Social-ecological model determinants of anaemia severity levels among children under five in Ashanti Region: A multinomial logistic regression based on the 2022 Ghana Demographic and Health Survey

Social-ecological model determinants of anaemia severity levels among children under five in Ashanti Region: A multinomial logistic regression based on the 2022 Ghana Demographic and Health Survey | 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 Social-ecological model determinants of anaemia severity levels among children under five in Ashanti Region: A multinomial logistic regression based on the 2022 Ghana Demographic and Health Survey Jonathan Sackey, Christiana Naa Momo Lokko, Emmanuel Mensah Baah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5654524/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Anaemia is a significant global health issue, particularly affecting children under five years and women of reproductive age, with children being disproportionately impacted due to rapid growth and increased iron demand. It is an indicator of poor nutrition and health and a major barrier to social and economic development. For children under five, anaemia is a haemoglobin concentration below 110 g/l. This study aimed to examine the severity of anaemia among children under five in Ghana and identify predictors based on the socio-ecological model. Using data from the 2022 Ghana Demographic and Health Survey (GDHS) and a sample of 662 children, multinomial logistic regression was applied for the analysis. The findings revealed that 32.4% of children experienced some form of anaemia, with 11.9% having moderate to severe anaemia. Individual (currently breastfeeding, had diarrhoea recently maternal age, maternal education, wealth index and ethnicity) and institutional (proximity to health facility) factors predicted anaemia severity. This study underscores the need for targeted interventions focused on individual factors, healthcare access, and breastfeeding support to reduce anaemia prevalence among children in Ghana. Children Under Five Community-Level factors Influencing factors Anemia Socio-ecological model of Health Introduction Anaemia is a global public health issue, disproportionately affecting children under five and women of reproductive age (Gupta et al., 2014; T. Kumar et al., 2014). Key causes include poor dietary intake, iron malabsorption, rapid growth, and chronic blood loss, as well as deficiencies in folate, vitamin B12, and vitamin A, malaria, and intestinal helminths (Bill & Foundation, 2023; Lopez et al., 2016). It negatively impacts health, social development, and economic progress globally, with children under five classified as anaemic if haemoglobin levels fall below 110 g/L (WHO, 2011). Immediate causes include inadequate nutrient absorption, malaria, and parasites, while household factors such as poor sanitation, limited health services, and insufficient dietary diversity also contribute (Larsen et al., 2017; Lombardo et al., 2017). Broader determinants include education, wealth, and cultural practices (Ngnie-Teta et al., 2009). Risk factors vary by context, including malaria, HIV, and sickle cell disease (Kassebaum et al., 2014). Anaemia reduces cognitive development, increases maternal and child mortality, and perpetuates cycles of poverty. Interventions such as dietary supplementation, deworming, and ITN usage have shown promise in addressing the burden (WHO, 2012). Efforts to reduce anaemia must prioritize integrated strategies targeting immediate, individual-level, institutional and community factors, which this current study seeks to achieve. Also, this study delves into regional level basis because most of the research seeks to understand anaemia in the context of country and community. Literature Anaemia affects approximately 600 million children worldwide, with 43% of children under five estimated to be anaemic (Antehunegn et al., 2021). Each year, 20 million babies are born with low birth weights, 3.6 million of whom die before 28 days, mostly in Sub-Saharan Africa and Southern Asia. Anaemia has long-lasting effects, such as diminished iron storage in newborns A. Kumar et al. (2022) and remains disproportionately high in Sub-Saharan Africa and South Asia despite socioeconomic improvements (Bill & Foundation, 2023). Prevalence rates in 2016 ranged from 62% in Benin to 86% in Burkina Faso, and from 37% in South Africa to 73% in Chad (Antehunegn et al., 2021). Key risk factors include sickle cell disease, malaria parasitemia, poor socioeconomic conditions, and caregiver unemployment (Simbauranga et al., 2015). Globally, 42.6% of children under five suffer from anaemia, contributing to 45% of child deaths despite interventions like iron supplementation (Shenton et al., 2020). Anaemia in children under five is linked to cognitive and behavioural delays and is caused by low birth weight, undernutrition, poor sanitation, and maternal anaemia (Egbi et al., 2014). In malaria-endemic areas, prevalence can reach 76% (WHO, 2021). Boys are more prone to anaemia than girls, with younger children also at higher risk (Aheto et al., 2023; Tadesse et al., 2022). In Ghana, anaemia prevalence among children under months is 49%, with 0.8% classified as severe (Ghana Statistical Service, 2021). Its predictors are context-specific, influenced by dietary habits, socioeconomic conditions, and local environments (Balarajan et al., 2011). Achieving Sustainable Development Goal targets requires identifying nation-specific predictors and implementing timely interventions to reduce childhood anaemia (Antehunegn et al., 2021). Continuous assessments are essential to inform prevention and treatment strategies. This study is grounded in the Social Ecological Model (SEM), developed by Bronfenbrenner (1978) and refined by McElroy (1988), which explores how factors at individual, institutional and community levels influence health behaviours. SEM is applied to understand the determinants of anaemia severity, offering a comprehensive approach that spans multiple disciplines, particularly in public health and education (McLaren & Hawe, 2005). The concept of ecology, derived from biological science, emphasizes the interactions between organisms and their environments (Stokols, 1996). Adopting a social ecology perspective is essential in human health research, as it considers the complex, multi-level factors affecting health outcomes (Stokols, 1996). While Bronfenbrenner's work primarily focused on how social factors shape individual development, it has been extended to study health behaviors such as physical activity and smoking (Simons-Morton, 2012). This study, therefore, aims to identify the prevalence and predictors of anaemia among children under 5 in the Ashanti region, Ghana. Setting The Ashanti Region, located in southern Ghana, is the third largest of the 16 administrative regions, covering 24,389 km² (10.2% of Ghana’s total land area). It is the most populated region, with 5,440,463 people GHANA STATISTICAL SERVICE (2021), accounting for about one-sixth of Ghana's population. Known for gold bar and cocoa production, its capital and largest city is Kumasi. Situated in Ghana's middle belt, it shares boundaries with six regions: Bono, Bono East, Ahafo (north), Eastern (east), Central (south), and Western (southwest). The region comprises 43 districts, each led by a District Chief Executive. Study Design and Sample Size This study utilized a quantitative cross-sectional analytical approach with data from the 2022 Ghana Demographic and Health Survey (GDHS), conducted nationwide across all 16 regions from October 17, 2022, to January 14, 2023. The survey employed a stratified two-stage cluster sampling design, achieving a 99% household response rate. Individual interviews included 15,014 women (98% response rate) and 7,044 men. For this study, data were filtered to focus on children under five, resulting in a final sample of 662 children from the Ashanti Region with complete data. The GDHS provides nationally representative data for urban and rural areas and all regions. Variable Codification Explained Variable The response variable of this study was the anaemic status of children, which is categorized into four categories; mildly anaemic (haemoglobin level 10.0–10.9g/dl, moderately anaemic (haemoglobin level 7.0–9.9g/dl), severely anaemic (haemoglobin level <7.0g/dl), and not anaemic (haemoglobin 11.0 g/dl). It was assessed based on the haemoglobin concentration in blood adjusted to the altitude in the Ghana Demographic and Health Survey. In DHS, before determining whether a child is anaemic or not, they take into account altitude. Then, they adjusted, the haemoglobin adjustment was done by subtracting or adding the adjusted Hgb value to each individual observed Hgb value. For this current study, the anaemia status was categorized anaemia into three levels; moderate to severe, mild and not anaemic. Explanatory Variables The study variables for the present study were selected based on the existing literature on anaemia and the availability of variables in the Ghana Demographic and Health Survey, 2022. Consistent with the study’s objectives and given the hierarchical nature of GDHS data where children and mothers were nested within the cluster. The explanatory variables were grouped into three, individual factors which are child age in months (6–11, 12–23, 24-35, 36-47, 48-59), sex of child (male, female), twin status (single, multiple), currently breastfeeding (no, yes), fever within the last two weeks (no, yes), taking iron in the past year (no, yes), had diarrhoea of diarrhoea (no, yes last two weeks), have mosquito bed net for sleeping (no, yes), maternal age (15–24, 25–34, 35–49), maternal education (no education, primary, secondary, tertiary), wealth index (poor, middle, rich), ethnicity (Akan, Ga/Dangme and Ewe, Mole-Dagbani), sex of family head (male, female), working status (not currently working, currently working) and parity (primiparous, multiparous, grandparous). The institutional factors variables considered in the study are access to healthcare (low, high) and proximity to health facilities (low, high). Finally, the community factors include place of residence (urban, rural), community media exposure (low media exposure, high media exposure and community source of water (unimproved, improved). Analysis Preliminary Analysis The data highlights several key characteristics of the sampled children and their households. Among the 662 children, 11.9% have moderate to severe anaemia, 20.4% have mild anaemia, and 67.6% are not anaemic, with 32.4% experiencing some level of anaemia. The gender distribution is slightly skewed towards females (54.3%). Age-wise, the largest group is 12-23 months (24.3%), while 92.3% are single births. Most children (63.7%) are no longer breastfeeding, 16.2% experienced fever in the last two weeks, and 62.2% received iron supplements in the past year. Diarrhoea affected 15.7% in the past two weeks, and 80.5% lived in households with mosquito nets. Mothers are predominantly aged 30-39 years (48%) and largely educated, with 62.5% completing secondary school. Nearly half of the households are wealthy (49.6%), with Akan children forming the majority (66.4%) and male-headed households dominating (61.3%). Most mothers (86.3%) are employed, with 78.6% having more than one child. Healthcare engagement is relatively high, with 65.6% of children from households frequently visiting health facilities, though 95.8% live far from such facilities. Urban children comprise 52.8% of the sample, and 63.3% of mothers have high media exposure. Most households (88.4%) have access to improved water sources, underscoring essential hygiene and health disparities. Further Analysis The logistic regression model is a statistical technique that analyses the relationship between multiple independent variables and a categorical dependent variable and models the probability of an event taking place by having the log odds for the event be a linear combination of one or more independent variables. Multinomial Logistic Regression (MLR) is an extension of binary logistic regression, employed when the dependent variable is categorical with more than two possible outcomes. MLR is designed to handle situations where the outcome variable can take on multiple categories that are not ordered and nominal. Table 1. Estimated Odds Ratios (OR) and confidence 95% confidence intervals (CI) from Multinomial Logistic Regression of Anemia Severity Moderate to Severe Anaemia Mild Anaemia Factors Exp (ß) [95% CI] P-Value Exp (ß) [95% CI] P-Value Sex of child Male Female 0.853 [0.460 – 1.581] 1.000 0.613 1.000 1.222 [0.764 – 1.953] 1.000 0.403 1.000 Child’s age 6-11 12-23 24-35 36-47 48-59 0.654 [0.126 – 3.392] 1.386 [0.514 – 3.738] 1.197 [0.488 – 2.935] 0.873 [0.363 – 2.101] 1.000 0.613 0.519 0.695 0.762 1.000 2.053 [0.856 – 4.925] 0.985 [0.449 – 2.163] 1.614 [0.750 – 3.475] 1.767 [0.837 – 3.729] 1.000 0.107 0.970 0.221 0.135 1.000 Twin status Single Multiple 0.797 [0.239 – 2.655] 1.000 0.712 1.000 0.891 [0.334 – 2.373] 1.000 0.817 1.000 Currently breastfeeding No Yes 6.271 [2.312 – 17.008] 1.000 0.000 1.000 0.498 [0.266 – 0.930] 1.000 0.029 1.000 Fever in the past 2 weeks No Yes 1.1709 [0.484 – 2.534] 1.000 0.809 1.000 0.808 [0.438– 1.491] 1.000 0.495 1.000 Taking iron in the past year No Yes 1.204 [0.598 – 2.424] 1.000 0.603 1.000 0.987 [0.585 – 1.665] 1.000 0.961 1.000 Had diarrhoea recently No Yes, the last 2 weeks 1.179 [0.491 – 2.830] 1.000 0.712 1.000 2.141 [1.044 – 4.391] 1.000 0.038 1.000 Have bed net for sleeping Yes No 0.614 [0.274 – 1.376] 1.000 0.236 1.000 0.846 [0.479 – 1.496] 1.000 0.566 1.000 Table 1. Estimated Odds Ratios (OR) and confidence 95% confidence intervals (CI) from Multinomial Logistic Regression of Anemia Severity Moderate to Severe Anaemia Mild Anaemia Factors Exp (ß) [95% CI] P-Value Exp (ß) [95% CI] P-Value Maternal age 15-19 20-24 25-29 30-34 35-39 40-45 44-49 1.057 [0.070 – 15.903] 0.164 [0.017 – 1.578] 0.084 [0.010 – 0.711] 0.020 [0.003 – 0.154] 0.057 [0.008 – 0.415] 0.145 [0.019 – 1.136] 1.000 0.968 0.118 0.023 0.000 0.005 0.066 1.000 0.032[0.003 – 0.311] 0.035 [0.005 – 0.251] 0.092 [0.014 – 0.591] 0.026 [0.004 – 0.168] 0.038 [0.006 – 0.233] 0.031 [0.004 – 0.221] 1.000 0.003 0.001 0.012 0.000 0.000 0.001 1.000 Maternal education No education Primary Secondary Tertiary 0.255 [0.991 – 3.855] 0.035 [0.146 – 0.610 0.039 [0.118 – 0.361] 1.000 0.989 0.008 0.000 1.000 0.460 [0.147 – 1.447] 0.396 [0.148 – 1.058] 0.200 [0.089 – 0.450] 1.000 0.184 0.065 0.000 1.000 Wealth index Poor Middle Rich 0.262 [ 0.085 – 0.808] 1.859 [0.769 – 4.340] 1.000 0.020 0.152 1.000 0.174 [ 0.075 – 0.404] 1.005 [0.520 – 1.941] 1.000 0.000 0.989 1.000 Ethnicity Akan Ga/Dangme and Ewe Mole-Dagbani 1.152 [0.517 – 2.567] 6.038 [1.238 – 29.454] 1.000 0.730 0.026 1.000 0.431 [0.244 – 0.763] 3.635 [1.022 – 12.922] 1.000 0.004 0.046 1.000 Sex of family head Male Female 3.258 [1.465 – 7.249] 1.000 0.004 1.000 0.892 [0.524 – 1.519] 1.000 0.674 1.000 Working status Not currently working Currently working 0.423 [0.118 – 1.514] 1.000 0.186 1.000 1.173 [0.533 – 2.582] 1.000 0.692 1.000 Table 1. Estimated Odds Ratios (OR) and confidence 95% confidence intervals (CI) from Multinomial Logistic Regression of Anemia Severity Moderate to Severe Anaemia Mild Anaemia Factors Exp (ß) [95% CI] P-Value Exp (ß) [95% CI] P-Value Parity Primiparous Multiparous Grandparous 0.581 [0.142 – 2.379] 0.438 [0.171– 1.119] 1.000 0.450 0.085 1.000 1.258 [0.410 – 3.858] 0.616 [0.271– 1.400] 1.000 0.688 0.247 1.000 Access to healthcare No Yes 0.921 [0.453 – 1.870] 1.000 0.819 1.000 1.238 [0.718 – 2.137] 1.000 0.443 1.000 Proximity to a health facility Low High 10.321 [24.239 -142.06] 1.000 0.000 1.000 8.123 [20.052 -140.06] 1.000 0.003 1.000 Place of residence Urban Rural 0.832 [0.375 – 1.845] 1.000 0.651 1.000 0.591 [0.329 -1.062] 1.000 0.079 1.000 Community media exposure Low media exposure High media exposure 3.085 [1.527 – 6.232] 1.000 0.020 1.000 1.661 [0.974 – 2.834] 1.000 0.063 1.000 Community source of water Unimproved Improved 2.037 [0.693 – 5.985] 1.000 0.196 0.732 0.412 [0.156 – 1.086] 1.000 0.073 1.000 Model P-Value 0.000 Nagelkerke R 2 41.8% Overall Percentage (%) 72.7 Reference Category: Not anaemic Source: SPSS output generated from 2022 GDHS data Discussion The study presented a general discussion of the results of the analysis. This section assesses how far the objectives of the research have been achieved. Comparisons and contrasts of the findings of the previous studies are also presented. In this study, the sex of the child is not a significant predictor of anaemia severity. Moreover, male children are less likely to have moderate to severe anaemia compared to female children rather than not anaemic. This finding contradicts the study conducted by Tadesse et al. (2022) which found that sex was a significant predictor of anaemia among under-five children. The study also indicated that being female is protective against anaemia among under-five children. Similarly, Mwakishalua et al. (2024) also found that infant gender significantly predicted anaemia status with male infants having an increased risk of anaemia compared to female infants. Evidence from the regression model in the Table suggests that the age of the child is a statistically significant predictor of anaemia severity (p = 0.049). However, children 6–11 months are less likely to have higher levels of anaemia compared to children 48–59 months rather than not anaemic. A study in line with this current study was conducted in Ethiopia, which found child age is a predictor of anaemia. This current study also contradicts previous studies by Alamneh et al., (2023); Shenton et al., (2020) which found that found that a child’s age was negatively associated with severe–moderate anaemia, as older children had lower odds of anaemia than younger children rather than not anaemic. The model shows that the twin status of the child is not a significant predictor of anaemia severity. Single children are less likely to have moderate to severe anaemia compared to children of twin status. Similarly to previous studies, Alamneh et al., (2023); Tesema et al., (2021) (Tesema et al., 2021) indicated that multiple births had 1.18 times [AOR = 1.18, 95% CI: 1.11, 1.25] higher odds of being at higher level anaemia status compared to a singleton. From the analysis results, currently breastfeeding is a statistically significant predictor of anaemia severity. Children who are not been currently breastfed are six times more likely to be moderately to severely anaemic compared to children who are currently breastfed rather than not anaemic. These results are in line with and at the same time contradict the previous results by (Khan et al., 2016) which asserted that currently breastfeeding is a significant predictor of anaemia severity and children who continued breastfeeding were more anaemic than the non-breast-fed children. Fever in the past two weeks is a significant predictor of anaemia severity. Children who have not had a fever in the past two weeks are more likely to have higher anaemic levels compared to children who have had a fever in the past two weeks rather than not anaemic. This current finding opposes the results of Seifu & Tesema,( 2022); Tesema et al., (2021) who stated that children who had a fever in the last two weeks had higher odds of higher levels of anaemia compared to their counterparts. Similarly, Shenton et al. (2020) asserted that fever in the previous 2 weeks was significantly associated with increased odds of severe–moderate anaemia. Again, the analysis indicated that taking iron in the past 2 years is not a significant predictor of anaemia. However, children who were not given iron within the last 12 months are more likely to be compared to their counterparts who were given iron within the last 12 months rather than not anaemic. This is consistent with the findings of Nambiema et al., (2019) who alluded that showed that taking iron supplements was not associated with childhood anaemia. Moreover, the model again showed that having diarrhoea recently is not a significant predictor of anaemia severity. Children who had a recent history of diarrhoea were less likely to have a higher level of anaemia compared to those who didn’t have any history of diarrhoea rather than not anaemic. Similarly to this finding, Shenton et al. (2020) also found no association between anaemia status and diarrhoea recently. Contrastingly, (Tesema et al., 2021) revealed that children with a history of diarrhoea had higher odds of higher anaemia levels than children who did not have diarrhoea. Again, Semba et al. (2008) demonstrated that current diarrhoea and a history of diarrhoea in the previous week were associated with anaemia among children under age five. The findings of this current can be because children who recently had diarrhoea might have been taken to healthcare facilities, where they received treatments that addressed multiple health issues, including anaemia. For example, Iron supplements or fortified foods are provided as part of recovery and deworming medications (intestinal worms can cause anaemia). Having a mosquito bed net for sleeping is not a significant predictor of anaemia severity. Children whose mothers didn’t have bed nets for sleeping were less likely to have moderate to severe anaemia compared to their counterparts whose mothers had bed nets for sleeping rather than not anaemic. The previous result of Ngesa & Mwambi (2014) indicating no significant relationship between having a bed net for sleeping and anaemia is in line with the result of this current study. However, this finding reversed that of Dutta et al. (2020) who indicated that the use of bed net decreased the likelihood of anaemia by 16%. The plausible reason is that families with bed nets might represent a group that has access to healthcare and awareness programs but might also have other underlying risk factors for anaemia (e.g., poor nutrition or higher prevalence of infectious diseases like malaria). Maternal age is a significant predictor of anaemia severity. Children whose mothers were 15–19 years old were more likely to have higher levels of anaemia. All children whose mothers were 20–44 years old had lower odds of having higher levels of anaemia, especially children with mothers aged 30–34 years rather than not anaemic. This is in line with the findings of Parbey et al. (2019); Tesema et al., (2021) indicated that children born to mothers aged less than 20 years had higher odds of a higher level of anaemia compared to children born to mothers aged 20 years and above and children whose mothers were aged less than 20 years were 4.69 times more likely to develop anaemia respectively. This current finding was expected as younger mothers may lack the experience or education to provide a balanced diet that prevents anaemia in their children. Moreover, maternal education significantly predicts anaemia severity. Children whose mothers had no education, primary and secondary education were less likely to have moderate to severe anaemia compared to children whose mothers had tertiary education rather than not anaemic. This current study supports the findings of Ofori et al. (2020); and Tadese et al. (2022) who concluded that maternal education was a significant predictor of anemia among under-five children. However, this current finding reverses that of Elmardi et al. (2020); and Kuziga et al. (2017) who alluded that a significant relationship and association between children’s mother's level of education or access to education facilities (e.g. listening to the radio) and anaemia respectively. However, the plausible reason for this is that more educated mothers may be more likely to seek medical attention and have their children diagnosed with anaemia, leading to higher reported cases compared to less-educated mothers whose children may remain undiagnosed. The relationship between wealth index and anaemia severity was significant. Children whose mothers were from poor homes were less likely to have moderate to severe anaemia rather than not anaemic. However, children whose mothers were from middle-class homes were more than 1.8 times more likely to have a high level of anaemia compared to children whose mothers were from rich homes rather than not anaemic. This finding is consistent with that of Alamneh et al. (2023) who found that the wealth index was a significant and independent predictor of anaemia level. However, it also opposes the result of (Kebede et al., 2021) indicating that low family income is an associated factor with anaemia among under-five children. The ethnicity of the mother affected the anaemia severity. Children whose mothers belong to Akan tribes were more likely to have moderate to severe anaemia rather than not anaemic. Also, children whose mothers are Ga/Dangmes and Ewes are six times more likely to have moderate to severe anaemia rather than not anaemic. This current study is in line with that of Parbey et al. (2019); Shenton et al. (2020) who alluded that a significant relationship between anaemia status and ethnicity and that children whose mothers were Ga/Dangme were also likely to have higher levels of anaemia in 2014 respectively. The sex of the family is significantly related to anaemia severity. Children from families headed by males were more than three times more likely to have higher levels of anaemia compared to children from female-headed families rather than not anaemic. The current result is consistent with that of Klu & Agordoh (2022) who inferred that children in MHH are more likely to be anaemic compared to children in FHH. However, it conflicts with the finding of Tesema et al. (2021) which found no association between the sex of the family head and anaemia severity. However, the relationship between working status and anaemia severity is not significant. Children whose mothers were currently not working were less likely to have moderate to severe anaemia compared to those whose mothers were currently not working rather than not anaemic. This result affirms that of (Gebreegziabiher et al., 2014), which indicated that maternal employment status was not associated with anaemia among children aged 6–59 months old in a study conducted in Kilte Awulaelo Woreda, Northern Ethiopia. Similarly, parity is not a significant predictor of anaemia severity. Children whose mothers were primiparous and multiparous had lower odds of having moderate to severe anaemia compared to children whose mothers were grandparous rather than not anaemic. This current result contradicts the odds that children with mothers of parity 2–3 and above 4 were significantly associated with anaemia. In model 2, 17% of parity 2–3 and 27% of parity 4 and above were more likely to have anaemic children than mothers with first parity, while in model 3, it was 11% (2–3 parity) and 14% (4 + parity) respectively (Dutta et al., 2020). Access to healthcare is not a significant predictor of anaemia severity. Children whose mothers had not visited a health facility within the last 12 months were less likely to have moderate to severe anaemia compared to those whose mothers had visited the health facility within the 12 months rather than not anaemic. Children whose mothers do not visit healthcare facilities might still engage in effective community-based or traditional practices that help mitigate anaemia, such as proper nutrition or supplementation through non-clinical sources. Nonetheless, proximity to a health facility is a significant predictor of anaemia severity. Children whose mothers are far from health facility are ten times more likely to have higher levels of anemia compared to children whose mothers are closer to health facility. This stems from the fact that longer travel times reduce the likelihood of regular visits, especially for children whose mothers manage multiple household responsibilities. Again, families far from healthcare facilities may face higher costs for transportation, making routine visits less feasible leading to untreated conditions such as infections (e.g., malaria, diarrhoea) or nutritional deficiencies that exacerbate anaemia. Place of residence is not a significant predictor of anaemia severity. Children from urban areas were less likely to have higher levels of anaemia compared to children from rural areas. This contradicts the findings of Gebreweld et al. (2019); Tadesse et al. (2022), who alluded that residence was a significant predictor of anaemia among under-five children and that those children from rural settings were 20% less likely to be affected by anaemia as compared to children from urban settings and children living in an urban area were 1.8 times more likely to be anaemic than those living in a rural area respectively. This current study, however, affirms that of Gebrie & Alebel (2020); Kebede et al. (2021) attesting that the distribution of anaemia is more prevalent among children from rural residents compared to urban ones and children from rural areas was 3.25 times more likely to be anaemic than their urban counterparts respectively. The relationship between the community water source and anaemia severity is not significant. Children from communities with improved sources of water had two times higher odds of having higher levels of anaemia compared to children from communities with unimproved sources of water. On an individual level, this agrees with that of Khan et al. (2016); Muchie (2016) indicating that the odds of being severely/moderately anaemic were higher for children whose households used non-improved sources of drinking water and children from households without access to ‘improved’ water sources were 1.34 times more likely than others to be anaemic. Community media exposure is a significant predictor of anaemia severity. Children who came from communities with low media exposure were three times more likely to have moderate to severe anaemia compared to children from communities with high media exposure. Confirming the result of the current study, Dutta et al. (2020) found that less exposure to mass media among mothers increases the likelihood of anaemia among children on an individual level. Confines and Strengths This study used cross-sectional data, so causal relationships between anaemia and the identified independent variables cannot be established. Additionally, due to the reliance on secondary data, factors such as eating habits, parasite infestations (malaria, Visceral Leishmaniasis, and hookworm), previous hospitalizations, and the use of nutritional supplements (e.g., vitamin B12 and folate) could not be examined. The cross-sectional nature of the GDHS data limits the ability to draw causal inferences between the independent and dependent variables. Moreover, since the GDHS data were collected retrospectively, recall bias may be a concern due to memory lapses. Despite these limitations, this study is one of the few to investigate anaemia prevalence and predictors at the regional level. The findings, based on a large, nationally representative dataset, are generalizable. The use of multinomial logistic regression provided insights into the factors influencing anaemia severity in the Ashanti region, offering valuable information for policymakers in developing targeted interventions. Conclusion The study examined anaemia severity among children under 5 in the Ashanti region and its relationship with individual, institutional and community factors to severe anaemia. Out of the 662 sample size, 20.4% had mild anaemia, and 67.6% were not anaemic, with 32.4% experiencing some level of anaemia. These findings suggest that anaemia in this sample is concentrated within a subset of children, reflecting variations in regional or contextual factors that might influence anaemia prevalence and severity. Individual (currently breastfeeding, had diarrhea recently maternal age, maternal education, wealth index and ethnicity) and institutional (proximity to health facility) factors predicted anemia severity. Maternal education is a critical area for intervention. Programs aimed at promoting formal education for women, particularly targeting adolescent mothers, can empower them with the knowledge to make informed health and nutrition decisions for their children. Community-based health campaigns should emphasize the importance of exclusive and prolonged breastfeeding, while healthcare providers offer robust counselling and support during antenatal and postnatal visits. These efforts can ensure that breastfeeding is sustained and accessible for all mothers, especially in rural and underserved areas. Improving healthcare accessibility is also paramount. Declarations Competing interests None declared. Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting, or dissemination plans of this research. Patient consent for publication Not required. Ethics approval We accessed the DHS data after getting permission from the DHS programme. Primary data were collected from each country by adhering to relevant local and international ethical guidelines. All procedures and questionnaires of DHS are reviewed and approved by the ICF International Institutional Review Board (IRB). Additionally, each country-specific DHS has been reviewed and approved by ICF IRB and an IRB in the host country. Data sharing statement Appendices to the extended report are available in English. Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. Author Contribution 1 and 1 wrote the main manuscript text and prepared Table 1.2 helped with the design of the manuscript, substantially reversed the paper and have approved the submitted version. Acknowledgements We would like to extend our earnest homage to the DHS programme for granting access to the data. Data Availability The data sets produced and/or examined during the present study are accessible in the MEASURE DHS database at the repository, https://www.dhsprogram.com References Aheto, J. M. K., Alhassan, Y., Puplampu, A. E., Boglo, J. K., & Sedzro, K. M. (2023). Anemia prevalence and its predictors among children under-five years in Ghana. A multilevel analysis of the cross-sectional 2019 Ghana Malaria Indicator Survey. Health Science Reports , 6 (10), e1643. https://doi.org/10.1002/HSR2.1643 Alamneh, T. S., Melesse, A. W., & Gelaye, K. A. (2023). Determinants of anemia severity levels among children aged 6–59 months in Ethiopia: Multilevel Bayesian statistical approach. Scientific Reports 2023 13:1 , 13 (1), 1–12. https://doi.org/10.1038/s41598-022-20381-7 Antehunegn, G., Id, T., Gebrie Worku, M., Tadesse Tessema, Z., Teshale, A. B., Alem, A. Z., Yeshaw Id, Y., Alamneh, T. S., & Liyew, A. M. (2021). Prevalence and determinants of severity levels of anemia among children aged 6-59 months in sub-Saharan Africa: A multilevel ordinal logistic regression analysis . https://doi.org/10.1371/journal.pone.0249978 Balarajan, Y., Ramakrishnan, U., Özaltin, E., Shankar, A. H., & Subramanian, S. V. (2011). Anaemia in low-income and middle-income countries. Lancet (London, England) , 378 (9809), 2123–2135. https://doi.org/10.1016/S0140-6736(10)62304-5 Bill, F., & Foundation, M. G. (2023). Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990–2021: findings from the Global Burden of Disease Study 2021. The Lancet Haematology , 10 (9), e713–e734. https://doi.org/10.1016/S2352-3026(23)00160-6 Dutta, M., Bhise, M., Prashad, L., Chaurasia, H., & Debnath, P. (2020). Prevalence and risk factors of anemia among children 6–59 months in India: A multilevel analysis. Clinical Epidemiology and Global Health , 8 (3), 868–878. https://doi.org/10.1016/J.CEGH.2020.02.015 Egbi, G., Steiner-Asiedu, M., Kwesi, F. S. aali., Ayi, I., Ofosu, W., Setorglo, J., Klobodu, S. S. elor., & Armar-Klemesu, M. (2014). Anaemia among school children older than five years in the Volta Region of Ghana. The Pan African Medical Journal , 17 Suppl 1 (Suppl 1), 10. https://doi.org/10.11694/PAMJ.SUPP.2014.17.1.3205 Elmardi, K. A., Adam, I., Malik, E. M., Ibrahim, A. A., Elhassan, A. H., Kafy, H. T., Nawai, L. M., Abdin, M. S., & Kremers, S. (2020). Anaemia prevalence and determinants in under 5 years children: findings of a cross-sectional population-based study in Sudan. 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BMC Pediatrics , 17 (1). https://doi.org/10.1186/S12887-017-0782-3 Larsen, D. A., Grisham, T., Slawsky, E., & Narine, L. (2017). An individual-level meta-analysis assessing the impact of community-level sanitation access on child stunting, anemia, and diarrhea: Evidence from DHS and MICS surveys. PLoS Neglected Tropical Diseases , 11 (6). https://doi.org/10.1371/JOURNAL.PNTD.0005591 Lombardo, P., Vaucher, P., Rarau, P., Mueller, I., Favrat, B., & Senn, N. (2017). Hemoglobin Levels and the Risk of Malaria in Papua New Guinean Infants: A Nested Cohort Study. The American Journal of Tropical Medicine and Hygiene , 97 (6), 1770. https://doi.org/10.4269/AJTMH.17-0093 Lopez, A., Cacoub, P., Macdougall, I. C., & Peyrin-Biroulet, L. (2016). Iron deficiency anaemia. Lancet (London, England) , 387 (10021), 907–916. https://doi.org/10.1016/S0140-6736(15)60865-0 McLaren, L., & Hawe, P. (2005). Ecological perspectives in health research. Journal of Epidemiology & Community Health , 59 (1), 6–14. https://doi.org/10.1136/JECH.2003.018044 Muchie, K. F. (2016). Determinants of severity levels of anemia among children aged 6-59 months in Ethiopia: Further analysis of the 2011 Ethiopian demographic and health survey. BMC Nutrition , 2 (1), 1–8. https://doi.org/10.1186/S40795-016-0093-3/TABLES/2 Mwakishalua, J., Karanja, S., Lihana, R., Okoyo, C., Stoffel, N., & Zimmermann, M. (2024). Prevalence and predictors of anemia among six-week-old infants in Kwale County, Kenya: A cross-sectional study. PLOS Global Public Health , 4 (3), e0003062. https://doi.org/10.1371/JOURNAL.PGPH.0003062 Nambiema, A., Robert, A., & Yaya, I. (2019). Prevalence and risk factors of anemia in children aged from 6 to 59 months in Togo: analysis from Togo demographic and health survey data, 2013–2014. BMC Public Health , 19 (1), 1–9. https://doi.org/10.1186/S12889-019-6547-1/TABLES/2 Ngesa, O., & Mwambi, H. (2014). Prevalence and Risk Factors of Anaemia among Children Aged between 6 Months and 14 Years in Kenya. PLoS ONE , 9 (11), e113756. https://doi.org/10.1371/JOURNAL.PONE.0113756 Ngnie-Teta, I., Kuate-Defo, B., & Receveur, O. (2009). Multilevel modelling of sociodemographic predictors of various levels of anaemia among women in Mali. Public Health Nutrition , 12 (9), 1462–1469. https://doi.org/10.1017/S1368980008004400 Ofori, L., Manortey, S., Vetsi, O., Nartey, C., & Ugorji, H. O. (2020). Factors Contributing to Anaemia in Children under Five Years in the Ga East Municipality, Ghana . Parbey, P. A., Tarkang, E., Manu, E., Amu, H., Ayanore, M. A., Aku, F. Y., Ziema, S. A., Bosoka, S. A., Adjuik, M., & Kweku, M. (2019). Risk Factors of Anaemia among Children under Five Years in the Hohoe Municipality, Ghana: A Case Control Study. Anemia , 2019 . https://doi.org/10.1155/2019/2139717 Seifu, B. L., & Tesema, G. A. (2022). Individual-and community-level factors associated with anemia among children aged 6–23 months in sub-Saharan Africa: evidence from 32 sub-Saharan African countries. Archives of Public Health , 80 (1), 1–12. https://doi.org/10.1186/S13690-022-00950-Y/TABLES/3 Semba, R. D., de Pee, S., Ricks, M. O., Sari, M., & Bloem, M. W. (2008). Diarrhea and fever as risk factors for anemia among children under age five living in urban slum areas of Indonesia. International Journal of Infectious Diseases , 12 (1), 62–70. https://doi.org/10.1016/J.IJID.2007.04.011 Shenton, L. M., Jones, A. D., & Wilson, M. L. (2020a). Factors Associated with Anemia Status Among Children Aged 6–59 months in Ghana, 2003–2014. Maternal and Child Health Journal , 24 (4), 483–502. https://doi.org/10.1007/S10995-019-02865-7/TABLES/5 Shenton, L. M., Jones, A. D., & Wilson, M. L. (2020b). Factors Associated with Anemia Status Among Children Aged 6–59 months in Ghana, 2003–2014. Maternal and Child Health Journal , 24 (4), 483–502. https://doi.org/10.1007/S10995-019-02865-7/TABLES/5 Simbauranga, R. H., Kamugisha, E., Hokororo, A., Kidenya, B. R., & Makani, J. (2015). Prevalence and factors associated with severe anaemia amongst under-five children hospitalized at Bugando Medical Centre, Mwanza, Tanzania. BMC Hematology , 15 (1). https://doi.org/10.1186/S12878-015-0033-5 Simons-Morton, B. (2012). Health Behavior in Ecological Context. Http://Dx.Doi.Org/10.1177/1090198112464494 , 40 (1), 6–10. https://doi.org/10.1177/1090198112464494 Stokols, D. (1996). Translating social ecological theory into guidelines for community health promotion. American Journal of Health Promotion : AJHP , 10 (4), 282–298. https://doi.org/10.4278/0890-1171-10.4.282 Tadese, M., Yeshaneh, A., & Mulu, G. B. (2022). Determinants of good academic performance among university students in Ethiopia : a cross ‑ sectional study. BMC Medical Education , 1–9. https://doi.org/10.1186/s12909-022-03461-0 Tadesse, S. E., Zerga, A. A., Mekonnen, T. C., Tadesse, A. W., Hussien, F. M., Feleke, Y. W., Anagaw, M. Y., & Ayele, F. Y. (2022a). Burden and Determinants of Anemia among Under-Five Children in Africa: Systematic Review and Meta-Analysis. Anemia , 2022 . https://doi.org/10.1155/2022/1382940 Tadesse, S. E., Zerga, A. A., Mekonnen, T. C., Tadesse, A. W., Hussien, F. M., Feleke, Y. W., Anagaw, M. Y., & Ayele, F. Y. (2022b). Burden and Determinants of Anemia among Under-Five Children in Africa: Systematic Review and Meta-Analysis. Anemia , 2022 . https://doi.org/10.1155/2022/1382940 Tadesse, S. E., Zerga, A. A., Mekonnen, T. C., Tadesse, A. W., Hussien, F. M., Feleke, Y. W., Anagaw, M. Y., & Ayele, F. Y. (2022c). Burden and Determinants of Anemia among Under-Five Children in Africa: Systematic Review and Meta-Analysis. Anemia , 2022 , 1382940. https://doi.org/10.1155/2022/1382940 Tesema, G. A., Worku, M. G., Tessema, Z. T., Teshale, A. B., Alem, A. Z., Yeshaw, Y., Alamneh, T. S., & Liyew, A. M. (2021a). Prevalence and determinants of severity levels of anemia among children aged 6-59 months in sub-Saharan Africa: A multilevel ordinal logistic regression analysis. PloS One , 16 (4). https://doi.org/10.1371/JOURNAL.PONE.0249978 Tesema, G. A., Worku, M. G., Tessema, Z. T., Teshale, A. B., Alem, A. Z., Yeshaw, Y., Alamneh, T. S., & Liyew, A. M. (2021b). Prevalence and determinants of severity levels of anemia among children aged 6–59 months in sub-Saharan Africa: A multilevel ordinal logistic regression analysis. PLOS ONE , 16 (4), e0249978. https://doi.org/10.1371/JOURNAL.PONE.0249978 Tesema, G. A., Worku, M. G., Tessema, Z. T., Teshale, A. B., Alem, A. Z., Yeshaw, Y., Alamneh, T. S., & Liyew, A. M. (2021c). Prevalence and determinants of severity levels of anemia among children aged 6–59 months in sub-Saharan Africa: A multilevel ordinal logistic regression analysis. PLoS ONE , 16 (4). https://doi.org/10.1371/JOURNAL.PONE.0249978 WHO. (2011). Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity . 1–6. WHO. (2012). Anaemia Policy Brief . 6 . WHO. (2021). Progress on malaria control in countries. Nursing Times , 90 (32), 20. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-5654524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":394082946,"identity":"a5b9ddde-a8dd-4b68-b39e-b4a4c7cd509e","order_by":0,"name":"Jonathan Sackey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIie3RsYrCMBjA8ZRAbgl2TSnoKzQUXPRhUoROEW4SNwtCuMEHqG+hb/BJoS7FuWOl4yHkuOE2MdbNIep2SP4QAoEf4UsQcrn+YWTQAog5o+RjCbcjs2Eb8VGaNLoa931aJhk8Q4JMxnyt0jjIJX+ORADDkJIi2dTyr/1RqN+rBf7+tJFdloaUGlKdttlOoTioBRnlNlKgMqTMkP20I+Y6QWJqI6WnQhoZAvJ4JYuHJFhhzHNhxv+S3pWIyMzS2ojPiNdo6B6Z59WB8XV1VNhGCPM1JOfuKxs9n40Hvf2k+LWR+5hZnmIviFtYv0xcLpfrnbsAyoJaHNBilPYAAAAASUVORK5CYII=","orcid":"","institution":"Takoradi Technical University","correspondingAuthor":true,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Sackey","suffix":""},{"id":394082947,"identity":"4a1cfc87-4622-41e1-abdd-aeebbd9844ed","order_by":1,"name":"Christiana Naa Momo Lokko","email":"","orcid":"","institution":"Takoradi Technical University","correspondingAuthor":false,"prefix":"","firstName":"Christiana","middleName":"Naa Momo","lastName":"Lokko","suffix":""},{"id":394082948,"identity":"bcd18fa5-5cfd-45bc-93a5-f775b8a6d488","order_by":2,"name":"Emmanuel Mensah Baah","email":"","orcid":"","institution":"Takoradi Technical University","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"Mensah","lastName":"Baah","suffix":""}],"badges":[],"createdAt":"2024-12-16 14:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5654524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5654524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72307420,"identity":"b3565ac7-0c75-4336-8930-ad6cf55c689f","added_by":"auto","created_at":"2024-12-25 04:42:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":841665,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5654524/v1/5e94e20f-b572-497f-9fb7-094e93220efa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social-ecological model determinants of anaemia severity levels among children under five in Ashanti Region: A multinomial logistic regression based on the 2022 Ghana Demographic and Health Survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnaemia is a global public health issue, disproportionately affecting children under five and women of reproductive age (Gupta et al., 2014; T. Kumar et al., 2014). Key causes include poor dietary intake, iron malabsorption, rapid growth, and chronic blood loss, as well as deficiencies in folate, vitamin B12, and vitamin A, malaria, and intestinal helminths (Bill \u0026amp; Foundation, 2023; Lopez et al., 2016). It negatively impacts health, social development, and economic progress globally, with children under five classified as anaemic if haemoglobin levels fall below 110 g/L (WHO, 2011). Immediate causes include inadequate nutrient absorption, malaria, and parasites, while household factors such as poor sanitation, limited health services, and insufficient dietary diversity also contribute (Larsen et al., 2017; Lombardo et al., 2017). Broader determinants include education, wealth, and cultural practices (Ngnie-Teta et al., 2009). Risk factors vary by context, including malaria, HIV, and sickle cell disease (Kassebaum et al., 2014). Anaemia reduces cognitive development, increases maternal and child mortality, and perpetuates cycles of poverty. Interventions such as dietary supplementation, deworming, and ITN usage have shown promise in addressing the burden (WHO, 2012). Efforts to reduce anaemia must prioritize integrated strategies targeting immediate, individual-level, institutional and community factors, which this current study seeks to achieve. Also, this study delves into regional level basis because most of the research seeks to understand anaemia in the context of country and community.\u003c/p\u003e"},{"header":"Literature","content":"\u003cp\u003eAnaemia affects approximately 600 million children worldwide, with 43% of children under five estimated to be anaemic (Antehunegn et al., 2021). Each year, 20 million babies are born with low birth weights, 3.6 million of whom die before 28 days, mostly in Sub-Saharan Africa and Southern Asia. Anaemia has long-lasting effects, such as diminished iron storage in newborns A. Kumar et al. (2022) and remains disproportionately high in Sub-Saharan Africa and South Asia despite socioeconomic improvements (Bill \u0026amp; Foundation, 2023). Prevalence rates in 2016 ranged from 62% in Benin to 86% in Burkina Faso, and from 37% in South Africa to 73% in Chad (Antehunegn et al., 2021). Key risk factors include sickle cell disease, malaria parasitemia, poor socioeconomic conditions, and caregiver unemployment (Simbauranga et al., 2015). Globally, 42.6% of children under five suffer from anaemia, contributing to 45% of child deaths despite interventions like iron supplementation (Shenton et al., 2020). Anaemia in children under five is linked to cognitive and behavioural delays and is caused by low birth weight, undernutrition, poor sanitation, and maternal anaemia (Egbi et al., 2014). In malaria-endemic areas, prevalence can reach 76% (WHO, 2021). Boys are more prone to anaemia than girls, with younger children also at higher risk (Aheto et al., 2023; Tadesse et al., 2022). In Ghana, anaemia prevalence among children under months is 49%, with 0.8% classified as severe (Ghana Statistical Service, 2021). Its predictors are context-specific, influenced by dietary habits, socioeconomic conditions, and local environments (Balarajan et al., 2011). Achieving Sustainable Development Goal targets requires identifying nation-specific predictors and implementing timely interventions to reduce childhood anaemia (Antehunegn et al., 2021). Continuous assessments are essential to inform prevention and treatment strategies.\u003c/p\u003e\n\u003cp\u003eThis study is grounded in the Social Ecological Model (SEM), developed by Bronfenbrenner (1978) and refined by McElroy (1988), which explores how factors at individual, institutional and community levels influence health behaviours. SEM is applied to understand the determinants of anaemia severity, offering a comprehensive approach that spans multiple disciplines, particularly in public health and education (McLaren \u0026amp; Hawe, 2005). The concept of ecology, derived from biological science, emphasizes the interactions between organisms and their environments (Stokols, 1996). Adopting a social ecology perspective is essential in human health research, as it considers the complex, multi-level factors affecting health outcomes (Stokols, 1996). While Bronfenbrenner\u0026apos;s work primarily focused on how social factors shape individual development, it has been extended to study health behaviors such as physical activity and smoking (Simons-Morton, 2012). This study, therefore, aims to identify the prevalence and predictors of anaemia among children under 5 in the Ashanti region, Ghana.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ashanti Region, located in southern Ghana, is the third largest of the 16 administrative regions, covering 24,389 km\u0026sup2; (10.2% of Ghana\u0026rsquo;s total land area). It is the most populated region, with 5,440,463 people GHANA STATISTICAL SERVICE (2021), accounting for about one-sixth of Ghana\u0026apos;s population. Known for gold bar and cocoa production, its capital and largest city is Kumasi. Situated in Ghana\u0026apos;s middle belt, it shares boundaries with six regions: Bono, Bono East, Ahafo (north), Eastern (east), Central (south), and Western (southwest). The region comprises 43 districts, each led by a District Chief Executive.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design and Sample Size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized a quantitative cross-sectional analytical approach with data from the 2022 Ghana Demographic and Health Survey (GDHS), conducted nationwide across all 16 regions from October 17, 2022, to January 14, 2023. The survey employed a stratified two-stage cluster sampling design, achieving a 99% household response rate. Individual interviews included 15,014 women (98% response rate) and 7,044 men. For this study, data were filtered to focus on children under five, resulting in a final sample of 662 children from the Ashanti Region with complete data. The GDHS provides nationally representative data for urban and rural areas and all regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable Codification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExplained Variable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe response variable of this study was the anaemic status of children, which is categorized into four categories; mildly anaemic (haemoglobin level 10.0\u0026ndash;10.9g/dl, moderately anaemic (haemoglobin level 7.0\u0026ndash;9.9g/dl), severely anaemic (haemoglobin level \u0026lt;7.0g/dl), and not anaemic (haemoglobin 11.0 g/dl). It was assessed based on the haemoglobin concentration in blood adjusted to the altitude in the Ghana Demographic and Health Survey. In DHS, before determining whether a child is anaemic or not, they take into account altitude. Then, they adjusted, the haemoglobin adjustment was done by subtracting or adding the adjusted Hgb value to each individual observed Hgb value. For this current study, the anaemia status was categorized anaemia into three levels; moderate to severe, mild and not anaemic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExplanatory Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study variables for the present study were selected based on the existing literature on anaemia and the availability of variables in the Ghana Demographic and Health Survey, 2022. Consistent with the study\u0026rsquo;s objectives and given the hierarchical nature of GDHS data where children and mothers were nested within the cluster. The explanatory variables were grouped into three, individual factors which are child age in months (6\u0026ndash;11, 12\u0026ndash;23, 24-35, 36-47, 48-59), sex of child (male, female), twin status (single, multiple), currently breastfeeding (no, yes), fever within the last two weeks (no, yes), taking iron in the past year (no, yes), had diarrhoea of diarrhoea (no, yes last two weeks), have mosquito bed net for sleeping (no, yes), maternal age (15\u0026ndash;24, 25\u0026ndash;34, 35\u0026ndash;49), maternal education (no education, primary, secondary, tertiary), wealth index (poor, middle, rich), ethnicity (Akan, Ga/Dangme and Ewe, Mole-Dagbani), sex of family head (male, female), working status (not currently working, currently working) and parity (primiparous, multiparous, grandparous). The institutional factors variables considered in the study are access to healthcare (low, high) and proximity to health facilities (low, high). Finally, the community factors include place of residence (urban, rural), community media exposure (low media exposure, high media exposure and community source of water (unimproved, improved).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreliminary Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data highlights several key characteristics of the sampled children and their households. Among the 662 children, 11.9% have moderate to severe anaemia, 20.4% have mild anaemia, and 67.6% are not anaemic, with 32.4% experiencing some level of anaemia. The gender distribution is slightly skewed towards females (54.3%). Age-wise, the largest group is 12-23 months (24.3%), while 92.3% are single births. Most children (63.7%) are no longer breastfeeding, 16.2% experienced fever in the last two weeks, and 62.2% received iron supplements in the past year. Diarrhoea affected 15.7% in the past two weeks, and 80.5% lived in households with mosquito nets. Mothers are predominantly aged 30-39 years (48%) and largely educated, with 62.5% completing secondary school. Nearly half of the households are wealthy (49.6%), with Akan children forming the majority (66.4%) and male-headed households dominating (61.3%). Most mothers (86.3%) are employed, with 78.6% having more than one child. Healthcare engagement is relatively high, with 65.6% of children from households frequently visiting health facilities, though 95.8% live far from such facilities. Urban children comprise 52.8% of the sample, and 63.3% of mothers have high media exposure. Most households (88.4%) have access to improved water sources, underscoring essential hygiene and health disparities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFurther Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe logistic regression model is a statistical technique that analyses the relationship between multiple independent variables and a categorical dependent variable and models the probability of an event taking place by having the log odds for the event be a linear combination of one or more independent variables. Multinomial Logistic Regression (MLR) is an extension of binary logistic regression, employed when the dependent variable is categorical with more than two possible outcomes. MLR is designed to handle situations where the outcome variable can take on multiple categories that are not ordered and nominal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1. Estimated Odds Ratios (OR) and confidence 95% confidence intervals (CI) from Multinomial Logistic Regression of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnemia Severity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"845\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 34.1398%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate to Severe Anaemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"10\" valign=\"top\" style=\"width: 0.9408%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32.7958%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild Anaemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp (\u0026szlig;) [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1398%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5216%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp (\u0026szlig;) [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex of child\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.853 [0.460 \u0026ndash; 1.581]\u003c/p\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1398%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5216%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.222 [0.764 \u0026ndash; 1.953]\u003c/p\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild\u0026rsquo;s age\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 6-11\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 12-23\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 24-35\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 36-47\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 48-59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.654 [0.126 \u0026ndash; 3.392]\u003c/p\u003e\n \u003cp\u003e1.386 [0.514 \u0026ndash; 3.738]\u003c/p\u003e\n \u003cp\u003e1.197 [0.488 \u0026ndash; 2.935]\u003c/p\u003e\n \u003cp\u003e0.873 [0.363 \u0026ndash; 2.101]\u003c/p\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1398%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5216%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e2.053 [0.856 \u0026ndash; 4.925]\u003c/p\u003e\n \u003cp\u003e0.985 [0.449 \u0026ndash; 2.163]\u003c/p\u003e\n \u003cp\u003e1.614 [0.750 \u0026ndash; 3.475]\u003c/p\u003e\n \u003cp\u003e1.767 [0.837 \u0026ndash; 3.729]\u003c/p\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTwin status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Single\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Multiple\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.797 [0.239 \u0026ndash; 2.655]\u003c/p\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1398%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5216%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.891 [0.334 \u0026ndash; 2.373]\u003c/p\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrently breastfeeding\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Yes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e6.271 [2.312 \u0026ndash; 17.008]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1398%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5216%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.498 [0.266 \u0026ndash; 0.930]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFever in the past 2 weeks\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Yes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.1709 [0.484 \u0026ndash; 2.534]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1398%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5216%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.808 [0.438\u0026ndash; 1.491]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTaking iron in the past year\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.204 [0.598 \u0026ndash; 2.424]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1398%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5216%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.987 [0.585 \u0026ndash; 1.665]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHad diarrhoea recently\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes, the last 2 weeks\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.179 [0.491 \u0026ndash; 2.830]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1398%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5216%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e2.141 [1.044 \u0026ndash; 4.391]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHave bed net for sleeping\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.614 [0.274 \u0026ndash; 1.376]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1398%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5216%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.846 [0.479 \u0026ndash; 1.496]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1. Estimated Odds Ratios (OR) and confidence 95% confidence intervals (CI) from Multinomial Logistic Regression of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnemia Severity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"845\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate to Severe Anaemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild Anaemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp (\u0026szlig;) [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp (\u0026szlig;) [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e15-19\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;20-24\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;25-29\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;30-34\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;35-39\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;40-45\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;44-49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.057 [0.070 \u0026ndash; 15.903]\u003c/p\u003e\n \u003cp\u003e0.164 [0.017 \u0026ndash; 1.578]\u003c/p\u003e\n \u003cp\u003e0.084 [0.010 \u0026ndash; 0.711]\u003c/p\u003e\n \u003cp\u003e0.020 [0.003 \u0026ndash; 0.154]\u003c/p\u003e\n \u003cp\u003e0.057 [0.008 \u0026ndash; 0.415]\u003c/p\u003e\n \u003cp\u003e0.145 [0.019 \u0026ndash; 1.136]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.032[0.003 \u0026ndash; 0.311]\u003c/p\u003e\n \u003cp\u003e0.035 [0.005 \u0026ndash; 0.251]\u003c/p\u003e\n \u003cp\u003e0.092 [0.014 \u0026ndash; 0.591]\u003c/p\u003e\n \u003cp\u003e0.026 [0.004 \u0026ndash; 0.168]\u003c/p\u003e\n \u003cp\u003e0.038 [0.006 \u0026ndash; 0.233]\u003c/p\u003e\n \u003cp\u003e0.031 [0.004 \u0026ndash; 0.221]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal education\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Primary\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Secondary\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Tertiary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.255 [0.991 \u0026ndash; 3.855]\u003c/p\u003e\n \u003cp\u003e0.035 [0.146 \u0026ndash; 0.610\u003c/p\u003e\n \u003cp\u003e0.039 [0.118 \u0026ndash; 0.361]\u003c/p\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.460 [0.147 \u0026ndash; 1.447]\u003c/p\u003e\n \u003cp\u003e0.396 [0.148 \u0026ndash; 1.058]\u003c/p\u003e\n \u003cp\u003e0.200 [0.089 \u0026ndash; 0.450]\u003c/p\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Poor\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Middle\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Rich\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.262 [ 0.085 \u0026ndash; 0.808]\u003c/p\u003e\n \u003cp\u003e1.859 [0.769 \u0026ndash; 4.340]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.174 [ 0.075 \u0026ndash; 0.404]\u003c/p\u003e\n \u003cp\u003e1.005 [0.520 \u0026ndash; 1.941]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Akan\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Ga/Dangme and Ewe\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Mole-Dagbani\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.152 [0.517 \u0026ndash; 2.567]\u003c/p\u003e\n \u003cp\u003e6.038 [1.238 \u0026ndash; 29.454]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.431 [0.244 \u0026ndash; 0.763]\u003c/p\u003e\n \u003cp\u003e3.635 [1.022 \u0026ndash; 12.922]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex of family head\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Female\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e3.258 [1.465 \u0026ndash; 7.249]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.892 [0.524 \u0026ndash; 1.519]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Not currently working\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Currently working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.423 [0.118 \u0026ndash; 1.514]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.173 [0.533 \u0026ndash; 2.582]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1. Estimated Odds Ratios (OR) and confidence 95% confidence intervals (CI) from Multinomial Logistic Regression of Anemia Severity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"845\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate to Severe Anaemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild Anaemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp (\u0026szlig;) [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp (\u0026szlig;) [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePrimiparous\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Multiparous\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Grandparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.581 [0.142 \u0026ndash; 2.379]\u003c/p\u003e\n \u003cp\u003e0.438 [0.171\u0026ndash; 1.119]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.258 [0.410 \u0026ndash; 3.858]\u003c/p\u003e\n \u003cp\u003e0.616 [0.271\u0026ndash; 1.400]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccess to healthcare\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.921 [0.453 \u0026ndash; 1.870]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.238 [0.718 \u0026ndash; 2.137]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProximity to a health facility\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLow\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e10.321 [24.239 -142.06]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e8.123 [20.052 -140.06]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eUrban\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRural\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.832 [0.375 \u0026ndash; 1.845]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.591 [0.329 -1.062]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity media exposure\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLow media exposure\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; High media exposure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e3.085 [1.527 \u0026ndash; 6.232]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.661 [0.974 \u0026ndash; 2.834]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity source of water\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eUnimproved\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Improved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e2.037 [0.693 \u0026ndash; 5.985]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.412 [0.156 \u0026ndash; 1.086]\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel P-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNagelkerke R\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e41.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Percentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eReference Category: Not anaemic\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: SPSS output generated from 2022 GDHS data\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study presented a general discussion of the results of the analysis. This section assesses how far the objectives of the research have been achieved. Comparisons and contrasts of the findings of the previous studies are also presented.\u003c/p\u003e \u003cp\u003eIn this study, the sex of the child is not a significant predictor of anaemia severity. Moreover, male children are less likely to have moderate to severe anaemia compared to female children rather than not anaemic. This finding contradicts the study conducted by Tadesse et al. (2022) which found that sex was a significant predictor of anaemia among under-five children. The study also indicated that being female is protective against anaemia among under-five children. Similarly, Mwakishalua et al. (2024) also found that infant gender significantly predicted anaemia status with male infants having an increased risk of anaemia compared to female infants.\u003c/p\u003e \u003cp\u003eEvidence from the regression model in the Table suggests that the age of the child is a statistically significant predictor of anaemia severity (p\u0026thinsp;=\u0026thinsp;0.049). However, children 6\u0026ndash;11 months are less likely to have higher levels of anaemia compared to children 48\u0026ndash;59 months rather than not anaemic. A study in line with this current study was conducted in Ethiopia, which found child age is a predictor of anaemia. This current study also contradicts previous studies by Alamneh et al., (2023); Shenton et al., (2020) which found that found that a child\u0026rsquo;s age was negatively associated with severe\u0026ndash;moderate anaemia, as older children had lower odds of anaemia than younger children rather than not anaemic.\u003c/p\u003e \u003cp\u003eThe model shows that the twin status of the child is not a significant predictor of anaemia severity. Single children are less likely to have moderate to severe anaemia compared to children of twin status. Similarly to previous studies, Alamneh et al., (2023); Tesema et al., (2021) (Tesema et al., 2021) indicated that multiple births had 1.18 times [AOR\u0026thinsp;=\u0026thinsp;1.18, 95% CI: 1.11, 1.25] higher odds of being at higher level anaemia status compared to a singleton.\u003c/p\u003e \u003cp\u003eFrom the analysis results, currently breastfeeding is a statistically significant predictor of anaemia severity. Children who are not been currently breastfed are six times more likely to be moderately to severely anaemic compared to children who are currently breastfed rather than not anaemic. These results are in line with and at the same time contradict the previous results by (Khan et al., 2016) which asserted that currently breastfeeding is a significant predictor of anaemia severity and children who continued breastfeeding were more anaemic than the non-breast-fed children.\u003c/p\u003e \u003cp\u003eFever in the past two weeks is a significant predictor of anaemia severity. Children who have not had a fever in the past two weeks are more likely to have higher anaemic levels compared to children who have had a fever in the past two weeks rather than not anaemic. This current finding opposes the results of Seifu \u0026amp; Tesema,( 2022); Tesema et al., (2021) who stated that children who had a fever in the last two weeks had higher odds of higher levels of anaemia compared to their counterparts. Similarly, Shenton et al. (2020) asserted that fever in the previous 2 weeks was significantly associated with increased odds of severe\u0026ndash;moderate anaemia.\u003c/p\u003e \u003cp\u003eAgain, the analysis indicated that taking iron in the past 2 years is not a significant predictor of anaemia. However, children who were not given iron within the last 12 months are more likely to be compared to their counterparts who were given iron within the last 12 months rather than not anaemic. This is consistent with the findings of Nambiema et al., (2019) who alluded that showed that taking iron supplements was not associated with childhood anaemia.\u003c/p\u003e \u003cp\u003eMoreover, the model again showed that having diarrhoea recently is not a significant predictor of anaemia severity. Children who had a recent history of diarrhoea were less likely to have a higher level of anaemia compared to those who didn\u0026rsquo;t have any history of diarrhoea rather than not anaemic. Similarly to this finding, Shenton et al. (2020) also found no association between anaemia status and diarrhoea recently. Contrastingly, (Tesema et al., 2021) revealed that children with a history of diarrhoea had higher odds of higher anaemia levels than children who did not have diarrhoea. Again, Semba et al. (2008) demonstrated that current diarrhoea and a history of diarrhoea in the previous week were associated with anaemia among children under age five. The findings of this current can be because children who recently had diarrhoea might have been taken to healthcare facilities, where they received treatments that addressed multiple health issues, including anaemia. For example, Iron supplements or fortified foods are provided as part of recovery and deworming medications (intestinal worms can cause anaemia).\u003c/p\u003e \u003cp\u003eHaving a mosquito bed net for sleeping is not a significant predictor of anaemia severity. Children whose mothers didn\u0026rsquo;t have bed nets for sleeping were less likely to have moderate to severe anaemia compared to their counterparts whose mothers had bed nets for sleeping rather than not anaemic. The previous result of Ngesa \u0026amp; Mwambi (2014) indicating no significant relationship between having a bed net for sleeping and anaemia is in line with the result of this current study. However, this finding reversed that of Dutta et al. (2020) who indicated that the use of bed net decreased the likelihood of anaemia by 16%. The plausible reason is that families with bed nets might represent a group that has access to healthcare and awareness programs but might also have other underlying risk factors for anaemia (e.g., poor nutrition or higher prevalence of infectious diseases like malaria).\u003c/p\u003e \u003cp\u003eMaternal age is a significant predictor of anaemia severity. Children whose mothers were 15\u0026ndash;19 years old were more likely to have higher levels of anaemia. All children whose mothers were 20\u0026ndash;44 years old had lower odds of having higher levels of anaemia, especially children with mothers aged 30\u0026ndash;34 years rather than not anaemic. This is in line with the findings of Parbey et al. (2019); Tesema et al., (2021) indicated that children born to mothers aged less than 20 years had higher odds of a higher level of anaemia compared to children born to mothers aged 20 years and above and children whose mothers were aged less than 20 years were 4.69 times more likely to develop anaemia respectively. This current finding was expected as younger mothers may lack the experience or education to provide a balanced diet that prevents anaemia in their children.\u003c/p\u003e \u003cp\u003eMoreover, maternal education significantly predicts anaemia severity. Children whose mothers had no education, primary and secondary education were less likely to have moderate to severe anaemia compared to children whose mothers had tertiary education rather than not anaemic. This current study supports the findings of Ofori et al. (2020); and Tadese et al. (2022) who concluded that maternal education was a significant predictor of anemia among under-five children. However, this current finding reverses that of Elmardi et al. (2020); and Kuziga et al. (2017) who alluded that a significant relationship and association between children\u0026rsquo;s mother's level of education or access to education facilities (e.g. listening to the radio) and anaemia respectively. However, the plausible reason for this is that more educated mothers may be more likely to seek medical attention and have their children diagnosed with anaemia, leading to higher reported cases compared to less-educated mothers whose children may remain undiagnosed.\u003c/p\u003e \u003cp\u003eThe relationship between wealth index and anaemia severity was significant. Children whose mothers were from poor homes were less likely to have moderate to severe anaemia rather than not anaemic. However, children whose mothers were from middle-class homes were more than 1.8 times more likely to have a high level of anaemia compared to children whose mothers were from rich homes rather than not anaemic. This finding is consistent with that of Alamneh et al. (2023) who found that the wealth index was a significant and independent predictor of anaemia level. However, it also opposes the result of (Kebede et al., 2021) indicating that low family income is an associated factor with anaemia among under-five children.\u003c/p\u003e \u003cp\u003eThe ethnicity of the mother affected the anaemia severity. Children whose mothers belong to Akan tribes were more likely to have moderate to severe anaemia rather than not anaemic. Also, children whose mothers are Ga/Dangmes and Ewes are six times more likely to have moderate to severe anaemia rather than not anaemic. This current study is in line with that of Parbey et al. (2019); Shenton et al. (2020) who alluded that a significant relationship between anaemia status and ethnicity and that children whose mothers were Ga/Dangme were also likely to have higher levels of anaemia in 2014 respectively.\u003c/p\u003e \u003cp\u003eThe sex of the family is significantly related to anaemia severity. Children from families headed by males were more than three times more likely to have higher levels of anaemia compared to children from female-headed families rather than not anaemic. The current result is consistent with that of Klu \u0026amp; Agordoh (2022) who inferred that children in MHH are more likely to be anaemic compared to children in FHH. However, it conflicts with the finding of Tesema et al. (2021) which found no association between the sex of the family head and anaemia severity.\u003c/p\u003e \u003cp\u003eHowever, the relationship between working status and anaemia severity is not significant. Children whose mothers were currently not working were less likely to have moderate to severe anaemia compared to those whose mothers were currently not working rather than not anaemic. This result affirms that of (Gebreegziabiher et al., 2014), which indicated that maternal employment status was not associated with anaemia among children aged 6\u0026ndash;59 months old in a study conducted in Kilte Awulaelo Woreda, Northern Ethiopia.\u003c/p\u003e \u003cp\u003eSimilarly, parity is not a significant predictor of anaemia severity. Children whose mothers were primiparous and multiparous had lower odds of having moderate to severe anaemia compared to children whose mothers were grandparous rather than not anaemic. This current result contradicts the odds that children with mothers of parity 2\u0026ndash;3 and above 4 were significantly associated with anaemia. In model 2, 17% of parity 2\u0026ndash;3 and 27% of parity 4 and above were more likely to have anaemic children than mothers with first parity, while in model 3, it was 11% (2\u0026ndash;3 parity) and 14% (4\u0026thinsp;+\u0026thinsp;parity) respectively (Dutta et al., 2020).\u003c/p\u003e \u003cp\u003eAccess to healthcare is not a significant predictor of anaemia severity. Children whose mothers had not visited a health facility within the last 12 months were less likely to have moderate to severe anaemia compared to those whose mothers had visited the health facility within the 12 months rather than not anaemic. Children whose mothers do not visit healthcare facilities might still engage in effective community-based or traditional practices that help mitigate anaemia, such as proper nutrition or supplementation through non-clinical sources.\u003c/p\u003e \u003cp\u003eNonetheless, proximity to a health facility is a significant predictor of anaemia severity. Children whose mothers are far from health facility are ten times more likely to have higher levels of anemia compared to children whose mothers are closer to health facility. This stems from the fact that longer travel times reduce the likelihood of regular visits, especially for children whose mothers manage multiple household responsibilities. Again, families far from healthcare facilities may face higher costs for transportation, making routine visits less feasible leading to untreated conditions such as infections (e.g., malaria, diarrhoea) or nutritional deficiencies that exacerbate anaemia.\u003c/p\u003e \u003cp\u003ePlace of residence is not a significant predictor of anaemia severity. Children from urban areas were less likely to have higher levels of anaemia compared to children from rural areas. This contradicts the findings of Gebreweld et al. (2019); Tadesse et al. (2022), who alluded that residence was a significant predictor of anaemia among under-five children and that those children from rural settings were 20% less likely to be affected by anaemia as compared to children from urban settings and children living in an urban area were 1.8 times more likely to be anaemic than those living in a rural area respectively. This current study, however, affirms that of Gebrie \u0026amp; Alebel (2020); Kebede et al. (2021) attesting that the distribution of anaemia is more prevalent among children from rural residents compared to urban ones and children from rural areas was 3.25 times more likely to be anaemic than their urban counterparts respectively.\u003c/p\u003e \u003cp\u003eThe relationship between the community water source and anaemia severity is not significant. Children from communities with improved sources of water had two times higher odds of having higher levels of anaemia compared to children from communities with unimproved sources of water. On an individual level, this agrees with that of Khan et al. (2016); Muchie (2016) indicating that the odds of being severely/moderately anaemic were higher for children whose households used non-improved sources of drinking water and children from households without access to \u0026lsquo;improved\u0026rsquo; water sources were 1.34 times more likely than others to be anaemic.\u003c/p\u003e \u003cp\u003eCommunity media exposure is a significant predictor of anaemia severity. Children who came from communities with low media exposure were three times more likely to have moderate to severe anaemia compared to children from communities with high media exposure. Confirming the result of the current study, Dutta et al. (2020) found that less exposure to mass media among mothers increases the likelihood of anaemia among children on an individual level.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eConfines and Strengths\u003c/h2\u003e \u003cp\u003eThis study used cross-sectional data, so causal relationships between anaemia and the identified independent variables cannot be established. Additionally, due to the reliance on secondary data, factors such as eating habits, parasite infestations (malaria, Visceral Leishmaniasis, and hookworm), previous hospitalizations, and the use of nutritional supplements (e.g., vitamin B12 and folate) could not be examined. The cross-sectional nature of the GDHS data limits the ability to draw causal inferences between the independent and dependent variables. Moreover, since the GDHS data were collected retrospectively, recall bias may be a concern due to memory lapses. Despite these limitations, this study is one of the few to investigate anaemia prevalence and predictors at the regional level. The findings, based on a large, nationally representative dataset, are generalizable. The use of multinomial logistic regression provided insights into the factors influencing anaemia severity in the Ashanti region, offering valuable information for policymakers in developing targeted interventions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study examined anaemia severity among children under 5 in the Ashanti region and its relationship with individual, institutional and community factors to severe anaemia. Out of the 662 sample size, 20.4% had mild anaemia, and 67.6% were not anaemic, with 32.4% experiencing some level of anaemia. These findings suggest that anaemia in this sample is concentrated within a subset of children, reflecting variations in regional or contextual factors that might influence anaemia prevalence and severity. Individual (currently breastfeeding, had diarrhea recently maternal age, maternal education, wealth index and ethnicity) and institutional (proximity to health facility) factors predicted anemia severity. Maternal education is a critical area for intervention. Programs aimed at promoting formal education for women, particularly targeting adolescent mothers, can empower them with the knowledge to make informed health and nutrition decisions for their children. Community-based health campaigns should emphasize the importance of exclusive and prolonged breastfeeding, while healthcare providers offer robust counselling and support during antenatal and postnatal visits. These efforts can ensure that breastfeeding is sustained and accessible for all mothers, especially in rural and underserved areas. Improving healthcare accessibility is also paramount.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003ch2\u003ePatient and public involvement\u003c/h2\u003e\n\u003cp\u003ePatients and/or the public were not involved in the design, conduct, reporting, or dissemination plans of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot required.\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eWe accessed the DHS data after getting permission from the DHS programme. Primary data were collected from each country by adhering to relevant local and international ethical guidelines. All procedures and questionnaires of DHS are reviewed and approved by the ICF International Institutional Review Board (IRB). Additionally, each country-specific DHS has been reviewed and approved by ICF IRB and an IRB in the host country.\u003c/p\u003e\n\u003ch2\u003eData sharing statement\u003c/h2\u003e\n\u003cp\u003eAppendices to the extended report are available in English.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003e1 and 1 wrote the main manuscript text and prepared Table 1.2 helped with the design of the manuscript, substantially reversed the paper and have approved the submitted version.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to extend our earnest homage to the DHS programme for granting access to the data.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data sets produced and/or examined during the present study are accessible in the MEASURE DHS database at the repository, https://www.dhsprogram.com\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAheto, J. 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Translating social ecological theory into guidelines for community health promotion. \u003cem\u003eAmerican Journal of Health Promotion : AJHP\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(4), 282\u0026ndash;298. https://doi.org/10.4278/0890-1171-10.4.282\u003c/li\u003e\n \u003cli\u003eTadese, M., Yeshaneh, A., \u0026amp; Mulu, G. B. (2022). Determinants of good academic performance among university students in Ethiopia : a cross ‑ sectional study. \u003cem\u003eBMC Medical Education\u003c/em\u003e, 1\u0026ndash;9. https://doi.org/10.1186/s12909-022-03461-0\u003c/li\u003e\n \u003cli\u003eTadesse, S. E., Zerga, A. A., Mekonnen, T. C., Tadesse, A. W., Hussien, F. M., Feleke, Y. W., Anagaw, M. Y., \u0026amp; Ayele, F. Y. (2022a). Burden and Determinants of Anemia among Under-Five Children in Africa: Systematic Review and Meta-Analysis. \u003cem\u003eAnemia\u003c/em\u003e, \u003cem\u003e2022\u003c/em\u003e. https://doi.org/10.1155/2022/1382940\u003c/li\u003e\n \u003cli\u003eTadesse, S. E., Zerga, A. A., Mekonnen, T. C., Tadesse, A. W., Hussien, F. M., Feleke, Y. W., Anagaw, M. Y., \u0026amp; Ayele, F. Y. (2022b). Burden and Determinants of Anemia among Under-Five Children in Africa: Systematic Review and Meta-Analysis. \u003cem\u003eAnemia\u003c/em\u003e, \u003cem\u003e2022\u003c/em\u003e. https://doi.org/10.1155/2022/1382940\u003c/li\u003e\n \u003cli\u003eTadesse, S. E., Zerga, A. A., Mekonnen, T. C., Tadesse, A. W., Hussien, F. M., Feleke, Y. W., Anagaw, M. Y., \u0026amp; Ayele, F. Y. (2022c). Burden and Determinants of Anemia among Under-Five Children in Africa: Systematic Review and Meta-Analysis. \u003cem\u003eAnemia\u003c/em\u003e, \u003cem\u003e2022\u003c/em\u003e, 1382940. https://doi.org/10.1155/2022/1382940\u003c/li\u003e\n \u003cli\u003eTesema, G. A., Worku, M. G., Tessema, Z. T., Teshale, A. B., Alem, A. Z., Yeshaw, Y., Alamneh, T. S., \u0026amp; Liyew, A. M. (2021a). Prevalence and determinants of severity levels of anemia among children aged 6-59 months in sub-Saharan Africa: A multilevel ordinal logistic regression analysis. \u003cem\u003ePloS One\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4). https://doi.org/10.1371/JOURNAL.PONE.0249978\u003c/li\u003e\n \u003cli\u003eTesema, G. A., Worku, M. G., Tessema, Z. T., Teshale, A. B., Alem, A. Z., Yeshaw, Y., Alamneh, T. S., \u0026amp; Liyew, A. M. (2021b). Prevalence and determinants of severity levels of anemia among children aged 6\u0026ndash;59 months in sub-Saharan Africa: A multilevel ordinal logistic regression analysis. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4), e0249978. https://doi.org/10.1371/JOURNAL.PONE.0249978\u003c/li\u003e\n \u003cli\u003eTesema, G. A., Worku, M. G., Tessema, Z. T., Teshale, A. B., Alem, A. Z., Yeshaw, Y., Alamneh, T. S., \u0026amp; Liyew, A. M. (2021c). Prevalence and determinants of severity levels of anemia among children aged 6\u0026ndash;59 months in sub-Saharan Africa: A multilevel ordinal logistic regression analysis. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4). https://doi.org/10.1371/JOURNAL.PONE.0249978\u003c/li\u003e\n \u003cli\u003eWHO. (2011). \u003cem\u003eHaemoglobin concentrations for the diagnosis of anaemia and assessment of severity\u003c/em\u003e. 1\u0026ndash;6.\u003c/li\u003e\n \u003cli\u003eWHO. (2012). \u003cem\u003eAnaemia Policy Brief\u003c/em\u003e. \u003cem\u003e6\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eWHO. (2021). Progress on malaria control in countries. \u003cem\u003eNursing Times\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(32), 20.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Children Under Five, Community-Level factors, Influencing factors, Anemia, Socio-ecological model of Health","lastPublishedDoi":"10.21203/rs.3.rs-5654524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5654524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnaemia is a significant global health issue, particularly affecting children under five years and women of reproductive age, with children being disproportionately impacted due to rapid growth and increased iron demand. It is an indicator of poor nutrition and health and a major barrier to social and economic development. For children under five, anaemia is a haemoglobin concentration below 110 g/l. This study aimed to examine the severity of anaemia among children under five in Ghana and identify predictors based on the socio-ecological model. Using data from the 2022 Ghana Demographic and Health Survey (GDHS) and a sample of 662 children, multinomial logistic regression was applied for the analysis. The findings revealed that 32.4% of children experienced some form of anaemia, with 11.9% having moderate to severe anaemia. Individual (currently breastfeeding, had diarrhoea recently maternal age, maternal education, wealth index and ethnicity) and institutional (proximity to health facility) factors predicted anaemia severity. This study underscores the need for targeted interventions focused on individual factors, healthcare access, and breastfeeding support to reduce anaemia prevalence among children in Ghana.\u003c/p\u003e","manuscriptTitle":"Social-ecological model determinants of anaemia severity levels among children under five in Ashanti Region: A multinomial logistic regression based on the 2022 Ghana Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-25 04:26:37","doi":"10.21203/rs.3.rs-5654524/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d96c558e-500a-47be-b7b7-3ee983885c3a","owner":[],"postedDate":"December 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-02T11:38:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-25 04:26:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5654524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5654524","identity":"rs-5654524","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}