The role of breastfeeding, antibiotics and antimalarial medicinal exposure in paediatric depression amongst infants and children aged under 5: A population-based study

The role of breastfeeding, antibiotics and antimalarial medicinal exposure in paediatric depression amongst infants and children aged under 5: A population-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The role of breastfeeding, antibiotics and antimalarial medicinal exposure in paediatric depression amongst infants and children aged under 5: A population-based study Kanayo Umeh, S Adaji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5036450/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 May, 2025 Read the published version in BMC Pediatrics → Version 1 posted 14 You are reading this latest preprint version Abstract Background Although paediatric depression is a recognised problem worldwide, there is limited data on its prevalence in children aged under 5 years, and the role of breastfeeding and medicinal exposure. This study examined whether lack of breastfeeding, and exposure to antimalarial and antibiotic medicines, contribute to depression in Nigerian children aged under 5. Methods The study examined data from 31,103 children (mean age, 2.11 years (SD, 1.421)), collected during the 6th round of the MICS (Multiple Indicator Cluster Surveys), completed in 2021. A series of binary logistic regression models were used to analyse the data. Results Children exposed to antimalarial medicines, specifically ACT (AOR = 1.760, 95%CI [1.316–2.355]), and artesunate injections (AOR = 1.757, 95%CI [1.150–2.684]), and those who were not breastfed (AOR = 0.776, 95%CI [0.625–0.963]), had greater odds of depression, after adjusting for potential confounders. The association between ACT medication use and depression was salient in male children (AOR = 2.347, 95%CI [1.547–3.559]), whereas the relationship between breastfeeding and depression was primarily observed in female children (AOR = 0.689, 95%CI [0.498–0.951]). Robustness checks including variations in managing missing data, breastfeeding measurement, and analysis across subgroups for multiple covariates (e.g., geographical area, mothers’ education, wealth index, medical symptoms), highlighted the importance of contextual factors. Conclusions This is the first population-based study to examine the role of breastfeeding and medicinal use, in suspected depression amongst children aged under 5, from sub-Sharan Africa. Overall, exposure to antimalaria treatment, particularly ACT, and inadequate breastfeeding may help identify infants and young children susceptible to paediatric depression. Clinical trial number Not applicable Figures Figure 1 Figure 2 BACKGROUND • Childhood depression in Sub-Saharan Africa Although paediatric depression is a growing public health concern worldwide (Patra, 2019 ; Lima et al., 2013 ; Mullen, 2018 ), there has been limited research on the scope of the problem and its associated factors, especially in sub-Saharan Africa (Tomlinson and Morgan, 2015 ). One reason for the limited evidence base is that diagnosing depression in children, particularly very young ones (e.g., under 5s), can be challenging (Dolinsky et al., 2011 ; Patra, 2019 ). Symptom presentation is complicated by age, and emotional, cognitive, and biological complexities (Selph and McDonagh, 2019 ). Infants/toddlers are unable to verbalise being depressed, and may not meet clinical diagnostic criteria (e.g., Diagnostic and Statistical Manual for Mental Disorders (DSM-5)) (Mullen, 2018 ). Research suggests that, for very young children (aged under 12), researchers must rely on parental reports, which are better at predicting mood disorders than child feedback (Lewis et al., 2012 ). Childhood depression is a recognised problem in sub-Saharan Africa, with 1 in 10 (9.5%) children and adolescents living with psychiatric conditions, including mood disorders (Cortina et al., 2012 ; Tomlinson and Morgan, 2015 ). However, existing research on depression in the region is almost entirely based on general populations (Gbadamosi et al., 2022 ; Yohani et al., 2023 ), adolescents (Sequeira et al., 2022 ; Partap et al., 2023 ; Aluh et al., 2018 ; Jorns-Presentati et al., 2021 ; Mabrouk et al., 2022 ), mixed populations (Owen et al., 2016 ), children aged over 5 years (Cortina et al., 2012 ; Sherr et al., 2020 ; Mokwena et al., 2023 ), or parents/caretakers (Motlhatlhedi et al., 2017 ; Huang et al., 2017 ; Saeed and Wemakor, 2019 ). There is almost no data on paediatric depression in children aged under 5 years, from sub-Saharan Africa (Tomlinson and Morgan, 2015 ). Epidemiological evidence on prevalence rates and/or causal factors in this age-group is extremely rare (Owen et al., 2016 ). This paucity of data is problematic because young children in sub-Saharan Africa face mental health challenges aggravated by chronic poverty, traumatic childhood events (e.g., witnessing violence), and illiteracy, compounded by parental difficulties, such as maternal depression (Tomlinson and Morgan, 2015 ; Kurtz et al., 2023 ; Amene et al., 2024 ; Jorns-Presentati et al., 2021 ). While a few studies from southern Africa have published data on depression in very young children (Mokwena et al., 2023 ; Sherr et al., 2020 ; Huang et al., 2017 ), including under 5’s (Drago et al., 2020 ; Motlhatlhedi et al., 2017 ), evidence from West African countries is more difficult to find. With regards to underlying factors of childhood depression, literature is even more scant and non-existent in sub-Saharan countries. Rarely explored factors that may contribute to depression in West African children include medicinal use, particularly antimalarial (Nevin and Croft, 2016 ) and antimicrobial (i.e., antibiotic) medication (Dinan and Dinan, 2022 ), and breastfeeding history, which have been subjects of much debates. • Exposure to antimalarial medicines Malaria infestation is a common cause of febrile illness among children in sub-Saharan Africa (Okiro and Snow, 2010 ; Trape et al., 2023 ; Oshagbemi et al., 2023 ). Malarial treatment among Nigerian children, as in adults, follows WHO guidelines with recommendation for artemisinin-based combination therapy (ACT) (FMoH, 2015 ). Among under-5 Nigerian children, there has been incremental resort to use ACT; from 7% in 2008 when the ACT was introduced to 74% in 2021 (NMEP, 2022 ). Hossain et al recently reviewed Malaria Survey datasets from 19 low- and middle-income countries (LMIC) and found that about one-fifth of Nigerian children under – 5 were suspected to have received antimalarial treatment (Hossain et al., 2024 ). Antimalarial drugs have been associated with mental and neurological dysfunction (Bitta et al., 2017 ; Nevin and Croft, 2016 ; Meier et al., 2004 ). For example, antimalarial medicines (e.g., chloroquine) may produce a wide spectrum of psychopathological experiences, including mood disorders (Maxwell et al., 2015 ). Although the psychiatric effects of antimalarial drugs in young children are complex and not well understood (Nevin and Croft, 2016 ), the term ‘cerebral malaria’ is a recognised neurological complication in children aged under 5 years, which entails a history of fever (2–3 days), and may include cognitive dysfunction (Song et al., 2022 ). Children aged under 5 account for two-thirds of all malaria deaths in sub-Saharan Africa (Sarfo et al., 2023 ). Thus, unsurprisingly, use of antimalarial medicines during pregnancy is common in the region (Nyeko et al., 2023 ; Hossain et al., 2023a ). An analysis of cross-sectional data from 32,397 children aged under 5, from five malaria-endemic African countries, including Guinea, Mali, Nigeria, and Senegal, revealed anti-malarial consumption rates as high as 66.3%, (Guinea), 70.5% (Mali), and 31.6% (Nigeria), over the 2-week period prior to the survey (Hossain et al., 2023a ). However, the psychiatric aspects of antimalarial drugs in infants and very young children is not well understood (Aneja et al., 2019 ). Hitherto, most studies on the neurological effects of antimalarial medicines have focused on adult or mixed populations outside sub-Saharan Africa (Meier et al., 2004 ; Bitta et al., 2017 ; Nevin and Croft, 2016 ; Maxwell et al., 2015 ; Song et al., 2022 ). • Exposure to antibiotics Use of antibiotics to treat febrile illnesses like respiratory tract infections and diarrhoeal diseases in young children is common in sub-Saharan Africa, especially in Nigeria (Auta et al., 2019 ). DHS data on antibiotic treatments in children under 5, from 45 countries (438 140 child-observations), showed that 38.4% (95% CI, 37.9% – 38.8%) of children with febrile symptoms received antibiotics (Levine et al., 2022 ). Other data from LMICs shows antibiotics were prescribed to 80·5% of children diagnosed with respiratory illness, 50·1% with diarrhoea, and 28·3% with malaria. The mean number of antibiotic prescriptions issued to children between birth and age 5 years across eight LMICs was 24·5 (95% CI 22·6–26·7), ranging from 7·1 (6·3–7·9) in Senegal to 59·1 (54·1–64·6) in Uganda (Fink et al., 2020 ). In Nigeria specifically, total antibiotic exposure in the first 5 years of life was 9.6 (95% CI 8.6–10.6) with formal health services being the main source of antibiotics in most cases (Levine et al., 2022 ). A study by Hossain et al estimated that 4.3% of Nigerian children with fever were exposed to antibiotic use (Hossain et al., 2023b ). Nigerian children aged under 5 years are more likely to be exposed to antibiotics by their mothers (Efunshile et al., 2019 ). An analysis of 450 out-patient case notes for under 5 years children from two Nigerian maternity hospitals, and medical records of 3700 under-five children treated at a third hospital, found that the majority (72%) of mothers administered antibiotics to their child, with only about half (48.5%) prescribed by a physician (Adisa et al., 2018 ). A meta-analysis of data from 30 countries in sub-Saharan Africa revealed that Nigeria has one of the highest rates of antibiotic usage in West African children under 5 years (Auta et al., 2019 ). Antibiotic usage has also been associated with an increased risk of psychiatric disorders, including depression (Lurie et al., 2015 ; Lavebratt et al., 2019 ; Prichett et al., 2022 ), through complex bidirectional biological interactions between gut bacteria and the brain ('brain–gut–microbiota axis') (Dinan and Dinan, 2022 ). However, most research in this area has focused on adult, adolescent, or mixed populations, and moreover isn’t specific to sub-Saharan Africa (Lurie et al., 2015 ; Prichett et al., 2022 ). Some limited data suggests antibiotics exposure may contribute to depression in children aged a under 5. For example, a population-based longitudinal study of 1 million births in Finland, from 1996 to 2012, followed up through 2014, found that antibiotics exposure in utero and during the first two postnatal years was associated with a slightly higher risk of psychiatric dysfunction, including mood disorders (e.g., depression), after adjusting for pregnancy- and birth-related covariates. Antibiotic exposure during pregnancy (trimesters 1 to 3) was associated with a 14–15% increased risk of offspring psychopathology. Antibiotics usage after 2 years age was also related to a slightly increased risk of psychiatric dysfunction (Lavebratt et al., 2019 ). • Exposure to breastfeeding Meta–analytic evidence from 29 sub-Saharan African countries suggests lower breast-feeding rates (both early initiation, and exclusive breastfeeding) in West and Central African countries (Issaka et al., 2017 ). Breastfeeding levels in these regions were below the WHO’s recommended 50% (Zong et al., 2021 ). Furthermore, inadequate breastfeeding has been implicated in paediatric depression (Kwok et al., 2013 ), albeit the data is inconclusive. A systematic review of 21 studies on the effects of breastfeeding on mental health in mothers and children, including five studies of childhood depressive disorders, found conflicting evidence (Bugaeva et al., 2023 ). While two studies found no link between breastfeeding and childhood depression, after adjusting for multiple covariates (Kwok et al., 2013 ; Loret de Mola et al., 2016 ), two other investigations observed a modest protective effect for breastfeeding (i.e., lack of breastfeeding was associated with a higher likelihood of paediatric depression later in life (6 − 16 years of age)) (Zhong et al., 2013 ; Allen et al., 1998 ). Other research suggests breastfeeding improves cognitive and brain functioning in children, partly due to fatty acids present in breastmilk (Krol and Grossmann, 2018 ). Regardless, data from sub-Sahara Africa is extremely limited, and more research is needed to better understand how inadequate breastfeeding relates to psychiatric disorders in infants/children from this region. • The present study This study explores the role of breastfeeding and commonly prescribed anti-microbials in suspected depression amongst children aged under 5. Although previous studies have assessed paediatric depression in very young children from sub-Saharan Africa, including under 5’s, most of this evidence is from southern Africa (Mokwena et al., 2023 ; Sherr et al., 2020 ; Huang et al., 2017 ; Drago et al., 2020 ; Motlhatlhedi et al., 2017 ). Data from West African countries is more difficult to find, despite the high rates of antimalarial and antibiotic consumption in West African children (Efunshile et al., 2019 ; Auta et al., 2019 ; Hossain et al., 2023a ), lower breast-feeding rates in the region (Issaka et al., 2017 ), and evidence implicating antimalarial/antibiotic drugs and breastfeeding in childhood depression (Bitta et al., 2017 ; Nevin and Croft, 2016 ; Lavebratt et al., 2019 ; Prichett et al., 2022 ). To address this gap in the literature, as well as generate a body of information that would improve understanding of childhood depression, the current study analysed survey data from 30,804 Nigerian children aged under 5. We hypothesised that children suspected to have depression are less likely to have been breastfed, and more likely to have received antimalarial and antibiotic medication, consistent with previous research. …………………………………………….. [Insert Figure 1 about here] Summary of data collection stages from the 2021 MICS/NICS conducted in Nigeria. …………………………………………… METHODS Design This study analysed data from the 2021 MICS/NICS conducted in Nigeria. The survey was funded by the Nigerian government, UNICEF, GAVI, and BMGF, and implemented by NBS, with technical support from UNICEF. Detailed and comprehensive information about MICS/NICS history in Nigeria, and organisation, methodology, sampling and implementation of the 2021 survey in the region, including personnel involved and questionnaires used, have been published elsewhere (MICS, 2022). For the purposes of this paper, we will report only information relevant to the current objectives. The MICS survey collected data on socio-demographic and socio-economic characteristics (e.g., household, and personal assets), and multiple health-related factors concerning women and children, including post-natal care, disease episodes such as diarrhoea and malaria, treatment of children with fever, antimalarial treatment, use of antibiotics, infant and young child feeding including breastfeeding, and equity-related topics, such as child functioning and subjective well-being. Data collection stages are summarised in Figure 1. Data was collected using five questionnaires, including a household survey on basic demographic information, a survey administered to all women in each household aged 15 to 49 years, and an under 5 questionnaire undertaken by mothers or caretakers of all children aged below 5 years, living in each household. Although the questions were based on standard MICS6 measures (MICS, 2022), they were pre-tested and customised in several regions (states) of Nigeria during July 2021. Data collection involved pre-tested computer-assisted personal interviewing (CAPI), whereby a respondent or interviewer uses an electronic device (tablet computers, running the Window 10 operating system), to answer the questions. The interviews and questionnaires were translated into English, or the respondent’s native language (e.g., Hausa, Igbo, Fulani), based on literacy testing. Data collection occurred from September to December 2021 and was conducted by 74 teams of interviewers (MICS, 2022). Sample The initial target sample size for the MICS was 37,000 households, covering 36 states, the Federal Capital Territory (FCT), and six geo-political zones in Nigeria. Within each region, a household listing was first generated in a selected number of census enumeration areas. A systematic sample of 20 households was then selected in each area. In total, 31,103 children aged under 5 years participated. Of this figure, more than half were male (50.4%), aged 24 to 59 months (two to five years) (62.4%), and lived in a rural area (63.4%). For most children (94.8%) their mother was the respondent to the under – 5 questionnaire/interview, while a primary caretaker responding on behalf of the other children. Most of the mothers either lacked any formal education, or had only primary school education (55.2%), and almost all the children (97%) lacked health insurance coverage. Childhood depression Infants, toddlers, and very young children (aged under 5) are generally unable to verbalise being depressed, and hence may not meet the clinical diagnostic criteria (e.g., Diagnostic and Statistical Manual for Mental Disorders (DSM-5)) for depression (Mullen, 2018). Consequently, previous research has relied on parental observations and reports, which can be better at establishing mood disorders in very young children (Lewis et al., 2012). Childhood 'depression' or 'sadness' in the MICS was assessed with a single question (MICS, 2022). This item was part of series of multiple-choice questions assessing early childhood development, including linguistic acuity and interactions with other children and household members. The data collector made a specific statement, directed at the child's mother or caretaker: 'The next two questions have five different options for answers. I am going to read these to you after each the question. How often does [name] seem to be very sad or depressed? Would you say: ‘daily’ (coded 1), ‘weekly’ (coded 2), ‘monthly’ (coded 3), ‘a few times a year’ (coded 4) or ‘never’ (coded 5)'? Depending on participant feedback the researcher could record two additional response options: ‘don’t know’ (coded 8) or ‘no response’ (coded 9). For the purposes of subsequent binary logistic regression analysis, we collapsed these response options into a dichotomous variable: ‘depressed’ (incorporating ‘daily’, ‘weekly’, ‘monthly’, ‘a few times a year’) (coded 1) and ‘never’ (comprising ‘never’ and the other response options) (coded 0). Medicinal exposure Consumption of antibiotic and antimalarial medicines was assessed using a series of multiple-choice questions, under a section labelled ‘Care of Illness’ (MICS, 2022). This section focused on the type and/or source of advice or treatment provided in response to childhood illnesses during the past fortnight, notably diarrhoea (‘In the last two weeks, has [name] had diarrhoea’? ‘Yes’/‘No’/‘Don’t know’), fever (‘At any time in the last two weeks, has [name] been ill with a fever’? ‘Yes’/‘No’/‘Don’t know’), and cough (‘At any time in the last two weeks, has [name] had an illness with a cough’? ‘Yes’/‘No’/‘Don’t know’). Medicinal use was assessed using several questions: ‘At any time during the illness, was [name] given any medicine for the illness? (‘Yes’/‘No’/‘Don’t know’), ‘What medicine was [name] given?’, ‘Any other medicine? The researcher recorded all medicines mentioned. If the type of medicine given could not be determined from a participant’s response, the researcher noted the brand name and then temporarily record ‘W' until the appropriate category of medicine could be established. Nine categories of antimalarial medicines were recorded: artemisinin combination therapy (ACT) (coded A), SP/fansidar (coded B), chloroquine (coded C), amodiaquine (coded D), quinine pills (coded E), quinine injection/IV (coded F), artesunate rectal (coded G), artesunate injection/IV (coded H), other anti-malarial (coded K). Due to its unidentified label, we excluded the last category from further analysis. Four types of antibiotics were identified: amoxicillin (coded A), cotrimoxazole (coded M), other antibiotic pill/syrup (coded N), and other antibiotic injection/IV (coded O). Given their unspecified labels, the two latter antibiotic categories were excluded from data analysis. Finally, use of home remedies and herbal medicines was also recorded as a separate medicinal category (coded Q). Exposure to each medicine type was treated as a separate dichotomous variable – used (coded 1) or not used (coded 0). Breastfeeding WHO and UNICEF guidelines both recommend exclusive breastfeeding for the first six months of life, and breastfeeding up to the age of 2 years, and beyond (Aryeetey and Dykes, 2018). MICS literature considers a broad range of multiple breastfeeding metrics (MICS, 2022). However, given the lower breast-feeding rates (both early initiation, and exclusive breastfeeding) in West and Central African countries (Issaka et al., 2017), for the purposes of this study we focused primarily on overall breast feeding history (i.e., prior exposure to breastfeeding). This was assessed using two items contained within the ‘breastfeeding and dietary’ intake section of the MICS questionnaire for children aged under 5: ‘Has [name] ever been breastfed?’ ‘Yes’ (coded 1)/ ‘No’ or ‘don’t know’ (coded 0), and ‘Is [name] still being breastfed?’ ‘Yes’ (coded 1)/ ‘No’ or ‘don’t know’ (coded 0). Confounding variables We examined a total of ten covariates: (a) gender (‘male’ (coded 1) / ‘female’ (coded 2)), (b) age, (c) weight, (d) geographical area ( ‘urban’ (coded 1) / ‘rural’ (coded 2)), (e) mother’s or caretaker’s educational level ( ‘none’ (coded 0), ‘primary’/ ‘secondary’/ ‘higher’ (coded 1)), (f) child’s functional difficulties (CFD), (g) ethnicity/tribe of the head of the household (‘Hausa’/ ‘Igbo’/ ‘Yoruba’/ ‘Fulani’ (coded 1), ‘others’ (coded 0)), (h) wealth index, (i) fever symptoms (‘Yes’ (coded 1)/ ‘No’ (coded 0)) and (j) cough symptoms (‘Yes’ (coded 1)/ ‘No’ (coded 0)). Age was based on birth history information, and recorded in days, months, and years. For the purposes of this paper, we analysed number of days. Weight was calibrated in kilogrammes (kg), and based on measurements of each child, read out by a measurer. CFD incorporated questions about physical difficulties that a child may have including hearing (e.g., ‘Does [name] use a hearing aid?’), vision (e.g., ‘Does [name] wear glasses?’), and mobility (e.g., ‘Does [name] use any equipment or receive assistance for walking?’). CFD data was only collected for children aged 2 to 4 years. Wealth index is a pre-computed composite numerical MICS metric, reflecting wealth or assets in each household (MICS, 2022). It incorporates various household characteristics, including ownership of personal assets (e.g., refrigerator, television, air conditioner, fan, water heater, bicycle, car, truck or van), and access to basic services (e.g., electricity, energy for cooking, internet) (Howe, 2009). Scores are organised into five wealth index quintiles: ‘poorest’ (coded 1), ‘second’ (coded 2), ‘middle’ (coded 3), ‘fourth’ (coded 4),‘richest’ (coded 5). Finally, we deemed it essential to account for fever and cough symptoms, as covariates. High fever in children is associated with malaria or bacterial infection (Schumacher and Spinelli, 2012; El-Radhi, 2019), and a cough may be caused by pneumonia but also by severe malaria (Oluwole et al., 2000), triggering the use of antibiotics or antimalarial medicines. Additionally, early childhood infections, including fever and cough, are associated with depression, through childhood up to mid adolescence (Chaplin et al., 2020), suggesting both symptoms may confound the association between medication use and childhood depression. Data Analysis Chi-square and independent samples t-tests were used to analyse descriptive data. To investigate the hypothesis that children who have experienced depression are more likely to have received antimalarial and antibiotic medication, and are less likely to have been breastfed, we conducted a hierarchical binary logistic regression. The outcome variable was childhood depression — 1 for presence and 0 for absence. Predictor variables comprised exposure to three types of antibiotic drugs (amoxicillin, cotrimoxazole, herbal), eight antimalarial medicines (ACT, fansidar, chloroquine, amodiaquine, quinine pills, quinine injection, artesunate rectal, and artesunate injection), and breastfeeding. We adjusted for ten covariates (age, gender, weight, urban/rural area, mothers’ education, child’s functional difficulties, ethnicity/tribe, wealth index, and fever/cough symptoms. Three logistic regression models were tested: Model 1 (childhood depression = Intercept + medication exposure), Model 2 (childhood depression = Intercept + medication exposure + breastfeeding exposure), Model 3 (childhood depression = Intercept + medication exposure + breastfeeding exposure + covariates). Of particular interest was whether any significant association of childhood depression with medication and breastfeeding (Model’s 1 and 2) persisted after accounting for covariates (Model 3). The analysis was first conducted for the whole sample and repeated separately for males and females. Further analyses were then performed to assess the robustness of the findings across various sources of uncertainty. RESULTS Descriptive data Rates of missing data ranged from 0 (e.g., medication use, cough/fever symptoms) to 37.2% (child functional difficulties), depression (46.2%), and 51.5% (cough symptoms). We opted for listwise deletion of missing cases (Rubin et al., 2007), given the large overall sample size (and hence statistical power) (Kang, 2013). However, the expected maximisation (EM) method, which replaces missing values with estimated parameters (Stavseth et al., 2019), was later used for sensitivity analysis (robustness checks). Descriptive characteristics for the whole sample, and children with and without depression, is shown in Table 1. Overall, 9774 children (31.4%) were suspected to have depression. Depressed children were more likely than their non-depressed counterparts to live in a rural area (75.3% versus 70.9%, respectively), (χ 2 (1, N = 16720) = 40.293, p < 0.001), less likely to have an educated mother (52.3% versus 60%, respectively), (χ 2 (1, N = 16718) = 97.570, p < 0.001), more likely to have functional difficulties (5.2% versus 3.3%, respectively), (χ 2 (1, N = 16720) = 35.140, p < 0.001), experience fever (26.2% versus 22.1%, respectively), (χ 2 (1, N = 16720) = 35.725, p < 0.001) and cough (19.1% versus 15.6%, respectively), (χ 2 (1, N = 16720) = 34.028, p < 0.001), have lower body weight (M = 0.932 versus 1.120, respectively) ( t (14708.053) = -12.011, p < 0.001), and live in a less wealthy household (M = 2.45 versus 2.61, respectively) ( t (14324.925) = -7.613, p < 0.001). Depressed children were more like than non-depressed participants to be administered cotrimoxazole (1% versus 0.6%, respectively), (χ 2 (1, N = 16720) = 6.495, p < 0.01), ACT (5.5% versus 3.9%, respectively), (χ 2 (1, N = 16720) = 22.225, p ≤ 0.001), fansidar (1.2% versus 0.7%, respectively), (χ 2 (1, N = 16720) = 6.900, p ≤ 0.01), chloroquine (1.7% versus 1%, respectively), (χ 2 (1, N = 16720) = 12.261, p ≤ 0.001), quinine (pills) (0.5% versus 0.3%, respectively), (χ 2 (1, N = 16720) = 3.350, p < 0.05), quinine (injection) (1.6% versus 1%, respectively), (χ 2 (1, N = 16720) = 10.619, p < 0.001), and artesunate (injection) (2.6% versus 1.9%, respectively), (χ 2 (1, N = 16720) = 9.727, p ≤ 0.001). By contrast, depressed children were less likely to have been breastfed (91.6% versus 93.8%, respectively), (χ 2 (1, N = 5253) = 9.218, p ≤ 0.001). We observed no group differences based on gender, age, ethnicity/tribe, and consumption of amoxicillin, herbal antibiotics, amodiaquine, and artesunate (rectal). Overall, most children were male (50.8%), lived in a rural area (73.5%), had an educated mother (55.5%), did not suffer functional difficulties (95.6%), belonged to the Hausa, Igbo, Yoruba, or Fulani tribe (60.9%), and had not experienced a fever (75.5%) or cough (82.3%). The average age (in days), body weight (kg), and wealth index were 975.11 (SD = 790.015), 0.990 (SD = 0.959), and 2.56 (SD = 1.349), respectively (the average age equates to 32.055 months). Only a small minority of children (≤ 4.8%) had been exposed to antibiotics or antimalarial medicines. However, most (92.2%) had been breastfed (25.7% were still breastfeeding). Breastfeeding children were significantly younger than their non-breastfeeding counterparts (mean age 348.42 days [0.954 years]) versus 1194.67 days [3.273 years] respectively, ( t (17890.621) = -105.279, p < 0.001). …………………………………………….. [Insert Table 1 about here] Descriptive data for study variables. …………………………………………… …………………………………………….. [Insert Table 2 about here] Logistic regression models predicting childhood depression from medication use, breastfeeding, and covariates in the whole sample. …………………………………………… …………………………………………….. [Insert Table 3 about here] Logistic regression models predicting childhood depression from medication use, breastfeeding, and covariates, in male children. …………………………………………… …………………………………………….. [Insert Table 4 about here] Logistic regression models predicting childhood depression from medication use, breastfeeding, and covariates, in female children. …………………………………………… Hypotheses testing The objective of this analysis was to assess the extent to which depression in Nigerian children aged under 5 relates to medicinal use and breastfeeding. The overall sample size (N = 31103) provided sufficient statistical power to detect significant associations, reducing type II error rates. To test the assumption of linearity in the logit, whereby the log-odds of the predicted probabilities of the outcome (depression) is a linear function of three continuous independent variables (age, weight, and wealth index), we conducted a Box-Tidwell test. This required adding a product term between each predictor and its natural log transformation, into the logistic regression model and examining its significance (Harris, 2021). We observed significant coefficients for all three interaction terms, suggesting the assumption of linearity in the logit was violated. However, due to the very large sample size, we decided to create and inspect scatterplots with the continuous predictors on the x -axis and the log-odds of the predicted probabilities on the y -axis. The observed curve estimates indicated a good fit for both linear and non-linear relationships. For the purposes of this investigation, we assumed linearity of the continuous predictors and log odds. …………………………………………….. [Insert Figure 2 about here] Adjusted odds ratios for significant predictor variables in the whole sample …………………………………………… Predicting childhood depression in the whole sample Table 2 shows logistic regression estimates, together with overall model parameters. Exposure to ACT, (OR = 1.647, 95%CI [1.262 – 2.150]), chloroquine, (OR = 1.632, 95%CI [1.022 –2.604]), and artesunate injections (OR = 1.710, 95%CI [1.137 –2.573]), predicted higher odds of childhood depression (Model 1) (Figure 2). T hus, for children administered ACT, chloroquine, and artesunate treatment, there was a significant 1.647, 1.632, and 1.710 increase, respectively, in the odds of experiencing depression, compared to children not given these medicines. Addition of breastfeeding (Model 2) also resulted in good fit: previous breastfeeding was associated with lower odds of depression (OR = 0.719, 95%CI [0.581 – 0.890]). This adjusted model was significant (χ²(1) = 46.854, p < .001), showing that ACT (AOR = 1.650, 95%CI [1.264 –2.153]), chloroquine (AOR = 1.612, 95%CI [1.009 –2.574]), artesunate injection (AOR = 1.726, 95%CI [1.147 –2.598]), and breastfeeding exposure (OR = 0.719, 95%CI [0.581 – 0.890]), robustly predict childhood depression. The model’s pseudo-R² value of 0.012 (Nagelkerke) indicated improvement over the null model. The contributions of ACT, artesunate injection, and breastfeeding to the model remained significant even after adjusting for covariates (Model 3), with the odds of depression higher in children exposed to ACT (OR = 1.760, 95%CI [1.316 – 2.355]) and artesunate injections (AOR = 1.757, 95%CI [1.150 –2.684]), and lower in breastfed children (AOR = 0.776, 95%CI [0.625 –0.963]). Predicting childhood depression in males Table 3 shows logit estimates and model parameters for male children. Exposure to ACT (OR = 2.188, 95%CI [1.490 – 3.212]) and artesunate injections (OR = 1.770, 95%CI [1.020 – 3.071]) was associated with a greater risk of childhood depression (Model 1). Male chi ldren given ACT were two times more likely to experience depression, while those receiving artesunate injections were 1.770 times more prone to depression, compared to male children who did not receive these treatments (χ²(1) = 32.023, p < .001, pseudo-R² = 0.016). Inclusion of breastfeeding (Model 2) failed to improve model fit. Addition of covariates (Model 3) generated a significant model (χ²(1) = 92.581, p < .001) , negating the contribution of artesunate exposure, albeit ACT remained significant: male children administered ACT were more than twice as likely to be depressed compared to those not given this treatment (AOR = 2.347, 95%CI [1.547 – 3.559]). Predicting childhood depression in females Table 4depicts results for female children. The initial logistic regression model comprising medicinal exposure (Model 1) failed to fit the data well: none of the predictor variables was significant. Addition of breastfeeding (Model 2) significantly improved model fit (χ²(1) = 24.474, p < .05) , with the pseudo-R² value of 0.013 (Nagelkerke) indicated a slight improvement over the null model. Female children who had been breastfed were less likely to experience depression, compared to those not breastfed (OR = 0.642, 95%CI [0.468 – 0.881]). Incorporating covariates (Model 3) improved model fit (χ²(1) = 86.606, p < .001) , increasing the proportion of variance explained ( pseudo-R² value of 0.045). B reastfeeding remained significant in the final model (AOR = 0.689, 95%CI [0.498 – 0.951]). Robustness checks We performed several robustness checks to determine how the results are affected by sources of uncertainty, including variations in analytic protocol, and management of missing data (Thabane et al., 2013). First, we repeated the logistic regression analysis using bootstrapping, whereby multiple (1000) simulated samples are tested. The emerging bootstrapped estimates supported the initial findings: exposure to ACT, ( β = 0.565, 95%CI [0.268 to 0.857]), artesunate injections ( β = 0.563, 95%CI [0.137 to 1.075]), and breastfeeding ( β = -0.254, 95%CI [-0.478 to -0.044]), predicted childhood depression in the whole sample (final model). ACT exposure and breastfeeding remained salient predictors in males ( β = 0.853, 95%CI [0.425 to 1.277]) and females ( β = -0.373, 95%CI [-0.700 to -0.037]), respectively. Next, we repeated the analyses using a different measure of breastfeeding (‘still’ breastfeeding rather than ‘ever’ breastfed). This produced partially identical results. In addition to ACT and artesunate (injection), exposure to chloroquine (AOR = 1.435, 95%CI [1.073 – 1.918]) and quinine (injection) (AOR = 1.372, 95%CI [1.026 – 1.836]) now predicted depression in the overall sample, helping improve model fit (χ²(22) = 357.907, p < .001). Breastfeeding was no longer significant. Analysis of the gender split showed that ACT exposure remained significant in males (AOR = 1.519, 95%CI [1.204 – 1.917]), whereas breastfeeding was no longer significant in females. Moreover, artesunate (injection) now improved model fit in the latter group (OR = 1.455, 95%CI [1.058 – 2.002]). Third, we re-ran the analyses across age groups (≤ versus > 2 years). Medication use and child functional difficulties were excluded from this analysis, due to low cell frequencies. Breastfeeding exposure remained a significant predictor, but only in older children (i.e., those aged > 2 years) (AOR = 0.757, 95%CI [0.610 – 0.941]). Next, we repeated the analyses across geographical (rural versus urban) regions. Antimalarial medicinal use remained a significant factor in both areas, with depression predicted by amodiaquine (AOR = 11.065, 95%CI [1.312 – 93.288]), and artesunate injection use (AOR = 0.757, 95%CI [0.610 – 0.941]) in urban children, and ACT exposure (AOR = 3.344, 95%CI [1.261 – 8.867]), in rural children. Breastfeeding was only significant in urban dwellers (AOR = 0.595, 95%CI [0.399 – 0.888]). We then tested the hypotheses based on mother’s educational level (no education versus some education). ACT use predicted paediatric depression in non-educated mothers (AOR = 1.677, 95%CI [1.066 – 2.638]), while exposure to three medicines – ACT (AOR = 1.783, 95%CI [1.212 – 2.623]), chloroquine (AOR = 2.114, 95%CI [1.033 – 4.327]), and artesunate rectal exposure (AOR = 2.455, 95%CI [1.337 – 4.509]), were significant for children of educated mothers. Breastfeeding predicted depression solely in the educated group (AOR = 0.627, 95%CI [0.451 – 0.873]). Next, we repeated the analyses by wealth index, comparing the lowest two wealth quintiles (‘poorest’, ‘second’) with the three higher categories (‘middle’, ‘fourth’, ‘richest’)). ACT exposure predicted depression in the less wealthy quintiles (AOR = 2.062, 95%CI [1.351 – 3.146]), whereas use of amodiaquine (AOR = 4.029, 95%CI [1.120 – 14.495]), and artesunate injections (AOR = 2.403, 95%CI [1.202 – 4.804]), were significant in children from wealthier backgrounds. Breastfeeding was not salient in either socio-economic group. We re-ran the analyses as a function of medical symptoms (cough, fever). Regarding fever, use of the antibiotic cotrimoxazole (AOR = 6.023, 95%CI [1.357– 26.721]) was significant in non-febrile children, whereas antimalarial medicines – ACT (AOR = 1.695, 95%CI [1.259 – 2.281]) and artesunate injections (AOR = 1.602, 95%CI [1.044 – 2.457]) – were significant in febrile participants. Breastfeeding was only relevant in the former group (AOR = 0.732, 95%CI [0.569 – 0.941]). For cough symptoms, ACT (AOR = 1.732, 95%CI [1.179 – 2.544]) and chloroquine (AOR = 2.063, 95%CI [1.043 – 4.081]) predicted depression in asymptomatic children, while ACT (AOR = 1.809, 95%CI [1.150 – 2.846]) and artesunate injections (AOR = 1.602, 95%CI [1.044 – 2.457]) were significant in symptomatic children. Breastfeeding was only salient in the latter group (AOR = 0.599, 95%CI [0.361 – 0.994]). Finally, we repeated the analyses using the EM approach for managing missing data. This produced roughly identical results with the original analyses (final model), whereby the odds of depression were elevated in children receiving ACT (AOR = 1.760, 95%CI [1.316 – 2.355]) and artesunate injections (AOR = 1.757, 95%CI [1.150 –2.684]), and lower in breastfed children (AOR = 0.776, 95%CI [0.625 –0.963]). Overall, the pattern of results varied as a function of the type of breastfeeding measure used, contextual factors (e.g., age, geographical area, mother’s educational level, child functional difficulties, wealth index), and the method used to manage missing data. Nevertheless, we observed a recurring pattern, whereby antimalarial medicinal use (particularly ACT treatment and artesunate injections), and breastfeeding history were persistent correlates of childhood depression, in most variations, with breastfeeding effects generally more salient in older children, living urban areas, from wealthier backgrounds, and a history of fever/cough. As in the original analysis, antibiotic exposure failed to predict depression, except in non-febrile children. DISCUSSION This population-based study helps address the lack of data on paediatric depression and its correlates, in infants and children from sub-Sharan Africa (Tomlinson and Morgan, 2015 ). Analysis of data from 30,804 Nigerian children aged under 5 years showed that those suspected to have depression were more likely to have received antimalarial medicines, specifically ACT and artesunate injections, and not been breastfed. The association between ACT exposure and depression was much stronger in male children, whereas the relationship of breastfeeding with depression applied primarily to female children. Crucially, these associations remained significant despite adjusting for important covariates, albeit multiple robustness checks suggest contextual factors including mother’s education, geographical area, and household wealth, and medical symptoms (fever, cough) have an important moderating effect. Overall, this evidence supports previous research linking antimalarial medication to psychiatric conditions, including depression (Bitta et al., 2017 ; Nevin and Croft, 2016 ; Maxwell et al., 2015 ). The data also supports past studies implicating breastfeeding in mental health (Zhong et al., 2013 ; Allen et al., 1998 ). Contrary to previous work with older demographics (Lurie et al., 2015 ; Lavebratt et al., 2019 ; Prichett et al., 2022 ), we found limited evidence linking antibiotics with depression in children under 5. Research on the psychiatric effects of antimalarial drugs in young children is very rare (Aneja et al., 2019 ), with most studies focusing on adults, or the general population (Meier et al., 2004 ; Bitta et al., 2017 ; Nevin and Croft, 2016 ; Maxwell et al., 2015 ; Song et al., 2022 ). Thus, hitherto the association between antimalarial drugs and depression in infants and children has been poorly understood (Aneja et al., 2019 ). Data for children from sub-Saharan Africa is particularly rare. Difficulty in identifying and diagnosing paediatric depression (Patra, 2019 ), combined with the lack of research on infant mental health in sub-Saharan Africa (Tomlinson and Morgan, 2015 ) adds additional ambiguity. Our findings suggest antimalarial treatment, and lack of breastfeeding may be independent risk factors for paediatric depression in Nigerian children aged under 5 years. While the psychiatric effects of antimalarial drugs have been attributed to neurological mechanisms, such as toxicity to the central nervous system (CNS) (Nevin and Croft, 2016 ), an arguably more plausible explanation for the current findings is the emotional aspect of malaria symptoms (e.g., fever, headache, gastrointestinal effects) and/or antimalarial treatment (medicines, injection). These medical experiences can be very unpleasant, especially for very young children, who may express their discomfort in ways that denote ‘depression’ or ‘sadness’ to an adult observer (Annan et al., 2023 ). For example, antimalarial drugs can induce vomiting in infants and children (Creek et al., 2010 ). Injections are painful to infants and very young children (Taddio et al., 2022 ), who often convey their distress through vocal/facial expressions denoting intense discomfort (Annan et al., 2023 ). As hypothesised, breastfed children were less likely to be depressed, even after accounting for covariates. Although previous research has implicated inadequate breastfeeding in paediatric depression, the evidence has been mixed (Kwok et al., 2013 ; Loret de Mola et al., 2016 ; Allen et al., 1998 ). A systematic review and meta-analysis of 18 studies on breastfeeding and mental disorders found conflicting evidence on the protective effect of breastfeeding on children's mental health in later life (Bugaeva et al., 2023 ). Some research found no association (Kwok et al., 2013 ; Loret de Mola et al., 2016 ), while other evidence indicated a protective effect for breastfeeding (Zhong et al., 2013 ; Allen et al., 1998 ). Our findings suggest a robust association between breastfeeding and depression in Nigerian infants/children that can’t be attributed to potential confounders, including body weight, maternal education, and child functional difficulties (Kanellopoulou et al., 2022 ; Malhotra and Sahoo, 2018 ). It is possible the low baseline rates of breastfeeding in Sub-Saharan African countries (Issaka et al., 2017 ), combined with a high prevalence of childhood trauma in the region (e.g., exposure violence, poverty) (Kurtz et al., 2023 ; Amene et al., 2024 ) may magnify any beneficial effects of breastfeeding on mental wellbeing in local infants/children (Krol and Grossmann, 2018 ). Gender played an important role in the relationship between breastfeeding and depression. We observed an association primarily in female children. The reason for this gender effect is unclear but may partly reflect gender differences in breastfeeding practices. However, a systematic analysis of qualitative data on breastfeeding in sub-Saharan Africa revealed no qualitative differences in how mothers breastfeed male and female infants (Ejie et al., 2021 ). One important consideration that may not be captured in qualitative data is the duration of breast feeding. Research suggests breastfeeding reduces the risk of depression only in children breastfed for longer than 6 (a shorter duration of breastfeeding was not associated with any protective effect) (Huang et al., 2019 ). Gender may determine breastfeeding duration (e.g., early initiation) in sub-Saharan Africa, whereby mothers breastfeed female infants for longer. However, evidence in this area is mixed. One study found female children were more likely to be breastfed early (Woldeamanuel, 2020 ), while another study found the opposite (Ayalew et al., 2019 ). Further research is necessary to determine if gender serves as a proxy for other variables affecting breastfeeding initiation/duration, such as local ethnicity, religious beliefs, and birth order (Shimizu et al., 2023 ). Use of antimalarial medicines, specifically ACT, was associated with paediatric depression, but only in male children (boys exposed to ACT were 2.3 times more likely to experience depression compared to male children administered ACT drugs). While the link between antimalarial drugs and psychiatric conditions is complex and not well understood (Nevin and Croft, 2016 ), the current findings may depict a gender-based disparity in exposure to anti malaria drugs, whereby male children are more likely to be administered ACT, and hence experience (and express) emotional discomfort from this treatment (Annan et al., 2023 ). Although research suggests malaria prevalence and associated risk factors in Sub-Saharan Africa are similar for male and female children aged under 5 (Nwaneli et al., 2020 ; Chilot et al., 2023 ), survey data from thirteen sub-Saharan African countries indicates gender affects whether children receive prompt access to antimalaria drugs (albeit not ACT medicines specifically) (Shah et al., 2015 ). However, evidence from a survey of antimalarial drug consumption in children under 5 from five countries in sub-Saharan African found no gender effect (Hossain et al., 2023a ). Another possible explanation is that male children are simply more susceptible to depression, and hence are more likely to experience emotional distress when exposed to uncomfortable antimalarial treatment (Creek et al., 2010 ). However, data suggests gender differences in depression amongst children is perfunctory (Salk et al., 2017 ), and more research is needed in this area. Poverty and childhood trauma might be important in understanding the link between ACT and depression in male children. Depression is more prevalent among African children exposed to violence (Jorns-Presentati et al., 2021 ) or exploitative child labour (Ibrahim et al., 2019 ). Furthermore, a survey of adverse childhood experiences in five sub-Saharan African countries found that male children are more likely than their female counterparts to witness and experience physical violence (Amene et al., 2024 ). Thus, the former group may already be more mentally distressed, from violent childhood traumas, prior to been administered antimalarial drugs. The medication may simply accentuate this pre-existing psychiatric vulnerability, manifesting as depression (Nevin and Croft, 2016 ). However, evidence for this view is weak. For example, a survey of the prevalence of emotional abuse experienced by children from Sub-Saharan Africa observed no gender effect (Kurtz et al., 2023 ). Furthermore, research with Nigerian children specifically has found no gender differences in exposure to violence, physical abuse, and other traumatic experiences, compared their female counterparts (Asagba et al., 2021 ). Moreover, data suggests girls are more likely to be victims of sexual abuse/trauma (Ibrahim et al., 2019 ; Amene et al., 2024 ). Overall, further research is necessary to better understand how gender affects the association between antimalarial treatment and depression in sub-Saharan African children. We found little evidence linking antibiotics and childhood depression, in contrast with previous research implicating antibiotic usage with an increased risk of mental disorders, including depression (Lurie et al., 2015 ; Lavebratt et al., 2019 ; Prichett et al., 2022 ). However, previous work in this area has been largely based on adult, adolescent, or mixed populations, and was not specific to sub-Saharan Africa (Lurie et al., 2015 ; Prichett et al., 2022 ). The observed association between antibiotics and depression solely in non-febrile children may denote a tendency for antimicrobials to be administered to children not diagnosed with malaria (i.e., not feverish), thereby increasing their risk of depression from antibiotics exposure (Dinan and Dinan, 2022 ). This view is supported by data from LMICs showing that antibiotics are less likely to be administered to children diagnosed with malaria (28.3%), compared to other medical conditions, such as respiratory illness (80·5%), and diarrhoea (50.1%) (Fink et al., 2020 ). Further research is needed to better understand how and why antibiotics may contribute to depression in non-febrile children under 5. Limitations This study has several limitations. Firstly, diagnosis of childhood depression was based on mother's or caretaker’s subjective judgements, in the form of a single-item question. While such parental feedback can be useful for understanding psychopathology in children (Lewis et al., 2012 ), including infants’ temperament (McGrath et al., 2008 ), it may not accurately capture a child’s emotional state (Caqueo-Urízar et al., 2022 ), and could be affected by the parents own mental health (McGrath et al., 2008 ). Paediatric depression is a complex disorder that is difficult to diagnose, and parental judgements may fail to capture this intricacy (Lima et al., 2013 ). Symptoms vary with age, level of development, and comorbidities, for example (Patra, 2019 ). While depression has been diagnosed in children as young as 3 years, the symptoms may not meet clinical diagnostic criteria (Mullen, 2018 ). Proper diagnosis often requires clinical interviews with both parental and child, conducted on an ongoing basis, and requiring the use of pictorial instruments and depression ratings scales (Patra, 2019 ). Secondly, robustness checks suggest the findings may vary depending on the type of breastfeeding measure used, and group differences in socio-demographic factors (e.g., age, geographical area, wealth index, mother’s education, medical symptoms). However, these inconsistencies did not dramatically alter the recurring pattern whereby antimalarial medicinal use, particularly ACT treatment, and breastfeeding predicted childhood depression. The data was also generally unaffected by the method used to manage missing data. It is notable that current breastfeeding failed to predict depression, perhaps reflecting the greater challenge of identifying ‘sadness’ in newborns and infants (breastfeeding children were under 12 months old, on average) (Dolinsky et al., 2011 ; Patra, 2019 ). Furthermore, breastfeeding duration not assessed. This is a potentially important metric that may moderate the association between breastfeeding and paediatric depression (children breastfed for > 6 months have a reduced risk of depression) (Huang et al., 2019 ). Since breastfeeding rates in West and Central African countries fall below the WHO’s recommended 50% (Issaka et al., 2017 ), breastfeeding duration might play a particularly important role in child health across the region, significantly impacting the odds of childhood depression. Thirdly, we did not control for several potential confounding factors implicated in childhood depression including family genetics, maternal depression, childhood maltreatment, and cognitive vulnerability (Lima et al., 2013 ; Malhotra and Sahoo, 2018 ). Severe childhood trauma (e.g., experiencing violence) in particular is highly prevalent in sub-Saharan Africa (Amene et al., 2024 ; Kurtz et al., 2023 ), and has been strongly associated with mood disorders in children (Jorns-Presentati et al., 2021 ). Further research is therefore needed to determine how this covariate interacts with medication use and breastfeeding in predicting paediatric depression. Despite these constraints, this study has several strengths. Firstly, to the best of our knowledge, this is the first ever investigation to demonstrate the role of antimalarial treatment, and breastfeeding, in the mental health of Nigerian children aged under 5 years. There is currently little or no published data on mood disorders and its correlates in infants and very young children from this geographical region (Tomlinson and Morgan, 2015 ). Most research in this area has focused on adolescents, adults, or mixed populations (Gbadamosi et al., 2022 ; Yohani et al., 2023 ; Sequeira et al., 2022 ; Partap et al., 2023 ; Aluh et al., 2018 ; Jorns-Presentati et al., 2021 ; Mabrouk et al., 2022 ; Owen et al., 2016 ; Cortina et al., 2012 ; Sherr et al., 2020 ; Mokwena et al., 2023 ). Secondly, we analysed data from a very large sample, providing smaller margins of error in adjusted odds ratio estimates, and hence generating more robust and reliable results. Third, we did control for several important covariates implicated in paediatric depression, including body weight (Kanellopoulou et al., 2022 ), and socio-demographic factors, such as maternal education, and households' cumulative living standards (wealth index) (Malhotra and Sahoo, 2018 ). Conclusions This is the first population-based study to examine the role of breastfeeding and medicinal use, in suspected depression amongst children aged under 5, from sub-Sharan Africa. Childhood depression was associated with exposure to antimalarial treatment, specifically ACT and artesunate injections, and lack of breastfeeding. Gender was an important moderating factor: treatment with antimalarial medicines may be an independent risk factor for paediatric depression primarily in male children, whereas the association between breast feeding and depression was stronger in female children. How breastfeeding was assessed, and variations in mother’s educational level, geographical area, household wealth, and medical symptoms (e.g., fever), were also important contextual factors. Given that infants and children in sub-Saharan Africa often face mental health challenges aggravated by chronic poverty, and childhood trauma, exposure to antimalaria treatment and inadequate breastfeeding may help identify Nigerian children under 5 who are susceptible to depression. Abbreviations ACT – Artemisinin Combination Therapy AOR – Adjusted Odds Ratio BMGF – Bill and Melinda Gates Foundation CFD – Child’s Functional Difficulties CNS – Central Nervous System EM – Expectation Maximisation FMoH – Federal Ministry of Health (Nigeria) GAVI – the Vaccine Alliance MICS – Multiple Indicator Cluster Survey NBS – National Bureau of Statistics NICS – National Immunization Coverage Survey NMEP – National Malaria Elimination Programme OR – Odds Ratio UNICEF – United Nations Children’s Fund WHO – World Health Organisation Declarations Ethics approval and consent to participate This study was performed in line with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Ethics approval was granted by the Liverpool John Moores University Research Ethics Committee (UREC reference: 16/NSP/035). Each participating adult provided verbal consent (adult consent was required for mother’s or caretakers aged 15 to 17), and all respondents were informed of their right to withdraw at any time, and the confidentiality and anonymity of the data. Consent for publication Not applicable Availability of data and materials Due to third party rights, other legal and ethical reasons, and the nature of data gathered, MICS data may not be redistributed or shared publicly, in any form (MICS, 2022). Access is restricted and facilitated directly by UNICEF/MICS (MICS, 2024). All MICS-related data and documentation can be viewed at http://www.childinfo.org/mics including questionnaires, manuals, data processing and tabulation plans as well as national reports, datasets and contact information. Furthermore, results from the surveys are made available in DevInfo, a powerful database designed to monitor progress towards the Millennium Development Goals. Access to MICS data requires registration at [email protected] . For further information please contact the Global MICS Coordinator, Strategic Information Section, Division of Policy and Planning, UNICEF, 3 U.N. Plaza, New York, NY 10017, USA. Tel: 212 303 7982 Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions KU conceived the study, extracted, and analysed the data, and wrote the manuscript. SA contributed to the final version of the manuscript. Acknowledgements We would like to thank the UNICEF MICS Team for providing access to the survey data. References Adisa R, Orherhe OM and Fakeye TO (2018) Evaluation of antibiotic prescriptions and use in under-five children in Ibadan, SouthWestern Nigeria. African health sciences 18(4): 1189-1201. Allen NB, Lewinsohn PM and Seeley (1998) Prenatal and perinatal influences on risk for psychopathology in childhood and adolescence. Development and psychopathology 10(3): 513-529. Aluh DO, Anyachebelu OC, Anosike C, et al. (2018) Mental health literacy: what do Nigerian adolescents know about depression? International journal of mental health systems 12: 8. Amene EW, Annor FB, Gilbert LK, et al. (2024) Prevalence of Adverse Childhood Experiences in sub-Saharan Africa: A multicountry analysis of the Violence Against Children and Youth Surveys (VACS). Child abuse & neglect 150: 106353. Aneja J, Goya D and Choudhary B (2019) Psychosis consequent to antimalarial drug use in a young child. Journal of family medicine and primary care 8(5): 1781-1783. Annan E, Ramukumba TS and Stevens BJ (2023) Perspectives of nurses regarding pain assessment and management during routine infant vaccination in Ghana. Nursing open 10(8): 5348-5355. Aryeetey R and Dykes F (2018) Global implications of the new WHO and UNICEF implementation guidance on the revised Baby-Friendly Hospital Initiative. Maternal & child nutrition 14(3): e12637. Asagba RB, Noibi OW and Ogueji IA (2021) Gender Differences in Children's Exposure to Domestic Violence in Nigeria. Journal of child & adolescent trauma 15(2): 423-426. Auta A, Ogbonna BO, Adewuyi EO, et al. (2019) Prevalence and factors associated with the use of antibiotics in non-bloody diarrhoea in children under 5 years of age in sub-Saharan Africa. Archives of disease in childhood 104(6): 518-521. Ayalew T, Tewabe T and Ayalew Y (2019) Timely initiation of breastfeeding among first time mothers in Bahir Dar city, North West, Ethiopia, 2016. Pediatric research 85(5): 612-616. Bitta MA, Kariuki SM, Mwita C, et al. (2017) Antimalarial drugs and the prevalence of mental and neurological manifestations: A systematic review and meta-analysis. Wellcome open research 2: 13. Bugaeva P, Arkusha I, Bikaev R, et al. (2023) Association of breastfeeding with mental disorders in mother and child: a systematic review and meta-analysis. BMC medicine 21(1): 393. Caqueo-Urízar A, Urzúa A, Villalonga-Olives E, et al. (2022) Children's Mental Health: Discrepancy between Child Self-Reporting and Parental Reporting. Behavioral Sciences 12(10). Chaplin AB, Jones PB and Khandaker GM (2020) Association between common early-childhood infection and subsequent depressive symptoms and psychotic experiences in adolescence: a population-based longitudinal birth cohort study. Psychological medicine . 1-11. Chilot D, Mondelaers A, Alem AZ, et al. (2023) Pooled prevalence and risk factors of malaria among children aged 6-59 months in 13 sub-Saharan African countries: A multilevel analysis using recent malaria indicator surveys. Plos One 18(5): e0285265. Cortina MA, Sodha A, Fazel M, et al. (2012) Prevalence of child mental health problems in sub-Saharan Africa: a systematic review. Archives of Pediatrics & Adolescent Medicine 166(3): 276-281. Creek D, Bigira V, Arinaitwe E, et al. (2010) Increased risk of early vomiting among infants and young children treated with dihydroartemisinin-piperaquine compared with artemether-lumefantrine for uncomplicated malaria. The American journal of tropical medicine and hygiene 83(4): 873-875. Dinan K and Dinan T (2022) Antibiotics and mental health: The good, the bad and the ugly. Journal of internal medicine 292(6): 858-869. Dolinsky DH, Brouwer RJ, Evenson KR, et al. (2011) Correlates of sedentary time and physical activity among preschool-aged children. Preventing chronic disease 8(6): A131. Drago F, Scharf RJ, Maphula A, et al. (2020) Psychosocial and environmental determinants of child cognitive development in rural south africa and tanzania: findings from the mal-ed cohort. BMC Public Health 20(1): 505. Efunshile AM, Ezeanosike O, Nwangwu CC, et al. (2019) Apparent overuse of antibiotics in the management of watery diarrhoea in children in Abakaliki, Nigeria. BMC infectious diseases 19(1): 275. Ejie IL, Eleje GU, Chibuzor MT, et al. (2021) A systematic review of qualitative research on barriers and facilitators to exclusive breastfeeding practice in sub-Saharan African countries. International breastfeeding journal 16(1): 44. El-Radhi AS (2019) Fever in common infectious diseases. Clinical Manual of Fever in Children . 85-140. Fink G, D'Acremont V, Leslie HH, et al. (2020) Antibiotic exposure among children younger than 5 years in low-income and middle-income countries: a cross-sectional study of nationally representative facility-based and household-based surveys. The Lancet Infectious diseases 20(2): 179-187. FMoH (2015) National Guidelines for Diagnosis and Treatment of Malaria. Reportno. Report Number|, Date. Place Published|: Institution|. Gbadamosi IT, Henneh IT, Aluko OM, et al. (2022) Depression in Sub-Saharan Africa. IBRO neuroscience reports 12: 309-322. Harris JK (2021) Primer on binary logistic regression. Family medicine and community health 9(Suppl 1). Hossain MS, Ahmed TS, Haque MA, et al. (2024) Prevalence of unqualified sources of antimalarial drug prescription for children under the age of five: A study in 19 low- and middle-income countries. Plos One 19(3): e0300347. Hossain MS, Ahmed TS, Sultana N, et al. (2023a) Examining the disparities of anti-malarial drug consumption among children under the age of five: a study of 5 malaria-endemic countries. Malaria journal 22(1): 370. Hossain MS, Islam MF, Arka PB, et al. (2023b) Antibiotic prescription from qualified sources for children with fever/cough: cross-sectional study from 59 low- and middle-income countries. EClinicalMedicine 61: 102055. Howe LD (2009) The wealth index as a measure of socio-economic position . London School of Hygiene and Tropical Medicine, University of London, London. Huang KY, Abura G, Theise R, et al. (2017) Parental Depression and Associations with Parenting and Children's Physical and Mental Health in a Sub-Saharan African Setting. Child psychiatry and human development 48(4): 517-527. Huang T, Yue Y, Wang H, et al. (2019) Infant Breastfeeding and Behavioral Disorders in School-Age Children. Breastfeeding medicine : the official journal of the Academy of Breastfeeding Medicine 14(2): 115-120. Ibrahim A, Abdalla SM, Jafer M, et al. (2019) Child labor and health: a systematic literature review of the impacts of child labor on child's health in low- and middle-income countries. Journal of public health (Oxford, England) 41(1): 18-26. Issaka AI, Agho KE and Renzaho AM (2017) Prevalence of key breastfeeding indicators in 29 sub-Saharan African countries: a meta-analysis of demographic and health surveys (2010-2015). Bmj Open 7(10): e014145. Jorns-Presentati A, Napp AK, Dessauvagie AS, et al. (2021) The prevalence of mental health problems in sub-Saharan adolescents: A systematic review. Plos One 16(5): e0251689. Kanellopoulou A, Antonogeorgos G, Douros K, et al. (2022) The Association between Obesity and Depression among Children and the Role of Family: A Systematic Review. Children (Basel, Switzerland) 9(8). Kang H (2013) The prevention and handling of the missing data. Korean Journal of Anesthesiology 54(5): 402-406. Krol KM and Grossmann T (2018) Psychological effects of breastfeeding on children and mothers. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz 61(8): 977-985. Kurtz M, Kawka H, Horstick O, et al. (2023) The prevalence of emotional abuse in children living in Sub-Saharan Africa - A systematic review. Child abuse & neglect 140: 106155. Kwok MK, Leung GM and Schooling CM (2013) Breast feeding and early adolescent behaviour, self-esteem and depression: Hong Kong's 'Children of 1997' birth cohort. Archives of disease in childhood 98(11): 887-894. Lavebratt C, Yang LL, Giacobini M, et al. (2019) Early exposure to antibiotic drugs and risk for psychiatric disorders: a population-based study. Translational psychiatry 9(1): 317. Levine GA, Bielicki J and Fink G (2022) Cumulative Antibiotic Exposure in the First 5 Years of Life: Estimates for 45 Low- and Middle-Income Countries From Demographic and Health Survey Data. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 75(9): 1537-1547. Lewis KJS, Mars B, Lewis G, et al. (2012) Do parents know best? Parent-reported vs. child-reported depression symptoms as predictors of future child mood disorder in a high-risk sample. Journal of Affective Disorders 141(2-3): 233-236. Lima NN, do Nascimento VB, de Carvalho SM, et al. (2013) Childhood depression: a systematic review. Neuropsychiatric disease and treatment 9: 1417-1425. Loret de Mola C, Horta BL, Goncalves H, et al. (2016) Breastfeeding and mental health in adulthood: A birth cohort study in Brazil. Journal of Affective Disorders 202: 115-119. Lurie I, Yang YX, Haynes K, et al. (2015) Antibiotic exposure and the risk for depression, anxiety, or psychosis: a nested case-control study. The Journal of clinical psychiatry 76(11): 1522-1528. Mabrouk A, Mbithi G, Chongwo E, et al. (2022) Mental health interventions for adolescents in sub-Saharan Africa: A scoping review. Frontiers in psychiatry 13: 937723. Malhotra S and Sahoo S (2018) Antecedents of depression in children and adolescents. Industrial psychiatry journal 27(1): 11-16. Maxwell NM, Nevin RL, Stahl S, et al. (2015) Prolonged neuropsychiatric effects following management of chloroquine intoxication with psychotropic polypharmacy. Clinical case reports 3(6): 379-387. McGrath JM, Records K and Rice M (2008) Maternal depression and infant temperament characteristics. Infant behavior & development 31(1): 71-80. Meier CR, Wilcock K and Jick SS (2004) The risk of severe depression, psychosis or panic attacks with prophylactic antimalarials. Drug safety 27(3): 203-213. MICS (2022) Nigeria - 2021 Multiple Indicator Cluster Survey (MICS) & National Immunization Coverage Survey (NICS): Survey Findings Report. Reportno. Report Number|, Date. Place Published|: Institution|. MICS (2024) Surveys . Available at: https://mics.unicef.org/surveys (accessed 18 June). Mokwena KE, Magabe S and Ntuli B (2023) Symptoms of Depression, Anxiety and Their Co-Occurrence among Orphaned Children in Sekhukhune District, Limpopo Province. Children (Basel, Switzerland) 10(8). Motlhatlhedi K, Setlhare V, Ganiyu A, et al. (2017) Association between depression in carers and malnutrition in children aged 6 months to 5 years. African journal of primary health care & family medicine 9(1): e1-e6. Mullen S (2018) Major depressive disorder in children and adolescents. The mental health clinician 8(6): 275-283. Nevin RL and Croft AM (2016) Psychiatric effects of malaria and anti-malarial drugs: historical and modern perspectives. Malaria journal 15: 332. NMEP (2022) Nigeria malaria indicator survey 2021 final report. Reportno. Report Number|, Date. Place Published|: Institution|. Nwaneli EI, Eguonu I, Ebenebe JC, et al. (2020) Malaria prevalence and its sociodemographic determinants in febrile children - a hospital-based study in a developing community in South-East Nigeria. Journal of preventive medicine and hygiene 61(2): E173-E180. Nyeko R, Otim F, Obiya EM, et al. (2023) Anti-malarial drug use, appropriateness and associated factors among children under-five with febrile illnesses presenting to a tertiary health facility: a cross sectional study. Malaria journal 22(1): 103. Okiro EA and Snow RW (2010) The relationship between reported fever and Plasmodium falciparum infection in African children. Malaria journal 9: 99. Oluwole D, Mason E and Costello A (2000) Management of childhood illness in Africa. Early evaluations show promising results. BMJ (Clinical research ed ) 320(7235): 594-595. Oshagbemi OA, Lopez-Romero P, Winnips C, et al. (2023) Estimated distribution of malaria cases among children in sub-Saharan Africa by specified age categories using data from the Global Burden of Diseases 2019. Malaria journal 22(1): 371. Owen JP, Baig B, Abbo C, et al. (2016) Child and adolescent mental health in sub-Saharan Africa: a perspective from clinicians and researchers. BJPsych international 13(2): 45-47. Partap U, Nyundo A, Manu A, et al. (2023) Depressive symptoms among adolescents in six sub-Saharan African countries: A pooled analysis of associated factors. Preventive medicine reports 36: 102499. Patra S (2019) Assessment and management of pediatric depression. Indian Journal of Psychiatry 61(3): 300-306. Prichett LM, Yolken RH, Wu L, et al. (2022) Relationship between antibiotic exposure and subsequent mental health disorders in a primary care health system. Brain, behavior, & immunity - health 21: 100430. Rubin LH, Witkiewitz K, Andre JS, et al. (2007) Methods for Handling Missing Data in the Behavioral Neurosciences: Don't Throw the Baby Rat out with the Bath Water. Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience 5(2): A71-77. Saeed N and Wemakor A (2019) Prevalence and determinants of depression in mothers of children under 5 years in Bole District, Ghana. BMC research notes 12(1): 373. Salk RH, Hyde JS and Abramson LY (2017) Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychological bulletin 143(8): 783-822. Sarfo JO, Amoadu M, Kordorwu PY, et al. (2023) Malaria amongst children under five in sub-Saharan Africa: a scoping review of prevalence, risk factors and preventive interventions. European journal of medical research 28(1): 80. Schumacher RF and Spinelli E (2012) Malaria in children. Mediterranean journal of hematology and infectious diseases 4(1): e2012073. Selph SS and McDonagh MS (2019) Depression in Children and Adolescents: Evaluation and Treatment. American family physician 100(10): 609-617. Sequeira M, Singh S, Fernandes L, et al. (2022) Adolescent Health Series: The status of adolescent mental health research, practice and policy in sub-Saharan Africa: A narrative review. Tropical medicine & international health : TM & IH 27(9): 758-766. Shah JA, Emina JB, Eckert E, et al. (2015) Prompt access to effective malaria treatment among children under five in sub-Saharan Africa: a multi-country analysis of national household survey data. Malaria journal 14: 329. Sherr L, Yakubovich AR, Skeen S, et al. (2020) Depressive symptoms among children attending community based support in South Africa - pathways for disrupting risk factors. Clinical child psychology and psychiatry 25(4): 984-1001. Shimizu M, Nakata Y and Takahashi K (2023) Current findings and gaps in early initiation of breastfeeding practices in sub-Saharan African countries: A scoping review. Journal of global health 13: 04036. Song X, Wei W, Cheng W, et al. (2022) Cerebral malaria induced by plasmodium falciparum : clinical features, pathogenesis, diagnosis, and treatment. Frontiers in cellular and infection microbiology 12: 939532. Stavseth MR, Clausen T and Roislien J (2019) How handling missing data may impact conclusions: A comparison of six different imputation methods for categorical questionnaire data. SAGE open medicine 7: 2050312118822912. Taddio A, McMurtry CM, Logeman C, et al. (2022) Prevalence of pain and fear as barriers to vaccination in children - Systematic review and meta-analysis. Vaccine 40(52): 7526-7537. Thabane L, Mbuagbaw L, Zhang S, et al. (2013) A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC medical research methodology 13: 92. Tomlinson M and Morgan B (2015) Infant Mental Health Research in Africa: a call for action for research in the next 10 years. Global mental health (Cambridge, England) 2: e7. Trape JF, Diagne N, Diene-Sarr F, et al. (2023) One hundred malaria attacks since birth. A longitudinal study of African children and young adults exposed to high malaria transmission. EClinicalMedicine 67: 102379. Woldeamanuel BT (2020) Trends and factors associated to early initiation of breastfeeding, exclusive breastfeeding and duration of breastfeeding in Ethiopia: evidence from the Ethiopia Demographic and Health Survey 2016. International breastfeeding journal 15(1): 3. Yohani S, Salami B, Taylor M, et al. (2023) African migrant children's mental health: A scoping review. International Journal of Intercultural Relations 97. Zhong BL, Ding J, Chen HH, et al. (2013) Depressive disorders among children in the transforming China: an epidemiological survey of prevalence, correlates, and service use. Depression and anxiety 30(9): 881-892. Zong X, Wu H, Zhao M, et al. (2021) Global prevalence of WHO infant feeding practices in 57 LMICs in 2010-2018 and time trends since 2000 for 44 LMICs. EClinicalMedicine 37: 100971. Tables Table 1 – Descriptive data for study variables Childhood depression Variables Whole sample No Yes P Antibiotics Amoxicillin 471 (2.8%) 180 (2.6%) 291 (3%) χ 2 (1, N = 16720) = 2.208, p > 0.05 Cotrimoxazole 139 (0.8%) 43 (0.6%) 96 (1.0%) χ 2 (1, N = 16720) = 6.495, p 0.05 Antimalarials ACT 810 (4.8%) 272 (3.9%) 538 (5.5%) χ 2 (1, N = 16720) = 22.225, p < 0.001 Fansidar 165 (1.0%) 52 (0.7%) 113 (1.2%) χ 2 (1, N = 16720) = 6.900, p < 0.01 Chloroquine 234 (1.4%) 71 (1.0%) 163 (1.7%) χ 2 (1, N = 16720) = 12.261, p 0.05 Quinine (pills) 74 (0.4%) 23 (0.3%) 51 (0.5%) χ 2 (1, N = 16720) = 3.350, p < 0.05 Quinine (injection) 229 (1.4%) 71 (1.0%) 158 (1.6%) χ 2 (1, N = 16720) = 10.619, p 0.05 Artesunate (injection) 382 (2.3%) 129 (1.9%) 253 (2.6%) χ 2 (1, N = 16720) = 9.727, p = 0.001 Breastfeeding (ever) 4864 (92.6%) 2238 (93.8%) 2626 (91.6%) χ 2 (1, N = 5253) = 9.218, p = 0.001 Covariates Age (number of days) M, 975.11 (SD: 790.015 ) M,1312.71 (SD: 782.450) M,1331.35 (SD: 667.279) t (13429.854) = 1.612, p > .05 Gender (% Female) 8231 (49.2%) 3449 (49.7%) 4782 (48.9%) χ 2 (1, N = 16720) = 0.863, p > 0.05 Weight (kg) (MICS + NICS) M, 0.990 (SD: 0.959) M, 1.120 (SD:1.00) M, 0.932 (SD:0.978) t (14708.053) = -12.011, p < .001 Area (% rural) 12285 (73.5%) 4925 (70.9%) 7360 (75.3%) χ 2 (1, N = 16720) = 40.293, p < 0.001 Education (% educated) 9273 (55.5%) 4165 (60.0%) 5108 (52.3%) χ 2 (1, N = 16718) = 97.570, p < 0.001 Functional difficulties 738 (4.4%) 229 (3.3%) 509 (5.2%) χ 2 (1, N = 16720) = 35.140, p 0.05 Wealth index (HH assets) M, 2.56 (SD: 1.349) M, 2.61 (SD:1.365) M, 2.45 (SD:1.276) t (14324.925) = -7.613, p < .001 Illness symptoms (fever) 4094 (24.5%) 1537 (22.1%) 2557 (26.2%) χ 2 (1, N = 16720) = 35.725, p < 0.001 Illness symptoms (cough) 2958 (17.7%) 1087 (15.6%) 1871 (19.1%) χ 2 (1, N = 16720) = 34.028, p < 0.001 Notes. NR = non-response, HH = Household. All values are sample size and percentages (%), unless means (M) and standard deviations (SDs) are stated. Percentages for all medicines depict the proportion of children exposed to each drug. Percentage for breastfeeding indicates the proportion of children who had ever been breastfed. Percentage for education (maternal) refers to the proportion with primary, secondary, or higher education. Percentage for ethnicity/tribe depicts proportion of children from Hausa, Igbo, or Yoruba backgrounds. P values relate to 1-sided comparisons between depressed and non-depressed children, based on Chi-square or independent samples t-tests. Table 2 Hierarchical logistic regression of predictors of paediatric depression in all infants and children. Model 1 Model 2 Model 3 Predictors ß Exp( ß ) (95% CI) ß Exp( ß ) (95% CI) ß Exp( ß ) (95% CI) Antibiotics (Amoxicillin) -0.003 0.997 (0.719, 1.382) -0.008 0.992 (0.715, 1.375) 0.000 1.019 (0.727, 1.427) Antibiotics (Cotrimoxaz) 0.595 1.812 (0.956, 3.435) 0.605 1.831 (0.966, 3.471) 0.444 1.559 (0.814, 2.988) Antibiotics (Herbal) 0.020 1.020 (0.654, 1.589) 0.015 1.015 (0.651, 1.583) -0.058 0.944 (0.597, 1.492) Antimalaria (ACT) 0.499 c 1.647 (1.262, 2.150) 0.501 c 1.650 (1.264, 2.153) 0.565 c 1.760 (1.316, 2.355) Antimalaria (Fansidar) 0.047 1.048 (0.606, 1.813) 0.026 1.026 (0.592, 1.777) 0.008 1.008 (0.575, 1.770) Antimalaria (Chloroquine) 0.490 a 1.632 (1.022, 2.604) 0.477 a 1.612 (1.009, 2.574) 0.460 1.584 (0.982, 2.556) Antimalaria (Amodiaquine) 0.411 1.508 (0.720, 3.157) 0.416 1.516 (0.724, 3.176) 0.569 1.766 (0.834, 3.738) Antimalaria (Quinine/pills) 0.601 1.824 (0.787, 4.227) 0.588 1.801 (0.777, 4.177) 0.649 1.914 (0.815, 4.494) Antimalaria (Quinine/inject) -0.307 0.736 (0.449, 1.205) -0.296 0.744 (0.454, 1.219) -0.255 0.775 (0.467, 1.285) Antimalaria (Artesunate/rectal) 0.275 1.316 (0.686, 2.523) 0.292 1.339 (0.698, 2.569) 0.420 1.522 (0.779, 2.973) Antimalaria (Artesunate/inject) 0.536 b 1.710 (1.137, 2.573) 0.546 b 1.726 (1.147, 2.598) 0.563 b 1.757 (1.150, 2.684) Breastfeeding (ever) -0.330 b 0.719 (0.581, 0.890) -0.254 a 0.776 (0.625, 0.963) Age (in days) 0.000 1.000 (1.000, 1.000) Gender (M/F) -0.011 0.989 (0.886, 1.105) Weight (MICS/NICS) 0.200 c 0.819 (0.768, 0.873) Area (urban/rural) 0.055 1.057 (0.910, 1.227) Education (mothers) -0.303 c 0.738 (0.649, 0.840) Childs functional difficulties 0.366 b 1.442 (1.146, 1.814) Ethnicity(tribe) 0.071 1.074 (0.954, 1.209) Wealth index -0.028 0.972 (0.920, 1.027) Medical (fever) -0.132 0.876 (0.746, 1.030) Medical (cough) 0.213 b 1.238 (1.067, 1.436) Nagelkerke R 2 0.010 0.012 0.040 Cox & Snell R 2 0.007 0.009 0.030 -2 Log likelihood 7199.049 7189.666 7076.719 No. of observations 31103 31103 31103 Model Chi-square 37.471 c 46.854 c 159.801 c Note . Coding: Unless otherwise indicated, all predictor variables except for age, weight, and wealth index, are dummy variables, coded as ‘Yes’ (1), ‘No’ (0)). Gender is coded as ‘Male’ (1), ‘Female’ (0). Geographical area is coded as ‘Urban’ (1) vs ‘Rural’ (0). Education (mothers) is coded as ‘None’ (0), ‘Primary’/‘Secondary’/‘Higher’ (1). Ethnicity (of household head) is coded as ‘Hausa’/‘Igbo’/‘Yoruba’/‘Fulani’ (1), ‘Other’ (0). ACT = Artemisinin-based combination therapy; MICS = Multiple Indicator Cluster Surveys; NICS = National Immunization Coverage Survey. ‘No’ includes ‘don’t know’ and no response. a p ≤ 0.05, b p ≤ 0.01, c p ≤ 0.001, Table 3 Hierarchical logistic regression of predictors of paediatric depression in male infants and children. Model 1 Model 2 Model 3 Predictors ß Exp( ß ) (95% CI) ß Exp( ß ) (95% CI) ß Exp( ß ) (95% CI) Antibiotics (Amoxicillin) -0.174 0.840 (0.526, 1.342) -0.175 0.840 (0.526, 1.341) -0.128 0.879 (0.544, 1.423) Antibiotics (Cotrimoxaz) 0.342 1.408 (0.579, 3.423) 0.349 1.418 (0.583, 3.448) 0.236 1.266 (0.508, 3.157) Antibiotics (Herbal) 0.462 1.587 (0.824, 3.054) 0.457 1.579 (0.820, 3.041) 0.371 1.449 (0.738, 2.846) Antimalaria (ACT) 0.783 c 2.188 (1.490, 3.212) 0.788 c 2.199 (1.498, 3.229) 0.853 c 2.347 (1.547, 3.559) Antimalaria (Fansidar) -0.180 0.835 (0.417, 1.674) -0.198 0.820 (0.409, 1.645) -0.159 0.853 (0.416, 1.746) Antimalaria (Chloroquine) 0.536 1.709 (0.865, 3.377) 0.524 1.689 (0.854, 3.339) 0.514 1.672 (0.833, 3.354) Antimalaria (Amodiaquine) 0.231 1.260 (0.351, 4.528) 0.226 1.254 (0.349, 4.508) 0.485 1.624 (0.444, 5.933) Antimalaria (Quinine/pills) 0.422 1.525 (0.506, 4.598) 0.406 0.501 (0.497, 4.528) 0.448 1.565 (0.509, 4.812) Antimalaria (Quinine/inject) -0.239 0.788 (0.412, 1.504) -0.231 0.793 (0.415, 1.516) -0.192 0.826 (0.420, 1.622) Antimalaria (Artesunate/rectal) 0.596 1.815 (0.769, 4.287) 0.607 1.834 (0.776, 4.335) 0.789 2.200 (0.914, 5.298) Antimalaria (Artesunate/inject) 0.571 a 1.770 (1.020, 3.071) 0.581 a 1.788 (1.030, 3.105) 0.552 1.736 (0.977, 3.084) Breastfeeding (ever) -0.241 0.786 (0.589, 1.050) -0.158 0.854 (0.637, 1.146) Age (in days) 0.000 1.000 (1.000, 1.000) Weight (MICS/NICS) -0.183 c 0.833 (0.762, 0.910) Area (urban/rural) 0.053 1.055 (0.858, 1.296) Education (mothers) -0.332 c 0.717 (0.599, 0.859) Childs functional difficulties 0.314 1.369 (0.991, 1.892) Ethnicity(tribe) 0.136 1.145 (0.970, 1.352) Wealth index -0.038 0.962 (0.892, 1.038) Medical (fever) -0.100 0.905 (0.719, 1.139) Medical (cough) 0.068 1.070 (0.869, 1.318) Nagelkerke R 2 0.016 0.017 0.045 Cox & Snell R 2 0.012 0.013 0.034 -2 Log likelihood 3682.286 3679.599 3621.728 No. of observations 15799 15799 15799 Model Chi-square 32.023 c 34.709 c 92.581 c Note . Coding: Unless otherwise indicated, all predictor variables except for age, weight, and wealth index, are dummy variables, coded as ‘Yes’ (1), ‘No’ (0)). Geographical area is coded as ‘Urban’ (1) vs ‘Rural’ (0). Education (mothers) is coded as ‘None’ (0), ‘Primary’/‘Secondary’/‘Higher’ (1). Ethnicity (of household head) is coded as ‘Hausa’/‘Igbo’/‘Yoruba’/‘Fulani’ (1), ‘Other’ (0). ACT = Artemisinin-based combination therapy; MICS = Multiple Indicator Cluster Surveys; NICS = National Immunization Coverage Survey. ‘No’ includes ‘don’t know’ and no response. a p ≤ 0.05, b p ≤ 0.01, c p ≤ 0.001, Table 4 Hierarchical logistic regression of predictors of paediatric depression in female infants and children. Model 1 Model 2 Model 3 Predictors ß Exp( ß ) (95% CI) ß Exp( ß ) (95% CI) ß Exp( ß ) (95% CI) Antibiotics (Amoxicillin) 0.099 1.104 (0.695, 1.752) 0.086 1.090 (0.686, 1.731) 0.075 1.078 (0.666, 1.745) Antibiotics (Cotrimoxaz) 0.863 2.370 (0.927, 6.060) 0.878 2.407 (0.941, 6.159) 0.677 1.968 (0.760, 5.100) Antibiotics (Herbal) -0.369 0.691 (0.368, 1.298) -0.373 0.689 (0.366, 1.294) -0.457 0.633 (0.329, 1.217) Antimalaria (ACT) 0.207 1.231 (0.842, 1.798) 0.199 1.220 (0.834, 1.783) 0.273 1.314 (0.864, 1.999) Antimalaria (Fansidar) 0.399 1.491 (0.591, 3.757) 0.380 1.462 (0.578, 3.695) 0.249 1.282 (0.501, 3.282) Antimalaria (Chloroquine) 0.435 1.546 (0.803, 2.976) 0.420 1.522 (0.789, 2.936) 0.367 1.443 (0.737, 2.828) Antimalaria (Amodiaquine) 0.525 1.690 (0.680, 4.199) 0.541 1.717 (0.691, 4.270) 0.627 1.871 (0.740, 4.730) Antimalaria (Quinine/pills) 0.803 2.233 (0.594, 8.398) 0.803 2.233 (0.593, 8.407) 0.956 2.602 (0.678, 9.985) Antimalaria (Quinine/inject) -0.349 0.705 (0.326, 1.528) -0.330 0.719 (0.332, 1.558) -0.270 0.764 (0.347, 1.682) Antimalaria (Artesunate/rectal) -0.072 0.930 (0.331, 2.616) -0.040 0.961 (0.342, 2.702) -0.038 0.963 (0.329, 2.822) Antimalaria (Artesunate/inject) 0.437 1.548 (0.835, 2.869) 0.442 1.555 (0.839, 2.884) 0.511 1.667 (0.879, 3.161) Breastfeeding (ever) -0.443 b 0.642 (0.468, 0.881) -0.373 a 0.689 (0.498, 0.951) Age (in days) 0.000 1.000 (1.000, 1.000) Weight (MICS/NICS) -0.206 c 0.813 (0.741, 0.893) Area (urban/rural) 0.054 1.056 (0.850, 1.311) Education (mothers) -0.280 b 0.756 (0.628, 0.911) Childs functional difficulties 0.422 1.525 (1.099, 2.116) Ethnicity(tribe) 0.006 1.006 (0.849, 1.193) Wealth index -0.021 0.979 (0.904, 1.062) Medical (fever) -0.154 0.857 (0.681, 1.078) Medical (cough) 0.378 c 1.460 (1.177, 1.810) Nagelkerke R 2 0.009 0.013 0.045 Cox & Snell R 2 0.007 0.010 0.033 -2 Log likelihood 3505.412 3497.668 3435.536 No. of observations 15304 15304 15304 Model Chi-square 16.730 24.474 a 86.606 c Note . Coding: Unless otherwise indicated, all predictor variables except for age, weight, and wealth index, are dummy variables, coded as ‘Yes’ (1), ‘No’ (0)). Geographical area is coded as ‘Urban’ (1) vs ‘Rural’ (0). Education (mothers) is coded as ‘None’ (0), ‘Primary’/‘Secondary’/‘Higher’ (1). Ethnicity (of household head) is coded as ‘Hausa’/‘Igbo’/‘Yoruba’/‘Fulani’ (1), ‘Other’ (0). ACT = Artemisinin-based combination therapy; MICS = Multiple Indicator Cluster Surveys; NICS = National Immunization Coverage Survey. ‘No’ includes ‘don’t know’ and no response. a p ≤ 0.05, b p ≤ 0.01, c p ≤ 0.001, Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 May, 2025 Read the published version in BMC Pediatrics → Version 1 posted Editorial decision: Revision requested 27 Oct, 2024 Reviews received at journal 25 Oct, 2024 Reviews received at journal 23 Oct, 2024 Reviewers agreed at journal 20 Oct, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviewers agreed at journal 15 Oct, 2024 Reviewers agreed at journal 15 Oct, 2024 Reviewers agreed at journal 03 Oct, 2024 Reviewers invited by journal 30 Sep, 2024 Editor invited by journal 16 Sep, 2024 Editor assigned by journal 13 Sep, 2024 Submission checks completed at journal 13 Sep, 2024 First submitted to journal 05 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5036450","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377093278,"identity":"102285ad-276f-4ceb-92e5-1d2e7fa10402","order_by":0,"name":"Kanayo Umeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBAC9gY2MJ3Az86QABVjw6+F5wBUi2QzyVoMDsPFCGlhYEtg5s2xyTM+zPB0Mw+DnTyDRFoCIS0HmHm3pRWbHWZIu83DkGzYIJF2AK8We2AAALUcTtwG0cKcwCCR3kDAFrCW/4mbm8Fa6onRAnbYgcQNzGAth4FaCDiMh5kt4eDcbcmJM4AOuznH4LhhG8+zBPxa2NsMH7zdZpfY396TduNNRbU8P3uaAV4tDMwMDFB38AANNyAYKyiA/QAJikfBKBgFo2AkAQCmCDxwy5f2tAAAAABJRU5ErkJggg==","orcid":"","institution":"Liverpool John Moores University","correspondingAuthor":true,"prefix":"","firstName":"Kanayo","middleName":"","lastName":"Umeh","suffix":""},{"id":377093279,"identity":"d2bdb83e-357d-4827-b27d-ee8286205a40","order_by":1,"name":"S Adaji","email":"","orcid":"","institution":"Bousfield Health Centre","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"","lastName":"Adaji","suffix":""}],"badges":[],"createdAt":"2024-09-05 08:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5036450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5036450/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12887-024-05265-1","type":"published","date":"2025-05-17T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69056122,"identity":"92e6a594-d804-4cc7-8356-8b29477e9672","added_by":"auto","created_at":"2024-11-15 06:24:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62201,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of data collection method and samples from the 2021 MICS/NICS conducted in Nigeria.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5036450/v1/bbfe6e36b3701ae40499b766.png"},{"id":69056121,"identity":"d48be1bd-10ae-4c2c-a191-6f2ac9576c39","added_by":"auto","created_at":"2024-11-15 06:24:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14753,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted odds ratios for significant predictor variables in the whole sample (N = 31,103 children aged under 5). The outcome measure is depression status. Estimates are from the final logistic regression model, which included all predictor variables including medicinal use (i.e., antibiotics, antimalarials), breastfeeding, and various covariates (e.g., age, gender, weight, geographical area, body weight, child’s functional difficulties, and wealth index). Only odds ratios for key predictors (medicinal use and breastfeeding) are shown, for better readability. Estimates for the regression models are in shown in Table 2.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5036450/v1/1674762d41ca4bf171501289.png"},{"id":83067952,"identity":"ef715599-6663-461b-810d-a06d41d6e65f","added_by":"auto","created_at":"2025-05-19 16:08:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1797736,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5036450/v1/8bf5bf5e-92d7-4714-87df-86d2479bbe22.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role of breastfeeding, antibiotics and antimalarial medicinal exposure in paediatric depression amongst infants and children aged under 5: A population-based study","fulltext":[{"header":"BACKGROUND","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Childhood depression in Sub-Saharan Africa\u003c/h2\u003e \u003cp\u003eAlthough paediatric depression is a growing public health concern worldwide (Patra, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lima et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mullen, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), there has been limited research on the scope of the problem and its associated factors, especially in sub-Saharan Africa (Tomlinson and Morgan, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). One reason for the limited evidence base is that diagnosing depression in children, particularly very young ones (e.g., under 5s), can be challenging (Dolinsky et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Patra, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Symptom presentation is complicated by age, and emotional, cognitive, and biological complexities (Selph and McDonagh, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Infants/toddlers are unable to verbalise being depressed, and may not meet clinical diagnostic criteria (e.g., Diagnostic and Statistical Manual for Mental Disorders (DSM-5)) (Mullen, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Research suggests that, for very young children (aged under 12), researchers must rely on parental reports, which are better at predicting mood disorders than child feedback (Lewis et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChildhood depression is a recognised problem in sub-Saharan Africa, with 1 in 10 (9.5%) children and adolescents living with psychiatric conditions, including mood disorders (Cortina et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tomlinson and Morgan, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, existing research on depression in the region is almost entirely based on general populations (Gbadamosi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yohani et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), adolescents (Sequeira et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Partap et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Aluh et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jorns-Presentati et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mabrouk et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), mixed populations (Owen et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), children aged over 5 years (Cortina et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sherr et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mokwena et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), or parents/caretakers (Motlhatlhedi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saeed and Wemakor, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There is almost no data on paediatric depression in children aged under 5 years, from sub-Saharan Africa (Tomlinson and Morgan, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Epidemiological evidence on prevalence rates and/or causal factors in this age-group is extremely rare (Owen et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This paucity of data is problematic because young children in sub-Saharan Africa face mental health challenges aggravated by chronic poverty, traumatic childhood events (e.g., witnessing violence), and illiteracy, compounded by parental difficulties, such as maternal depression (Tomlinson and Morgan, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kurtz et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Amene et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jorns-Presentati et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While a few studies from southern Africa have published data on depression in very young children (Mokwena et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sherr et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), including under 5\u0026rsquo;s (Drago et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Motlhatlhedi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), evidence from West African countries is more difficult to find.\u003c/p\u003e \u003cp\u003eWith regards to underlying factors of childhood depression, literature is even more scant and non-existent in sub-Saharan countries. Rarely explored factors that may contribute to depression in West African children include medicinal use, particularly antimalarial (Nevin and Croft, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and antimicrobial (i.e., antibiotic) medication (Dinan and Dinan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and breastfeeding history, which have been subjects of much debates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Exposure to antimalarial medicines\u003c/h2\u003e \u003cp\u003eMalaria infestation is a common cause of febrile illness among children in sub-Saharan Africa (Okiro and Snow, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Trape et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Oshagbemi et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Malarial treatment among Nigerian children, as in adults, follows WHO guidelines with recommendation for artemisinin-based combination therapy (ACT) (FMoH, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Among under-5 Nigerian children, there has been incremental resort to use ACT; from 7% in 2008 when the ACT was introduced to 74% in 2021 (NMEP, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hossain et al recently reviewed Malaria Survey datasets from 19 low- and middle-income countries (LMIC) and found that about one-fifth of Nigerian children under \u0026ndash; 5 were suspected to have received antimalarial treatment (Hossain et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAntimalarial drugs have been associated with mental and neurological dysfunction (Bitta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nevin and Croft, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Meier et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). For example, antimalarial medicines (e.g., chloroquine) may produce a wide spectrum of psychopathological experiences, including mood disorders (Maxwell et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although the psychiatric effects of antimalarial drugs in young children are complex and not well understood (Nevin and Croft, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the term \u0026lsquo;cerebral malaria\u0026rsquo; is a recognised neurological complication in children aged under 5 years, which entails a history of fever (2\u0026ndash;3 days), and may include cognitive dysfunction (Song et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChildren aged under 5 account for two-thirds of all malaria deaths in sub-Saharan Africa (Sarfo et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, unsurprisingly, use of antimalarial medicines during pregnancy is common in the region (Nyeko et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). An analysis of cross-sectional data from 32,397 children aged under 5, from five malaria-endemic African countries, including Guinea, Mali, Nigeria, and Senegal, revealed anti-malarial consumption rates as high as 66.3%, (Guinea), 70.5% (Mali), and 31.6% (Nigeria), over the 2-week period prior to the survey (Hossain et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). However, the psychiatric aspects of antimalarial drugs in infants and very young children is not well understood (Aneja et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hitherto, most studies on the neurological effects of antimalarial medicines have focused on adult or mixed populations outside sub-Saharan Africa (Meier et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bitta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nevin and Croft, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Maxwell et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Exposure to antibiotics\u003c/h2\u003e \u003cp\u003eUse of antibiotics to treat febrile illnesses like respiratory tract infections and diarrhoeal diseases in young children is common in sub-Saharan Africa, especially in Nigeria (Auta et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). DHS data on antibiotic treatments in children under 5, from 45 countries (438 140 child-observations), showed that 38.4% (95% CI, 37.9% \u0026ndash; 38.8%) of children with febrile symptoms received antibiotics (Levine et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Other data from LMICs shows antibiotics were prescribed to 80\u0026middot;5% of children diagnosed with respiratory illness, 50\u0026middot;1% with diarrhoea, and 28\u0026middot;3% with malaria. The mean number of antibiotic prescriptions issued to children between birth and age 5 years across eight LMICs was 24\u0026middot;5 (95% CI 22\u0026middot;6\u0026ndash;26\u0026middot;7), ranging from 7\u0026middot;1 (6\u0026middot;3\u0026ndash;7\u0026middot;9) in Senegal to 59\u0026middot;1 (54\u0026middot;1\u0026ndash;64\u0026middot;6) in Uganda (Fink et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Nigeria specifically, total antibiotic exposure in the first 5 years of life was 9.6 (95% CI 8.6\u0026ndash;10.6) with formal health services being the main source of antibiotics in most cases (Levine et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A study by Hossain et al estimated that 4.3% of Nigerian children with fever were exposed to antibiotic use (Hossain et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Nigerian children aged under 5 years are more likely to be exposed to antibiotics by their mothers (Efunshile et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). An analysis of 450 out-patient case notes for under 5 years children from two Nigerian maternity hospitals, and medical records of 3700 under-five children treated at a third hospital, found that the majority (72%) of mothers administered antibiotics to their child, with only about half (48.5%) prescribed by a physician (Adisa et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A meta-analysis of data from 30 countries in sub-Saharan Africa revealed that Nigeria has one of the highest rates of antibiotic usage in West African children under 5 years (Auta et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAntibiotic usage has also been associated with an increased risk of psychiatric disorders, including depression (Lurie et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lavebratt et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Prichett et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), through complex bidirectional biological interactions between gut bacteria and the brain ('brain\u0026ndash;gut\u0026ndash;microbiota axis') (Dinan and Dinan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, most research in this area has focused on adult, adolescent, or mixed populations, and moreover isn\u0026rsquo;t specific to sub-Saharan Africa (Lurie et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Prichett et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Some limited data suggests antibiotics exposure may contribute to depression in children aged a under 5. For example, a population-based longitudinal study of 1\u0026nbsp;million births in Finland, from 1996 to 2012, followed up through 2014, found that antibiotics exposure in utero and during the first two postnatal years was associated with a slightly higher risk of psychiatric dysfunction, including mood disorders (e.g., depression), after adjusting for pregnancy- and birth-related covariates. Antibiotic exposure during pregnancy (trimesters 1 to 3) was associated with a 14\u0026ndash;15% increased risk of offspring psychopathology. Antibiotics usage after 2 years age was also related to a slightly increased risk of psychiatric dysfunction (Lavebratt et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Exposure to breastfeeding\u003c/h2\u003e \u003cp\u003eMeta\u0026ndash;analytic evidence from 29 sub-Saharan African countries suggests lower breast-feeding rates (both early initiation, and exclusive breastfeeding) in West and Central African countries (Issaka et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Breastfeeding levels in these regions were below the WHO\u0026rsquo;s recommended 50% (Zong et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, inadequate breastfeeding has been implicated in paediatric depression (Kwok et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), albeit the data is inconclusive. A systematic review of 21 studies on the effects of breastfeeding on mental health in mothers and children, including five studies of childhood depressive disorders, found conflicting evidence (Bugaeva et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While two studies found no link between breastfeeding and childhood depression, after adjusting for multiple covariates (Kwok et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Loret de Mola et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), two other investigations observed a modest protective effect for breastfeeding (i.e., lack of breastfeeding was associated with a higher likelihood of paediatric depression later in life (6 \u0026minus;\u0026thinsp;16 years of age)) (Zhong et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Allen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Other research suggests breastfeeding improves cognitive and brain functioning in children, partly due to fatty acids present in breastmilk (Krol and Grossmann, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Regardless, data from sub-Sahara Africa is extremely limited, and more research is needed to better understand how inadequate breastfeeding relates to psychiatric disorders in infants/children from this region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; The present study\u003c/h2\u003e \u003cp\u003eThis study explores the role of breastfeeding and commonly prescribed anti-microbials in suspected depression amongst children aged under 5. Although previous studies have assessed paediatric depression in very young children from sub-Saharan Africa, including under 5\u0026rsquo;s, most of this evidence is from southern Africa (Mokwena et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sherr et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Drago et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Motlhatlhedi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Data from West African countries is more difficult to find, despite the high rates of antimalarial and antibiotic consumption in West African children (Efunshile et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Auta et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e), lower breast-feeding rates in the region (Issaka et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and evidence implicating antimalarial/antibiotic drugs and breastfeeding in childhood depression (Bitta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nevin and Croft, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lavebratt et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Prichett et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To address this gap in the literature, as well as generate a body of information that would improve understanding of childhood depression, the current study analysed survey data from 30,804 Nigerian children aged under 5. We hypothesised that children suspected to have depression are less likely to have been breastfed, and more likely to have received antimalarial and antibiotic medication, consistent with previous research.\u003c/p\u003e\u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u003c/p\u003e\n\u003cp\u003e[Insert \u003cstrong\u003eFigure 1\u003c/strong\u003e about here]\u003c/p\u003e\n\u003cp\u003eSummary of data collection stages from the 2021 MICS/NICS conducted in Nigeria.\u003c/p\u003e\n\u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e"},{"header":"METHODS","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis study analysed data from the 2021 MICS/NICS conducted in Nigeria. The survey was funded by the Nigerian government, UNICEF, GAVI, and BMGF, and implemented by NBS, with technical support from UNICEF. Detailed and comprehensive information about MICS/NICS history in Nigeria, and organisation, methodology, sampling and implementation of the 2021 survey in the region, including personnel involved and questionnaires used, have been published elsewhere\u0026nbsp;(MICS, 2022). For the purposes of this paper, we will report only information relevant to the current objectives. The MICS survey collected data on socio-demographic and socio-economic characteristics (e.g., household, and personal assets), and multiple health-related factors concerning women and children, including post-natal care, disease episodes such as diarrhoea and malaria, treatment of children with fever, antimalarial treatment, use of antibiotics, infant and young child feeding including breastfeeding, and equity-related topics, such as child functioning and subjective well-being.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData collection stages are summarised in Figure 1. Data was collected using five questionnaires, including a household survey on basic demographic information, a survey administered to all women in each household aged 15 to 49 years, and an under 5 questionnaire undertaken by mothers or caretakers of all children aged below 5 years, living in each household. Although the questions were based on standard MICS6 \u0026nbsp;measures\u0026nbsp;(MICS, 2022), they were pre-tested and customised in several regions (states) of Nigeria during July 2021. Data collection involved pre-tested computer-assisted personal interviewing (CAPI), whereby a respondent or interviewer uses an electronic device (tablet computers, running the Window 10 operating system), to answer the questions. The interviews and questionnaires were translated into English, or the respondent’s native language (e.g., Hausa, Igbo, Fulani), based on literacy testing. Data collection occurred from September to December 2021 and was conducted by 74 teams of interviewers\u0026nbsp;(MICS, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe initial target sample size for the MICS was 37,000 households, covering 36 states, the Federal Capital Territory (FCT), and six geo-political zones in Nigeria. Within each region, a household listing was first generated in a selected number of census enumeration areas. A systematic sample of 20 households was then selected in each area. In total, 31,103 \u0026nbsp;children aged under 5 years participated. Of this figure, more than half were male (50.4%), aged 24 to 59 months (two to five years) (62.4%), and lived in a rural area (63.4%). For most children (94.8%) their mother was the respondent to the under – 5 questionnaire/interview, while a primary caretaker responding on behalf of the other children. Most of the mothers either lacked any formal education, or had only primary school education (55.2%), and almost all the children (97%) lacked health insurance coverage.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eChildhood depression\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInfants, toddlers, and very young children (aged under 5) are generally unable to verbalise being depressed, and hence may not meet the clinical diagnostic criteria (e.g., Diagnostic and Statistical Manual for Mental Disorders (DSM-5)) for depression\u0026nbsp;(Mullen, 2018). Consequently, previous research has relied on parental observations and reports, which can be better at establishing mood disorders in very young children\u0026nbsp;(Lewis et al., 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChildhood 'depression' or 'sadness' in the MICS was assessed with a single question\u0026nbsp;(MICS, 2022). This item was part of series of multiple-choice questions assessing early childhood development, including linguistic acuity and interactions with other children and household members. The data collector made a specific statement, directed at the child's mother or caretaker: 'The next two questions have five different options for answers. I am going to read these to you after each the question. How often does [name] seem to be very sad or depressed? Would you say: ‘daily’ (coded 1), ‘weekly’ (coded 2), ‘monthly’ (coded 3), ‘a few times a year’ (coded 4) or ‘never’ (coded 5)'? Depending on participant feedback the researcher could record two additional response options: ‘don’t know’ (coded 8) or ‘no response’ (coded 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the purposes of subsequent binary logistic regression analysis, we collapsed these response options into a dichotomous variable: ‘depressed’ (incorporating ‘daily’, ‘weekly’, ‘monthly’, ‘a few times a year’) (coded 1) and ‘never’ (comprising ‘never’ and the other response options) (coded 0).\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eMedicinal exposure\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eConsumption of antibiotic and antimalarial medicines was assessed using a series of multiple-choice questions, under a section labelled ‘Care of Illness’\u0026nbsp;(MICS, 2022). This section focused on the type and/or source of advice or treatment provided in response to childhood illnesses during the past fortnight, notably diarrhoea (‘In the last two weeks, has [name] had diarrhoea’? ‘Yes’/‘No’/‘Don’t know’), fever (‘At any time in the last two weeks, has [name] been ill with a fever’? ‘Yes’/‘No’/‘Don’t know’), and cough (‘At any time in the last two weeks, has [name] had an illness with a cough’? ‘Yes’/‘No’/‘Don’t know’).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMedicinal use was assessed using several questions: ‘At any time during the illness, was [name] given any medicine for the illness? (‘Yes’/‘No’/‘Don’t know’), ‘What medicine was [name] given?’, ‘Any other medicine? The researcher recorded all medicines mentioned. If the type of medicine given could not be determined from a participant’s response, the researcher noted the brand name and then temporarily record ‘W' until the appropriate category of medicine could be established.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNine categories of antimalarial medicines were recorded: artemisinin combination therapy (ACT) (coded A), SP/fansidar (coded B), chloroquine (coded C), amodiaquine (coded D), quinine pills (coded E), quinine injection/IV (coded F), artesunate rectal (coded G), artesunate injection/IV (coded H), other anti-malarial (coded K). Due to its unidentified label, we excluded the last category from further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFour types of antibiotics were identified: amoxicillin (coded A), cotrimoxazole (coded M), other antibiotic pill/syrup (coded N), and other antibiotic injection/IV (coded O). Given their unspecified labels, the two latter antibiotic categories were excluded from data analysis. Finally, use of home remedies and herbal medicines was also recorded as a separate medicinal category (coded Q). Exposure to each medicine type was treated as a separate dichotomous variable – used (coded 1) or not used (coded 0).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eBreastfeeding\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWHO and UNICEF guidelines both recommend exclusive breastfeeding for the first six months of life, and breastfeeding up to the age of 2 years, and beyond\u0026nbsp;(Aryeetey and Dykes, 2018). MICS literature considers a broad range of multiple breastfeeding metrics\u0026nbsp;(MICS, 2022). However, given the\u0026nbsp;lower breast-feeding rates (both early initiation, and exclusive breastfeeding) in West and Central African countries\u0026nbsp;(Issaka et al., 2017), for the purposes of this study we focused primarily on overall breast feeding history (i.e., prior exposure to breastfeeding). This was assessed using two items contained within the ‘breastfeeding and dietary’ intake section of the MICS questionnaire for children aged under 5: ‘Has [name] ever been breastfed?’ ‘Yes’ (coded 1)/ ‘No’ or ‘don’t know’ (coded 0), and ‘Is [name] still being breastfed?’ ‘Yes’ (coded 1)/ ‘No’ or ‘don’t know’ (coded 0).\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eConfounding variables\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe examined a total of ten covariates: (a) gender (‘male’ (coded 1) / ‘female’ (coded 2)), (b) age, (c) weight, (d) geographical area ( ‘urban’ (coded 1) / ‘rural’ (coded 2)), (e) mother’s or caretaker’s educational level ( ‘none’ (coded 0), ‘primary’/ ‘secondary’/ ‘higher’ (coded 1)), (f) child’s functional difficulties (CFD), (g) ethnicity/tribe of the head of the household (‘Hausa’/ ‘Igbo’/ ‘Yoruba’/ ‘Fulani’ (coded 1), ‘others’ (coded 0)), (h) wealth index, (i) fever symptoms (‘Yes’ (coded 1)/ ‘No’ (coded 0)) and (j) cough symptoms (‘Yes’ (coded 1)/ ‘No’ (coded 0)).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAge was based on birth history information, and recorded in days, months, and years. For the purposes of this paper, we analysed number of days. Weight was calibrated in kilogrammes (kg), and based on measurements of each child, read out by a measurer. CFD incorporated questions about physical difficulties that a child may have including hearing (e.g., ‘Does [name] use a hearing aid?’), vision (e.g., ‘Does [name] wear glasses?’), and mobility (e.g., ‘Does [name] use any equipment or receive assistance for walking?’). CFD data was only collected for children aged 2 to 4 years.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWealth index is a pre-computed composite numerical MICS metric, reflecting wealth or assets in each household\u0026nbsp;(MICS, 2022). It incorporates various household characteristics, including ownership of personal assets (e.g., refrigerator, television, air conditioner, fan, water heater, bicycle, car, truck or van), and access to basic services (e.g., electricity, energy for cooking, internet)\u0026nbsp;(Howe, 2009). Scores are organised into five wealth index quintiles: ‘poorest’ (coded 1), ‘second’ (coded 2), ‘middle’ (coded 3), ‘fourth’ (coded 4),‘richest’ (coded 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, we deemed it essential to account for fever and cough symptoms, as covariates.\u0026nbsp;High fever in children is associated with malaria or bacterial infection\u0026nbsp;(Schumacher and Spinelli, 2012; El-Radhi, 2019), and a cough may be caused by pneumonia but also by severe malaria\u0026nbsp;(Oluwole et al., 2000), triggering the use of antibiotics or antimalarial medicines. Additionally, early childhood infections, including fever and cough, are associated with depression, through childhood up to mid adolescence\u0026nbsp;(Chaplin et al., 2020), suggesting both symptoms may confound the association between medication use and childhood depression.\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eChi-square and independent samples t-tests were used to analyse descriptive data.\u0026nbsp;To investigate the hypothesis that children who have experienced depression are more likely to have received antimalarial and antibiotic medication, and are less likely to have been breastfed, we conducted a hierarchical binary logistic regression. The outcome variable was childhood depression — 1 for presence and 0 for absence. Predictor variables comprised exposure to three types of antibiotic drugs (amoxicillin, cotrimoxazole, herbal), eight antimalarial medicines (ACT, fansidar, chloroquine, amodiaquine, quinine pills, quinine injection, artesunate rectal, and artesunate injection), and breastfeeding. We adjusted for ten covariates (age, gender, weight, urban/rural area, mothers’ education, child’s functional difficulties, ethnicity/tribe, wealth index, and fever/cough symptoms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree logistic regression models were tested:\u0026nbsp;Model 1 (childhood depression = Intercept + medication exposure),\u0026nbsp;Model 2 (childhood depression = Intercept + medication exposure + breastfeeding exposure), Model 3 (childhood depression = Intercept + medication exposure + breastfeeding exposure + covariates). Of particular interest was whether any significant association of childhood depression with medication and breastfeeding (Model’s 1 and 2) persisted after accounting for covariates (Model 3). The analysis was first conducted for the whole sample and repeated separately for males and females. Further analyses were then performed to assess the robustness of the findings across various sources of uncertainty.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eDescriptive data\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRates of missing data ranged from 0 (e.g., medication use, cough/fever symptoms) to 37.2% (child functional difficulties), depression (46.2%), and 51.5% (cough symptoms). We opted for listwise deletion of missing cases\u0026nbsp;(Rubin et al., 2007), given the large overall sample size (and hence statistical power)\u0026nbsp;(Kang, 2013). However, the expected maximisation (EM) method, which replaces missing values with estimated parameters\u0026nbsp;(Stavseth et al., 2019), was later used for sensitivity analysis (robustness checks).\u003c/p\u003e\n\u003cp\u003eDescriptive characteristics for the whole sample, and children with and without depression, is shown in Table 1. Overall, 9774 children (31.4%) were suspected to have depression. Depressed children were more likely than their non-depressed counterparts to live in a rural area (75.3% versus 70.9%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 40.293, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), less likely to have an educated mother (52.3% versus 60%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16718) = 97.570, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), more likely to have functional difficulties (5.2% versus 3.3%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 35.140, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), experience fever (26.2% versus 22.1%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 35.725, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and cough (19.1% versus 15.6%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 34.028, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), have lower body weight (M = 0.932 versus 1.120, respectively) (\u003cem\u003et\u003c/em\u003e(14708.053) = -12.011,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001), and live in a less wealthy household (M = 2.45 versus 2.61, respectively) (\u003cem\u003et\u003c/em\u003e(14324.925) = -7.613,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepressed children were more like than non-depressed participants to be administered cotrimoxazole (1% versus 0.6%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 6.495, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), ACT (5.5% versus 3.9%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 22.225, \u003cem\u003ep\u003c/em\u003e ≤ 0.001), fansidar (1.2% versus 0.7%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 6.900, \u003cem\u003ep\u003c/em\u003e ≤ 0.01), chloroquine (1.7% versus 1%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 12.261, \u003cem\u003ep\u003c/em\u003e ≤ 0.001), quinine (pills) (0.5% versus 0.3%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 3.350, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), quinine (injection) (1.6% versus 1%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 10.619, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and artesunate (injection) (2.6% versus 1.9%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 16720) = 9.727, \u003cem\u003ep\u003c/em\u003e ≤ 0.001). By contrast, depressed children were less likely to have been breastfed (91.6% versus 93.8%, respectively), (χ\u003csup\u003e2\u003c/sup\u003e (1, N = 5253) = 9.218, \u003cem\u003ep\u003c/em\u003e ≤ 0.001). We observed no group differences based on gender, age, ethnicity/tribe, and consumption of amoxicillin, herbal antibiotics, amodiaquine, and artesunate (rectal).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, most children were male (50.8%), lived in a rural area (73.5%), had an educated mother (55.5%), did not suffer functional difficulties (95.6%), belonged to the Hausa, Igbo, Yoruba, or Fulani tribe (60.9%), and had not experienced a fever (75.5%) or cough (82.3%). The average age (in days), body weight (kg), and wealth index were 975.11 (SD = 790.015), 0.990 (SD = 0.959), and 2.56 (SD = 1.349), respectively (the average age equates to 32.055 months). Only a small minority of children (≤ 4.8%) had been exposed to antibiotics or antimalarial medicines. However, most (92.2%) had been breastfed (25.7% were still breastfeeding). Breastfeeding children were significantly younger than their non-breastfeeding counterparts (mean age 348.42 days [0.954 years]) versus 1194.67 days [3.273 years] respectively, (\u003cem\u003et\u003c/em\u003e(17890.621) = -105.279, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e……………………………………………..\u003c/p\u003e\n\u003cp\u003e[Insert \u003cstrong\u003eTable 1\u003c/strong\u003e about here]\u003c/p\u003e\n\u003cp\u003eDescriptive data for study variables.\u003c/p\u003e\n\u003cp\u003e……………………………………………\u003c/p\u003e\n\u003cp\u003e……………………………………………..\u003c/p\u003e\n\u003cp\u003e[Insert \u003cstrong\u003eTable 2\u003c/strong\u003e about here]\u003c/p\u003e\n\u003cp\u003eLogistic regression models predicting childhood depression from medication use, breastfeeding, and covariates in the whole sample.\u003c/p\u003e\n\u003cp\u003e……………………………………………\u003c/p\u003e\n\u003cp\u003e……………………………………………..\u003c/p\u003e\n\u003cp\u003e[Insert \u003cstrong\u003eTable 3\u003c/strong\u003e about here]\u003c/p\u003e\n\u003cp\u003eLogistic regression models predicting childhood depression from medication use, breastfeeding, and covariates, in male children.\u003c/p\u003e\n\u003cp\u003e……………………………………………\u003c/p\u003e\n\u003cp\u003e……………………………………………..\u003c/p\u003e\n\u003cp\u003e[Insert \u003cstrong\u003eTable 4\u003c/strong\u003e about here]\u003c/p\u003e\n\u003cp\u003eLogistic regression models predicting childhood depression from medication use, breastfeeding, and covariates, in female children.\u003c/p\u003e\n\u003cp\u003e……………………………………………\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eHypotheses testing\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe objective of this analysis was to assess the extent to which depression in Nigerian children aged under 5 relates to medicinal use and breastfeeding. The overall sample size (N = 31103) provided sufficient statistical power to detect significant associations, reducing type II error rates. To test the assumption of linearity in the logit, whereby the log-odds of the predicted probabilities of the outcome (depression) is a linear function of three \u003cem\u003econtinuous\u003c/em\u003e independent variables (age, weight, and wealth index), we conducted a Box-Tidwell test. This required adding a product term between each predictor and its natural log transformation, into the logistic regression model and examining its significance\u0026nbsp;(Harris, 2021). We observed significant coefficients for all three interaction terms, suggesting the assumption of linearity in the logit was violated. However, due to the very large sample size, we decided to create and inspect scatterplots with the continuous predictors on the \u003cem\u003ex\u003c/em\u003e-axis and the log-odds of the predicted probabilities on the \u003cem\u003ey\u003c/em\u003e-axis. The observed curve estimates indicated a good fit for both linear and non-linear relationships. For the purposes of this investigation, we assumed linearity of the continuous predictors and log odds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e……………………………………………..\u003c/p\u003e\n\u003cp\u003e[Insert \u003cstrong\u003eFigure 2\u003c/strong\u003e about here]\u003c/p\u003e\n\u003cp\u003eAdjusted odds ratios for significant predictor variables in the whole sample\u003c/p\u003e\n\u003cp\u003e……………………………………………\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003ePredicting childhood depression in the whole sample\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTable 2 shows logistic regression estimates, together with overall model parameters. Exposure to ACT, (OR = 1.647, 95%CI [1.262 – 2.150]),\u0026nbsp;chloroquine, (OR = 1.632, 95%CI [1.022 –2.604]),\u0026nbsp;and artesunate injections (OR = 1.710, 95%CI [1.137 –2.573]),\u0026nbsp;predicted higher odds of childhood depression (Model 1) (Figure 2). T\u003cem\u003ehus, for children administered ACT, chloroquine, and artesunate treatment, there was a significant 1.647, 1.632, and 1.710 increase, respectively, in the odds of experiencing depression, compared to children not given these medicines.\u0026nbsp;\u003c/em\u003eAddition of breastfeeding (Model 2) also resulted in good fit: previous breastfeeding was associated with lower odds of depression (OR = 0.719, 95%CI [0.581 – 0.890]).\u0026nbsp;\u003cem\u003eThis adjusted model was significant (χ²(1) = 46.854, p \u0026lt; .001), showing that\u0026nbsp;\u003c/em\u003eACT (AOR = 1.650, 95%CI [1.264 –2.153]), chloroquine (AOR = 1.612, 95%CI [1.009 –2.574]), artesunate injection (AOR = 1.726, 95%CI [1.147 –2.598]), and breastfeeding exposure (OR = 0.719, 95%CI [0.581 – 0.890]),\u0026nbsp;robustly predict childhood depression. The model’s \u003cem\u003epseudo-R² value of 0.012 (Nagelkerke) indicated improvement over the null model.\u0026nbsp;\u003c/em\u003eThe contributions of ACT, artesunate injection, and breastfeeding to the model remained significant even after adjusting for covariates (Model 3), with the odds of depression higher in children exposed\u0026nbsp;to ACT (OR = 1.760, 95%CI [1.316 – 2.355]) and\u0026nbsp;artesunate injections (AOR = 1.757, 95%CI [1.150 –2.684]), and lower in breastfed children (AOR = 0.776, 95%CI [0.625 –0.963]).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003ePredicting childhood depression in males\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTable 3 shows logit estimates and model parameters for male children. Exposure to ACT (OR = 2.188, 95%CI [1.490 – 3.212]) and\u0026nbsp;artesunate injections (OR = 1.770, 95%CI [1.020 – 3.071]) was associated with a greater risk of childhood depression (Model 1). Male chi\u003cem\u003eldren given ACT were two times more likely to experience depression, while those receiving artesunate injections were 1.770 times more prone to depression, compared to male children who did not receive these treatments (χ²(1) = 32.023, p \u0026lt; .001, pseudo-R² = 0.016).\u0026nbsp;Inclusion of\u0026nbsp;\u003c/em\u003ebreastfeeding (Model 2) failed to improve model fit. Addition of covariates (Model 3) generated a significant model\u0026nbsp;\u003cem\u003e(χ²(1) = 92.581, p \u0026lt; .001)\u003c/em\u003e, negating the contribution of artesunate exposure, albeit ACT remained significant: male children administered ACT were more than twice as likely to be depressed compared to those not given this treatment (AOR = 2.347, 95%CI [1.547 – 3.559]).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003ePredicting childhood depression in females\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTable 4depicts results for female children. The initial logistic regression model comprising medicinal exposure (Model 1) failed to fit the data well: none of the predictor variables was significant. Addition of breastfeeding (Model 2) significantly improved model fit \u003cem\u003e(χ²(1) = 24.474, p \u0026lt; .05)\u003c/em\u003e, with the \u003cem\u003epseudo-R² value of 0.013 (Nagelkerke) indicated a slight improvement over the null model. Female children who had been breastfed were less likely to experience depression, compared to those not breastfed\u0026nbsp;\u003c/em\u003e(OR = 0.642, 95%CI [0.468 – 0.881]). Incorporating covariates (Model 3) improved model fit\u0026nbsp;\u003cem\u003e(χ²(1) = 86.606, p \u0026lt; .001)\u003c/em\u003e, increasing the proportion of variance explained (\u003cem\u003epseudo-R² value of 0.045). B\u003c/em\u003ereastfeeding remained significant in the final model (AOR = 0.689, 95%CI [0.498 – 0.951]).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eRobustness checks\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe performed several robustness checks to determine how the results are affected by sources of uncertainty, including variations in analytic protocol, and management of missing data\u0026nbsp;(Thabane et al., 2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, we repeated the logistic regression analysis using bootstrapping, whereby multiple (1000) simulated samples are tested. The emerging bootstrapped estimates supported the initial findings: exposure to ACT, (\u003cem\u003eβ\u003c/em\u003e = 0.565, 95%CI [0.268 to 0.857]),\u0026nbsp;artesunate injections (\u003cem\u003eβ\u003c/em\u003e = 0.563, 95%CI [0.137 to 1.075]),\u0026nbsp;and breastfeeding (\u003cem\u003eβ\u003c/em\u003e = -0.254, 95%CI [-0.478 to -0.044]),\u0026nbsp;predicted childhood depression in the whole sample (final model). ACT exposure and breastfeeding remained salient predictors in males (\u003cem\u003eβ\u003c/em\u003e = 0.853, 95%CI [0.425 to 1.277])\u0026nbsp;and females (\u003cem\u003eβ\u003c/em\u003e = -0.373, 95%CI [-0.700 to -0.037]), respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we repeated the analyses using a different measure of breastfeeding (‘still’ breastfeeding rather than ‘ever’ breastfed). This produced partially identical results. In addition to ACT and artesunate (injection), exposure to chloroquine (AOR = 1.435, 95%CI [1.073 – 1.918]) and quinine (injection) (AOR = 1.372, 95%CI [1.026 – 1.836]) now predicted depression in the overall sample, helping improve model fit \u003cem\u003e(χ²(22) = 357.907, p \u0026lt; .001).\u0026nbsp;\u003c/em\u003eBreastfeeding was no longer significant. Analysis of the gender split showed that ACT exposure remained significant in males (AOR = 1.519, 95%CI [1.204 – 1.917]), whereas breastfeeding was no longer significant in females. Moreover, artesunate (injection) now improved model fit in the latter group (OR = 1.455, 95%CI [1.058 – 2.002]).\u003c/p\u003e\n\u003cp\u003eThird, we re-ran the analyses across age groups (≤ versus \u0026gt; 2 years). Medication use and child functional difficulties were excluded from this analysis, due to low cell frequencies. Breastfeeding exposure remained a significant predictor, but only in older children (i.e., those aged \u0026gt; 2 years) (AOR = 0.757, 95%CI [0.610 – 0.941]).\u003c/p\u003e\n\u003cp\u003eNext, we repeated the analyses across geographical (rural versus urban) regions. Antimalarial medicinal use remained a significant factor in both areas, with depression predicted by amodiaquine (AOR = 11.065, 95%CI [1.312 – 93.288]), and artesunate injection use (AOR = 0.757, 95%CI [0.610 – 0.941])\u0026nbsp;in urban children, and ACT exposure (AOR = 3.344, 95%CI [1.261 – 8.867]), in rural children. Breastfeeding was only significant in urban dwellers (AOR = 0.595, 95%CI [0.399 – 0.888]).\u003c/p\u003e\n\u003cp\u003eWe then tested the hypotheses based on mother’s educational level (no education versus some education). ACT use\u0026nbsp;predicted paediatric depression in non-educated mothers (AOR = 1.677, 95%CI [1.066 – 2.638]), while exposure to three medicines – ACT (AOR = 1.783, 95%CI [1.212 – 2.623]), chloroquine (AOR = 2.114, 95%CI [1.033 – 4.327]), and artesunate rectal exposure (AOR = 2.455, 95%CI [1.337 – 4.509]), were significant for children of educated mothers. Breastfeeding predicted depression solely in the educated group (AOR = 0.627, 95%CI [0.451 – 0.873]).\u003c/p\u003e\n\u003cp\u003eNext, we repeated the analyses by wealth index, comparing the lowest two wealth quintiles (‘poorest’, ‘second’) with the three higher categories (‘middle’, ‘fourth’, ‘richest’)). ACT exposure\u0026nbsp;predicted depression in the less wealthy quintiles (AOR = 2.062, 95%CI [1.351 – 3.146]), whereas use of amodiaquine (AOR = 4.029, 95%CI [1.120 – 14.495]), and artesunate injections (AOR = 2.403, 95%CI [1.202 – 4.804]), were significant in children from wealthier backgrounds. Breastfeeding was not salient in either socio-economic group.\u003c/p\u003e\n\u003cp\u003eWe re-ran the analyses as a function of medical symptoms (cough, fever). Regarding fever, use of the antibiotic cotrimoxazole (AOR = 6.023, 95%CI [1.357– 26.721]) was significant in non-febrile children, whereas antimalarial medicines – ACT (AOR = 1.695, 95%CI [1.259 – 2.281]) and artesunate injections (AOR = 1.602, 95%CI [1.044 – 2.457]) – were significant in febrile participants. Breastfeeding was only relevant in the former group (AOR = 0.732, 95%CI [0.569 – 0.941]). For cough symptoms, ACT (AOR = 1.732, 95%CI [1.179 – 2.544]) and chloroquine (AOR = 2.063, 95%CI [1.043 – 4.081]) predicted depression in asymptomatic children, while ACT (AOR = 1.809, 95%CI [1.150 – 2.846]) and artesunate injections (AOR = 1.602, 95%CI [1.044 – 2.457]) were significant in symptomatic children. Breastfeeding was only salient in the latter group (AOR = 0.599, 95%CI [0.361 – 0.994]).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, we repeated the analyses using the EM approach for managing missing data. This produced roughly identical results with the original analyses (final model), whereby the odds of depression were elevated in children receiving ACT (AOR = 1.760, 95%CI [1.316 – 2.355]) and\u0026nbsp;artesunate injections (AOR = 1.757, 95%CI [1.150 –2.684]), and lower in breastfed children (AOR = 0.776, 95%CI [0.625 –0.963]).\u003c/p\u003e\n\u003cp\u003eOverall, the pattern of results varied as a function of the type of breastfeeding measure used, contextual factors (e.g., age, geographical area, mother’s educational level, child functional difficulties, wealth index), and the method used to manage missing data. Nevertheless, we observed a recurring pattern, whereby antimalarial medicinal use (particularly ACT treatment and artesunate injections), and breastfeeding history were persistent correlates of childhood depression, in most variations, with breastfeeding effects generally more salient in older children, living urban areas, from wealthier backgrounds, and a history of fever/cough. As in the original analysis, antibiotic exposure failed to predict depression, except in non-febrile children.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis population-based study helps address the lack of data on paediatric depression and its correlates, in infants and children from sub-Sharan Africa (Tomlinson and Morgan, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Analysis of data from 30,804 Nigerian children aged under 5 years showed that those suspected to have depression were more likely to have received antimalarial medicines, specifically ACT and artesunate injections, and not been breastfed. The association between ACT exposure and depression was much stronger in male children, whereas the relationship of breastfeeding with depression applied primarily to female children. Crucially, these associations remained significant despite adjusting for important covariates, albeit multiple robustness checks suggest contextual factors including mother\u0026rsquo;s education, geographical area, and household wealth, and medical symptoms (fever, cough) have an important moderating effect. Overall, this evidence supports previous research linking antimalarial medication to psychiatric conditions, including depression (Bitta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nevin and Croft, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Maxwell et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The data also supports past studies implicating breastfeeding in mental health (Zhong et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Allen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Contrary to previous work with older demographics (Lurie et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lavebratt et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Prichett et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we found limited evidence linking antibiotics with depression in children under 5.\u003c/p\u003e \u003cp\u003eResearch on the psychiatric effects of antimalarial drugs in young children is very rare (Aneja et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with most studies focusing on adults, or the general population (Meier et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bitta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nevin and Croft, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Maxwell et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, hitherto the association between antimalarial drugs and depression in infants and children has been poorly understood (Aneja et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Data for children from sub-Saharan Africa is particularly rare. Difficulty in identifying and diagnosing paediatric depression (Patra, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), combined with the lack of research on infant mental health in sub-Saharan Africa (Tomlinson and Morgan, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) adds additional ambiguity. Our findings suggest antimalarial treatment, and lack of breastfeeding may be independent risk factors for paediatric depression in Nigerian children aged under 5 years. While the psychiatric effects of antimalarial drugs have been attributed to neurological mechanisms, such as toxicity to the central nervous system (CNS) (Nevin and Croft, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), an arguably more plausible explanation for the current findings is the emotional aspect of malaria symptoms (e.g., fever, headache, gastrointestinal effects) and/or antimalarial treatment (medicines, injection). These medical experiences can be very unpleasant, especially for very young children, who may express their discomfort in ways that denote \u0026lsquo;depression\u0026rsquo; or \u0026lsquo;sadness\u0026rsquo; to an adult observer (Annan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, antimalarial drugs can induce vomiting in infants and children (Creek et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Injections are painful to infants and very young children (Taddio et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who often convey their distress through vocal/facial expressions denoting intense discomfort (Annan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs hypothesised, breastfed children were less likely to be depressed, even after accounting for covariates. Although previous research has implicated inadequate breastfeeding in paediatric depression, the evidence has been mixed (Kwok et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Loret de Mola et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Allen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). A systematic review and meta-analysis of 18 studies on breastfeeding and mental disorders found conflicting evidence on the protective effect of breastfeeding on children's mental health in later life (Bugaeva et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Some research found no association (Kwok et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Loret de Mola et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while other evidence indicated a protective effect for breastfeeding (Zhong et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Allen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Our findings suggest a robust association between breastfeeding and depression in Nigerian infants/children that can\u0026rsquo;t be attributed to potential confounders, including body weight, maternal education, and child functional difficulties (Kanellopoulou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Malhotra and Sahoo, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is possible the low baseline rates of breastfeeding in Sub-Saharan African countries (Issaka et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), combined with a high prevalence of childhood trauma in the region (e.g., exposure violence, poverty) (Kurtz et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Amene et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) may magnify any beneficial effects of breastfeeding on mental wellbeing in local infants/children (Krol and Grossmann, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGender played an important role in the relationship between breastfeeding and depression. We observed an association primarily in female children. The reason for this gender effect is unclear but may partly reflect gender differences in breastfeeding practices. However, a systematic analysis of qualitative data on breastfeeding in sub-Saharan Africa revealed no qualitative differences in how mothers breastfeed male and female infants (Ejie et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One important consideration that may not be captured in qualitative data is the \u003cem\u003eduration\u003c/em\u003e of breast feeding. Research suggests breastfeeding reduces the risk of depression only in children breastfed for longer than 6 (a shorter duration of breastfeeding was not associated with any protective effect) (Huang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Gender may determine breastfeeding duration (e.g., early initiation) in sub-Saharan Africa, whereby mothers breastfeed female infants for longer. However, evidence in this area is mixed. One study found female children were more likely to be breastfed early (Woldeamanuel, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while another study found the opposite (Ayalew et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Further research is necessary to determine if gender serves as a proxy for other variables affecting breastfeeding initiation/duration, such as local ethnicity, religious beliefs, and birth order (Shimizu et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUse of antimalarial medicines, specifically ACT, was associated with paediatric depression, but only in male children (boys exposed to ACT were 2.3 times more likely to experience depression compared to male children administered ACT drugs). While the link between antimalarial drugs and psychiatric conditions is complex and not well understood (Nevin and Croft, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the current findings may depict a gender-based disparity in exposure to anti malaria drugs, whereby male children are more likely to be administered ACT, and hence experience (and express) emotional discomfort from this treatment (Annan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although research suggests malaria prevalence and associated risk factors in Sub-Saharan Africa are similar for male and female children aged under 5 (Nwaneli et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chilot et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), survey data from thirteen sub-Saharan African countries indicates gender affects whether children receive prompt access to antimalaria drugs (albeit not ACT medicines specifically) (Shah et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, evidence from a survey of antimalarial drug consumption in children under 5 from five countries in sub-Saharan African found no gender effect (Hossain et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Another possible explanation is that male children are simply more susceptible to depression, and hence are more likely to experience emotional distress when exposed to uncomfortable antimalarial treatment (Creek et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, data suggests gender differences in depression amongst children is perfunctory (Salk et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and more research is needed in this area.\u003c/p\u003e \u003cp\u003ePoverty and childhood trauma might be important in understanding the link between ACT and depression in male children. Depression is more prevalent among African children exposed to violence (Jorns-Presentati et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or exploitative child labour (Ibrahim et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, a survey of adverse childhood experiences in five sub-Saharan African countries found that male children are more likely than their female counterparts to witness and experience physical violence (Amene et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, the former group may already be more mentally distressed, from violent childhood traumas, prior to been administered antimalarial drugs. The medication may simply accentuate this pre-existing psychiatric vulnerability, manifesting as depression (Nevin and Croft, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, evidence for this view is weak. For example, a survey of the prevalence of emotional abuse experienced by children from Sub-Saharan Africa observed no gender effect (Kurtz et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, research with Nigerian children specifically has found no gender differences in exposure to violence, physical abuse, and other traumatic experiences, compared their female counterparts (Asagba et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, data suggests girls are more likely to be victims of sexual abuse/trauma (Ibrahim et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Amene et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overall, further research is necessary to better understand how gender affects the association between antimalarial treatment and depression in sub-Saharan African children.\u003c/p\u003e \u003cp\u003eWe found little evidence linking antibiotics and childhood depression, in contrast with previous research implicating antibiotic usage with an increased risk of mental disorders, including depression (Lurie et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lavebratt et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Prichett et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, previous work in this area has been largely based on adult, adolescent, or mixed populations, and was not specific to sub-Saharan Africa (Lurie et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Prichett et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The observed association between antibiotics and depression solely in non-febrile children may denote a tendency for antimicrobials to be administered to children not diagnosed with malaria (i.e., not feverish), thereby increasing their risk of depression from antibiotics exposure (Dinan and Dinan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This view is supported by data from LMICs showing that antibiotics are less likely to be administered to children diagnosed with malaria (28.3%), compared to other medical conditions, such as respiratory illness (80\u0026middot;5%), and diarrhoea (50.1%) (Fink et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Further research is needed to better understand how and why antibiotics may contribute to depression in non-febrile children under 5.\u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. Firstly, diagnosis of childhood depression was based on mother's or caretaker\u0026rsquo;s subjective judgements, in the form of a single-item question. While such parental feedback can be useful for understanding psychopathology in children (Lewis et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), including infants\u0026rsquo; temperament (McGrath et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), it may not accurately capture a child\u0026rsquo;s emotional state (Caqueo-Ur\u0026iacute;zar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and could be affected by the parents own mental health (McGrath et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Paediatric depression is a complex disorder that is difficult to diagnose, and parental judgements may fail to capture this intricacy (Lima et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Symptoms vary with age, level of development, and comorbidities, for example (Patra, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While depression has been diagnosed in children as young as 3 years, the symptoms may not meet clinical diagnostic criteria (Mullen, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Proper diagnosis often requires clinical interviews with both parental and child, conducted on an ongoing basis, and requiring the use of pictorial instruments and depression ratings scales (Patra, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecondly, robustness checks suggest the findings may vary depending on the type of breastfeeding measure used, and group differences in socio-demographic factors (e.g., age, geographical area, wealth index, mother\u0026rsquo;s education, medical symptoms). However, these inconsistencies did not dramatically alter the recurring pattern whereby antimalarial medicinal use, particularly ACT treatment, and breastfeeding predicted childhood depression. The data was also generally unaffected by the method used to manage missing data.\u003c/p\u003e \u003cp\u003eIt is notable that current breastfeeding failed to predict depression, perhaps reflecting the greater challenge of identifying \u0026lsquo;sadness\u0026rsquo; in newborns and infants (breastfeeding children were under 12 months old, on average) (Dolinsky et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Patra, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, breastfeeding duration not assessed. This is a potentially important metric that may moderate the association between breastfeeding and paediatric depression (children breastfed for \u0026gt;\u0026thinsp;6 months have a reduced risk of depression) (Huang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Since breastfeeding rates in West and Central African countries fall below the WHO\u0026rsquo;s recommended 50% (Issaka et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), breastfeeding duration might play a particularly important role in child health across the region, significantly impacting the odds of childhood depression.\u003c/p\u003e \u003cp\u003eThirdly, we did not control for several potential confounding factors implicated in childhood depression including family genetics, maternal depression, childhood maltreatment, and cognitive vulnerability (Lima et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Malhotra and Sahoo, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Severe childhood trauma (e.g., experiencing violence) in particular is highly prevalent in sub-Saharan Africa (Amene et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kurtz et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and has been strongly associated with mood disorders in children (Jorns-Presentati et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further research is therefore needed to determine how this covariate interacts with medication use and breastfeeding in predicting paediatric depression.\u003c/p\u003e \u003cp\u003eDespite these constraints, this study has several strengths. Firstly, to the best of our knowledge, this is the first ever investigation to demonstrate the role of antimalarial treatment, and breastfeeding, in the mental health of Nigerian children aged under 5 years. There is currently little or no published data on mood disorders and its correlates in infants and very young children from this geographical region (Tomlinson and Morgan, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Most research in this area has focused on adolescents, adults, or mixed populations (Gbadamosi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yohani et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sequeira et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Partap et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Aluh et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jorns-Presentati et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mabrouk et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Owen et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cortina et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sherr et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mokwena et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Secondly, we analysed data from a very large sample, providing smaller margins of error in adjusted odds ratio estimates, and hence generating more robust and reliable results. Third, we did control for several important covariates implicated in paediatric depression, including body weight (Kanellopoulou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and socio-demographic factors, such as maternal education, and households' cumulative living standards (wealth index) (Malhotra and Sahoo, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis is the first population-based study to examine the role of breastfeeding and medicinal use, in suspected depression amongst children aged under 5, from sub-Sharan Africa. Childhood depression was associated with exposure to antimalarial treatment, specifically ACT and artesunate injections, and lack of breastfeeding. Gender was an important moderating factor: treatment with antimalarial medicines may be an independent risk factor for paediatric depression primarily in male children, whereas the association between breast feeding and depression was stronger in female children. How breastfeeding was assessed, and variations in mother\u0026rsquo;s educational level, geographical area, household wealth, and medical symptoms (e.g., fever), were also important contextual factors. Given that infants and children in sub-Saharan Africa often face mental health challenges aggravated by chronic poverty, and childhood trauma, exposure to antimalaria treatment and inadequate breastfeeding may help identify Nigerian children under 5 who are susceptible to depression.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eACT\u0026nbsp;\u003c/strong\u003e\u0026ndash; Artemisinin Combination Therapy\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAOR\u003c/strong\u003e \u0026ndash; Adjusted Odds Ratio\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBMGF\u0026nbsp;\u003c/strong\u003e\u0026ndash; Bill and Melinda Gates Foundation\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCFD\u0026nbsp;\u003c/strong\u003e\u0026ndash; Child\u0026rsquo;s Functional Difficulties\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCNS\u0026nbsp;\u003c/strong\u003e\u0026ndash; Central Nervous System\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEM\u003c/strong\u003e \u0026ndash; Expectation Maximisation\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFMoH\u003c/strong\u003e \u0026ndash; Federal Ministry of Health (Nigeria)\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGAVI\u003c/strong\u003e \u0026ndash; the Vaccine Alliance\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMICS\u0026nbsp;\u003c/strong\u003e\u0026ndash; Multiple Indicator Cluster Survey\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNBS\u003c/strong\u003e \u0026ndash; National Bureau of Statistics\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNICS\u003c/strong\u003e \u0026ndash; National Immunization Coverage Survey\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNMEP\u0026nbsp;\u003c/strong\u003e\u0026ndash;\u0026nbsp;National Malaria Elimination Programme\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOR\u003c/strong\u003e \u0026ndash; Odds Ratio\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eUNICEF\u0026nbsp;\u003c/strong\u003e\u0026ndash; United Nations Children\u0026rsquo;s Fund\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWHO\u003c/strong\u003e \u0026ndash; World Health Organisation\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis study was performed in line with the\u0026nbsp;ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Ethics\u0026nbsp;approval was granted by the Liverpool John Moores University Research Ethics Committee (UREC reference: 16/NSP/035).\u0026nbsp;Each participating adult provided verbal consent (adult consent was required for mother\u0026rsquo;s or caretakers aged 15 to 17), and all respondents were informed of their right to withdraw at any time, and the confidentiality and anonymity of the data.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDue to third party rights, other legal and ethical reasons, and the nature of data gathered, MICS data may not be redistributed or shared publicly, in any form (MICS, 2022). Access is restricted and facilitated directly by UNICEF/MICS (MICS, 2024). All MICS-related data and documentation can be viewed at http://www.childinfo.org/mics including questionnaires, manuals, data processing and tabulation plans as well as national reports, datasets and contact information. Furthermore, results from the surveys are made available in DevInfo, a powerful database designed to monitor progress towards the Millennium Development Goals. Access to MICS data requires registration at [email protected]. For further information please contact the Global MICS Coordinator, Strategic Information Section, Division of Policy and Planning, UNICEF, 3 U.N. Plaza, New York, NY 10017, USA. Tel: 212 303 7982\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eKU\u0026nbsp;conceived the study, extracted, and\u0026nbsp;analysed the data, and wrote the manuscript. SA contributed to the final version of the manuscript.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe would like to thank the UNICEF MICS Team for providing access to the survey data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdisa R, Orherhe OM and Fakeye TO (2018) Evaluation of antibiotic prescriptions and use in under-five children in Ibadan, SouthWestern Nigeria. \u003cem\u003eAfrican health sciences\u003c/em\u003e 18(4): 1189-1201.\u003c/li\u003e\n\u003cli\u003eAllen NB, Lewinsohn PM and Seeley (1998) Prenatal and perinatal influences on risk for psychopathology in childhood and adolescence. \u003cem\u003eDevelopment and psychopathology\u003c/em\u003e 10(3): 513-529.\u003c/li\u003e\n\u003cli\u003eAluh DO, Anyachebelu OC, Anosike C, et al. (2018) Mental health literacy: what do Nigerian adolescents know about depression? \u003cem\u003eInternational journal of mental health systems\u003c/em\u003e 12: 8.\u003c/li\u003e\n\u003cli\u003eAmene EW, Annor FB, Gilbert LK, et al. (2024) Prevalence of Adverse Childhood Experiences in sub-Saharan Africa: A multicountry analysis of the Violence Against Children and Youth Surveys (VACS). \u003cem\u003eChild abuse \u0026amp; neglect\u003c/em\u003e 150: 106353.\u003c/li\u003e\n\u003cli\u003eAneja J, Goya D and Choudhary B (2019) Psychosis consequent to antimalarial drug use in a young child. \u003cem\u003eJournal of family medicine and primary care\u003c/em\u003e 8(5): 1781-1783.\u003c/li\u003e\n\u003cli\u003eAnnan E, Ramukumba TS and Stevens BJ (2023) Perspectives of nurses regarding pain assessment and management during routine infant vaccination in Ghana. \u003cem\u003eNursing open\u003c/em\u003e 10(8): 5348-5355.\u003c/li\u003e\n\u003cli\u003eAryeetey R and Dykes F (2018) Global implications of the new WHO and UNICEF implementation guidance on the revised Baby-Friendly Hospital Initiative. \u003cem\u003eMaternal \u0026amp; child nutrition\u003c/em\u003e 14(3): e12637.\u003c/li\u003e\n\u003cli\u003eAsagba RB, Noibi OW and Ogueji IA (2021) Gender Differences in Children\u0026apos;s Exposure to Domestic Violence in Nigeria. \u003cem\u003eJournal of child \u0026amp; adolescent trauma\u003c/em\u003e 15(2): 423-426.\u003c/li\u003e\n\u003cli\u003eAuta A, Ogbonna BO, Adewuyi EO, et al. (2019) Prevalence and factors associated with the use of antibiotics in non-bloody diarrhoea in children under 5 years of age in sub-Saharan Africa. \u003cem\u003eArchives of disease in childhood\u003c/em\u003e 104(6): 518-521.\u003c/li\u003e\n\u003cli\u003eAyalew T, Tewabe T and Ayalew Y (2019) Timely initiation of breastfeeding among first time mothers in Bahir Dar city, North West, Ethiopia, 2016. \u003cem\u003ePediatric research\u003c/em\u003e 85(5): 612-616.\u003c/li\u003e\n\u003cli\u003eBitta MA, Kariuki SM, Mwita C, et al. (2017) Antimalarial drugs and the prevalence of mental and neurological manifestations: A systematic review and meta-analysis. \u003cem\u003eWellcome open research\u003c/em\u003e 2: 13.\u003c/li\u003e\n\u003cli\u003eBugaeva P, Arkusha I, Bikaev R, et al. (2023) Association of breastfeeding with mental disorders in mother and child: a systematic review and meta-analysis. \u003cem\u003eBMC medicine\u003c/em\u003e 21(1): 393.\u003c/li\u003e\n\u003cli\u003eCaqueo-Ur\u0026iacute;zar A, Urz\u0026uacute;a A, Villalonga-Olives E, et al. (2022) Children\u0026apos;s Mental Health: Discrepancy between Child Self-Reporting and Parental Reporting. \u003cem\u003eBehavioral Sciences\u003c/em\u003e 12(10).\u003c/li\u003e\n\u003cli\u003eChaplin AB, Jones PB and Khandaker GM (2020) Association between common early-childhood infection and subsequent depressive symptoms and psychotic experiences in adolescence: a population-based longitudinal birth cohort study. \u003cem\u003ePsychological medicine\u003c/em\u003e. 1-11.\u003c/li\u003e\n\u003cli\u003eChilot D, Mondelaers A, Alem AZ, et al. (2023) Pooled prevalence and risk factors of malaria among children aged 6-59 months in 13 sub-Saharan African countries: A multilevel analysis using recent malaria indicator surveys. \u003cem\u003ePlos One\u003c/em\u003e 18(5): e0285265.\u003c/li\u003e\n\u003cli\u003eCortina MA, Sodha A, Fazel M, et al. (2012) Prevalence of child mental health problems in sub-Saharan Africa: a systematic review. \u003cem\u003eArchives of Pediatrics \u0026amp; Adolescent Medicine\u003c/em\u003e 166(3): 276-281.\u003c/li\u003e\n\u003cli\u003eCreek D, Bigira V, Arinaitwe E, et al. (2010) Increased risk of early vomiting among infants and young children treated with dihydroartemisinin-piperaquine compared with artemether-lumefantrine for uncomplicated malaria. \u003cem\u003eThe American journal of tropical medicine and hygiene\u003c/em\u003e 83(4): 873-875.\u003c/li\u003e\n\u003cli\u003eDinan K and Dinan T (2022) Antibiotics and mental health: The good, the bad and the ugly. \u003cem\u003eJournal of internal medicine\u003c/em\u003e 292(6): 858-869.\u003c/li\u003e\n\u003cli\u003eDolinsky DH, Brouwer RJ, Evenson KR, et al. (2011) Correlates of sedentary time and physical activity among preschool-aged children. \u003cem\u003ePreventing chronic disease\u003c/em\u003e 8(6): A131.\u003c/li\u003e\n\u003cli\u003eDrago F, Scharf RJ, Maphula A, et al. (2020) Psychosocial and environmental determinants of child cognitive development in rural south africa and tanzania: findings from the mal-ed cohort. \u003cem\u003eBMC Public Health\u003c/em\u003e 20(1): 505.\u003c/li\u003e\n\u003cli\u003eEfunshile AM, Ezeanosike O, Nwangwu CC, et al. (2019) Apparent overuse of antibiotics in the management of watery diarrhoea in children in Abakaliki, Nigeria. \u003cem\u003eBMC infectious diseases\u003c/em\u003e 19(1): 275.\u003c/li\u003e\n\u003cli\u003eEjie IL, Eleje GU, Chibuzor MT, et al. (2021) A systematic review of qualitative research on barriers and facilitators to exclusive breastfeeding practice in sub-Saharan African countries. \u003cem\u003eInternational breastfeeding journal\u003c/em\u003e 16(1): 44.\u003c/li\u003e\n\u003cli\u003eEl-Radhi AS (2019) Fever in common infectious diseases. \u003cem\u003eClinical Manual of Fever in Children\u003c/em\u003e. 85-140.\u003c/li\u003e\n\u003cli\u003eFink G, D\u0026apos;Acremont V, Leslie HH, et al. (2020) Antibiotic exposure among children younger than 5 years in low-income and middle-income countries: a cross-sectional study of nationally representative facility-based and household-based surveys. \u003cem\u003eThe Lancet Infectious diseases\u003c/em\u003e 20(2): 179-187.\u003c/li\u003e\n\u003cli\u003eFMoH (2015) National Guidelines for Diagnosis and Treatment of Malaria. Reportno. Report Number|, Date. Place Published|: Institution|.\u003c/li\u003e\n\u003cli\u003eGbadamosi IT, Henneh IT, Aluko OM, et al. (2022) Depression in Sub-Saharan Africa. \u003cem\u003eIBRO neuroscience reports\u003c/em\u003e 12: 309-322.\u003c/li\u003e\n\u003cli\u003eHarris JK (2021) Primer on binary logistic regression. \u003cem\u003eFamily medicine and community health\u003c/em\u003e 9(Suppl 1).\u003c/li\u003e\n\u003cli\u003eHossain MS, Ahmed TS, Haque MA, et al. (2024) Prevalence of unqualified sources of antimalarial drug prescription for children under the age of five: A study in 19 low- and middle-income countries. \u003cem\u003ePlos One\u003c/em\u003e 19(3): e0300347.\u003c/li\u003e\n\u003cli\u003eHossain MS, Ahmed TS, Sultana N, et al. (2023a) Examining the disparities of anti-malarial drug consumption among children under the age of five: a study of 5 malaria-endemic countries. \u003cem\u003eMalaria journal\u003c/em\u003e 22(1): 370.\u003c/li\u003e\n\u003cli\u003eHossain MS, Islam MF, Arka PB, et al. (2023b) Antibiotic prescription from qualified sources for children with fever/cough: cross-sectional study from 59 low- and middle-income countries. \u003cem\u003eEClinicalMedicine\u003c/em\u003e 61: 102055.\u003c/li\u003e\n\u003cli\u003eHowe LD (2009) \u003cem\u003eThe wealth index as a measure of socio-economic position\u003c/em\u003e. London School of Hygiene and Tropical Medicine, University of London, London.\u003c/li\u003e\n\u003cli\u003eHuang KY, Abura G, Theise R, et al. (2017) Parental Depression and Associations with Parenting and Children\u0026apos;s Physical and Mental Health in a Sub-Saharan African Setting. \u003cem\u003eChild psychiatry and human development\u003c/em\u003e 48(4): 517-527.\u003c/li\u003e\n\u003cli\u003eHuang T, Yue Y, Wang H, et al. (2019) Infant Breastfeeding and Behavioral Disorders in School-Age Children. \u003cem\u003eBreastfeeding medicine : the official journal of the Academy of Breastfeeding Medicine\u003c/em\u003e 14(2): 115-120.\u003c/li\u003e\n\u003cli\u003eIbrahim A, Abdalla SM, Jafer M, et al. (2019) Child labor and health: a systematic literature review of the impacts of child labor on child\u0026apos;s health in low- and middle-income countries. \u003cem\u003eJournal of public health (Oxford, England)\u003c/em\u003e 41(1): 18-26.\u003c/li\u003e\n\u003cli\u003eIssaka AI, Agho KE and Renzaho AM (2017) Prevalence of key breastfeeding indicators in 29 sub-Saharan African countries: a meta-analysis of demographic and health surveys (2010-2015). \u003cem\u003eBmj Open\u003c/em\u003e 7(10): e014145.\u003c/li\u003e\n\u003cli\u003eJorns-Presentati A, Napp AK, Dessauvagie AS, et al. (2021) The prevalence of mental health problems in sub-Saharan adolescents: A systematic review. \u003cem\u003ePlos One\u003c/em\u003e 16(5): e0251689.\u003c/li\u003e\n\u003cli\u003eKanellopoulou A, Antonogeorgos G, Douros K, et al. (2022) The Association between Obesity and Depression among Children and the Role of Family: A Systematic Review. \u003cem\u003eChildren (Basel, Switzerland)\u003c/em\u003e 9(8).\u003c/li\u003e\n\u003cli\u003eKang H (2013) The prevention and handling of the missing data. \u003cem\u003eKorean Journal of Anesthesiology\u003c/em\u003e 54(5): 402-406.\u003c/li\u003e\n\u003cli\u003eKrol KM and Grossmann T (2018) Psychological effects of breastfeeding on children and mothers. \u003cem\u003eBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz\u003c/em\u003e 61(8): 977-985.\u003c/li\u003e\n\u003cli\u003eKurtz M, Kawka H, Horstick O, et al. (2023) The prevalence of emotional abuse in children living in Sub-Saharan Africa - A systematic review. \u003cem\u003eChild abuse \u0026amp; neglect\u003c/em\u003e 140: 106155.\u003c/li\u003e\n\u003cli\u003eKwok MK, Leung GM and Schooling CM (2013) Breast feeding and early adolescent behaviour, self-esteem and depression: Hong Kong\u0026apos;s \u0026apos;Children of 1997\u0026apos; birth cohort. \u003cem\u003eArchives of disease in childhood\u003c/em\u003e 98(11): 887-894.\u003c/li\u003e\n\u003cli\u003eLavebratt C, Yang LL, Giacobini M, et al. (2019) Early exposure to antibiotic drugs and risk for psychiatric disorders: a population-based study. \u003cem\u003eTranslational psychiatry\u003c/em\u003e 9(1): 317.\u003c/li\u003e\n\u003cli\u003eLevine GA, Bielicki J and Fink G (2022) Cumulative Antibiotic Exposure in the First 5 Years of Life: Estimates for 45 Low- and Middle-Income Countries From Demographic and Health Survey Data. \u003cem\u003eClinical infectious diseases : an official publication of the Infectious Diseases Society of America\u003c/em\u003e 75(9): 1537-1547.\u003c/li\u003e\n\u003cli\u003eLewis KJS, Mars B, Lewis G, et al. (2012) Do parents know best? Parent-reported vs. child-reported depression symptoms as predictors of future child mood disorder in a high-risk sample. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e 141(2-3): 233-236.\u003c/li\u003e\n\u003cli\u003eLima NN, do Nascimento VB, de Carvalho SM, et al. (2013) Childhood depression: a systematic review. \u003cem\u003eNeuropsychiatric disease and treatment\u003c/em\u003e 9: 1417-1425.\u003c/li\u003e\n\u003cli\u003eLoret de Mola C, Horta BL, Goncalves H, et al. (2016) Breastfeeding and mental health in adulthood: A birth cohort study in Brazil. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e 202: 115-119.\u003c/li\u003e\n\u003cli\u003eLurie I, Yang YX, Haynes K, et al. (2015) Antibiotic exposure and the risk for depression, anxiety, or psychosis: a nested case-control study. \u003cem\u003eThe Journal of clinical psychiatry\u003c/em\u003e 76(11): 1522-1528.\u003c/li\u003e\n\u003cli\u003eMabrouk A, Mbithi G, Chongwo E, et al. (2022) Mental health interventions for adolescents in sub-Saharan Africa: A scoping review. \u003cem\u003eFrontiers in psychiatry\u003c/em\u003e 13: 937723.\u003c/li\u003e\n\u003cli\u003eMalhotra S and Sahoo S (2018) Antecedents of depression in children and adolescents. \u003cem\u003eIndustrial psychiatry journal\u003c/em\u003e 27(1): 11-16.\u003c/li\u003e\n\u003cli\u003eMaxwell NM, Nevin RL, Stahl S, et al. (2015) Prolonged neuropsychiatric effects following management of chloroquine intoxication with psychotropic polypharmacy. \u003cem\u003eClinical case reports\u003c/em\u003e 3(6): 379-387.\u003c/li\u003e\n\u003cli\u003eMcGrath JM, Records K and Rice M (2008) Maternal depression and infant temperament characteristics. \u003cem\u003eInfant behavior \u0026amp; development\u003c/em\u003e 31(1): 71-80.\u003c/li\u003e\n\u003cli\u003eMeier CR, Wilcock K and Jick SS (2004) The risk of severe depression, psychosis or panic attacks with prophylactic antimalarials. \u003cem\u003eDrug safety\u003c/em\u003e 27(3): 203-213.\u003c/li\u003e\n\u003cli\u003eMICS (2022) Nigeria - 2021 Multiple Indicator Cluster Survey (MICS) \u0026amp; National Immunization Coverage Survey (NICS): Survey Findings Report. Reportno. Report Number|, Date. Place Published|: Institution|.\u003c/li\u003e\n\u003cli\u003eMICS (2024) \u003cem\u003eSurveys\u003c/em\u003e. Available at: https://mics.unicef.org/surveys (accessed 18 June).\u003c/li\u003e\n\u003cli\u003eMokwena KE, Magabe S and Ntuli B (2023) Symptoms of Depression, Anxiety and Their Co-Occurrence among Orphaned Children in Sekhukhune District, Limpopo Province. \u003cem\u003eChildren (Basel, Switzerland)\u003c/em\u003e 10(8).\u003c/li\u003e\n\u003cli\u003eMotlhatlhedi K, Setlhare V, Ganiyu A, et al. (2017) Association between depression in carers and malnutrition in children aged 6 months to 5 years. \u003cem\u003eAfrican journal of primary health care \u0026amp; family medicine\u003c/em\u003e 9(1): e1-e6.\u003c/li\u003e\n\u003cli\u003eMullen S (2018) Major depressive disorder in children and adolescents. \u003cem\u003eThe mental health clinician\u003c/em\u003e 8(6): 275-283.\u003c/li\u003e\n\u003cli\u003eNevin RL and Croft AM (2016) Psychiatric effects of malaria and anti-malarial drugs: historical and modern perspectives. \u003cem\u003eMalaria journal\u003c/em\u003e 15: 332.\u003c/li\u003e\n\u003cli\u003eNMEP (2022) Nigeria malaria indicator survey 2021 final report. Reportno. Report Number|, Date. Place Published|: Institution|.\u003c/li\u003e\n\u003cli\u003eNwaneli EI, Eguonu I, Ebenebe JC, et al. (2020) Malaria prevalence and its sociodemographic determinants in febrile children - a hospital-based study in a developing community in South-East Nigeria. \u003cem\u003eJournal of preventive medicine and hygiene\u003c/em\u003e 61(2): E173-E180.\u003c/li\u003e\n\u003cli\u003eNyeko R, Otim F, Obiya EM, et al. (2023) Anti-malarial drug use, appropriateness and associated factors among children under-five with febrile illnesses presenting to a tertiary health facility: a cross sectional study. \u003cem\u003eMalaria journal\u003c/em\u003e 22(1): 103.\u003c/li\u003e\n\u003cli\u003eOkiro EA and Snow RW (2010) The relationship between reported fever and Plasmodium falciparum infection in African children. \u003cem\u003eMalaria journal\u003c/em\u003e 9: 99.\u003c/li\u003e\n\u003cli\u003eOluwole D, Mason E and Costello A (2000) Management of childhood illness in Africa. Early evaluations show promising results. \u003cem\u003eBMJ (Clinical research ed )\u003c/em\u003e 320(7235): 594-595.\u003c/li\u003e\n\u003cli\u003eOshagbemi OA, Lopez-Romero P, Winnips C, et al. (2023) Estimated distribution of malaria cases among children in sub-Saharan Africa by specified age categories using data from the Global Burden of Diseases 2019. \u003cem\u003eMalaria journal\u003c/em\u003e 22(1): 371.\u003c/li\u003e\n\u003cli\u003eOwen JP, Baig B, Abbo C, et al. (2016) Child and adolescent mental health in sub-Saharan Africa: a perspective from clinicians and researchers. \u003cem\u003eBJPsych international\u003c/em\u003e 13(2): 45-47.\u003c/li\u003e\n\u003cli\u003ePartap U, Nyundo A, Manu A, et al. (2023) Depressive symptoms among adolescents in six sub-Saharan African countries: A pooled analysis of associated factors. \u003cem\u003ePreventive medicine reports\u003c/em\u003e 36: 102499.\u003c/li\u003e\n\u003cli\u003ePatra S (2019) Assessment and management of pediatric depression. \u003cem\u003eIndian Journal of Psychiatry\u003c/em\u003e 61(3): 300-306.\u003c/li\u003e\n\u003cli\u003ePrichett LM, Yolken RH, Wu L, et al. (2022) Relationship between antibiotic exposure and subsequent mental health disorders in a primary care health system. \u003cem\u003eBrain, behavior, \u0026amp; immunity - health\u003c/em\u003e 21: 100430.\u003c/li\u003e\n\u003cli\u003eRubin LH, Witkiewitz K, Andre JS, et al. (2007) Methods for Handling Missing Data in the Behavioral Neurosciences: Don\u0026apos;t Throw the Baby Rat out with the Bath Water. \u003cem\u003eJournal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience\u003c/em\u003e 5(2): A71-77.\u003c/li\u003e\n\u003cli\u003eSaeed N and Wemakor A (2019) Prevalence and determinants of depression in mothers of children under 5 years in Bole District, Ghana. \u003cem\u003eBMC research notes\u003c/em\u003e 12(1): 373.\u003c/li\u003e\n\u003cli\u003eSalk RH, Hyde JS and Abramson LY (2017) Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. \u003cem\u003ePsychological bulletin\u003c/em\u003e 143(8): 783-822.\u003c/li\u003e\n\u003cli\u003eSarfo JO, Amoadu M, Kordorwu PY, et al. (2023) Malaria amongst children under five in sub-Saharan Africa: a scoping review of prevalence, risk factors and preventive interventions. \u003cem\u003eEuropean journal of medical research\u003c/em\u003e 28(1): 80.\u003c/li\u003e\n\u003cli\u003eSchumacher RF and Spinelli E (2012) Malaria in children. \u003cem\u003eMediterranean journal of hematology and infectious diseases\u003c/em\u003e 4(1): e2012073.\u003c/li\u003e\n\u003cli\u003eSelph SS and McDonagh MS (2019) Depression in Children and Adolescents: Evaluation and Treatment. \u003cem\u003eAmerican family physician\u003c/em\u003e 100(10): 609-617.\u003c/li\u003e\n\u003cli\u003eSequeira M, Singh S, Fernandes L, et al. (2022) Adolescent Health Series: The status of adolescent mental health research, practice and policy in sub-Saharan Africa: A narrative review. \u003cem\u003eTropical medicine \u0026amp; international health : TM \u0026amp; IH\u003c/em\u003e 27(9): 758-766.\u003c/li\u003e\n\u003cli\u003eShah JA, Emina JB, Eckert E, et al. (2015) Prompt access to effective malaria treatment among children under five in sub-Saharan Africa: a multi-country analysis of national household survey data. \u003cem\u003eMalaria journal\u003c/em\u003e 14: 329.\u003c/li\u003e\n\u003cli\u003eSherr L, Yakubovich AR, Skeen S, et al. (2020) Depressive symptoms among children attending community based support in South Africa - pathways for disrupting risk factors. \u003cem\u003eClinical child psychology and psychiatry\u003c/em\u003e 25(4): 984-1001.\u003c/li\u003e\n\u003cli\u003eShimizu M, Nakata Y and Takahashi K (2023) Current findings and gaps in early initiation of breastfeeding practices in sub-Saharan African countries: A scoping review. \u003cem\u003eJournal of global health\u003c/em\u003e 13: 04036.\u003c/li\u003e\n\u003cli\u003eSong X, Wei W, Cheng W, et al. (2022) Cerebral malaria induced by plasmodium falciparum : clinical features, pathogenesis, diagnosis, and treatment. \u003cem\u003eFrontiers in cellular and infection microbiology\u003c/em\u003e 12: 939532.\u003c/li\u003e\n\u003cli\u003eStavseth MR, Clausen T and Roislien J (2019) How handling missing data may impact conclusions: A comparison of six different imputation methods for categorical questionnaire data. \u003cem\u003eSAGE open medicine\u003c/em\u003e 7: 2050312118822912.\u003c/li\u003e\n\u003cli\u003eTaddio A, McMurtry CM, Logeman C, et al. (2022) Prevalence of pain and fear as barriers to vaccination in children - Systematic review and meta-analysis. \u003cem\u003eVaccine\u003c/em\u003e 40(52): 7526-7537.\u003c/li\u003e\n\u003cli\u003eThabane L, Mbuagbaw L, Zhang S, et al. (2013) A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. \u003cem\u003eBMC medical research methodology\u003c/em\u003e 13: 92.\u003c/li\u003e\n\u003cli\u003eTomlinson M and Morgan B (2015) Infant Mental Health Research in Africa: a call for action for research in the next 10 years. \u003cem\u003eGlobal mental health (Cambridge, England)\u003c/em\u003e 2: e7.\u003c/li\u003e\n\u003cli\u003eTrape JF, Diagne N, Diene-Sarr F, et al. (2023) One hundred malaria attacks since birth. A longitudinal study of African children and young adults exposed to high malaria transmission. \u003cem\u003eEClinicalMedicine\u003c/em\u003e 67: 102379.\u003c/li\u003e\n\u003cli\u003eWoldeamanuel BT (2020) Trends and factors associated to early initiation of breastfeeding, exclusive breastfeeding and duration of breastfeeding in Ethiopia: evidence from the Ethiopia Demographic and Health Survey 2016. \u003cem\u003eInternational breastfeeding journal\u003c/em\u003e 15(1): 3.\u003c/li\u003e\n\u003cli\u003eYohani S, Salami B, Taylor M, et al. (2023) African migrant children\u0026apos;s mental health: A scoping review. \u003cem\u003eInternational Journal of Intercultural Relations\u003c/em\u003e 97.\u003c/li\u003e\n\u003cli\u003eZhong BL, Ding J, Chen HH, et al. (2013) Depressive disorders among children in the transforming China: an epidemiological survey of prevalence, correlates, and service use. \u003cem\u003eDepression and anxiety\u003c/em\u003e 30(9): 881-892.\u003c/li\u003e\n\u003cli\u003eZong X, Wu H, Zhao M, et al. (2021) Global prevalence of WHO infant feeding practices in 57 LMICs in 2010-2018 and time trends since 2000 for 44 LMICs. \u003cem\u003eEClinicalMedicine\u003c/em\u003e 37: 100971.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e \u003cstrong\u003e\u0026ndash;\u0026nbsp;\u003c/strong\u003eDescriptive data for study variables\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 24%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChildhood depression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhole sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26%;\"\u003e\n \u003cp\u003eAntibiotics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eAmoxicillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e471 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e180 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e291 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 2.208, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eCotrimoxazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e139 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e43 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e96 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 6.495, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eHerbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e241 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e93 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e148 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 0.879, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26%;\"\u003e\n \u003cp\u003eAntimalarials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e810 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e272 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e538 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 22.225, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eFansidar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e165 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e52 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e113 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 6.900, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eChloroquine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e234 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e71 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e163 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 12.261, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eAmodiaquine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e124 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e45 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e79 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 1.419, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eQuinine (pills)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e74 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e23 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e51 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 3.350, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eQuinine (injection)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e229 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e71 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e158 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 10.619, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eArtesunate (rectal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e150 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e60 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e90 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 0.148, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eArtesunate (injection)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e382 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e129 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e253 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 9.727, \u003cem\u003ep\u003c/em\u003e = 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26%;\"\u003e\n \u003cp\u003eBreastfeeding (ever)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e4864 (92.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e2238 (93.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e2626 (91.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 5253) = 9.218, \u003cem\u003ep\u003c/em\u003e = 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26%;\"\u003e\n \u003cp\u003eCovariates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eAge (number of days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eM, 975.11 (SD: 790.015 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eM,1312.71 (SD: 782.450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eM,1331.35 (SD: 667.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e(13429.854) = 1.612, \u003cem\u003ep\u003c/em\u003e \u0026gt; .05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eGender (% Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e8231 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e3449 (49.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e4782 (48.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 0.863, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eWeight (kg) (MICS + NICS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eM, 0.990 (SD: 0.959)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eM, 1.120 (SD:1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eM, 0.932 (SD:0.978)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e(14708.053) = -12.011, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eArea (% rural)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e12285 (73.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e4925 (70.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e7360 (75.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 40.293, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eEducation (% educated)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e9273 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e4165 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e5108 (52.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16718) = 97.570, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eFunctional difficulties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e738 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e229 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e509 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 35.140, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eEthnicity/tribe (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e10189 (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e4253 (61.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e5936 (60.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 0.421, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eWealth index (HH assets)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eM, 2.56 (SD: 1.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eM, 2.61 (SD:1.365)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eM, 2.45 (SD:1.276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e(14324.925) = -7.613, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eIllness symptoms (fever)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e4094 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e1537 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e2557 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 35.725, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23%;\"\u003e\n \u003cp\u003eIllness symptoms (cough)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e2958 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e1087 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e1871 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e = 16720) = 34.028, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNotes.\u003c/em\u003e NR = non-response, HH = Household. All values are sample size and percentages (%), unless means (M) and standard deviations (SDs) are stated. Percentages for all medicines depict the proportion of children exposed to each drug. Percentage for breastfeeding indicates the proportion of children who had ever been breastfed. Percentage for education (maternal) refers to the proportion with primary, secondary, or higher education. Percentage for ethnicity/tribe depicts proportion of children from Hausa, Igbo, or Yoruba backgrounds. \u003cem\u003eP\u003c/em\u003e values relate to 1-sided comparisons between depressed and non-depressed children, based on Chi-square or independent samples t-tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eHierarchical logistic regression of predictors of paediatric depression in all infants and children.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026szlig;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(\u003cem\u003e\u0026szlig;\u003c/em\u003e) (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026szlig;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(\u003cem\u003e\u0026szlig;\u003c/em\u003e) (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026szlig;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(\u003cem\u003e\u0026szlig;\u003c/em\u003e) (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntibiotics (Amoxicillin)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.997 (0.719, 1.382)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.992 (0.715, 1.375)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.019 (0.727, 1.427)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntibiotics (Cotrimoxaz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.812 (0.956, 3.435)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.831 (0.966, 3.471)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.559 (0.814, 2.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntibiotics (Herbal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.020 (0.654, 1.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.015 (0.651, 1.583)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.944 (0.597, 1.492)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (ACT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.499\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.647 (1.262, 2.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.501\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.650 (1.264, 2.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.565\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.760 (1.316, 2.355)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Fansidar)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.048 (0.606, 1.813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.026 (0.592, 1.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.008 (0.575, 1.770)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Chloroquine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.490\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.632 (1.022, 2.604)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.477\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.612 (1.009, 2.574)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.584 (0.982, 2.556)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Amodiaquine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.508 (0.720, 3.157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.516 (0.724, 3.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.766 (0.834, 3.738)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Quinine/pills)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.824 (0.787, 4.227)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.801 (0.777, 4.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.914 (0.815, 4.494)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Quinine/inject)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.736 (0.449, 1.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.744 (0.454, 1.219)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.775 (0.467, 1.285)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Artesunate/rectal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.316 (0.686, 2.523)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.339 (0.698, 2.569)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.522 (0.779, 2.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Artesunate/inject)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.536\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.710 (1.137, 2.573)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.546\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.726 (1.147, 2.598)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.563\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.757 (1.150, 2.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eBreastfeeding (ever)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.330\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.719 (0.581, 0.890)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.254\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.776 (0.625, 0.963)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAge (in days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.000 (1.000, 1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eGender (M/F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.989 (0.886, 1.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eWeight (MICS/NICS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.200\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.819 (0.768, 0.873)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eArea (urban/rural)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.057 (0.910, 1.227)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eEducation (mothers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.303\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.738 (0.649, 0.840)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eChilds functional difficulties\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.366\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.442 (1.146, 1.814)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eEthnicity(tribe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.074 (0.954, 1.209)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eWealth index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.972 (0.920, 1.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eMedical (fever)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.876 (0.746, 1.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eMedical (cough)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.213\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.238 (1.067, 1.436)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eNagelkerke \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eCox \u0026amp; Snell \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e-2 Log likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e7199.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e7189.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e7076.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eNo. of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e31103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e31103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e31103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eModel Chi-square\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e37.471\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e46.854\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e159.801\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Coding: Unless otherwise indicated, all predictor variables except for age, weight, and wealth index, are dummy variables, coded as \u0026lsquo;Yes\u0026rsquo; (1), \u0026lsquo;No\u0026rsquo; (0)). Gender is coded as \u0026lsquo;Male\u0026rsquo; (1), \u0026lsquo;Female\u0026rsquo; (0). Geographical area is coded as \u0026lsquo;Urban\u0026rsquo; (1) vs \u0026lsquo;Rural\u0026rsquo; (0). Education (mothers) is coded as \u0026lsquo;None\u0026rsquo; (0), \u0026lsquo;Primary\u0026rsquo;/\u0026lsquo;Secondary\u0026rsquo;/\u0026lsquo;Higher\u0026rsquo; (1). Ethnicity (of household head) is coded as \u0026lsquo;Hausa\u0026rsquo;/\u0026lsquo;Igbo\u0026rsquo;/\u0026lsquo;Yoruba\u0026rsquo;/\u0026lsquo;Fulani\u0026rsquo; (1), \u0026lsquo;Other\u0026rsquo; (0). ACT = Artemisinin-based combination therapy; MICS = Multiple Indicator Cluster Surveys; NICS = National Immunization Coverage Survey. \u0026lsquo;No\u0026rsquo; includes \u0026lsquo;don\u0026rsquo;t know\u0026rsquo; and no response. \u003csup\u003ea\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.05, \u003csup\u003eb\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.01, \u003csup\u003ec\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.001,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eHierarchical logistic regression of predictors of paediatric depression in male infants and children.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026szlig;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(\u003cem\u003e\u0026szlig;\u003c/em\u003e) (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026szlig;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(\u003cem\u003e\u0026szlig;\u003c/em\u003e) (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026szlig;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(\u003cem\u003e\u0026szlig;\u003c/em\u003e) (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntibiotics (Amoxicillin)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.840 (0.526, 1.342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.840 (0.526, 1.341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.879 (0.544, 1.423)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntibiotics (Cotrimoxaz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.408 (0.579, 3.423)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.418 (0.583, 3.448)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.266 (0.508, 3.157)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntibiotics (Herbal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.587 (0.824, 3.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.579 (0.820, 3.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.449 (0.738, 2.846)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (ACT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.783\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e2.188 (1.490, 3.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.788\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e2.199 (1.498, 3.229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.853\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e2.347 (1.547, 3.559)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Fansidar)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.835 (0.417, 1.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.820 (0.409, 1.645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.853 (0.416, 1.746)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Chloroquine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.709 (0.865, 3.377)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.689 (0.854, 3.339)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.672 (0.833, 3.354)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Amodiaquine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.260 (0.351, 4.528)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.254 (0.349, 4.508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.624 (0.444, 5.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Quinine/pills)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.525 (0.506, 4.598)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.501 (0.497, 4.528)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.565 (0.509, 4.812)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Quinine/inject)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.788 (0.412, 1.504)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.793 (0.415, 1.516)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.826 (0.420, 1.622)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Artesunate/rectal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.815 (0.769, 4.287)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.834 (0.776, 4.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e2.200 (0.914, 5.298)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Artesunate/inject)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.571\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.770 (1.020, 3.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.581\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.788 (1.030, 3.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.736 (0.977, 3.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eBreastfeeding (ever)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.786 (0.589, 1.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.854 (0.637, 1.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAge (in days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.000 (1.000, 1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eWeight (MICS/NICS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.183\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.833 (0.762, 0.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eArea (urban/rural)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.055 (0.858, 1.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eEducation (mothers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.332\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.717 (0.599, 0.859)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eChilds functional difficulties\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.369 (0.991, 1.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eEthnicity(tribe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.145 (0.970, 1.352)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eWealth index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.962 (0.892, 1.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eMedical (fever)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.905 (0.719, 1.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eMedical (cough)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.070 (0.869, 1.318)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eNagelkerke \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eCox \u0026amp; Snell \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e-2 Log likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e3682.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e3679.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e3621.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eNo. of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e15799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e15799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e15799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eModel Chi-square\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e32.023\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e34.709\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e92.581\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Coding: Unless otherwise indicated, all predictor variables except for age, weight, and wealth index, are dummy variables, coded as \u0026lsquo;Yes\u0026rsquo; (1), \u0026lsquo;No\u0026rsquo; (0)). Geographical area is coded as \u0026lsquo;Urban\u0026rsquo; (1) vs \u0026lsquo;Rural\u0026rsquo; (0). Education (mothers) is coded as \u0026lsquo;None\u0026rsquo; (0), \u0026lsquo;Primary\u0026rsquo;/\u0026lsquo;Secondary\u0026rsquo;/\u0026lsquo;Higher\u0026rsquo; (1). Ethnicity (of household head) is coded as \u0026lsquo;Hausa\u0026rsquo;/\u0026lsquo;Igbo\u0026rsquo;/\u0026lsquo;Yoruba\u0026rsquo;/\u0026lsquo;Fulani\u0026rsquo; (1), \u0026lsquo;Other\u0026rsquo; (0). ACT = Artemisinin-based combination therapy; MICS = Multiple Indicator Cluster Surveys; NICS = National Immunization Coverage Survey. \u0026lsquo;No\u0026rsquo; includes \u0026lsquo;don\u0026rsquo;t know\u0026rsquo; and no response. \u003csup\u003ea\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.05, \u003csup\u003eb\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.01, \u003csup\u003ec\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.001,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eHierarchical logistic regression of predictors of paediatric depression in female infants and children.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25.5102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026szlig;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(\u003cem\u003e\u0026szlig;\u003c/em\u003e) (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026szlig;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(\u003cem\u003e\u0026szlig;\u003c/em\u003e) (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026szlig;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(\u003cem\u003e\u0026szlig;\u003c/em\u003e) (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntibiotics (Amoxicillin)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.104 (0.695, 1.752)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.090 (0.686, 1.731)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.078 (0.666, 1.745)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntibiotics (Cotrimoxaz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e2.370 (0.927, 6.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e2.407 (0.941, 6.159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.968 (0.760, 5.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntibiotics (Herbal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.691 (0.368, 1.298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.689 (0.366, 1.294)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.633 (0.329, 1.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (ACT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.231 (0.842, 1.798)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.220 (0.834, 1.783)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.314 (0.864, 1.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Fansidar)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.491 (0.591, 3.757)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.462 (0.578, 3.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.282 (0.501, 3.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Chloroquine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.546 (0.803, 2.976)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.522 (0.789, 2.936)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.443 (0.737, 2.828)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Amodiaquine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.690 (0.680, 4.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.717 (0.691, 4.270)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.871 (0.740, 4.730)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Quinine/pills)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e2.233 (0.594, 8.398)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e2.233 (0.593, 8.407)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e2.602 (0.678, 9.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Quinine/inject)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.705 (0.326, 1.528)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.719 (0.332, 1.558)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.764 (0.347, 1.682)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Artesunate/rectal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.930 (0.331, 2.616)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.961 (0.342, 2.702)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.963 (0.329, 2.822)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAntimalaria (Artesunate/inject)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.548 (0.835, 2.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.555 (0.839, 2.884)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.667 (0.879, 3.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eBreastfeeding (ever)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.443\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.642 (0.468, 0.881)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.373\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.689 (0.498, 0.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eAge (in days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.000 (1.000, 1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eWeight (MICS/NICS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.206\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.813 (0.741, 0.893)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eArea (urban/rural)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.056 (0.850, 1.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eEducation (mothers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.280\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.756 (0.628, 0.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eChilds functional difficulties\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.525 (1.099, 2.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eEthnicity(tribe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.006 (0.849, 1.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eWealth index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.979 (0.904, 1.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eMedical (fever)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.857 (0.681, 1.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eMedical (cough)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.378\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.460 (1.177, 1.810)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eNagelkerke \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eCox \u0026amp; Snell \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e-2 Log likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e3505.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e3497.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e3435.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eNo. of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e15304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e15304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e15304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eModel Chi-square\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e16.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e24.474\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e86.606\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Coding: Unless otherwise indicated, all predictor variables except for age, weight, and wealth index, are dummy variables, coded as \u0026lsquo;Yes\u0026rsquo; (1), \u0026lsquo;No\u0026rsquo; (0)). Geographical area is coded as \u0026lsquo;Urban\u0026rsquo; (1) vs \u0026lsquo;Rural\u0026rsquo; (0). Education (mothers) is coded as \u0026lsquo;None\u0026rsquo; (0), \u0026lsquo;Primary\u0026rsquo;/\u0026lsquo;Secondary\u0026rsquo;/\u0026lsquo;Higher\u0026rsquo; (1). Ethnicity (of household head) is coded as \u0026lsquo;Hausa\u0026rsquo;/\u0026lsquo;Igbo\u0026rsquo;/\u0026lsquo;Yoruba\u0026rsquo;/\u0026lsquo;Fulani\u0026rsquo; (1), \u0026lsquo;Other\u0026rsquo; (0). ACT = Artemisinin-based combination therapy; MICS = Multiple Indicator Cluster Surveys; NICS = National Immunization Coverage Survey. \u0026lsquo;No\u0026rsquo; includes \u0026lsquo;don\u0026rsquo;t know\u0026rsquo; and no response. \u003csup\u003ea\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.05, \u003csup\u003eb\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.01, \u003csup\u003ec\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.001,\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5036450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5036450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough paediatric depression is a recognised problem worldwide, there is limited data on its prevalence in children aged under 5 years, and the role of breastfeeding and medicinal exposure. This study examined whether lack of breastfeeding, and exposure to antimalarial and antibiotic medicines, contribute to depression in Nigerian children aged under 5.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study examined data from 31,103 children (mean age, 2.11 years (SD, 1.421)), collected during the 6th round of the MICS (Multiple Indicator Cluster Surveys), completed in 2021. A series of binary logistic regression models were used to analyse the data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eChildren exposed to antimalarial medicines, specifically ACT (AOR\u0026thinsp;=\u0026thinsp;1.760, 95%CI [1.316\u0026ndash;2.355]), and artesunate injections (AOR\u0026thinsp;=\u0026thinsp;1.757, 95%CI [1.150\u0026ndash;2.684]), and those who were not breastfed (AOR\u0026thinsp;=\u0026thinsp;0.776, 95%CI [0.625\u0026ndash;0.963]), had greater odds of depression, after adjusting for potential confounders. The association between ACT medication use and depression was salient in male children (AOR\u0026thinsp;=\u0026thinsp;2.347, 95%CI [1.547\u0026ndash;3.559]), whereas the relationship between breastfeeding and depression was primarily observed in female children (AOR\u0026thinsp;=\u0026thinsp;0.689, 95%CI [0.498\u0026ndash;0.951]). Robustness checks including variations in managing missing data, breastfeeding measurement, and analysis across subgroups for multiple covariates (e.g., geographical area, mothers\u0026rsquo; education, wealth index, medical symptoms), highlighted the importance of contextual factors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis is the first population-based study to examine the role of breastfeeding and medicinal use, in suspected depression amongst children aged under 5, from sub-Sharan Africa. Overall, exposure to antimalaria treatment, particularly ACT, and inadequate breastfeeding may help identify infants and young children susceptible to paediatric depression.\u003c/p\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e","manuscriptTitle":"The role of breastfeeding, antibiotics and antimalarial medicinal exposure in paediatric depression amongst infants and children aged under 5: A population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 06:24:24","doi":"10.21203/rs.3.rs-5036450/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-27T11:30:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-25T13:36:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-23T06:42:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268475176849547625759840051853646810856","date":"2024-10-20T06:02:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261959286843659730053289774611998670680","date":"2024-10-19T01:36:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109760825760105894072256169632734888918","date":"2024-10-18T07:42:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3745591618756041495517037576037690049","date":"2024-10-15T09:32:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48883964365607173353375687120180135602","date":"2024-10-15T06:29:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87419671302468334472085226885808327226","date":"2024-10-04T03:44:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-01T02:39:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-16T07:57:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-13T12:27:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-13T12:26:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2024-09-05T08:12:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d3047be-41d7-4b4f-b675-0ea7b278c03d","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:04:05+00:00","versionOfRecord":{"articleIdentity":"rs-5036450","link":"https://doi.org/10.1186/s12887-024-05265-1","journal":{"identity":"bmc-pediatrics","isVorOnly":false,"title":"BMC Pediatrics"},"publishedOn":"2025-05-17 15:57:39","publishedOnDateReadable":"May 17th, 2025"},"versionCreatedAt":"2024-11-15 06:24:24","video":"","vorDoi":"10.1186/s12887-024-05265-1","vorDoiUrl":"https://doi.org/10.1186/s12887-024-05265-1","workflowStages":[]},"version":"v1","identity":"rs-5036450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5036450","identity":"rs-5036450","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}