Socio-economic and demographic determinants of undernutrition among 6-59 months old children living in Malawian stunting hotspots: A cross-sectional community study

Socio-economic and demographic determinants of undernutrition among 6-59 months old children living in Malawian stunting hotspots: A cross-sectional community study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Socio-economic and demographic determinants of undernutrition among 6-59 months old children living in Malawian stunting hotspots: A cross-sectional community study Patrick Ndovie, Numeri Geresomo, Smith G. Nkhata, Robert Fungo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5105657/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background Globally, malnutrition is prevalent, particularly in Sub-Saharan Africa, where 171 million under-five children suffer from stunting, and 45% of mortalities are reported. This study aimed to identify socio-economic and demographic factors contributing to undernutrition among 6–59 months old children in Malawian stunting hotspots. Methods Stata 17.0 was used to analyse descriptive statistics and logistic regression for nutritional status associations, using WHO Z-Scores and 95% confidence. This cross-sectional community study was conducted in Mzimba, Mchinji and Mangochi and involved 1,275 caregivers and children. Results The results in the study revealed that 6–59 months old children had varying risks of wasting and stunting. Caregivers who engaged in farming, business, or unemployed had lower risk of being wasted, whereas unemployed and student caregivers had higher odds of stunting (OR = 1.64, 95% CI: 1.14–2.36, p = 0.008) and underweight (OR = 3.66, 95% CI: 1.52–8.80, p = 0.004). Caregivers who had attained junior education level had increased odds of having wasted and underweight children, while those who attained junior and senior secondary education showed reduced risks. Low household income (below 50,000MK per month) increase the risk wasting (OR = 8.35, 95% CI: 5.09–13.68, p = 0.000), while those with higher incomes had a decreased risk. Christian caregivers had lower odds of having a wasted child while muslim caregivers had higher odds (OR = 8.35, 95% CI: 5.09–13.68, p = 0.000). Households with less than five members had reduced underweight odds, in contrast to those with more than five members who had increased odds. Conclusion Caregivers in farming, business, and with higher education have lower risks of child wasting and stunting. Unemployed, less educated, and low-income or larger households face higher risks. Malnutrition unemployment religious affiliations caregivers Odds Ratio Figures Figure 1 Figure 2 Introduction Malnutrition, encompassing deficiencies, excesses, or imbalances in essential nutrients and energy intake ( 1 ), remains a public global health issue, particularly prevalent in developing countries such as Malawi (Endris & Asefa, 2017). Despite recent commitments by member states of the World Health Organization (WHO) to achieve nine global targets by 2025, progress has been limited, with no country fully meeting these objectives (NPC, 2014). Globally, undernutrition in under-five children accounts for 45% of the deaths ( 1 ). Approximately 171 million children worldwide suffer from stunting because of inadequate nutrition during infancy. In the year 2022, data from UNICEF/WHO/World Bank Group, (2023), revealed that 148.1 million children under the age of five, constituting 23.3% globally, suffered from stunting, while 45 million children under the age of five, accounting for 6.8% of the population, experienced wasting. In Africa, the prevalence is even more alarming, with two out of every five children experiencing stunting, totaling around 60 million children ( 5 ). Malnutrition has a profound implication on public health, economic prosperity, and societal stability ( 6 ). In the region of Sub-Saharan Africa, the prevalence of child stunting stood at 34%, with a slightly lower rate of 29.2% specifically in the West African Sub-region, as reported in 2017 by the FAO, IFAD, UNICEF, (2019). Research conducted by Psaki et al., (2012) indicates that approximately 20% of children residing in developing nations suffer from malnutrition. In Malawi, undernutrition cases such as stunting are still a public health concern although the prevalence rates have been reduced in the past decade ( 9 ). As per the 2015-16 MDHS findings, 37% of children under the age of 5 exhibit stunted growth, indicating chronic undernutrition. A smaller proportion, approximately 3%, are classified as wasted, suggesting acute undernutrition, while 5% are identified as overweight, signaling over-nutrition. Moreover, 12% of children under the age of 5 are categorized as underweight, reflecting inadequate nutrition for their age (NSO [Malawi], 2017). Malnutrition is caused by so many factors such as socioeconomic and demographic factors. Socio-demographic factors, like the age of the mother or surrogate, income level, religious beliefs, literacy, maternal education, and the child's sex, can impact a child's nutritional status. However, these influences might manifest differently in urban areas, particularly within the complex and unpredictable environment of urban slums ( 10 ). Henceforth, this study assessed the socio-economic determinants of undernutrition among 6–59 months old children living in Malawian stunting hotspots. Methods Study Area This research focused in three districts: Mzimba in the northern region, Mchinji in the central region, and Mangochi in the southern region. Specifically, data was collected at TA Twalo and SC Kampingo-Sibande in Mzimba, TA Zulu and SC Mduwa in Mchinji, and SC Namabvi and SC Chowe in Mangochi district (see Fig. 1 ). The study spanned from August to September 2023. Source population and study design The target population for this study included all children aged 6–59 months old residing in the study areas as illustrated in Fig. 1 . This research study used a cross-sectional study design in data collection. Sample size determination and sampling technique A multi-stage sampling was employed in selecting respondents in this study. Firstly, three districts (Mzimba, Mchinji and Mangochi) known for high stunting prevalence within each region were purposively selected. Secondly, T/As were randomly selected from each of the three districts. Villages were randomly chosen, and within these villages, households with children aged 6–59 months old were purposefully selected. Initially, a sample size of 1267 caregiver/mothers with 6–59 months old children. Criteria for inclusion and exclusion in the selection of mother-child pairs caregiving The study included caregivers/mothers and their 6–59 months old children who had lived in the study areas for more than one year before the survey and gave a consent to take part in the study. The study excluded mothers and caregivers who had not given their consent to participate. In addition, according to medical records or the child health card, the study did not include respondents (mothers/caregivers of children aged 6–59 months) who were mentally challenged, physically deformed, or chronically ill because these conditions would have an impact on their anthropometric measurements and require special feeding attention. Data collection tools and procedures Prior to collecting anthropometric data from children, consent was obtained from the households. For children aged 24–59 months, weight was measured in underwear and without shoes using an electronic scale (SECA 861 or SECA 813, Hamburg, Germany) accurate to 0.1 kg, and height was measured in the Frankfort plane with a telescopic height instrument (SECA 225 or SECA 214) accurate to 0.1 cm. Conversely, data for children aged 6–23 months were obtained by measuring the combined weight of the child and mother/caregiver, then subtracting the mother/caregiver's weight. Height was measured using a precisely graduated length board and recorded to the nearest millimeter. Child age was determined through parental recall using an events calendar. Measurements of height and weight were taken twice, and the average was recorded. The measuring instruments were calibrated at least twice daily. Wasting, stunting, and underweight were defined by the World Health Organization (WHO) as Z-scores of less than − 2 standard deviations for weight-for-height, height-for-age, and weight-for-age, respectively. Socio-economic and demographic data such as sex, age (months) and gender of the children and education level, economic activities, marital status, and religion of the caregiver/mother was collected using a semi-structure questionnaire through face-to-face interview. Additionally, data on household characteristics such as household income and size was collected using the same questionnaire. The structured questionnaire was uploaded into SurveyCTO Collect v. 2.80 ( 11 ) platform and enumerators used electronic gadgets during data collection. Data analysis Stata version 17.0 (Standard edition) was used to generate descriptive statistics such means. Standard deviations (SD), frequencies, and percentages. In addition, multivariate logistic regression was also used to determine the association between the variables. All the values were significant at 95% confidence level (P < 0.05). Z-Scores for anthropometric indices was generated from WHO Anthro version 3.2.2 and values were entered in Stata for further analysis and age specific cut off points were used. A multivariate logistic regression analysis was run in STATA to analyse the association between nutritional status of children (weight-for-height, height-for-age, and weight-for-age) and independent variables such as socioeconomic and demographic characteristics. Results Socioeconomic and demographic factors among 6–59 months old children and their caregivers Table 1 presents the socioeconomic and demographic determinants of the study population. The mean age of the children was 28.42 months, with approximately 48.3% being male. These findings are consistent with those reported by State et al. (2022) and Eshete et al. (2018). Most children were in the 24–35-month age range (26.8%). Regarding caregiver occupation, 72.2% were engaged in farming, while only 0.6% worked as casual labourers. Most caregivers were married (83.0%), with a small proportion having separated from their spouses (0.7%). Financially, a significant majority of households earned less than 50,000 MK (< 28.85 USD) per month (93.5%), while a minimal percentage earned between 100,000 MK and 150,000 MK per month (0.39%). Table 1 Socioeconomic and demographic characteristics among 6–59 months old children and their caregivers (n = 1275) VARIABLE CATEGORY MEAN ± SD N(%) Sex of Child Male 616(48.3) Female 659(51.7) Age of the child (Months) 6–11 28.42 ± 15.10 231(18.1) 12–23 298(23.4) 24–35 342(26.8) 36–47 222(17.4) 48–60 182(14.3) Occupation Farmer 921(72.2) Permanent employee 16(1.3) Casual laborer 8(0.6) Business/Trader 93(7.3) Housewife 86(6.8) Student 21(1.7) Unemployed 130(10.2) Education None 23(1.8) Junior primary 473(37.1) Senior primary 468(36.7) Junior secondary 143(11.2) Senior secondary 155(12.2) University diploma 8(0.63) University degree 5(0.39) Marital Status Single 66(5.2) Married 1058(83.0) Divorced 66(5.2) Separated 9(0.71) Widowed 76(6.0) Household Incomes (MK) 150,000 6(0.47) Household Size 4 members 759(59.5) Religion Christians 791(62.0) Muslims 479(37.6) No Religion 5(0.4) Fully Vaccinated Yes 644 (50.5) No 631 (49.5) Furthermore, the study showed that more than half of the households had more than four members (59.5%), whereas only 4.2% had fewer than three members. Regarding religious affiliation, 62% of caregivers were identified as Christians, while the smallest proportion had no religious affiliation. Additionally, the study found that more than half of the children (50.5%) were fully vaccinated. Nutritional status of 6–59 months old children Figure 2 displays the prevalence rates of underweight, stunting, and wasting among children aged 6–59 months. The findings indicate that 83% of the children were of normal weight, while 3.1% were moderately wasted and 3.3% were severely wasted. The results further indicate that 57.2% of the children were classified as having normal height for age, while 26.1% were moderately stunted and 16.7% were severely stunted. Additionally, the prevalence of underweight among the children was as follows: 82% were of normal weight, 13% were moderately underweight, and 4.5% were severely underweight. Association between socio-economic and demographic factors with undernutrition among study participants Table 3 presents the results of the multiple logistic regression analysis examining the association between socio-demographic factors and undernutrition among the study participants. The analysis revealed no significant association between the gender of the children and undernutrition. Significant associations were found between certain age ranges and undernutrition. Children aged 6–11 months were significantly more likely to be wasted (OR = 1.86, 95% CI: 0.74–1.65, p = 0.008) and less likely to be stunted (OR = 0.55, 95% CI: 0.41–0.76, p = 0.000). Specifically, children in this age group were 1.86 times more likely to be wasted compared to other age ranges, and 0.55 times less likely to be stunted. Children aged 24–37 months (OR = 2.08, 95% CI: 1.62–2.76, p = 0.000) and 38–47 months (OR = 7.07, 95% CI: 5.09–9.81, p = 0.000) were 2.08 and 7.07 times more likely to be stunted and underweight, respectively. Children aged 48–60 months were 20.25 times more likely to be underweight (OR = 20.25, 95% CI: 13.80–29.71, p = 0.000). Additionally, children aged 38–47 months, and 12–23 months were 0.12 and 0.64 times less likely to be wasted and stunted, respectively. Regarding caregiver occupation, children of caregivers who were farmers (OR = 0.55, 95% CI: 0.37–0.82, p = 0.003), businesspersons (OR = 0.12, 95% CI: 0.07–0.88, p = 0.037), and unemployed (OR = 0.08, 95% CI: 0.01–0.55, p = 0.011) were less likely to be wasted, with odds ratios of 0.55, 0.12, and 0.08, respectively. Additionally, unemployed caregivers were 0.54 times less likely to have underweight children. Conversely, caregivers who were unemployed (OR = 1.64, 95% CI: 1.14–2.36, p = 0.008) were 1.64 times more likely to have stunted children. Caregivers engaged in business (OR = 1.67, 95% CI: 1.02–2.75, p = 0.043) and those who were students (OR = 3.66, 95% CI: 1.52–8.80, p = 0.004) were 1.67 and 3.66 times more likely to have underweight children, respectively. Table 3 Multiple Logistic regression model of association between socioeconomic and demographic factors with under-nutrition among study participants Variable Wasting Stunting Underweight OR (95% CI) P-Value OR (95% CI) P-Value OR (95% CI) P-Value GENDER Male 1.10(0.74–1.65) 0.630 1.10(0.88–1.37) 0.414 1.19(0.89–1.59) 0.243 Female 0.91(0.61–1.35) 0.630 0.91(0.73–1.14) 0.414 0.84(0.63–1.13) 0.243 AGE (MONTHS) 6–11 1.86(1.18–2.95) 0.008 0.55(0.41–0.76) 0.000 1.00 12–23 1.66(1.08–2.56) 0.021 0.64(0.49–0.83) 0.001 1.00 24–37 0.97(0.62–1.53) 0.898 2.08(1.62–2.67) 0.000 1.00 38–47 0.12(0.04–0.40) 0.000 1.30(0.97–1.74) 0.075 7.07(5.09–9.81) 0.000 48–60 0.74(0.40–1.38) 0.343 0.85(0.62–1.18) 0.337 20.25(13.80-29.71) 0.000 Caregivers’ occupation Farmer 0.55(0.37–0.82) 0.003 0.99(0.79–1.26) 0.959 1.13(0.83–1.55) 0.431 Business 0.12(0.07–0.88) 0.037 0.68(0.44–1.06) 0.090 1.67(1.02–2.75) 0.043 Unemployed 0.08(0.01–0.55) 0.011 1.64(1.14–2.36) 0.008 0.54(0.30–0.96) 0.037 Student 0.54(0.72–4.07) 0.551 1.48(0.62–3.51) 0.375 3.66(1.52–8.80) 0.004 Housewife 1.30(0.63–2.68) 0.476 1.06(0.68–1.65) 0.791 0.83(0.45–1.53) 0.557 Caregivers Education level None 1.04(0.24–4.49) 0.959 1.03(0.45–2.36) 0.949 0.21(0.03–1.58) 0.130 Junior Primary 4.17(2.72–6.42) 0.000 1.27(1.01–1.60) 0.041 1.30(0.96–1.75) 0.086 Senior Primary 0.44(0.27–0.72) 0.001 1.19(0.95–1.50) 0.140 1.19(0.88–1.60) 0.256 Junior Secondary 0.14(0.03–0.57) 0.006 0.60(0.41–0.87) 0.007 0.77(0.46–1.28) 0.312 Senior Secondary 0.48(0.22–1.06) 0.071 0.68(0.48–0.97) 0.033 0.36(0.20–0.67) 0.001 University Reference Reference Reference Caregiver’s Marital Status Single 0.69(0.25–1.94) 0.484 1.12(0.68–1.84) 0.657 1.85(1.05–3.24) 0.033 Married 1.10(0.64–1.88) 0.741 0.80(0.59–1.07) 0.130 0.86(0.59–1.25) 0.424 Widowed 0.76(0.30–1.92) 0.556 1.15(0.72–1.83) 0.558 1.07(0.59–1.95) 0.816 Separated 1.37(0.17–11.03) 0.769 2.69(0.671-0.80) 0.163 1.00 Divorced 1.31(0.58–2.96) 0.508 1.27(0.77–2.09) 0.341 0.81(0.39–1.67) 0.571 Household Income < 50,000mk 8.02(1.11–58.23) 0.039 1.60(1.00-2.57) 0.052 0.63(0.37–1.07) 0.089 ≥ 50,000mk 0.12(0.12–0.90) 0.039 0.63(0.39–1.01) 0.053 1.59(0.94–2.69) 0.085 Religious Affiliations Christian 0.12(0.07–0.20) 0.000 1.26(1.00-1.58) 0.049 1.01(0.75–1.36) 0.961 Muslim 8.35(5.09–13.68) 0.000 0.82(0.65–1.03) 0.086 0.99(0.73–1.34) 0.945 No religion Reference Reference 1.18(0.13–10.65) 0.880 Household Size < 5 1.16(0.77–1.73) 0.479 0.95(0.76–1.19) 0.647 0.69(0.51–0.94) ≥ 5 0.86(0.56–1.29) 0.479 1.05(0.84–1.32) 0.647 1.45(1.06–1.97) Full vaccination No Reference Reference Reference Yes 0.289(0.18–0.46) 0.000 1.44(1.15–1.80) 0.001 1.00 *Significant at p < 0.05, CI: Confidence Interval, OR: Odds ratio The findings indicate that caregivers' economic activities and educational attainment can significantly impact children's nutritional status, likely through resource access and dietary diversity. Caregivers with junior primary education were 4.17 times more likely to have wasted children (OR = 4.17, 95% CI: 2.72–6.42, p = 0.000) and 1.27 times more likely to have stunted children (OR = 1.27, 95% CI: 1.01–1.60, p = 0.041). Conversely, caregivers with senior primary and junior secondary education were 0.44 and 0.14 times less likely to have wasted children, respectively. Additionally, caregivers with senior secondary education were 0.68 times less likely to have stunted children (OR = 0.68, 95% CI: 0.48–0.97, p = 0.033) and 0.36 times less likely to have underweight children (OR = 0.36, 95% CI: 0.20–0.67, p = 0.000). Those with junior secondary education were also 0.60 times less likely to have stunted children (OR = 0.60, 95% CI: 0.41–0.87, p = 0.007). Children of single caregivers had significantly higher odds of being underweight (OR = 1.85, 95% CI: 1.05–3.24, p = 0.033), though no significant associations were found with wasting or stunting. Regarding religious affiliation, Christian caregivers were associated with lower odds of wasting (OR = 0.12, 95% CI: 0.07–0.20, p = 0.000), while Muslim caregivers were associated with higher odds of wasting (OR = 8.35, 95% CI: 5.09–13.68, p = 0.000). Children from larger households (≥ 5 members) were 1.45 times more likely to be stunted compared to those from smaller households (OR = 1.45, 95% CI: 1.06–1.97, p = 0.018), and 0.69 times less likely to be underweight (OR = 0.69, 95% CI: 0.51–0.94, p = 0.018). Discussion Socioeconomic and demographic factors among 6–59 months old children and their caregivers Maternal education is a key determinant of children's diet, health, and survival, as evidenced by a study by Rahmawati et al., (2018). This study found that most caregivers had attained junior primary education (37.1%), while only a small fraction had a university degree (0.39%). This distribution is consistent with Cox et al., (2017), who reported that 53.1% of caregivers had completed primary education. Maternal education emerged as a crucial factor in improving child nutrition, aligning with Muche et al., (2021) and other studies (Kavosi, 2014; Abeway et al., 2018; Abuya et al., 2012 Abuya et al., 2012). Maternal education is associated with better dietary practices. Educated mothers are more likely to understand nutritional requirements and make informed food choices, which directly influences the dietary intake of their children. Educated mothers are better equipped to implement effective childcare practices and access healthcare services, which can reduce malnutrition. Education also enhances income opportunities, further supporting nutritional needs (Akombi, et al., 2017). Household income also plays a crucial role in children's dietary intake. Increased income is generally associated with better nutrition. Simelane et al., (2020) found that wealthier households had a 0.45 times lower likelihood of having stunted children compared to very poor households. The World Bank Group, (2015) reported that 62% of Malawians lived on less than $ 1.25 per day in 2010, highlighting a significant disparity from the international average. Despite an overall decline in poverty, the wealthiest have become richer, while poverty rates among the poorer segments of the population remain high. This study has shown that households with higher incomes tend to have better nutrition outcomes for children than those with lower incomes. Educated mothers are often better positioned to secure higher-paying jobs, contributing to improved household income, which is crucial for accessing nutritious foods. Olamijuwon et al., (2017) found no significant association between single motherhood and marasmus, but our study revealed a significant association between lower household income and wasting. Children from households earning less than 50,000 MK were 8.02 times more likely to be wasted compared to those from wealthier households. No significant associations were found for stunting or underweight, emphasizing the need for targeted interventions to address poverty and acute malnutrition. Improved household income positively impacts dietary diversity, nutrient intake, and overall nutritional status ( 21 , 22 ). Ensuring adequate resources is vital for supporting optimal nutrition and health for vulnerable populations. Findings on household size are similar to what was found by NSO, (2019), who reported an average household size of 4.4. Studies by Galgamuwa et al., (2017) and Fentaw R, Bogale A, (2013) indicate that larger households often experience poorer access to nutritious food due to resource constraints. The Malawi Poverty Report (2020) revealed that 20.5% of Malawians live in extreme poverty, with the central region having the highest proportion (55.8%), followed by the southern (51.0%) and northern regions (32.9%) (NSO, 2021). Households with more members tend to face challenges in providing adequate nutrition due to resource reduction, where food and financial resources are spread thinly across more individuals. This can lead to insufficient dietary intake and poor nutritional outcomes for children in these households. Furthermore, caregivers in households with many members may have less time and attention to devote to each child's feeding and care practices, which are crucial for healthy growth and development. In terms of religious affiliation, NSO, (2019) found that most of Malawians are Christians, with smaller proportions of Muslims (13% of women and 11% of men) and a minimal number of individuals without religious affiliation (1% of women and 3% of men). Galgamuwa et al., (2017) noted that religious beliefs could influence dietary practices, potentially affecting children’s nutrition. The differences in nutritional outcomes may be linked to varying dietary practices and cultural beliefs associated with different religions. For instance, religious dietary restrictions or practices may influence the types of food consumed, impacting overall nutrition. For example, Islamic dietary restrictions might limit access to certain protein sources. Gender, Age and undernutrition. Our study's finding that stunting remains a significant public health issue in Malawi is consistent with NSO, (2017), which reported a 37.1% stunting rate among children. However, discrepancies were noted in wasting and underweight measures. This suggests that while stunting is a persistent problem, other forms of malnutrition may vary. Gender was not a significant predictor of nutritional status in this study, contrasting with findings by Obasohan et al., (2024) and Addae et al., (2024), who reported that male children were more likely to be undernourished. Differences in care based on gender, as discussed by Girma et al., (2019), might contribute to this variation. Similarly, while Mya et al., (2019) found higher stunting rates among older children, Obasohan et al., (2024) observed greater malnutrition in children aged 24–35 months compared to younger children. Conclusion In conclusion, the nutritional outcomes of children are significantly influenced by caregiver roles and socio-economic factors. Caregivers engaged in farming or business, or those with higher education levels, tend to foster healthier environments, reducing instances of wasting and stunting in children. Conversely, caregivers who are unemployed, students, or have lower education levels, along with those in low-income or larger households, face greater challenges in providing adequate nutrition, leading to higher risks of underweight and wasting. While the study shows that religious affiliation such as Muslims had an impact on child nutrition, it also highlights that household size and socio-economic conditions play critical roles in food security and resource allocation. The evidence suggests that larger household sizes and lower income are linked to increased malnutrition risks, supporting earlier studies that indicate higher risks of underweight and food shortages in larger or poorer families. Abbreviations CI Confidence Interval FAO Food and Agriculture Organisation IFAD International Fund for Agricultural Development USD United States Dollar MDHS Malawi Demographic Health Survey MK Malawi Kwacha NHSRC National Health Sciences Research Committee NPC National Planning Commission NSO National Statistical Office OR Odds ratio SC Senior Chief SD Standard Deviation TA Traditional Authority UNICEF United Nations Children’s Fund WHO World Health Organisation Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of NHSRC. Ethical approval was sought from The National Health Sciences Research Committee (NHSRC) (Approval number 23/01/4301). District councils from the three districts also approved the study, and the confidentiality and significance of the research were clearly explained to all participants. Verbal informed consent was obtained from all respondents involved in the study. Consent for publication Not applicable to this study Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was funded in whole by the United States Agency for International Development (USAID) under Agreement #7200AA18LE00003 as part of Feed the Future Innovation Lab for Legume Systems Research Clinical trial number: not applicable Authors' contributions Patrick Ndovie: Designed the research, conducted the research, analyzed the data, wrote the paper and had primary responsibility for final content. Numeri Geresomo: Wrote the paper and had primary responsibility for final content. Smith G. Nkhata: Wrote the paper and had primary responsibility for final content. Robert Fungo: Wrote the paper and had primary responsibility for final content. 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FAO, IFAD, UNICEF, WFP, WHO. The State Of Food Security And Nutrition In The World 2020. Transforming Food Systems For Affordable Healthy Diets. The State of Food Security and Nutrition in the World 2020. 2020. 20–315 p. FAO, IFAD, UNICEF W and W. The state of food security and nutrition in the world 2019: safeguarding against economic slowdowns and downturns (Vol. 2019). Food & Agriculture Org.. [Internet]. Vol. 2019, The state of food security and nutrition in the world 2019: safeguarding against economic slowdowns and downturns. 2019. 212 p. Available from: https://books.google.com/books?hl=en&lr=&id=0lWkDwAAQBAJ&oi=fnd&pg=PR1&dq=FAO+IFAD+UNICEF,+WFP,+WHO.+(2019).+The+State+of+Food+Security+and+Nutrition+in+the+World.+Safeguarding+Against+Economic+Slowdowns +and+Downturns.+Rome:+FAO&ots=0quieOKpNe&sig=gfA0FoK Psaki, S., Bhutta, Z.A., Ahmed, T., Ahmend, S., Bessong, P., Islam, M., John, S., Kosek, M., Lima, A., Nesamvuni, C., Shrestha, P., Svensen, E., McGarth, M., Richard, S., Seidman, J., Caulfield, L., Miller, M. and Checkley, W.Psaki, S., Bhutta, Z.A., Ahme W. Household food access and child malnutrition: results from the eight-country MAL-ED study. Popul Heal Metr. 2012;10:v. NSO [Malawi] & I. Malawi Demographic Health Survey Report [Internet]. NSO & ICF International. 2017. Available from: http://dhsprogram.com/pubs/pdf/FR319/FR319.pdf Mondi DO, Kirabira P. Socio-demographic factors influencing nutritional status of children. Public Heal Res. 2016;6(2):62–75. Dobility. SurvetyCTO Collect software. [Internet]. 2019 [cited 2024 Feb 1]. Available from: https://www.surveycto.com/ Rahmawati VE, Pamungkasari EP, Murti B. Determinants of Stunting and Child Development in Jombang District. J Matern Child Heal. 2018;03(01):68–80. Cox M, Rose L, Kalua K, Wildt G De, Bailey R, Hart J. The prevalence and risk factors for acute respiratory infections in children aged 0- ­ 59 months in rural Malawi : A cross- ­ sectional study. 2017;(November 2011):489–96. Muche A, Gezie LD, Baraki AG egzabher, Amsalu ET. Predictors of stunting among children age 6–59 months in Ethiopia using Bayesian multi-level analysis. Sci Rep [Internet]. 2021;11(1):1–12. Available from: https://doi.org/10.1038/s41598-021-82755-7 Kavosi E et al. Prevalence and determinants of under-nutrition among children under six: A cross-sectional survey in Fars province, Iran. Int J Heal Policy Manag. 2014;3(2):71. Abeway, S., Gebremichael, B., Murugan, R., Assefa, M. & Adinew YM. Stunting and its determinants among children aged 6–59 months in northern Ethiopia: A cross-sectional study. J Nutr Metab. 2018; Abuya, B. A., Ciera, J. & Kimani-Murage E. Effect of mother’s education on child’s nutritional status in the slums of Nairobi. BMC Pediatr. 2012;12(1):80. Simelane MS, Chemhaka GB, Zwane E. A multilevel analysis of individual, household and community level factors on stunting among children aged 6–59 months in Eswatini: A secondary analysis of the Eswatini 2010 and 2014 Multiple Indicator Cluster Surveys. PLoS One [Internet]. 2020;15(10 October):24–35. Available from: http://dx.doi.org/10.1371/journal.pone.0241548 The World Bank Group. World Bank. 2015. World Development Indicators. Olamijuwon EO, Odimegwu CO, Gumbo J, Chisumpa VH. Single motherhood and marasmus among under-five children in Sub-Saharan Africa: a regional analysis of prevalence and correlates. African Popul Stud. 2017;31(1). Taruvinga, A., Muchenje, V. & Mushunje A. Determinants of rural household dietary diversity: The case of Amatole and Nyandeni districts, South Africa. Int J Dev Sustain. 2013;2(4):2233–2247. Doan D. Does income growth improve diet diversity in China? 2014; NSO. 2018 Malawi Population and Housing Census report. NSO. 2019. Galgamuwa LS, Iddawela D, Dharmaratne SD, Galgamuwa GLS. Nutritional status and correlated socio-economic factors among preschool and school children in plantation communities, Sri Lanka. BMC Public Health. 2017;17(1):1–11. Fentaw R, Bogale A AD. Prevalence of child malnutrition in agro-pastoral households in afar regional state of Ethiopia. Nutr Res Pr. 2013;7:122–31. Malawi National Statistical Office. Malawi Poverty Report 2020. Gov Malawi [Internet]. 2021;(August). Available from: www.nsomalawi.mw National Statistical Office. MALAWI POPULATION AND HOUSING CENSUS REPORT-2018 2018 Malawi Population and Housing Main Report. 2019;(May). Available from: http://www.nsomalawi.mw/images/stories/data_on_line/demography/census_2018/2018 Malawi Population and Housing Census Main Report.pdf NSO. Malawi Demographic and Health Survey 2015-16 [Internet]. National Statistics Office The DHS Program. 2017. Available from: http://dhsprogram.com/pubs/pdf/FR319/FR319.pdf Obasohan PE, Walters SJ, Jacques R, Khatab K. Socio-economic, demographic, and contextual predictors of malnutrition among children aged 6–59 months in Nigeria. BMC Nutr. 2024;10(1):1. Addae HY, Sulemana M, Yakubu T, Atosona A, Tahiru R, Azupogo F. Low birth weight, household socio-economic status, water and sanitation are associated with stunting and wasting among children aged 6–23 months: Results from a national survey in Ghana. PLoS One [Internet]. 2024;19(3 March). Available from: http://dx.doi.org/10.1371/journal.pone.0297698 Girma A, Woldie H, Mekonnen FA, Gonete KA, Sisay M. Undernutrition and associated factors among urban children aged 24-59 months in Northwest Ethiopia: A community based cross sectional study. BMC Pediatr. 2019;19(1):1–11. Mya KS, Kyaw AT, Tun T. Feeding practices and nutritional status of children age 6-23 months in Myanmar: A secondary analysis of the 2015-16 Demographic and Health Survey. PLoS One. 2019;14(1):1–13. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Nov, 2024 Reviews received at journal 22 Nov, 2024 Reviews received at journal 28 Oct, 2024 Reviewers agreed at journal 24 Oct, 2024 Reviewers agreed at journal 22 Oct, 2024 Reviewers agreed at journal 21 Oct, 2024 Reviewers agreed at journal 19 Oct, 2024 Reviews received at journal 19 Oct, 2024 Reviewers agreed at journal 16 Oct, 2024 Reviewers invited by journal 16 Oct, 2024 Editor invited by journal 01 Oct, 2024 Editor assigned by journal 18 Sep, 2024 Submission checks completed at journal 18 Sep, 2024 First submitted to journal 17 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5105657","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":387125115,"identity":"b470440e-5dfa-4db4-b535-f35db49e96ad","order_by":0,"name":"Patrick Ndovie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYHACAxCS4yNZizEbiVoYGBLbiFZvLt288TFPgU16m0Tyswc/9zDI8zdwp0ng02I551ixMY9BWm6bRJq5Yc8zBsMZB3i34dVicCPHTHKGwWGglgQzCZ4DDIwbGHi33SBCy/90Non0b5J/DjDYE6VF4oPBgQQ2iRwzaaAtiQS1WM5IKzb4YJBs2Mbzpkxa5oBE8ozDvNt/4NNiLpG88UHCHzt5fvb0bZJvDtjY9rf3bjbA6zA0PjCsmPGpx6JlFIyCUTAKRgEmAACn9kLCpHI+BQAAAABJRU5ErkJggg==","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources","correspondingAuthor":true,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Ndovie","suffix":""},{"id":387125116,"identity":"c4dd1d55-697c-467a-afd6-6ea025e3096e","order_by":1,"name":"Numeri Geresomo","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Numeri","middleName":"","lastName":"Geresomo","suffix":""},{"id":387125117,"identity":"e42f5f81-a0f4-40b8-a0fe-2a3f0e40c953","order_by":2,"name":"Smith G. Nkhata","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Smith","middleName":"G.","lastName":"Nkhata","suffix":""},{"id":387125118,"identity":"c73f9fa4-bb46-4398-9b93-ad61a1067ddc","order_by":3,"name":"Robert Fungo","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Fungo","suffix":""},{"id":387125119,"identity":"5fee2b3c-a81c-4278-9e42-90363f7925e1","order_by":4,"name":"Justice Munthali","email":"","orcid":"","institution":"International Center for Tropical Agriculture (CIAT)","correspondingAuthor":false,"prefix":"","firstName":"Justice","middleName":"","lastName":"Munthali","suffix":""},{"id":387125120,"identity":"e4a0a06e-6f2b-4380-a32f-6cdb34a75055","order_by":5,"name":"Vincent Nyau","email":"","orcid":"","institution":"University of Zambia","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Nyau","suffix":""}],"badges":[],"createdAt":"2024-09-17 21:29:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5105657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5105657/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71474183,"identity":"f410c906-5dba-473f-9465-6c88c34ba5e2","added_by":"auto","created_at":"2024-12-16 04:42:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76965,"visible":true,"origin":"","legend":"\u003cp\u003eResearch study areas\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5105657/v1/a4c23933bae10c8961807885.jpg"},{"id":71474184,"identity":"38285a33-b0da-4b61-9fd1-363bc73f2c11","added_by":"auto","created_at":"2024-12-16 04:42:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41864,"visible":true,"origin":"","legend":"\u003cp\u003eOverall prevalence rates of underweight, stunting and wasting\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5105657/v1/f6bd1ffc0902f30295fa1428.jpg"},{"id":71474810,"identity":"065b2722-a432-4a1d-9a3d-1f1cfc1f3eb7","added_by":"auto","created_at":"2024-12-16 04:50:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":860252,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5105657/v1/c179192d-395d-4f36-be29-ed1d6bac3bf2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socio-economic and demographic determinants of undernutrition among 6-59 months old children living in Malawian stunting hotspots: A cross-sectional community study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalnutrition, encompassing deficiencies, excesses, or imbalances in essential nutrients and energy intake (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), remains a public global health issue, particularly prevalent in developing countries such as Malawi (Endris \u0026amp; Asefa, 2017). Despite recent commitments by member states of the World Health Organization (WHO) to achieve nine global targets by 2025, progress has been limited, with no country fully meeting these objectives (NPC, 2014). Globally, undernutrition in under-five children accounts for 45% of the deaths (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Approximately 171\u0026nbsp;million children worldwide suffer from stunting because of inadequate nutrition during infancy. In the year 2022, data from UNICEF/WHO/World Bank Group, (2023), revealed that 148.1\u0026nbsp;million children under the age of five, constituting 23.3% globally, suffered from stunting, while 45\u0026nbsp;million children under the age of five, accounting for 6.8% of the population, experienced wasting.\u003c/p\u003e \u003cp\u003eIn Africa, the prevalence is even more alarming, with two out of every five children experiencing stunting, totaling around 60\u0026nbsp;million children (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Malnutrition has a profound implication on public health, economic prosperity, and societal stability (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In the region of Sub-Saharan Africa, the prevalence of child stunting stood at 34%, with a slightly lower rate of 29.2% specifically in the West African Sub-region, as reported in 2017 by the FAO, IFAD, UNICEF, (2019). Research conducted by Psaki et al., (2012) indicates that approximately 20% of children residing in developing nations suffer from malnutrition. In Malawi, undernutrition cases such as stunting are still a public health concern although the prevalence rates have been reduced in the past decade (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). As per the 2015-16 MDHS findings, 37% of children under the age of 5 exhibit stunted growth, indicating chronic undernutrition. A smaller proportion, approximately 3%, are classified as wasted, suggesting acute undernutrition, while 5% are identified as overweight, signaling over-nutrition. Moreover, 12% of children under the age of 5 are categorized as underweight, reflecting inadequate nutrition for their age (NSO [Malawi], 2017). Malnutrition is caused by so many factors such as socioeconomic and demographic factors. Socio-demographic factors, like the age of the mother or surrogate, income level, religious beliefs, literacy, maternal education, and the child's sex, can impact a child's nutritional status. However, these influences might manifest differently in urban areas, particularly within the complex and unpredictable environment of urban slums (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Henceforth, this study assessed the socio-economic determinants of undernutrition among 6\u0026ndash;59 months old children living in Malawian stunting hotspots.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThis research focused in three districts: Mzimba in the northern region, Mchinji in the central region, and Mangochi in the southern region. Specifically, data was collected at TA Twalo and SC Kampingo-Sibande in Mzimba, TA Zulu and SC Mduwa in Mchinji, and SC Namabvi and SC Chowe in Mangochi district (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study spanned from August to September 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSource population and study design\u003c/h3\u003e\n\u003cp\u003eThe target population for this study included all children aged 6\u0026ndash;59 months old residing in the study areas as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This research study used a cross-sectional study design in data collection.\u003c/p\u003e\n\u003ch3\u003eSample size determination and sampling technique\u003c/h3\u003e\n\u003cp\u003eA multi-stage sampling was employed in selecting respondents in this study. Firstly, three districts (Mzimba, Mchinji and Mangochi) known for high stunting prevalence within each region were purposively selected. Secondly, T/As were randomly selected from each of the three districts. Villages were randomly chosen, and within these villages, households with children aged 6\u0026ndash;59 months old were purposefully selected. Initially, a sample size of 1267 caregiver/mothers with 6\u0026ndash;59 months old children.\u003c/p\u003e\n\u003ch3\u003eCriteria for inclusion and exclusion in the selection of mother-child pairs caregiving\u003c/h3\u003e\n\u003cp\u003eThe study included caregivers/mothers and their 6\u0026ndash;59 months old children who had lived in the study areas for more than one year before the survey and gave a consent to take part in the study.\u003c/p\u003e \u003cp\u003e The study excluded mothers and caregivers who had not given their consent to participate. In addition, according to medical records or the child health card, the study did not include respondents (mothers/caregivers of children aged 6\u0026ndash;59 months) who were mentally challenged, physically deformed, or chronically ill because these conditions would have an impact on their anthropometric measurements and require special feeding attention.\u003c/p\u003e\n\u003ch3\u003eData collection tools and procedures\u003c/h3\u003e\n\u003cp\u003e Prior to collecting anthropometric data from children, consent was obtained from the households. For children aged 24\u0026ndash;59 months, weight was measured in underwear and without shoes using an electronic scale (SECA 861 or SECA 813, Hamburg, Germany) accurate to 0.1 kg, and height was measured in the Frankfort plane with a telescopic height instrument (SECA 225 or SECA 214) accurate to 0.1 cm. Conversely, data for children aged 6\u0026ndash;23 months were obtained by measuring the combined weight of the child and mother/caregiver, then subtracting the mother/caregiver's weight. Height was measured using a precisely graduated length board and recorded to the nearest millimeter. Child age was determined through parental recall using an events calendar. Measurements of height and weight were taken twice, and the average was recorded. The measuring instruments were calibrated at least twice daily. Wasting, stunting, and underweight were defined by the World Health Organization (WHO) as Z-scores of less than \u0026minus;\u0026thinsp;2 standard deviations for weight-for-height, height-for-age, and weight-for-age, respectively.\u003c/p\u003e \u003cp\u003eSocio-economic and demographic data such as sex, age (months) and gender of the children and education level, economic activities, marital status, and religion of the caregiver/mother was collected using a semi-structure questionnaire through face-to-face interview. Additionally, data on household characteristics such as household income and size was collected using the same questionnaire. The structured questionnaire was uploaded into SurveyCTO Collect v. 2.80 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) platform and enumerators used electronic gadgets during data collection.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eStata version 17.0 (Standard edition) was used to generate descriptive statistics such means. Standard deviations (SD), frequencies, and percentages. In addition, multivariate logistic regression was also used to determine the association between the variables. All the values were significant at 95% confidence level (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Z-Scores for anthropometric indices was generated from WHO Anthro version 3.2.2 and values were entered in Stata for further analysis and age specific cut off points were used. A multivariate logistic regression analysis was run in STATA to analyse the association between nutritional status of children (weight-for-height, height-for-age, and weight-for-age) and independent variables such as socioeconomic and demographic characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSocioeconomic and demographic factors among 6\u0026ndash;59 months old children and their caregivers\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the socioeconomic and demographic determinants of the study population. The mean age of the children was 28.42 months, with approximately 48.3% being male. These findings are consistent with those reported by State et al. (2022) and Eshete et al. (2018). Most children were in the 24\u0026ndash;35-month age range (26.8%). Regarding caregiver occupation, 72.2% were engaged in farming, while only 0.6% worked as casual labourers. Most caregivers were married (83.0%), with a small proportion having separated from their spouses (0.7%). Financially, a significant majority of households earned less than 50,000 MK (\u0026lt;\u0026thinsp;28.85 USD) per month (93.5%), while a minimal percentage earned between 100,000 MK and 150,000 MK per month (0.39%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocioeconomic and demographic characteristics among 6\u0026ndash;59 months old children and their caregivers (n\u0026thinsp;=\u0026thinsp;1275)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCATEGORY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMEAN\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex of Child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e616(48.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e659(51.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAge of the child (Months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e28.42\u0026thinsp;\u0026plusmn;\u0026thinsp;15.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e231(18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298(23.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e342(26.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u0026ndash;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e222(17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e182(14.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e921(72.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePermanent employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16(1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCasual laborer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness/Trader\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93(7.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86(6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21(1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130(10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23(1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e473(37.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e468(36.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143(11.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e155(12.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(0.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(0.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66(5.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1058(83.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66(5.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9(0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76(6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHousehold Incomes (MK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1192(93.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50,001\u0026ndash;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72(5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100,001\u0026ndash;150,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(0.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;150,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53(4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;4 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e463(36.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e759(59.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e791(62.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslims\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e479(37.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Religion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFully Vaccinated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e644 (50.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e631 (49.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the study showed that more than half of the households had more than four members (59.5%), whereas only 4.2% had fewer than three members. Regarding religious affiliation, 62% of caregivers were identified as Christians, while the smallest proportion had no religious affiliation. Additionally, the study found that more than half of the children (50.5%) were fully vaccinated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNutritional status of 6\u0026ndash;59 months old children\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the prevalence rates of underweight, stunting, and wasting among children aged 6\u0026ndash;59 months. The findings indicate that 83% of the children were of normal weight, while 3.1% were moderately wasted and 3.3% were severely wasted.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results further indicate that 57.2% of the children were classified as having normal height for age, while 26.1% were moderately stunted and 16.7% were severely stunted. Additionally, the prevalence of underweight among the children was as follows: 82% were of normal weight, 13% were moderately underweight, and 4.5% were severely underweight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between socio-economic and demographic factors with undernutrition among study participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the multiple logistic regression analysis examining the association between socio-demographic factors and undernutrition among the study participants. The analysis revealed no significant association between the gender of the children and undernutrition. Significant associations were found between certain age ranges and undernutrition. Children aged 6\u0026ndash;11 months were significantly more likely to be wasted (OR\u0026thinsp;=\u0026thinsp;1.86, 95% CI: 0.74\u0026ndash;1.65, p\u0026thinsp;=\u0026thinsp;0.008) and less likely to be stunted (OR\u0026thinsp;=\u0026thinsp;0.55, 95% CI: 0.41\u0026ndash;0.76, p\u0026thinsp;=\u0026thinsp;0.000). Specifically, children in this age group were 1.86 times more likely to be wasted compared to other age ranges, and 0.55 times less likely to be stunted. Children aged 24\u0026ndash;37 months (OR\u0026thinsp;=\u0026thinsp;2.08, 95% CI: 1.62\u0026ndash;2.76, p\u0026thinsp;=\u0026thinsp;0.000) and 38\u0026ndash;47 months (OR\u0026thinsp;=\u0026thinsp;7.07, 95% CI: 5.09\u0026ndash;9.81, p\u0026thinsp;=\u0026thinsp;0.000) were 2.08 and 7.07 times more likely to be stunted and underweight, respectively. Children aged 48\u0026ndash;60 months were 20.25 times more likely to be underweight (OR\u0026thinsp;=\u0026thinsp;20.25, 95% CI: 13.80\u0026ndash;29.71, p\u0026thinsp;=\u0026thinsp;0.000). Additionally, children aged 38\u0026ndash;47 months, and 12\u0026ndash;23 months were 0.12 and 0.64 times less likely to be wasted and stunted, respectively.\u003c/p\u003e \u003cp\u003eRegarding caregiver occupation, children of caregivers who were farmers (OR\u0026thinsp;=\u0026thinsp;0.55, 95% CI: 0.37\u0026ndash;0.82, p\u0026thinsp;=\u0026thinsp;0.003), businesspersons (OR\u0026thinsp;=\u0026thinsp;0.12, 95% CI: 0.07\u0026ndash;0.88, p\u0026thinsp;=\u0026thinsp;0.037), and unemployed (OR\u0026thinsp;=\u0026thinsp;0.08, 95% CI: 0.01\u0026ndash;0.55, p\u0026thinsp;=\u0026thinsp;0.011) were less likely to be wasted, with odds ratios of 0.55, 0.12, and 0.08, respectively. Additionally, unemployed caregivers were 0.54 times less likely to have underweight children. Conversely, caregivers who were unemployed (OR\u0026thinsp;=\u0026thinsp;1.64, 95% CI: 1.14\u0026ndash;2.36, p\u0026thinsp;=\u0026thinsp;0.008) were 1.64 times more likely to have stunted children. Caregivers engaged in business (OR\u0026thinsp;=\u0026thinsp;1.67, 95% CI: 1.02\u0026ndash;2.75, p\u0026thinsp;=\u0026thinsp;0.043) and those who were students (OR\u0026thinsp;=\u0026thinsp;3.66, 95% CI: 1.52\u0026ndash;8.80, p\u0026thinsp;=\u0026thinsp;0.004) were 1.67 and 3.66 times more likely to have underweight children, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Logistic regression model of association between socioeconomic and demographic factors with under-nutrition among study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eWasting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eStunting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eGENDER\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10(0.74\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.10(0.88\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.19(0.89\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91(0.61\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.91(0.73\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.84(0.63\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eAGE (MONTHS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86(1.18\u0026ndash;2.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.55(0.41\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.66(1.08\u0026ndash;2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.64(0.49\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97(0.62\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.08(1.62\u0026ndash;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u0026ndash;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12(0.04\u0026ndash;0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.30(0.97\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e7.07(5.09\u0026ndash;9.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74(0.40\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.85(0.62\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e20.25(13.80-29.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eCaregivers\u0026rsquo; occupation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55(0.37\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.99(0.79\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.13(0.83\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12(0.07\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.68(0.44\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.67(1.02\u0026ndash;2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08(0.01\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.64(1.14\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.54(0.30\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54(0.72\u0026ndash;4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.48(0.62\u0026ndash;3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.66(1.52\u0026ndash;8.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30(0.63\u0026ndash;2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.06(0.68\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.83(0.45\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eCaregivers Education level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04(0.24\u0026ndash;4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.03(0.45\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.21(0.03\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior Primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.17(2.72\u0026ndash;6.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.27(1.01\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.30(0.96\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior Primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44(0.27\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.19(0.95\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.19(0.88\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14(0.03\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.60(0.41\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.77(0.46\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior Secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48(0.22\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.68(0.48\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.36(0.20\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eCaregiver\u0026rsquo;s Marital Status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69(0.25\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.12(0.68\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.85(1.05\u0026ndash;3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10(0.64\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.80(0.59\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.86(0.59\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76(0.30\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.15(0.72\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.07(0.59\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37(0.17\u0026ndash;11.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.69(0.671-0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31(0.58\u0026ndash;2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.27(0.77\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.81(0.39\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eHousehold Income\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50,000mk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.02(1.11\u0026ndash;58.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.60(1.00-2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.63(0.37\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50,000mk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12(0.12\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.63(0.39\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.59(0.94\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eReligious Affiliations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12(0.07\u0026ndash;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.26(1.00-1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.01(0.75\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.35(5.09\u0026ndash;13.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.82(0.65\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.99(0.73\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo religion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.18(0.13\u0026ndash;10.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16(0.77\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.95(0.76\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.69(0.51\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86(0.56\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.05(0.84\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.45(1.06\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eFull vaccination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.289(0.18\u0026ndash;0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.44(1.15\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, CI: Confidence Interval, OR: Odds ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe findings indicate that caregivers' economic activities and educational attainment can significantly impact children's nutritional status, likely through resource access and dietary diversity. Caregivers with junior primary education were 4.17 times more likely to have wasted children (OR\u0026thinsp;=\u0026thinsp;4.17, 95% CI: 2.72\u0026ndash;6.42, p\u0026thinsp;=\u0026thinsp;0.000) and 1.27 times more likely to have stunted children (OR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 1.01\u0026ndash;1.60, p\u0026thinsp;=\u0026thinsp;0.041). Conversely, caregivers with senior primary and junior secondary education were 0.44 and 0.14 times less likely to have wasted children, respectively. Additionally, caregivers with senior secondary education were 0.68 times less likely to have stunted children (OR\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.48\u0026ndash;0.97, p\u0026thinsp;=\u0026thinsp;0.033) and 0.36 times less likely to have underweight children (OR\u0026thinsp;=\u0026thinsp;0.36, 95% CI: 0.20\u0026ndash;0.67, p\u0026thinsp;=\u0026thinsp;0.000). Those with junior secondary education were also 0.60 times less likely to have stunted children (OR\u0026thinsp;=\u0026thinsp;0.60, 95% CI: 0.41\u0026ndash;0.87, p\u0026thinsp;=\u0026thinsp;0.007). Children of single caregivers had significantly higher odds of being underweight (OR\u0026thinsp;=\u0026thinsp;1.85, 95% CI: 1.05\u0026ndash;3.24, p\u0026thinsp;=\u0026thinsp;0.033), though no significant associations were found with wasting or stunting.\u003c/p\u003e \u003cp\u003eRegarding religious affiliation, Christian caregivers were associated with lower odds of wasting (OR\u0026thinsp;=\u0026thinsp;0.12, 95% CI: 0.07\u0026ndash;0.20, p\u0026thinsp;=\u0026thinsp;0.000), while Muslim caregivers were associated with higher odds of wasting (OR\u0026thinsp;=\u0026thinsp;8.35, 95% CI: 5.09\u0026ndash;13.68, p\u0026thinsp;=\u0026thinsp;0.000). Children from larger households (\u0026ge;\u0026thinsp;5 members) were 1.45 times more likely to be stunted compared to those from smaller households (OR\u0026thinsp;=\u0026thinsp;1.45, 95% CI: 1.06\u0026ndash;1.97, p\u0026thinsp;=\u0026thinsp;0.018), and 0.69 times less likely to be underweight (OR\u0026thinsp;=\u0026thinsp;0.69, 95% CI: 0.51\u0026ndash;0.94, p\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSocioeconomic and demographic factors among 6\u0026ndash;59 months old children and their caregivers\u003c/h2\u003e \u003cp\u003eMaternal education is a key determinant of children's diet, health, and survival, as evidenced by a study by Rahmawati et al., (2018). This study found that most caregivers had attained junior primary education (37.1%), while only a small fraction had a university degree (0.39%). This distribution is consistent with Cox et al., (2017), who reported that 53.1% of caregivers had completed primary education. Maternal education emerged as a crucial factor in improving child nutrition, aligning with Muche et al., (2021) and other studies (Kavosi, 2014; Abeway et al., 2018; Abuya et al., 2012 Abuya et al., 2012). Maternal education is associated with better dietary practices. Educated mothers are more likely to understand nutritional requirements and make informed food choices, which directly influences the dietary intake of their children. Educated mothers are better equipped to implement effective childcare practices and access healthcare services, which can reduce malnutrition. Education also enhances income opportunities, further supporting nutritional needs (Akombi, et al., 2017).\u003c/p\u003e \u003cp\u003e Household income also plays a crucial role in children's dietary intake. Increased income is generally associated with better nutrition. Simelane et al., (2020) found that wealthier households had a 0.45 times lower likelihood of having stunted children compared to very poor households. The World Bank Group, (2015) reported that 62% of Malawians lived on less than \u003cspan\u003e$\u003c/span\u003e1.25 per day in 2010, highlighting a significant disparity from the international average. Despite an overall decline in poverty, the wealthiest have become richer, while poverty rates among the poorer segments of the population remain high. This study has shown that households with higher incomes tend to have better nutrition outcomes for children than those with lower incomes. Educated mothers are often better positioned to secure higher-paying jobs, contributing to improved household income, which is crucial for accessing nutritious foods. Olamijuwon et al., (2017) found no significant association between single motherhood and marasmus, but our study revealed a significant association between lower household income and wasting. Children from households earning less than 50,000 MK were 8.02 times more likely to be wasted compared to those from wealthier households. No significant associations were found for stunting or underweight, emphasizing the need for targeted interventions to address poverty and acute malnutrition. Improved household income positively impacts dietary diversity, nutrient intake, and overall nutritional status (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Ensuring adequate resources is vital for supporting optimal nutrition and health for vulnerable populations.\u003c/p\u003e \u003cp\u003eFindings on household size are similar to what was found by NSO, (2019), who reported an average household size of 4.4. Studies by Galgamuwa et al., (2017) and Fentaw R, Bogale A, (2013) indicate that larger households often experience poorer access to nutritious food due to resource constraints. The Malawi Poverty Report (2020) revealed that 20.5% of Malawians live in extreme poverty, with the central region having the highest proportion (55.8%), followed by the southern (51.0%) and northern regions (32.9%) (NSO, 2021). Households with more members tend to face challenges in providing adequate nutrition due to resource reduction, where food and financial resources are spread thinly across more individuals. This can lead to insufficient dietary intake and poor nutritional outcomes for children in these households. Furthermore, caregivers in households with many members may have less time and attention to devote to each child's feeding and care practices, which are crucial for healthy growth and development.\u003c/p\u003e \u003cp\u003eIn terms of religious affiliation, NSO, (2019) found that most of Malawians are Christians, with smaller proportions of Muslims (13% of women and 11% of men) and a minimal number of individuals without religious affiliation (1% of women and 3% of men). Galgamuwa et al., (2017) noted that religious beliefs could influence dietary practices, potentially affecting children\u0026rsquo;s nutrition. The differences in nutritional outcomes may be linked to varying dietary practices and cultural beliefs associated with different religions. For instance, religious dietary restrictions or practices may influence the types of food consumed, impacting overall nutrition. For example, Islamic dietary restrictions might limit access to certain protein sources.\u003c/p\u003e \u003cp\u003eGender, Age and undernutrition.\u003c/p\u003e \u003cp\u003eOur study's finding that stunting remains a significant public health issue in Malawi is consistent with NSO, (2017), which reported a 37.1% stunting rate among children. However, discrepancies were noted in wasting and underweight measures. This suggests that while stunting is a persistent problem, other forms of malnutrition may vary. Gender was not a significant predictor of nutritional status in this study, contrasting with findings by Obasohan et al., (2024) and Addae et al., (2024), who reported that male children were more likely to be undernourished. Differences in care based on gender, as discussed by Girma et al., (2019), might contribute to this variation. Similarly, while Mya et al., (2019) found higher stunting rates among older children, Obasohan et al., (2024) observed greater malnutrition in children aged 24\u0026ndash;35 months compared to younger children.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the nutritional outcomes of children are significantly influenced by caregiver roles and socio-economic factors. Caregivers engaged in farming or business, or those with higher education levels, tend to foster healthier environments, reducing instances of wasting and stunting in children. Conversely, caregivers who are unemployed, students, or have lower education levels, along with those in low-income or larger households, face greater challenges in providing adequate nutrition, leading to higher risks of underweight and wasting. While the study shows that religious affiliation such as Muslims had an impact on child nutrition, it also highlights that household size and socio-economic conditions play critical roles in food security and resource allocation. The evidence suggests that larger household sizes and lower income are linked to increased malnutrition risks, supporting earlier studies that indicate higher risks of underweight and food shortages in larger or poorer families.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Confidence Interval\u003c/p\u003e\n\u003cp\u003eFAO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Food and Agriculture Organisation\u003c/p\u003e\n\u003cp\u003eIFAD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;International Fund for Agricultural Development\u003c/p\u003e\n\u003cp\u003eUSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;United States Dollar\u003c/p\u003e\n\u003cp\u003eMDHS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Malawi Demographic Health Survey\u003c/p\u003e\n\u003cp\u003eMK\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Malawi Kwacha\u003c/p\u003e\n\u003cp\u003eNHSRC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;National Health Sciences Research Committee\u003c/p\u003e\n\u003cp\u003eNPC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;National Planning Commission\u003c/p\u003e\n\u003cp\u003eNSO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;National Statistical Office\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Odds ratio\u003c/p\u003e\n\u003cp\u003eSC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Senior Chief\u003c/p\u003e\n\u003cp\u003eSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard Deviation\u003c/p\u003e\n\u003cp\u003eTA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Traditional Authority\u003c/p\u003e\n\u003cp\u003eUNICEF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;United Nations Children\u0026rsquo;s Fund\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organisation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of NHSRC. Ethical approval was sought from The National Health Sciences Research Committee (NHSRC) (Approval number 23/01/4301). District councils from the three districts also approved the study, and the confidentiality and significance of the research were clearly explained to all participants. Verbal informed consent was obtained from all respondents involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable to this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded in whole by the United States Agency for International Development (USAID) under Agreement #7200AA18LE00003 as part of Feed the Future Innovation Lab for Legume Systems Research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003ePatrick Ndovie:\u003c/strong\u003e Designed the research, conducted the research, analyzed the data, wrote the paper and had primary responsibility for final content.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNumeri Geresomo:\u003c/strong\u003e Wrote the paper and had primary responsibility for final content.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSmith G. Nkhata:\u003c/strong\u003e Wrote the paper and had primary responsibility for final content.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRobert Fungo:\u003c/strong\u003e Wrote the paper and had primary responsibility for final content.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eJustice Munthali:\u003c/strong\u003e Wrote the paper and had primary responsibility for final content.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eVincent Nyau:\u003c/strong\u003e Wrote the paper and had primary responsibility for final content.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. Fact sheets - Malnutrition [Internet]. 2020 [cited 2024 May 14]. Available from: https://www.who.int/ news-room/fact-sheets/detail/malnutrition\u003c/li\u003e\n\u003cli\u003eEndris N, Asefa H DL. 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A multilevel analysis of individual, household and community level factors on stunting among children aged 6\u0026ndash;59 months in Eswatini: A secondary analysis of the Eswatini 2010 and 2014 Multiple Indicator Cluster Surveys. PLoS One [Internet]. 2020;15(10 October):24\u0026ndash;35. Available from: http://dx.doi.org/10.1371/journal.pone.0241548\u003c/li\u003e\n\u003cli\u003eThe World Bank Group. World Bank. 2015. World Development Indicators. \u003c/li\u003e\n\u003cli\u003eOlamijuwon EO, Odimegwu CO, Gumbo J, Chisumpa VH. Single motherhood and marasmus among under-five children in Sub-Saharan Africa: a regional analysis of prevalence and correlates. African Popul Stud. 2017;31(1). \u003c/li\u003e\n\u003cli\u003eTaruvinga, A., Muchenje, V. \u0026amp; Mushunje A. Determinants of rural household dietary diversity: The case of Amatole and Nyandeni districts, South Africa. Int J Dev Sustain. 2013;2(4):2233\u0026ndash;2247. \u003c/li\u003e\n\u003cli\u003eDoan D. Does income growth improve diet diversity in China? 2014; \u003c/li\u003e\n\u003cli\u003eNSO. 2018 Malawi Population and Housing Census report. NSO. 2019. \u003c/li\u003e\n\u003cli\u003eGalgamuwa LS, Iddawela D, Dharmaratne SD, Galgamuwa GLS. Nutritional status and correlated socio-economic factors among preschool and school children in plantation communities, Sri Lanka. BMC Public Health. 2017;17(1):1\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eFentaw R, Bogale A AD. Prevalence of child malnutrition in agro-pastoral households in afar regional state of Ethiopia. Nutr Res Pr. 2013;7:122\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eMalawi National Statistical Office. Malawi Poverty Report 2020. Gov Malawi [Internet]. 2021;(August). Available from: www.nsomalawi.mw\u003c/li\u003e\n\u003cli\u003eNational Statistical Office. MALAWI POPULATION AND HOUSING CENSUS REPORT-2018 2018 Malawi Population and Housing Main Report. 2019;(May). Available from: http://www.nsomalawi.mw/images/stories/data_on_line/demography/census_2018/2018 Malawi Population and Housing Census Main Report.pdf\u003c/li\u003e\n\u003cli\u003eNSO. Malawi Demographic and Health Survey 2015-16 [Internet]. National Statistics Office The DHS Program. 2017. Available from: http://dhsprogram.com/pubs/pdf/FR319/FR319.pdf\u003c/li\u003e\n\u003cli\u003eObasohan PE, Walters SJ, Jacques R, Khatab K. Socio-economic, demographic, and contextual predictors of malnutrition among children aged 6\u0026ndash;59 months in Nigeria. BMC Nutr. 2024;10(1):1. \u003c/li\u003e\n\u003cli\u003eAddae HY, Sulemana M, Yakubu T, Atosona A, Tahiru R, Azupogo F. Low birth weight, household socio-economic status, water and sanitation are associated with stunting and wasting among children aged 6\u0026ndash;23 months: Results from a national survey in Ghana. PLoS One [Internet]. 2024;19(3 March). Available from: http://dx.doi.org/10.1371/journal.pone.0297698\u003c/li\u003e\n\u003cli\u003eGirma A, Woldie H, Mekonnen FA, Gonete KA, Sisay M. Undernutrition and associated factors among urban children aged 24-59 months in Northwest Ethiopia: A community based cross sectional study. BMC Pediatr. 2019;19(1):1\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eMya KS, Kyaw AT, Tun T. Feeding practices and nutritional status of children age 6-23 months in Myanmar: A secondary analysis of the 2015-16 Demographic and Health Survey. PLoS One. 2019;14(1):1\u0026ndash;13. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malnutrition, unemployment, religious affiliations, caregivers, Odds Ratio","lastPublishedDoi":"10.21203/rs.3.rs-5105657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5105657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGlobally, malnutrition is prevalent, particularly in Sub-Saharan Africa, where 171\u0026nbsp;million under-five children suffer from stunting, and 45% of mortalities are reported. This study aimed to identify socio-economic and demographic factors contributing to undernutrition among 6\u0026ndash;59 months old children in Malawian stunting hotspots.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eStata 17.0 was used to analyse descriptive statistics and logistic regression for nutritional status associations, using WHO Z-Scores and 95% confidence. This cross-sectional community study was conducted in Mzimba, Mchinji and Mangochi and involved 1,275 caregivers and children.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results in the study revealed that 6\u0026ndash;59 months old children had varying risks of wasting and stunting. Caregivers who engaged in farming, business, or unemployed had lower risk of being wasted, whereas unemployed and student caregivers had higher odds of stunting (OR\u0026thinsp;=\u0026thinsp;1.64, 95% CI: 1.14\u0026ndash;2.36, p\u0026thinsp;=\u0026thinsp;0.008) and underweight (OR\u0026thinsp;=\u0026thinsp;3.66, 95% CI: 1.52\u0026ndash;8.80, p\u0026thinsp;=\u0026thinsp;0.004). Caregivers who had attained junior education level had increased odds of having wasted and underweight children, while those who attained junior and senior secondary education showed reduced risks. Low household income (below 50,000MK per month) increase the risk wasting (OR\u0026thinsp;=\u0026thinsp;8.35, 95% CI: 5.09\u0026ndash;13.68, p\u0026thinsp;=\u0026thinsp;0.000), while those with higher incomes had a decreased risk. Christian caregivers had lower odds of having a wasted child while muslim caregivers had higher odds (OR\u0026thinsp;=\u0026thinsp;8.35, 95% CI: 5.09\u0026ndash;13.68, p\u0026thinsp;=\u0026thinsp;0.000). Households with less than five members had reduced underweight odds, in contrast to those with more than five members who had increased odds.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCaregivers in farming, business, and with higher education have lower risks of child wasting and stunting. Unemployed, less educated, and low-income or larger households face higher risks.\u003c/p\u003e","manuscriptTitle":"Socio-economic and demographic determinants of undernutrition among 6-59 months old children living in Malawian stunting hotspots: A cross-sectional community study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 04:42:30","doi":"10.21203/rs.3.rs-5105657/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-26T06:06:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-22T06:27:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-28T18:12:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140255765925102805444999536183745376760","date":"2024-10-24T12:10:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248534412665174005700444477726144708754","date":"2024-10-22T04:52:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60172526323581265336015924365219529885","date":"2024-10-21T12:09:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136077640238012711454629419379285015491","date":"2024-10-19T20:10:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-19T09:47:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78208673004973801694745943165300795617","date":"2024-10-16T16:29:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-16T15:36:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-01T10:13:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-19T02:02:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-19T02:00:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2024-09-17T21:28:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"329ba095-8be3-4b84-920e-eaa29b7a1bb3","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T06:39:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 04:42:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5105657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5105657","identity":"rs-5105657","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}