Sibling Gender Dynamics and Childhood Malnutrition in Ghana

Sibling Gender Dynamics and Childhood Malnutrition in Ghana | 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 Sibling Gender Dynamics and Childhood Malnutrition in Ghana Peter Annor Mensah, Ruth Tobi Sawyerr, Aaron Kobina Christian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4601625/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2024 Read the published version in BMC Nutrition → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Stunting remains a public health concern in sub-Saharan Africa. Despite the evolving awareness of the effect of family composition on child health outcomes, the influence of sibling gender on stunting has seldom been consistent. The current study investigated the association between sibling composition and stunting among children under 5 years in Ghana. Methods This cross-sectional study utilized data from the most recent Ghana Demographic and Health Survey (GDHS 2022), focusing on 4412 mother-child dyads. Stunting prevalence was assessed through descriptive analysis, while logistic regression analysis was employed to examine the association between sibling composition and identify other risk factors associated with stunting. Results The prevalence of stunting among children under five years of age was 18%. It was observed from the composite and sex-stratified models that having male siblings increased children’s odds of being stunted. Furthermore, being a male child (OR: 1.49; 95% CI: 1.15, 1.94) and belonging to a household with an unimproved water supply (OR: 10.9; 95% CI: 1.03, 1.82) increased children's likelihood of stunting. Conclusion This study revealed that the extra nutrients male children require for healthy growth and development may heighten competition for nutrients, especially in resource-constrained households. Parents and guardians are advised to be consciously aware of the subtle and apparent competition between siblings and take appropriate measures to prevent children’s deprivation of nutrition by their male siblings. Childhood stunting Sibling composition Sex of siblings Background The evidence provided in the literature regarding the economic and public health burden of childhood malnutrition is staggering. Childhood stunting annually costs the private sector in 95 low- and middle-income countries (LMICs) a minimum of USD 135.4 billion in sales, while private sector workers in East Asia and the Pacific alone lose approximately USD 16.5 billion due to stunting ( 1 ). From a broader perspective, it is estimated that childhood malnutrition among children can cost countries 4–11% of their GDP ( 2 ). Childhood stunting can cause irreparable and long-lasting damage to the cognitive function and neurodevelopment of children while impacting them later in adulthood. Compared to a stunted child's growth, a well-nourished child achieves a greater number of years in education, gains improved learning outcomes, and reaches higher income levels in adulthood, thus increasing the likelihood of avoiding poverty ( 3 ). Globally, of the 21% (149 million) of stunted children under the age of five, more than 90% live in LMICs ( 1 ). Although sub-Saharan Africa (SSA) is making headways towards stemming childhood stunting in the region, the trend is believed to be too slow to meet global targets ( 4 ). Currently, the average prevalence of stunting among children under 5 years of age is 40% in SSA ( 5 ). Similarly, evidence from the Ghana Demographic and Health Survey over the years suggests a consistent decrease in the prevalence of stunting in the country. However, the evidence further indicates that aside from the significant decline observed between 2008 and 2014 (from 28–19%) (6;7), the period between 2014 and 2022 witnessed a marginal decline (from 19–18%) ( 7 , 8 ) in stunting prevalence. The evidence on stunting prevalence in Ghana is troubling, particularly amid numerous interventions to address it. The literature on the correlates of stunting in SSA has predominantly focused on maternal and broader inter and intra-household factors, with little focus on the sex composition of a child’s siblings. The argument for the consideration of the sibling gender effect in stunting research is based on findings from previous research suggesting that sibling composition influences a child’s health outcomes ( 9 , 10 ). Previous research has attempted to provide reasons for siblings’ gender considerations in child nutritional studies. For instance, in their first-of-its-kind study of sibling rivalry and child health outcomes in Ghana, Garg and Morduch found that the concept of “spillover/reference effects” matters significantly in explaining patterns of child health ( 11 ). They further explain that there may be a spillover effect when parents attempt to instil more masculine traits (enhanced physical activity and greater self-confidence) in their daughters when they have at least one brother. This influences the treatment given to girls with brothers compared to girls with sisters. On the other hand, the reference effect may occur such that different treatment is given to girls with only brothers compared to girls with at least one sister. In the absence of sisters, a single daughter might conceivably be treated similarly to male offspring within the family context; however, differences may become more noticeable with the inclusion of another girl in the family structure, thereby changing the criteria used to assess differential treatment ( 11 ). Furthermore, Raj et al. argue that in contexts where preference for a particular sex is paramount, parents tend to invest differently in the gender that is deemed to be valuable (either economically or socially) to the parents ( 12 ). Considering that most of the ethnic groups in Ghana practice the patrilineal system of inheritance, which forms a key component of traditional families, it is expected that there might be a preference for a male child compared to a female child. As a result, parents might want to invest household resources differently among children of opposite sexes based on the value (social and economic) they ascribe to their children ( 13 ). Given the limited evidence on the influence of sibling composition on child nutritional outcomes in Ghana and the potential impact of the sibling composition of a child on health outcomes, this research aimed to provide new evidence on the role of the gender of a sibling in influencing childhood stunting in Ghana using representative data. This study is relevant to the African context due to the uniqueness of cultural systems relating to gender norms, roles and expectations that influence the differential treatment (caregiving) and socialization of children. Examining the relationship between a child's nutritional status and sibling composition is important because it can offer valuable insights into the gender dynamics within households, which impact the short- and long-term health and economic outcomes of household members. Materials and Methods Source of Data This analysis used data from the 2022 Demographic and Health Survey (DHS) of Ghana. The demographic and health survey is a nationally representative survey that collects data on basic health and demographic indicators in developing countries. The DHS enables policymakers to measure trends and statuses of health and demographic indicators. The survey also helps governments and development actors streamline population and health interventions. The 2022 GDHS employed a stratified two-stage sampling design to select respondents. The first stage involved the selection of 618 clusters obtained using the 2021 Population and Housing Census as a sampling frame. Equal probability systematic random sampling was used to select the clusters. The second stage also involved the listing of households from which the survey respondents were selected. A total of 15,014 women aged 15–49 years and 15–59 years were interviewed. The 2022 GDHS gathered information on various population and health indicators, such as maternal and child health, nutritional status of children, and fertility levels. This analysis used data from the children’s files since the unit of analysis was children younger than 5 years. The children's files contained 9353 responses; however, only 4412 children. The 4,412 children included in the study met specific criteria: their height-for-age Z scores were recorded during data collection, and they were either the only child of their mothers or had only male or only female siblings. To accurately assess the nuanced gender effects of siblings on childhood stunting, children with both male and female siblings were excluded from the analysis. Variables Dependent variable Childhood stunting as the outcome variable was measured by the height-for-age index, which was compared with the World Health Organization (WHO) growth standard reference population ( 8 ). The height-for-age index is expressed in standard deviation units (z scores) from the median of the reference population. Therefore, a child is considered stunted when the height-for-age Z score (HAZ) is less than − 2 standard deviations (< -2 SD) from the median of the reference population. Childhood stunting was thus categorized as stunted (HAZ = -2 SD). Height measurements were taken using the ShorrBoard measuring board. Recumbent length was measured for children younger than 24 months, and the heights of older children were measured while standing. Independent variables The main independent variable – sibling composition – was measured by the sex of the sibling(s) of the index child. This variable was generated from the ‘daughters at home’ and ‘sons at home’ variables in the children’s files. In the present study, sibling composition was categorized as “only child”, “brothers only”, or “sisters only”. Control variables The control variables were grouped under three broad categories: child, maternal, and household context factors. The child factors included the sex of the child (male or female), breastfeeding duration (categories: still breastfeeding, ever breastfed, not currently breastfeeding, or never breastfed), birth weight, and birth order number. Birth weight was measured in grams and categorized as low ( 4000 grams), and birth order number had the following categories: 1st, 2nd, 3rd, 4th, 5th+. The maternal factors included maternal body mass index (BMI), maternal education (categories: no education, primary, secondary, higher), and parity (categorized as 2 or fewer, 3–5, and 6+). Maternal BMI was measured by the mother’s weight (in kilograms) divided by her height (measured in meters squared). Thus, maternal BMI was categorized as underweight ( 30 kg/m 2 ). The household context factors included household size [categorized as small (i.e., household membership less than 3), medium (i.e., household membership between 3 and 6), and large (i.e., household membership of more than 6)], place of residence (categorized as rural and urban), water supply (categorized as improved and unimproved), toilet facility (categorized as improved and unimproved), and household wealth status. Household wealth status measures the cumulative standard of living of households. It is computed using data on household assets (bicycles and televisions), the materials used for house construction, the type of water access, and sanitation facilities, using principal component analysis (PCA). The household wealth variable was categorized as poorest, poorer, middle, richer, or richest. Methods of analysis Analysis was performed on 4412 mother-child dyads at two main levels: bivariate and multivariate. The bivariate analysis involved assessing the relationship between sociodemographic factors and stunting using cross-tabulations. The Pearson chi-square test of association was further performed to test the significance of the association between the independent/control variables and the dependent variable. Binary logistic regression was employed to examine the extent to which the independent variable influenced the dependent variable after controlling for other predictor variables. Two separate adjusted models were fitted: the composite model, which included the ‘ sex of child’ variable, and the sex-stratified model, which excluded the ‘ sex of child’ variable. In each of the models, one category of each of the nominal variables was used as the reference category against which the remaining categories of the variables were compared. The results from the binary logistic regression analysis are presented as odds ratios (ORs) together with 95% confidence intervals (CIs) and p values. A test of multicollinearity was performed, and the results showed no evidence of collinearity among the variables. Analyses were performed using the R programming language. Results Table 1 presents the two-way bivariate results for stunted children as well as their distribution across sociodemographic characteristics. Pearson’s chi-square test was performed to determine the associations between the independent variables and stunting. An 18% stunting prevalence was recorded from the analysis. The results from Table 1 show that a greater proportion of stunted children had only male siblings (21%), while 16% of children with only sisters were stunted. Stunting was more prevalent among boys (20%) than among girls (15%). A greater percentage of children who had never been breastfed (28%) than of those who were still being breastfed (17%) had stunting. In terms of birth weight and maternal BMI, children with low birth weight and those born to underweight mothers accounted for the greatest proportion of stunted children (29% and 28%, respectively) among their peers. Additionally, the prevalence of stunting decreases with improvements in mothers’ education, as does household wealth. However, stunting was more prevalent among children from richer households than among those from middle-class households. Moreover, children from households with improved toilet facilities are less stunted than are children from households with unimproved toilet facilities (16% and 21%, respectively). The chi-square test revealed that all sociodemographic characteristics, except birth order number, parity, household size, and water supply, were significantly associated with stunting. Table 1 Distribution of stunted children by sociodemographic characteristics Characteristic Categories Stunted Children n % p value 1 Sibling Composition Only Child 185 17 0.01 Brothers Only 186 21 Sisters Only 141 16 Sex of Child Female 211 15 < 0.001 Male 301 20 Breastfeeding Duration Ever breastfed, not currently breastfeeding 127 20 0.016 Never breastfed 24 28 Still breastfeeding 208 17 Birth Weight (grams) Low 49 29 < 0.001 Normal 223 17 High 87 20 Birth Order Number 1st 209 19 0.07 2nd 122 15 3rd 95 20 4th 48 18 5th 38 14 Mother's BMI Normal Weight 333 20 < 0.001 Obese 34 9 Overweight 98 14 Underweight 47 28 Maternal Education No Education 156 24 < 0.001 Primary 90 20 Secondary 252 17 Higher 14 5.1 Parity 2 or fewer 325 18 0.70 3–5 167 17 6+ 20 15 Household Size Small 106 15 0.07 Medium 276 18 Large 130 20 Place of Residence Urban 192 15 0.002 Rural 320 20 Water Supply Improved 323 18 0.60 Unimproved 181 17 Toilet Facility Improved 142 16 < 0.001 Unimproved 311 21 Household Wealth Status Poorest 191 24 < 0.001 Poorer 149 22 Middle 74 14 Richer 73 15 Richest 25 6.4 Stunting Prevalence 18 1 Pearson’s Chi-squared test; Abbreviations/Symbols: n = Number; % = percent Source: Analysis of 2022 GDHS Table 2 displays the binary logistic regression analysis results to assess the association between sibling composition and childhood stunting after controlling for other predictor variables. The results of the composite model, which included both male and female children, showed that having only male siblings was significantly associated with childhood stunting (p-value 0.009). Thus, children with brothers only were more likely to be stunted compared to those who were the only children (OR: 1.84; 95% CI: 1.17, 2.92). However, having only female siblings was not significantly associated with childhood stunting. Similar results were also observed in the sex-stratified models. The results from the male sex-adjusted model suggest that compared to male children who are also the only children of their mothers, male children with only male siblings were more likely to be stunted (OR: 1.96; 95% CI: 1.10,3.54). Likewise, female children with only male siblings are 2.17 times as likely as those who are the only children of their mothers. Remarkably, having female siblings was not associated with childhood stunting in any of the three models. Additionally, the sex of the child was significantly associated with childhood stunting (p-value 0.003). Compared with female children, male children were more likely to be stunted (OR: 1.49; 95% CI: 1.15, 1.94). Breastfeeding duration was more a significant predictor of childhood stunting among females than among males. It is observed from the female sex-stratified model that compared to female children who had ever breastfed and were not currently being breastfed, female children who had never been breastfed were 1.68 times more likely to be stunted. On the other hand, female children who were still breastfeeding were 7.78 times more likely to be stunted than were their counterparts who had ever been breastfed and were not currently breastfeeding. Table 2 Multivariate model showing the association with childhood stunting Characteristic Adjusted Model: Composite Adjusted Model: Sex-Stratified (Male) Adjusted Model: Sex-Stratified (Female) Categories OR 1 95% CI 1 p-value OR 1 95% CI 1 p-value OR 1 95% CI 1 p-value Child Factors Sibling Composition Only Child ** — — — — — — Brothers Only 1.84 [1.17, 2.92] 0.009 1.96 [1.10, 3.54] 0.02 2.17 [1.00, 4.86] 0.05 Sisters Only 1.42 [0.89, 2.28] 0.14 1.35 [0.74, 2.47] 0.30 1.65 [0.75, 3.71] 0.20 Sex of Child Female ** — — Male 1.49 [1.15, 1.94] 0.003 Breastfeeding Duration Ever breastfed, not currently breastfeeding ** — — — — — — Never breastfed 1.34 [1.00, 1.78] 0.045 1.21 [0.82, 1.77] 0.30 1.68 [1.06, 2.65] 0.03 Still breastfeeding 2.54 [1.43, 4.41] 0.001 1.14 [0.50, 2.39] 0.70 7.78 [3.13, 19.3] < 0.001 Birth Weight (grams) Low ** — — — — — — Normal 2.12 [1.40, 3.20] < 0.001 2.07 [1.17, 3.61] 0.01 2.61 [1.34, 4.97] 0.004 High 0.97 [0.71, 1.33] 0.90 0.74 [0.47, 1.13] 0.20 1.30 [0.80, 2.09] 0.30 Birth Order 1st ** — — — — — — 2nd 0.52 [0.33, 0.81] 0.005 0.61 [0.34, 1.07] 0.09 0.36 [0.16, 0.77] 0.01 3rd 1.01 [0.50, 1.99] > 0.90 0.68 [0.27, 1.62] 0.40 1.74 [0.55, 5.45] 0.30 4th 0.93 [0.41, 2.06] 0.90 0.43 [0.14, 1.27] 0.13 2.05 [0.58, 7.33] 0.30 5th 0.52 [0.19, 1.35] 0.20 0.32 [0.09, 1.11] 0.08 1.01 [0.19, 4.59] > 0.90 Maternal Factors Maternal BMI Normal Weight ** — — — — — — Obese 0.69 [0.38, 1.20] 0.20 0.49 [0.19, 1.11] 0.11 1.05 [0.45, 2.26] > 0.90 Overweight 0.66 [0.45, 0.97] 0.04 0.79 [0.48, 1.26] 0.30 0.47 [0.23, 0.92] 0.04 Underweight 1.41 [0.87, 2.23] 0.20 1.20 [0.65, 2.14] 0.50 2.08 [0.90, 4.52] 0.07 Maternal Education No Education ** — — — — — — Primary 2.36 [0.98, 6.65] 0.07 1.39 [0.48, 4.69] 0.60 6.40 [1.14, 121] 0.09 Secondary 2.00 [0.83, 5.65] 0.20 0.88 [0.29, 3.06] 0.80 7.02 [1.27, 132] 0.07 Higher 1.86 [0.81, 5.04] 0.20 1.26 [0.47, 4.07] 0.70 4.20 [0.81, 77.5] 0.20 Parity 2 or fewer ** — — — — — — 3–5 0.62 [0.34, 1.14] 0.12 0.88 [0.41, 1.93] 0.80 0.35 [0.12, 0.97] 0.05 6+ 0.59 [0.18, 1.90] 0.40 0.87 [0.18, 4.08] 0.90 0.32 [0.05, 2.04] 0.20 Household Context Factors Place of Residence Urban ** — — — — — — Rural 0.78 0.56, 1.08 0.13 0.91 [0.59, 1.41] 0.70 0.67 [0.40, 1.14] 0.13 Household Size Large ** — — — — — — Medium 1.13 [0.82, 1.56] 0.40 1.01 [0.67, 1.52] > 0.90 1.33 [0.78, 2.32] 0.30 Small 0.84 [0.57, 1.24] 0.40 0.62 [0.37, 1.04] 0.08 1.25 [0.66, 2.38] 0.50 Water Supply Improved ** — — — — — — Unimproved 1.37 [1.03, 1.82] 0.03 1.23 [0.83, 1.80] 0.30 1.53 [0.96, 2.42] 0.07 Toilet Facility Improved ** — — — — — — Unimproved 1.09 [0.80, 1.49] 0.60 1.04 [0.70, 1.55] 0.90 1.21 [0.73, 2.05] 0.50 Household Wealth Status Richest ** — — — — — — Poorest 3.12 [1.30, 8.47] 0.01 3.70 [1.19, 14.3] 0.04 2.52 [0.62, 13.2] 0.20 Poorer 3.09 [1.31, 8.26] 0.01 3.39 [1.11, 13.0] 0.05 2.74 [0.72, 13.9] 0.20 Middle 1.52 [0.65, 4.02] 0.40 1.53 [0.50, 5.83] 0.50 1.49 [0.40, 7.28] 0.60 Richer 2.04 [0.88, 5.35] 0.11 2.97 [1.00, 11.0] 0.07 1.35 [0.35, 6.78] 0.70 1 OR = Odds Ratio, CI = Confidence Interval, ** = Reference Category Source: Analysis of 2022 GDHS Birth weight is significantly associated with childhood stunting. According to the composite model, compared to children with low birth weights, children with normal birth weights are 2.12 times more likely to be stunted. High birth weight, however, was not associated with stunting. Additionally, children of overweight mothers are 34% less likely to be stunted than are children of mothers with normal weight. Furthermore, belonging to a household with an unimproved water supply was significantly associated with childhood stunting (p value = 0.03). Compared to children from the richest households, the likelihood of stunting among children from the poorest and poorer households is not significantly different. Specifically, compared to children from the richest households, children from the poorest households are 3.12 times more likely to be stunted. However, compared to children from the richest households, children from poorer households are also more likely to be stunted (OR: 3.09; 95% CI: 1.31, 8.26). Moreover, maternal education, parity, place of residence, household size, and toilet facility did not show any significant relationships with childhood stunting. Discussion This study sought to examine the association between sibling composition (in terms of sex) and chronic malnutrition among children in Ghana. It further aimed to answer the question of whether having siblings of either the same or opposite sex matters in stunting analysis. Evidence from this study suggests that sibling composition influences stunting among children under the age of 5. This finding is consistent with the findings of previous studies on the role of siblings’ sex in influencing chronic malnutrition( 14 , 9 , 12 , 15 ). The analysis further revealed that, unlike having only female siblings, having only male siblings significantly predicts stunting among children under 5 years of age. Specifically, children with only male siblings have a heightened risk of being stunted. However, having female siblings was not a threat of being stunted. This is in stark contradiction to the findings of prior research by Raj et al., which suggested a lower risk of stunting among boys with many male siblings than among girls with many female siblings ( 12 ). Their research suggested that having more female siblings increased girls’ odds of being stunted, which is inconsistent with the findings of the present study. In an attempt to understand the nuanced relationship between sibling composition and childhood stunting, a sex-stratified analysis was performed for both male and female children. In the context of childhood stunting, having only male siblings was a problem for both male and female children, which is consistent with the findings from the composite model. This finding also agrees with findings from a study by Chaudhuri that examined the effect of sibling sex on nutritional outcomes in children ( 16 ). The study revealed that having male siblings negatively impacted both boys and girls, even though the impact was more severe among boys than girls. Chaudhuri explains that in households where there are only male children, there is strong competition for household resources, including food and nutrition ( 16 ). A plausible explanation is given by Helfrecht & Meehan, Kramer et al., and Magvanjav et al., who opine that there is fierce competition for resources, including nutrition, among children, especially among those from resource-constrained households ( 17 , 18 , 15 ). Within the Ghanaian cultural context, where the majority of ethnic groups practice the patrilineal inheritance system and where priority is given to male children, parents might want to invest greater resources such as attention, food, and nutrition in male children at the expense of female children because they (male children) are thought to be successors and future breadwinners of their families. Additionally, in all-male-children families, parents may invest scarce resources equally in their children. As is well articulated by Helfrecht and Meehan, the seemingly equal treatment given to male children in a household tends to deepen their rivalry, extending to competition over scarce resources ( 17 ). Biologically, boys and girls experience significant growth and development during childhood ( 19 ). However, boys tend to grow faster in height, weight, and muscle development ( 20 ). This rapid growth necessitates additional nutrients for tissue building and repair ( 21 ). As a result, male children may require more energy and nutrients to support their growth and development. The need for the nutrients required for healthy growth and development among boys may exacerbate competition for scarce resources such as food, especially in resource-constrained households ( 17 ). Adding to the hypothesized objectives of the gendered influences of siblings on chronic childhood malnutrition, this study documents some of the child, maternal, and household context factors that put children at risk of being stunted. Consistent with prior research, the sex of the child ( 22 , 23 ), breastfeeding duration ( 24 , 25 ), and birth weight ( 26 , 27 , 28 ) were the child factors that showed a significant association with childhood stunting. Specifically, while male children had greater odds of being stunted than female children, children with normal birth weight had greater odds of stunting than children with low birth weight. Like other cross-sectional studies, the present study has several limitations. Because this was a cross-sectional study where data collection was performed at a single point in time, the study is limited in making causal inferences or observing variable changes with time. Even though a robust sampling technique was utilized to ensure that a representative sample was obtained, the DHS survey may be fraught with biases due to errors in measurement, underrepresentation of certain population subgroups, or nonresponse. Additionally, because the DHS allows for self-reporting of some measurements, such as birth weight (which is a key variable in this analysis), the data may face errors of social desirability bias. Furthermore, since the sibling composition variable was generated from both the ‘sons at home’ and ‘daughters at home’ variables, it may bias the results. This is because it is assumed in this study that mothers’ resource allocation is limited to only children living in the same household as their parents. This assumption may not be accurate, especially in instances where mothers allocate resources (food and nutrition) to children who are not presently living in the same household as them. Nevertheless, children living with their mothers are usually considered a good representation for sibling composition analysis. Conclusion and Recommendations This study examined the association between sibling composition and stunting among children under 5 years of age. This study reports the gender effect of siblings on stunting for male and female children. Although having sisters had no effect on stunting for children, having male siblings increased children’s likelihood of being stunted. The findings from this study indicate that in households where there are male children, in addition to the extra nutrients that boys need for healthy growth and development, resource (food) constraints reinforce rivalry and competition between siblings, which may result in unequal access to food and adequate nutrition. Parents and guardians are advised to consciously be aware of the subtle and apparent competition between children and take measures to prevent children’s disproportionate access to nutrition. Abbreviations BMI: Body Mass Index; DHS: Demographic and Health Survey; GDHS: Ghana Demographic and Health Survey; HAZ: Height-for-age Z-scores; LMICs: Low and Middle-Income Countries; SD: Standard Deviation; SSA: Sub-Saharan Africa; WHO: World Health Organization. Declarations Acknowledgements The authors are grateful to Josephine Ackah of the London School of Hygiene and Tropical Medicine for her initial review of the manuscript. The authors also appreciate the Ghana Statistical Service for granting the request to utilise the 2022 GDHS for this paper. Consent to participate Interviewers trained by the Ghana Statistical Service sought consent from participants to participate in the survey. Respondents then agreed to participate in the survey. The data for this study anonymised the respondents so that no personally identifiable information could be traced to individual respondents. Authors’ Contribution PAM conceptualised this study, performed the analysis, and drafted the initial manuscript. RTS reviewed the initial manuscript and contributed to the discussion. AKC supervised and directed the drafting of the manuscript. All three authors read and approved the final manuscript. Funding Not applicable Availability of data and materials The Ghana Statistical Service host the data for the study and shall be given access to upon written request. Ethical approval This study used data from the 2022 GDHS. Ethical clearance for the conduct of the survey was given by the Ethical Review Committee of the Ghana Health Service, and ICF Institutional Review Board. Access to the data was granted by the Ghana Statistical Service. Consent for publication Not applicable Competing interests The authors declare no competing interests. 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Cham: Springer International Publishing; 2021. pp. 295–308. Magvanjav O, Undurraga EA, Eisenberg DTA, Zeng W, Dorjgochoo T, Leonard WR, Godoy RA. Sibling composition and children’s anthropometric indicators of nutritional status: Evidence from native Amazonians in Bolivia. Ann Hum Biol. 2013;40(1):23–34. Chaudhuri A. Impact of Sibling Rivalry on the Nutritional Status of Children: Evidence from Matlab, Bangladesh. SSRN J. 2008. https://doi.org/10.2139/ssrn.1158815 . Helfrecht C, Meehan CL. Sibling effects on nutritional status: Intersections of cooperation and competition across development: sibling effects on nutritional status. Am J Hum Biol. 2016;28(2):159–70. Kramer KL, Veile A, Otárola-Castillo E. Sibling Competition & Growth Tradeoffs. Biological vs. Statistical Significance. Sear R, editor. PLoS ONE. 2016;11(3):e0150126. Bundy DAP, De Silva N, Horton S, Jamison DT, Patton GC, editors. Disease Control Priorities, Third Edition: Child and Adolescent Health and Development [Internet]. Vol. 8. The World Bank; 2017. http://elibrary.worldbank.org/doi/book/ 10.1596/978-1-4648-0423-6 . Graber EG. Physical Growth of Infants and Children. MSD Manual Professional Version. 2023. https://www.msdmanuals.com/professional/pediatrics/growth-and-development/physical-growth-of-infants-and-children?query=physical%20growth%20of%20infants%20and%20children . De Sanctis V, Soliman A, Alaaraj N, Ahmed S, Alyafei F, Hamed N. Early and Long-term Consequences of Nutritional Stunting: From Childhood to Adulthood. Acta Biomed. 2021;92(1):e2021168. Amare ZY, Ahmed ME, Mehari AB. Determinants of nutritional status among children under age 5 in Ethiopia: further analysis of the 2016 Ethiopia demographic and health survey. Global Health. 2019;15(1):62. Boah M, Azupogo F, Amporfro DA, Abada LA. The epidemiology of undernutrition and its determinants in children under five years in Ghana. Zereyesus Y, editor. PLoS ONE. 2019;14(7):e0219665. Kumwenda C, Zgambo LM, Umugwaneza M, Nthani D, Silavwe HN, Audain K. Breastfeeding Duration Is Associated With Growth Among Children Aged 0 to 23 Months; Analysis of the Zambia 2018 Demographic and Healthy Survey Data. 2021. https://www.researchsquare.com/article/rs-148598/v1 . Syeda B, Agho K, Wilson L, Maheshwari GK, Raza MQ. Relationship between breastfeeding duration and undernutrition conditions among children aged 0–3 Years in Pakistan. Int J Pediatr Adolesc Med. 2021;8(1):10–7. Jana A, Dey D, Ghosh R. Contribution of low birth weight to childhood undernutrition in India: evidence from the national family health survey 2019–2021. BMC Public Health. 2023;23(1):1336. Aboagye RG, Ahinkorah BO, Seidu AA, Frimpong JB, Archer AG, Adu C, Hagan JE, Amu H, Yaya S. Birth weight and nutritional status of children under five in sub-Saharan Africa. PLoS ONE. 2022;17(6):e0269279. Ntenda PAM. Association of low birth weight with undernutrition in preschool-aged children in Malawi. Nutr J. 2019;18(1):51. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Dec, 2024 Read the published version in BMC Nutrition → Version 1 posted Editorial decision: Revision requested 22 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviewers agreed at journal 09 Jul, 2024 Reviews received at journal 06 Jul, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviewers agreed at journal 22 Jun, 2024 Reviewers invited by journal 20 Jun, 2024 Editor invited by journal 19 Jun, 2024 Editor assigned by journal 19 Jun, 2024 Submission checks completed at journal 19 Jun, 2024 First submitted to journal 18 Jun, 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. 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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-4601625","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322428593,"identity":"3f8136b8-3859-4a12-970b-36a8cc094a8d","order_by":0,"name":"Peter Annor Mensah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYHADxvYPH4AUGztBlcxwxjHGGSAtzHgUo2lhS2PmQRHAAczZ+w8+rqi5k8cvfcbssc2vbfJ8zAyMHz7m4NZi2XOY2fDMsWfFkn055sa5fbcN25gZmCVnbsOtxeBGMptkA9vhxA1neAykc3tuMwK1sDHz4tNy/zH7z4Z/UC2WPbftCWu5wczG2NgG0sKWJs3w43YiQS2WPcnGko19hxNn9jAfNuxtuJ3cxszYjNcv5uwHH35s+HY4sZ+HsfHBjz+3bee3Nx/88BGfw1B4jG1gsgG3egwtDH/wKh4Fo2AUjIIRCgCYTVGduTX2gAAAAABJRU5ErkJggg==","orcid":"","institution":"Regional Institute for Population Studies, University of Ghana, Legon","correspondingAuthor":true,"prefix":"","firstName":"Peter","middleName":"Annor","lastName":"Mensah","suffix":""},{"id":322428595,"identity":"aa88ddd8-a4b4-4ed9-96b0-59f0995d030a","order_by":1,"name":"Ruth Tobi Sawyerr","email":"","orcid":"","institution":"Regional Institute for Population Studies, University of Ghana, Legon","correspondingAuthor":false,"prefix":"","firstName":"Ruth","middleName":"Tobi","lastName":"Sawyerr","suffix":""},{"id":322428597,"identity":"e058c130-c7f9-4000-b40c-5933e618c3f4","order_by":2,"name":"Aaron Kobina Christian","email":"","orcid":"","institution":"Regional Institute for Population Studies, University of Ghana, Legon","correspondingAuthor":false,"prefix":"","firstName":"Aaron","middleName":"Kobina","lastName":"Christian","suffix":""}],"badges":[],"createdAt":"2024-06-18 18:03:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4601625/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4601625/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40795-024-00969-0","type":"published","date":"2024-12-06T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70964785,"identity":"24b4cc9a-5b32-4bbb-8388-ea6e33c7c5aa","added_by":"auto","created_at":"2024-12-09 16:15:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":850563,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4601625/v1/2116cc0a-382d-474d-8ec7-38001e26bd94.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sibling Gender Dynamics and Childhood Malnutrition in Ghana","fulltext":[{"header":"Background","content":"\u003cp\u003eThe evidence provided in the literature regarding the economic and public health burden of childhood malnutrition is staggering. Childhood stunting annually costs the private sector in 95 low- and middle-income countries (LMICs) a minimum of USD 135.4\u0026nbsp;billion in sales, while private sector workers in East Asia and the Pacific alone lose approximately USD 16.5\u0026nbsp;billion due to stunting (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). From a broader perspective, it is estimated that childhood malnutrition among children can cost countries 4\u0026ndash;11% of their GDP (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Childhood stunting can cause irreparable and long-lasting damage to the cognitive function and neurodevelopment of children while impacting them later in adulthood. Compared to a stunted child's growth, a well-nourished child achieves a greater number of years in education, gains improved learning outcomes, and reaches higher income levels in adulthood, thus increasing the likelihood of avoiding poverty (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, of the 21% (149\u0026nbsp;million) of stunted children under the age of five, more than 90% live in LMICs (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although sub-Saharan Africa (SSA) is making headways towards stemming childhood stunting in the region, the trend is believed to be too slow to meet global targets (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Currently, the average prevalence of stunting among children under 5 years of age is 40% in SSA (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Similarly, evidence from the Ghana Demographic and Health Survey over the years suggests a consistent decrease in the prevalence of stunting in the country. However, the evidence further indicates that aside from the significant decline observed between 2008 and 2014 (from 28\u0026ndash;19%) (6;7), the period between 2014 and 2022 witnessed a marginal decline (from 19\u0026ndash;18%) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) in stunting prevalence. The evidence on stunting prevalence in Ghana is troubling, particularly amid numerous interventions to address it.\u003c/p\u003e \u003cp\u003eThe literature on the correlates of stunting in SSA has predominantly focused on maternal and broader inter and intra-household factors, with little focus on the sex composition of a child\u0026rsquo;s siblings. The argument for the consideration of the sibling gender effect in stunting research is based on findings from previous research suggesting that sibling composition influences a child\u0026rsquo;s health outcomes (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Previous research has attempted to provide reasons for siblings\u0026rsquo; gender considerations in child nutritional studies. For instance, in their first-of-its-kind study of sibling rivalry and child health outcomes in Ghana, Garg and Morduch found that the concept of \u0026ldquo;spillover/reference effects\u0026rdquo; matters significantly in explaining patterns of child health (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). They further explain that there may be a spillover effect when parents attempt to instil more masculine traits (enhanced physical activity and greater self-confidence) in their daughters when they have at least one brother. This influences the treatment given to girls with brothers compared to girls with sisters. On the other hand, the reference effect may occur such that different treatment is given to girls with only brothers compared to girls with at least one sister. In the absence of sisters, a single daughter might conceivably be treated similarly to male offspring within the family context; however, differences may become more noticeable with the inclusion of another girl in the family structure, thereby changing the criteria used to assess differential treatment (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, Raj et al. argue that in contexts where preference for a particular sex is paramount, parents tend to invest differently in the gender that is deemed to be valuable (either economically or socially) to the parents (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Considering that most of the ethnic groups in Ghana practice the patrilineal system of inheritance, which forms a key component of traditional families, it is expected that there might be a preference for a male child compared to a female child. As a result, parents might want to invest household resources differently among children of opposite sexes based on the value (social and economic) they ascribe to their children (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the limited evidence on the influence of sibling composition on child nutritional outcomes in Ghana and the potential impact of the sibling composition of a child on health outcomes, this research aimed to provide new evidence on the role of the gender of a sibling in influencing childhood stunting in Ghana using representative data. This study is relevant to the African context due to the uniqueness of cultural systems relating to gender norms, roles and expectations that influence the differential treatment (caregiving) and socialization of children. Examining the relationship between a child's nutritional status and sibling composition is important because it can offer valuable insights into the gender dynamics within households, which impact the short- and long-term health and economic outcomes of household members.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSource of Data\u003c/h2\u003e \u003cp\u003eThis analysis used data from the 2022 Demographic and Health Survey (DHS) of Ghana. The demographic and health survey is a nationally representative survey that collects data on basic health and demographic indicators in developing countries. The DHS enables policymakers to measure trends and statuses of health and demographic indicators. The survey also helps governments and development actors streamline population and health interventions. The 2022 GDHS employed a stratified two-stage sampling design to select respondents. The first stage involved the selection of 618 clusters obtained using the 2021 Population and Housing Census as a sampling frame. Equal probability systematic random sampling was used to select the clusters. The second stage also involved the listing of households from which the survey respondents were selected. A total of 15,014 women aged 15\u0026ndash;49 years and 15\u0026ndash;59 years were interviewed. The 2022 GDHS gathered information on various population and health indicators, such as maternal and child health, nutritional status of children, and fertility levels. This analysis used data from the children\u0026rsquo;s files since the unit of analysis was children younger than 5 years. The children's files contained 9353 responses; however, only 4412 children. The 4,412 children included in the study met specific criteria: their height-for-age Z scores were recorded during data collection, and they were either the only child of their mothers or had only male or only female siblings. To accurately assess the nuanced gender effects of siblings on childhood stunting, children with both male and female siblings were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eDependent variable\u003c/h2\u003e \u003cp\u003eChildhood stunting as the outcome variable was measured by the height-for-age index, which was compared with the World Health Organization (WHO) growth standard reference population (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The height-for-age index is expressed in standard deviation units (z scores) from the median of the reference population. Therefore, a child is considered stunted when the height-for-age Z score (HAZ) is less than \u0026minus;\u0026thinsp;2 standard deviations (\u0026lt; -2 SD) from the median of the reference population. Childhood stunting was thus categorized as stunted (HAZ \u0026lt; -2 SD) or not stunted (HAZ \u0026gt;= -2 SD). Height measurements were taken using the ShorrBoard measuring board. Recumbent length was measured for children younger than 24 months, and the heights of older children were measured while standing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eIndependent variables\u003c/h2\u003e \u003cp\u003eThe main independent variable \u0026ndash; sibling composition \u0026ndash; was measured by the sex of the sibling(s) of the index child. This variable was generated from the \u0026lsquo;daughters at home\u0026rsquo; and \u0026lsquo;sons at home\u0026rsquo; variables in the children\u0026rsquo;s files. In the present study, sibling composition was categorized as \u0026ldquo;only child\u0026rdquo;, \u0026ldquo;brothers only\u0026rdquo;, or \u0026ldquo;sisters only\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eControl variables\u003c/h2\u003e \u003cp\u003eThe control variables were grouped under three broad categories: child, maternal, and household context factors. The child factors included the sex of the child (male or female), breastfeeding duration (categories: still breastfeeding, ever breastfed, not currently breastfeeding, or never breastfed), birth weight, and birth order number. Birth weight was measured in grams and categorized as low (\u0026lt;\u0026thinsp;2500 grams), normal (2500\u0026ndash;4000 grams), or high (\u0026gt;\u0026thinsp;4000 grams), and birth order number had the following categories: 1st, 2nd, 3rd, 4th, 5th+. The maternal factors included maternal body mass index (BMI), maternal education (categories: no education, primary, secondary, higher), and parity (categorized as 2 or fewer, 3\u0026ndash;5, and 6+). Maternal BMI was measured by the mother\u0026rsquo;s weight (in kilograms) divided by her height (measured in meters squared). Thus, maternal BMI was categorized as underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal weight (18.5\u0026ndash;25 kg/m\u003csup\u003e2\u003c/sup\u003e), or obese (\u0026gt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe household context factors included household size [categorized as small (i.e., household membership less than 3), medium (i.e., household membership between 3 and 6), and large (i.e., household membership of more than 6)], place of residence (categorized as rural and urban), water supply (categorized as improved and unimproved), toilet facility (categorized as improved and unimproved), and household wealth status. Household wealth status measures the cumulative standard of living of households. It is computed using data on household assets (bicycles and televisions), the materials used for house construction, the type of water access, and sanitation facilities, using principal component analysis (PCA). The household wealth variable was categorized as poorest, poorer, middle, richer, or richest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMethods of analysis\u003c/h2\u003e \u003cp\u003eAnalysis was performed on 4412 mother-child dyads at two main levels: bivariate and multivariate. The bivariate analysis involved assessing the relationship between sociodemographic factors and stunting using cross-tabulations. The Pearson chi-square test of association was further performed to test the significance of the association between the independent/control variables and the dependent variable.\u003c/p\u003e \u003cp\u003eBinary logistic regression was employed to examine the extent to which the independent variable influenced the dependent variable after controlling for other predictor variables. Two separate adjusted models were fitted: the composite model, which included the \u0026lsquo;\u003cem\u003esex of child\u0026rsquo;\u003c/em\u003e variable, and the sex-stratified model, which excluded the \u0026lsquo;\u003cem\u003esex of child\u0026rsquo;\u003c/em\u003e variable. In each of the models, one category of each of the nominal variables was used as the reference category against which the remaining categories of the variables were compared. The results from the binary logistic regression analysis are presented as odds ratios (ORs) together with 95% confidence intervals (CIs) and p values. A test of multicollinearity was performed, and the results showed no evidence of collinearity among the variables. Analyses were performed using the R programming language.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the two-way bivariate results for stunted children as well as their distribution across sociodemographic characteristics. Pearson\u0026rsquo;s chi-square test was performed to determine the associations between the independent variables and stunting. An 18% stunting prevalence was recorded from the analysis. The results from Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show that a greater proportion of stunted children had only male siblings (21%), while 16% of children with only sisters were stunted. Stunting was more prevalent among boys (20%) than among girls (15%). A greater percentage of children who had never been breastfed (28%) than of those who were still being breastfed (17%) had stunting. In terms of birth weight and maternal BMI, children with low birth weight and those born to underweight mothers accounted for the greatest proportion of stunted children (29% and 28%, respectively) among their peers. Additionally, the prevalence of stunting decreases with improvements in mothers\u0026rsquo; education, as does household wealth. However, stunting was more prevalent among children from richer households than among those from middle-class households. Moreover, children from households with improved toilet facilities are less stunted than are children from households with unimproved toilet facilities (16% and 21%, respectively). The chi-square test revealed that all sociodemographic characteristics, except birth order number, parity, household size, and water supply, were significantly associated with stunting.\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\u003eDistribution of stunted children by sociodemographic characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eStunted Children\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep value\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSibling Composition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnly Child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrothers Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSisters Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBreastfeeding Duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEver breastfed, not currently breastfeeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever breastfed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStill breastfeeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBirth Weight (grams)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBirth Order Number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMother's BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMaternal Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 or fewer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlace of Residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWater Supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnimproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eToilet Facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnimproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHousehold Wealth Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStunting Prevalence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;\u003cem\u003ePearson\u0026rsquo;s Chi-squared test; Abbreviations/Symbols: n\u0026thinsp;=\u0026thinsp;Number; % = percent\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Analysis of 2022 GDHS\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the binary logistic regression analysis results to assess the association between sibling composition and childhood stunting after controlling for other predictor variables. The results of the composite model, which included both male and female children, showed that having only male siblings was significantly associated with childhood stunting (p-value 0.009). Thus, children with brothers only were more likely to be stunted compared to those who were the only children (OR: 1.84; 95% CI: 1.17, 2.92). However, having only female siblings was not significantly associated with childhood stunting. Similar results were also observed in the sex-stratified models. The results from the male sex-adjusted model suggest that compared to male children who are also the only children of their mothers, male children with only male siblings were more likely to be stunted (OR: 1.96; 95% CI: 1.10,3.54). Likewise, female children with only male siblings are 2.17 times as likely as those who are the only children of their mothers. Remarkably, having female siblings was not associated with childhood stunting in any of the three models.\u003c/p\u003e \u003cp\u003eAdditionally, the sex of the child was significantly associated with childhood stunting (p-value 0.003). Compared with female children, male children were more likely to be stunted (OR: 1.49; 95% CI: 1.15, 1.94). Breastfeeding duration was more a significant predictor of childhood stunting among females than among males. It is observed from the female sex-stratified model that compared to female children who had ever breastfed and were not currently being breastfed, female children who had never been breastfed were 1.68 times more likely to be stunted. On the other hand, female children who were still breastfeeding were 7.78 times more likely to be stunted than were their counterparts who had ever been breastfed and were not currently breastfeeding.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate model showing the association with childhood stunting\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eAdjusted Model: Composite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eAdjusted Model: Sex-Stratified (Male)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eAdjusted Model: Sex-Stratified (Female)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCategories\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"15\" rowspan=\"16\"\u003e \u003cp\u003e\u003cb\u003eChild Factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSibling Composition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOnly Child **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrothers Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.17, 2.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[1.10, 3.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[1.00, 4.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSisters Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.89, 2.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.74, 2.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.75, 3.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex of Child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.15, 1.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBreastfeeding Duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEver breastfed, not currently breastfeeding **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNever breastfed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.00, 1.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.82, 1.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[1.06, 2.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStill breastfeeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.43, 4.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.50, 2.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[3.13, 19.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBirth Weight (grams)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.40, 3.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[1.17, 3.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[1.34, 4.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.71, 1.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.47, 1.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.80, 2.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBirth Order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1st **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.33, 0.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.34, 1.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.16, 0.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.50, 1.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.27, 1.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.55, 5.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.41, 2.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.14, 1.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.58, 7.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.19, 1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.09, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.19, 4.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e\u003cb\u003eMaternal Factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMaternal BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal Weight **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.38, 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.19, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.45, 2.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.45, 0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.48, 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.23, 0.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.87, 2.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.65, 2.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.90, 4.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMaternal Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Education **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.98, 6.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.48, 4.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[1.14, 121]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.83, 5.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.29, 3.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[1.27, 132]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.81, 5.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.47, 4.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.81, 77.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 or fewer **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.34, 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.41, 1.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.12, 0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.18, 1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.18, 4.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.05, 2.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003e\u003cb\u003eHousehold Context Factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlace of Residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56, 1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.59, 1.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.40, 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLarge **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.82, 1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.67, 1.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.78, 2.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.57, 1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.37, 1.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.66, 2.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWater Supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImproved **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnimproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.03, 1.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.83, 1.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.96, 2.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eToilet Facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImproved **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnimproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.80, 1.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.70, 1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.73, 2.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHousehold Wealth Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRichest **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.30, 8.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[1.19, 14.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.62, 13.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.31, 8.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[1.11, 13.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.72, 13.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.65, 4.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.50, 5.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.40, 7.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.88, 5.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[1.00, 11.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.35, 6.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c12\" namest=\"c3\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;OR\u0026thinsp;=\u0026thinsp;Odds Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval, ** = Reference Category\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eSource: Analysis of 2022 GDHS\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBirth weight is significantly associated with childhood stunting. According to the composite model, compared to children with low birth weights, children with normal birth weights are 2.12 times more likely to be stunted. High birth weight, however, was not associated with stunting. Additionally, children of overweight mothers are 34% less likely to be stunted than are children of mothers with normal weight. Furthermore, belonging to a household with an unimproved water supply was significantly associated with childhood stunting (p value\u0026thinsp;=\u0026thinsp;0.03). Compared to children from the richest households, the likelihood of stunting among children from the poorest and poorer households is not significantly different. Specifically, compared to children from the richest households, children from the poorest households are 3.12 times more likely to be stunted. However, compared to children from the richest households, children from poorer households are also more likely to be stunted (OR: 3.09; 95% CI: 1.31, 8.26). Moreover, maternal education, parity, place of residence, household size, and toilet facility did not show any significant relationships with childhood stunting.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study sought to examine the association between sibling composition (in terms of sex) and chronic malnutrition among children in Ghana. It further aimed to answer the question of whether having siblings of either the same or opposite sex matters in stunting analysis. Evidence from this study suggests that sibling composition influences stunting among children under the age of 5. This finding is consistent with the findings of previous studies on the role of siblings’ sex in influencing chronic malnutrition(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The analysis further revealed that, unlike having only female siblings, having only male siblings significantly predicts stunting among children under 5 years of age. Specifically, children with only male siblings have a heightened risk of being stunted. However, having female siblings was not a threat of being stunted. This is in stark contradiction to the findings of prior research by Raj et al., which suggested a lower risk of stunting among boys with many male siblings than among girls with many female siblings (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Their research suggested that having more female siblings increased girls’ odds of being stunted, which is inconsistent with the findings of the present study.\u003c/p\u003e \u003cp\u003eIn an attempt to understand the nuanced relationship between sibling composition and childhood stunting, a sex-stratified analysis was performed for both male and female children. In the context of childhood stunting, having only male siblings was a problem for both male and female children, which is consistent with the findings from the composite model. This finding also agrees with findings from a study by Chaudhuri that examined the effect of sibling sex on nutritional outcomes in children (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The study revealed that having male siblings negatively impacted both boys and girls, even though the impact was more severe among boys than girls. Chaudhuri explains that in households where there are only male children, there is strong competition for household resources, including food and nutrition (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). A plausible explanation is given by Helfrecht \u0026amp; Meehan, Kramer et al., and Magvanjav et al., who opine that there is fierce competition for resources, including nutrition, among children, especially among those from resource-constrained households (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the Ghanaian cultural context, where the majority of ethnic groups practice the patrilineal inheritance system and where priority is given to male children, parents might want to invest greater resources such as attention, food, and nutrition in male children at the expense of female children because they (male children) are thought to be successors and future breadwinners of their families. Additionally, in all-male-children families, parents may invest scarce resources equally in their children. As is well articulated by Helfrecht and Meehan, the seemingly equal treatment given to male children in a household tends to deepen their rivalry, extending to competition over scarce resources (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Biologically, boys and girls experience significant growth and development during childhood (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, boys tend to grow faster in height, weight, and muscle development (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This rapid growth necessitates additional nutrients for tissue building and repair (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). As a result, male children may require more energy and nutrients to support their growth and development. The need for the nutrients required for healthy growth and development among boys may exacerbate competition for scarce resources such as food, especially in resource-constrained households (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdding to the hypothesized objectives of the gendered influences of siblings on chronic childhood malnutrition, this study documents some of the child, maternal, and household context factors that put children at risk of being stunted. Consistent with prior research, the sex of the child (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), breastfeeding duration (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and birth weight (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) were the child factors that showed a significant association with childhood stunting. Specifically, while male children had greater odds of being stunted than female children, children with normal birth weight had greater odds of stunting than children with low birth weight.\u003c/p\u003e \u003cp\u003eLike other cross-sectional studies, the present study has several limitations. Because this was a cross-sectional study where data collection was performed at a single point in time, the study is limited in making causal inferences or observing variable changes with time. Even though a robust sampling technique was utilized to ensure that a representative sample was obtained, the DHS survey may be fraught with biases due to errors in measurement, underrepresentation of certain population subgroups, or nonresponse. Additionally, because the DHS allows for self-reporting of some measurements, such as birth weight (which is a key variable in this analysis), the data may face errors of social desirability bias.\u003c/p\u003e \u003cp\u003eFurthermore, since the sibling composition variable was generated from both the ‘sons at home’ and ‘daughters at home’ variables, it may bias the results. This is because it is assumed in this study that mothers’ resource allocation is limited to only children living in the same household as their parents. This assumption may not be accurate, especially in instances where mothers allocate resources (food and nutrition) to children who are not presently living in the same household as them. Nevertheless, children living with their mothers are usually considered a good representation for sibling composition analysis.\u003c/p\u003e "},{"header":"Conclusion and Recommendations","content":"\u003cp\u003eThis study examined the association between sibling composition and stunting among children under 5 years of age. This study reports the gender effect of siblings on stunting for male and female children. Although having sisters had no effect on stunting for children, having male siblings increased children’s likelihood of being stunted. The findings from this study indicate that in households where there are male children, in addition to the extra nutrients that boys need for healthy growth and development, resource (food) constraints reinforce rivalry and competition between siblings, which may result in unequal access to food and adequate nutrition. Parents and guardians are advised to consciously be aware of the subtle and apparent competition between children and take measures to prevent children’s disproportionate access to nutrition.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: Body Mass Index; DHS: Demographic and Health Survey; GDHS: Ghana Demographic and Health Survey; HAZ: Height-for-age Z-scores; LMICs: Low and Middle-Income Countries; SD: Standard Deviation; SSA: Sub-Saharan Africa; WHO: World Health Organization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to Josephine Ackah of the London School of Hygiene and Tropical Medicine for her initial review of the manuscript. The authors also appreciate the Ghana Statistical Service for granting the request to utilise the 2022 GDHS for this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInterviewers trained by the Ghana Statistical Service sought consent from participants to participate in the survey. Respondents then agreed to participate in the survey. The data for this study anonymised the respondents so that no personally identifiable information could be traced to individual respondents.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePAM conceptualised this study, performed the analysis, and drafted the initial manuscript. RTS reviewed the initial manuscript and contributed to the discussion. AKC supervised and directed the drafting of the manuscript. All three authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ghana Statistical Service host the data for the study and shall be given access to upon written request.\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used data from the 2022 GDHS. Ethical clearance for the conduct of the survey was given by the Ethical Review Committee of the Ghana Health Service, and ICF Institutional Review Board. Access to the data was granted by the Ghana Statistical Service.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkseer N, Tasic H, Nnachebe Onah M, Wigle J, Rajakumar R, Sanchez-Hernandez D, Akuoko J, Black RE, Horta BL, Nwuneli N, Shine R, Wazny K, Japra N, Shekar M, Hoddinott J. Economic costs of childhood stunting to the private sector in low- and middle-income countries. eClinicalMedicine. 2022;45:101320.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorton S, Steckel RH. Malnutrition: Global Economic Losses Attributable to Malnutrition 1900\u0026ndash;2000 and Projections to 2050. In: Lomborg B, editor. 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MSD Manual Professional Version. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.msdmanuals.com/professional/pediatrics/growth-and-development/physical-growth-of-infants-and-children?query=physical%20growth%20of%20infants%20and%20children\u003c/span\u003e\u003cspan address=\"https://www.msdmanuals.com/professional/pediatrics/growth-and-development/physical-growth-of-infants-and-children?query=physical%20growth%20of%20infants%20and%20children\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Sanctis V, Soliman A, Alaaraj N, Ahmed S, Alyafei F, Hamed N. Early and Long-term Consequences of Nutritional Stunting: From Childhood to Adulthood. Acta Biomed. 2021;92(1):e2021168.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmare ZY, Ahmed ME, Mehari AB. Determinants of nutritional status among children under age 5 in Ethiopia: further analysis of the 2016 Ethiopia demographic and health survey. Global Health. 2019;15(1):62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoah M, Azupogo F, Amporfro DA, Abada LA. The epidemiology of undernutrition and its determinants in children under five years in Ghana. Zereyesus Y, editor. PLoS ONE. 2019;14(7):e0219665.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumwenda C, Zgambo LM, Umugwaneza M, Nthani D, Silavwe HN, Audain K. Breastfeeding Duration Is Associated With Growth Among Children Aged 0 to 23 Months; Analysis of the Zambia 2018 Demographic and Healthy Survey Data. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchsquare.com/article/rs-148598/v1\u003c/span\u003e\u003cspan address=\"https://www.researchsquare.com/article/rs-148598/v1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSyeda B, Agho K, Wilson L, Maheshwari GK, Raza MQ. Relationship between breastfeeding duration and undernutrition conditions among children aged 0\u0026ndash;3 Years in Pakistan. Int J Pediatr Adolesc Med. 2021;8(1):10\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJana A, Dey D, Ghosh R. Contribution of low birth weight to childhood undernutrition in India: evidence from the national family health survey 2019\u0026ndash;2021. BMC Public Health. 2023;23(1):1336.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAboagye RG, Ahinkorah BO, Seidu AA, Frimpong JB, Archer AG, Adu C, Hagan JE, Amu H, Yaya S. Birth weight and nutritional status of children under five in sub-Saharan Africa. PLoS ONE. 2022;17(6):e0269279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNtenda PAM. Association of low birth weight with undernutrition in preschool-aged children in Malawi. Nutr J. 2019;18(1):51.\u003c/span\u003e\u003c/li\u003e\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":"Childhood stunting, Sibling composition, Sex of siblings","lastPublishedDoi":"10.21203/rs.3.rs-4601625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4601625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eStunting remains a public health concern in sub-Saharan Africa. Despite the evolving awareness of the effect of family composition on child health outcomes, the influence of sibling gender on stunting has seldom been consistent. The current study investigated the association between sibling composition and stunting among children under 5 years in Ghana.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study utilized data from the most recent Ghana Demographic and Health Survey (GDHS 2022), focusing on 4412 mother-child dyads. Stunting prevalence was assessed through descriptive analysis, while logistic regression analysis was employed to examine the association between sibling composition and identify other risk factors associated with stunting.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of stunting among children under five years of age was 18%. It was observed from the composite and sex-stratified models that having male siblings increased children\u0026rsquo;s odds of being stunted. Furthermore, being a male child (OR: 1.49; 95% CI: 1.15, 1.94) and belonging to a household with an unimproved water supply (OR: 10.9; 95% CI: 1.03, 1.82) increased children's likelihood of stunting.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study revealed that the extra nutrients male children require for healthy growth and development may heighten competition for nutrients, especially in resource-constrained households. Parents and guardians are advised to be consciously aware of the subtle and apparent competition between siblings and take appropriate measures to prevent children\u0026rsquo;s deprivation of nutrition by their male siblings.\u003c/p\u003e","manuscriptTitle":"Sibling Gender Dynamics and Childhood Malnutrition in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-04 11:00:40","doi":"10.21203/rs.3.rs-4601625/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-22T16:35:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-10T18:06:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215111582906940873174503955477682849862","date":"2024-07-09T09:34:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-06T11:06:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199732879105701755055445624130025496082","date":"2024-06-26T10:29:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176422146692724410776853437534843532826","date":"2024-06-22T20:21:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-20T19:48:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-19T15:53:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-19T07:46:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-19T07:45:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2024-06-18T18:00:49+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":"c66c26dc-90a4-4316-af3f-b497433e3091","owner":[],"postedDate":"July 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:04:27+00:00","versionOfRecord":{"articleIdentity":"rs-4601625","link":"https://doi.org/10.1186/s40795-024-00969-0","journal":{"identity":"bmc-nutrition","isVorOnly":false,"title":"BMC Nutrition"},"publishedOn":"2024-12-06 15:57:46","publishedOnDateReadable":"December 6th, 2024"},"versionCreatedAt":"2024-07-04 11:00:40","video":"","vorDoi":"10.1186/s40795-024-00969-0","vorDoiUrl":"https://doi.org/10.1186/s40795-024-00969-0","workflowStages":[]},"version":"v1","identity":"rs-4601625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4601625","identity":"rs-4601625","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}