The Socio-economic, Demographic, and Contextual Predictors of Malnutrition among Children aged 6-59 months in Nigeria

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Walters, Richard Jacques, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3157817/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2024 Read the published version in BMC Nutrition → Version 1 posted 4 You are reading this latest preprint version Abstract Malnutrition has remained a global public health issue, particularly in low- and middle-income countries (LMICs). Researchers have committed to studying malnutrition (especially in children under the age of five) to address the nine malnutrition targets, set by the WHO to be achieved by 2025. With barely three years to the end of target date, there is so much yet undone to meet the target especially in developing countries. In fact, there can be no effective interventions without a strong evidence base. This study seeks to evaluate the prevalence of malnutrition among children aged 6–59 months across Nigeria and its states, as well as its individual and contextual factors. Two separates, independently collected, nationally representative cross-sectional surveys, the National Human Development Report (NHDR 2018) and the 2018 Nigeria Demographic and Health Survey (2018 NDHS) were linked for the study. Spatial map was used to describe the prevalence of malnutrition, a 3-level multivariate multilevel logistic regression models were fitted where children/individuals (at level 1) were nested in communities/clusters (at level 2) and nested in states (at level 3). The results showed that an estimated 43.6% of children aged 6–59 months are poorly nourished in Nigeria. The proportions of poorly nourished children were generally highest in the Northern Nigeria. Child’s gender, age, birth size, preceding birth order, anaemia status, maternal education, work status, body weight, household wealth status, number of bedrooms were among individual/household predictors of malnutrition. On the community level, being from community with high wealth index, distance to nearest health facilities is no big problem. Regional variations and gender inequality index were state level predictors of malnutrition among children in Nigeria. This study has shown that two-third of children aged 6–59 months in Nigeria were poorly nourished. The high rates of constant insecurities that have distorted farming activities in the northern part of the country should be addressed urgently. Figures Figure 1 Figure 2 Introduction Malnutrition which “refers to deficiencies, excesses, or imbalances in a person’s intake of energy and nutrients” [ 1 ], has continued to be a public health concern world over and especially in developing countries [ 2 ]. Over 200 million children under-five are either undernourished or overweight [ 1 ]. Undernutrition in children is contributing about 45% of under-five mortality worldwide [ 1 ]. The World Health Organisation (WHO) member states recently ratified a commitment to nine global targets by 2025, including a 40% decrease in childhood stunting, a prevalence of childhood wasting of less than 5%, a guarantee that the number of overweight children will not rise, and the elimination of all forms of malnutrition by 2030 [ 3 , 4 ]. With less than three years till the goal date, the development has remained minimal, and none of the nine targets have been fully attained by any nation [ 5 ]. Prevalence of malnutrition in under-five years is highest by age classification [ 2 ]. However, in the last two decades, stunting in children has witnessed significant reduction, while overweight is on the increase [ 3 , 4 ]. In Nigeria, the 2013 Nigeria Demographic and Health Survey (NDHS) reported that 37%, 18% and 29% of the children under-five years are stunted, wasted and underweight, respectively [ 5 ]. Understanding how different socio-economic, demographic, and contextual factors determine child malnutrition is a great way to improve the distribution of scare resources for the desired interventions. Variations also exist in the indicators of malnutrition as per the place of residence where urban children are likely to be more nourished than children in rural areas [ 6 , 7 ]. To reach the nine targets, set forth by the WHO on malnutrition by 2025, researchers have committed to investigating malnutrition (particularly in children under the age of five). Effective interventions cannot be created until a solid evidence base is built [ 8 ]. This study aims to assess the prevalence, and its individual and contextual predictors of malnutrition among children aged 6–59 months in Nigeria Methods Source of data This study is a secondary analysis of data from two separate, independently collected, nationally representative cross-sectional surveys. We took the Gender Inequality Index (GII), Human Development Index (HDI) and Multi-dimensional Poverty Index (MPI) out of the National Human Development Report (NHDR 2018) to use as the state variables and were included in the 2018 Nigeria Demographic and Health Survey (2018 NDHS), which is the primary data set. Using a two-stage stratified cluster design on each stratum obtained from the demarcation of the identified enumeration in the 2006 census, samples were independently chosen from each stratum in the NDHS. 1,400 Enumeration Areas (EAs) were chosen as sampling units at the first stage. At the second stage equal probability sampling was used to select 30 households at random from each EA, yielding a target sample size for the survey of 42,000 (30 x 1400) households. Variables definition Outcome or dependent variable In NDHS, the malnutrition status (MNS) of children under-five years was determined through the measurement of anthropometric indices expressed as (i) Stunting: measured as height-for-age Z-score (HAZ) of less than minus two standard deviations from the median of the reference population. It is an indicator of growth retardation. (ii) Wasting: a measure of weight-for-height Z-score (WHZ) of less than minus two standard deviations from the median of the reference population. (iii) Underweight: a measure of weight-for-age Z-score (WAZ) of less than minus two standard deviations from the median of the reference population [ 9 , 10 ]; these are measures of undernutrition. (iv) Overweight is also a measure of weight-for-height Z-score (WHZ) of more than plus (+) two standard deviations above the median of the reference population [ 10 ], considered as a measure of 'overnutrition’. The measure of malnutrition was considered in this study as the outcome of either undernutrition or overnutrition indicators. A recumbent length for children less than two years old was taken lying down, while children two years and above had their height standing up using Shorr Board measuring instrument. On the other hand, the weights of the children were measured with the SECA scale (model 878U) [ 11 ]. In this study, the composite index for malnutrition was computed using the four indicators (stunting, wasting, underweight, and overweight) as a measure of the overall description of malnutrition status among children aged 6–59 months in Nigeria. Children with no trace of anthropometric failure were classified as '0', labelled as 'well nourished', and those that have at least one of the four indicators were classified as '1', labelled as 'poorly nourished' [ 12 – 14 ] Independent variables The predictor variables considered for this study along with full descriptions and classifications have previously been reported [ 15 ]. These variables were grouped into children/individual-related variables to include, child’s sex, age, perceived birth size, order, malaria status, nutritional status, fever, acute respiratory infection status, had diarrhoea, duration of breastfeeding, deworming, iron pills/syrup, and child took vitamin A; parental-related to include, place of delivery; preceding birth interval, maternal religious status, age group, age at first birth, educational status, working status, body mass index, anaemia status, autonomy level, marital status, ante-natal care visit, maternal ethnicity, religious status, maternal iron supplement during pregnancy, and paternal education status and work status; others are household-related to include, household socioeconomic status (wealth index), household size, number rooms for sleeping, number of under-five in the household, age and sex of household head, under-five slept under bed net last; and community-related variables to include, household region of residence, place of residence, community distance to health facility; and area-related variables to include, the state multidimensional poverty index, and the state human development index. Statistical analysis At the first level of analysis, charts and spatial map were used to describe the prevalence of malnutrition. At the second level, multivariate multilevel logistic regression models were fitted to determine the predictors of malnutrition status among 6–59 months of age in Nigeria due to the complex/hierarchical nature of the data sets, where children/individuals (at level 1) are nested in communities/clusters (at level 2) and nested in states (at level 3). The 3-level model was found to be more appropriate than the 2-level model and single-level model using a likelihood ratio test. Results Prevalence of malnutrition Four indicators were used to establish the nutrition status of the children: stunting, wasting, underweight, and overweight. The 'composite of anthropometric failure' was used to classify the children as poorly nourished if the child had any indicators and represented as '1', and well-nourished otherwise. Figure 1 indicates that 56.6% (5931/10481), 95%CI (55.63–57.53) of the children is well nourished. In addition, about 38% (3942/10481), 95%CI (36.7–38.5) of the children aged 6–59 months in Nigeria are stunted, 7% (709/10481), 95%CI (6.30–7.26) are wasted, 22% (2296/10481), 95%CI (21.12–22.71) are underweight, while about 2% (172/10481), 95%CI (1.42–1.91) are overweight. Figure 2 presents the spatial variations in the proportion of children aged 6–59 months in Nigeria that were poorly nourished by states and FCT. The proportions of poorly nourished children were generally highest in the Northern Nigeria- Kebbi, Zamfara, Katsina, Kano, Jigawa, Bauchi and Gombe states. Edo and Lagos states are among the states with the least poorly nourished children. Multilevel analysis of malnutrition status Multilevel multivariate models of predictors of malnutrition status In the first instance, a multicollinearity test was conducted to check for highly correlated predictors. The outcome has already been reported elsewhere [ 16 ]. The backward and forward stepwise procedures were used to test all 48 variables that scaled through the multicollinearity checks. The removal was by p ≥ 0.20 for backward, and entry was p < 0.20 for stepwise forward methods, and then combined the outcomes of both forward and backward stepwise methods. Furthermore, the goodness of fits (Table 1 ) from these three methods using AIC and BIC shows that the combination of the backward and forward stepwise variables yielded the least AIC (9124.917). Table 1 Evaluation of goodness of fit for variables selection methods in malnutrition Goodness of fit Backward stepwise Forward stepwise Backward + forward AIC 9262.6 9154.846 9124.917 BIC 9729.8 9635.162 9639.811 Given above, 31 potential variables (child's age, sex, birth size, preceding birth interval, child took iron syrup in the last six months before the survey, duration of breastfeeding, malaria status, anaemia status, cough status, diarrhoeal status, and child's place of delivery, mother lives with partner, maternal education status, maternal ethnic group, maternal religious status, paternal education status, maternal body weight status, maternal anaemia status, household wealth, household head age group, number of bedrooms, low cluster wealth level, cluster distance to a health facility is no big problem, low cluster maternal education level, low cluster household with bed net, state human development index, state gender inequality index, region of residence, and place of residence), were finally retained for the multilevel model building. Model Set-up Given the complex/hierarchical nature of the data sets, such that children/parental/household in individual units at Level-1 (since children from the same parent, and household tend to be more similar than children from other households because they share the same characteristics) [ 16 ], are nested in communities/clusters at Level-2 and nested in states at Level-3, multivariate multilevel logistic regression models were fitted to determine the predictors of malnutrition status among 6–59 months of age in Nigeria. Furthermore, a likelihood ratio test was carried out to establish that the three-level model was more appropriate than the two-level model (the likelihood-ratio test is LR χ2 = 448.73, p < 0.001 for Level-2 nested in Level-3). Model Building The analysis built four multilevel logistic models. Model 1 is a null model (or empty model) with no predictors. The essence is to measure the variations across the communities and the states. Model 2 (level-1 factors only. Model 3 contains level-1 and level-2 factors only; that is, community-related variables were added to Model 2; Model 4 (full model) was derived for all the selected variables, including the area-related variables (level-3 variables). Intra-class correlation coefficients (both the communities and the states), variance partition coefficients (both for the communities and states), and median odds ratios (MOR) were the post-estimation techniques used to evaluate the models. The goodness of fit was determined using Log-likelihood (LLH), Akaike's information criteria (AIC) and Bayesian information criteria (BIC), such that the model with the highest LLH and lowest AIC and/or BIC was chosen as the best fit [ 16 ] Multilevel Model Results This section presents the results from the analysis using multilevel mixed-effects logistic regression. A weighted total sample of 7770 children were analysed. These result into 1361 communities (level 2) with an average of 6 persons per community, nested in 37 states (level 3) with the number of children ranging between 92 and 373 and on average of 210 children per state. A Measure of Variation (Random Effects) Model 1 is the null model (no predictors) with the random effect showing that the proportions of the total variations due to differences in the communities and the states were, respectively, 0.322 (95%CI: 0.234 to 0.443) and 0.595 (95%CI: 0.367 to 0.966), while the variance due to individual level is 3.29 (π 2 /3), which is fixed for logit. Therefore, the variations in the prevalence of malnutrition status among children aged 6–59 months in Nigeria due to the three-level factors were assessed through an intrastate correlation coefficient of 0.1415 (95% CI: 0.092–0.210) and intracommunity correlation coefficient of 0.218 (95% CI: 0.165–0.281), indicating that 14.15% and 21.8% of the total variation in the odds of malnutrition were respectively due to state and community levels. Therefore, the variance partition coefficient (VPC) at the state level corresponds with the ICC at the state level. However, the VPC at the community level is 0.076, meaning that 7.6% of the total variance is collectively attributed to both the state and community levels. The performance of models was established using AIC and likelihood ratio. Improvements in model fit were achieved at Model 4 (full model), with AIC = 9118.5 and log-likelihood = − 4490.3. Therefore, this study interpreted the results of model 4. From this chosen model (Model 4) in Table 2 , the ICC at the community level in the null model has dropped from 21.8–4.6% (95% CI:17–25%), meaning the correlation between two children/individuals (unit of analysis) within the same community and the same state is 0.046, and the ICC at state-level also dropped from 14.1–0.1% (95% CI: 0.1–4.4%), both had remained significant. The variance partition coefficient (VPC) is the same as the ICC at the state level (0.002). Nevertheless, the VPC at the community level is 0.045, meaning that 4.5% of the total variance is collectively attributed to both the state and the community levels. Similarly, in model 4, the median odds ratio (MOR) computed for states was 1.077, signifying that there exists a mean difference of the risk of being poorly nourished for two children with the same level-1 characteristics and picked randomly from two states. It means there is a 7.7% increased risk of a child being poorly nourished if he/she moves from one state to another state of increased risk of poorly nourished. Additionally, there is a 46% increased risk of a child being poorly nourished if he/she moves to another community with a higher risk of being poorly nourished. Measures of Association (Fixed Effects) Table 2 presents the results of the adjusted odds ratios (AOR) for the variables used in the analysis after adjusting for the other variables in the model. Model 4 represented the model with the best fit and contained all the variables from the three levels (individual variables at level 1, community variables at level 2 and state/area variables at level 3). Variables at level-1 are child's age, sex, birth size, preceding birth interval; the child took iron syrup in the last six months before the survey, anaemia status, the child had diarrhoea in the last two weeks before the survey, mother currently works, maternal education status, maternal ethnicity, paternal education status, maternal body weight status, household wealth, and several bedrooms in the household were statistically significant predictors of malnutrition among children 6–59 months of age in Nigeria. Among the community-related variables (level 2-related), cluster distance to a health facility is no big problem and was a statistically significant predictor of malnutrition. Gender inequality index and region of residence among the area variables were also significant predictors of malnutrition. On the other hand, the child who took iron syrup in the last six months before the survey, the child's duration of breast-feeding status, malaria RDT status, the child had cough two weeks before the survey, and place of delivery were not statistically significant predictors of malnutrition. Also, maternal religious status, maternal anaemia status, household head age group, number of people in the household, community wealth status, the proportion of community maternal education status, proportion of community households with no net, and type of place of residence, were not statistically significant predictors of malnutrition among children aged 6–59 months in Nigeria. The odds of female children being poorly nourished (AOR = 0.74, 95% CI 0.67–0.82) are significantly lower than their male counterparts. Also, the odds of children aged 24–35 months developing malnutrition were 2.22 times the odds of children 6–11 months of age (AOR = 2.22, 95% CI: 1.72–2.86). The smaller the birth size of the children is, the more likely they will be poorly nourished. Children who were born with average birth size (AOR = 1.26, 95% CI: 1.05–1.51), and born with small birth size (AOR = 1.79, 95% CI: 1.45–2.26), had 26% and 79% increased odds of contracting malnutrition when compared with children born with large birth size. Anaemic children (AOR = 1.33, 95% CI: 1.18–1.51) and those who had diarrhoeal (AOR = 1.27, 95% CI: 1.08–1.49) two weeks preceding the survey were more prone to being poorly nourished compared with children who do not have any of these conditions. Table 2 Multilevel multivariate logistic models of predictors of malnutrition with adjusted odds ratios (AOR) among children 6–59 months in Nigeria Model 2 (N = 7770): Level-1 variables (child, parental, and households) Model 3 (N = 7770): Level-1 & 2 variables (Model 2 + community-related) Model 4 (N = 7770): Level 1, 2, & 3 variables (Model 3 + area-related) Variables OR 95%CI OR 95%CI OR 95%CI Child’s sex Male 1.00 1.00 1.00 Female 0.742*** (0.669, 0.823) 0.743*** (0.669, 0.825) 0.742*** (0.669, 0.824) Child's age in group 6–11 months 1.00 1.00 1.00 12–23 months 1.69*** (1.402, 2.036) 1.688*** (1.4, 2.034) 1.664*** (1.381, 2.005) 24–35 months 2.282*** (1.771, 2.941) 2.268*** (1.759, 2.923) 2.219*** (1.722, 2.858) 36–47 months 1.848*** (1.428, 2.393) 1.847*** (1.426, 2.391) 1.791*** (1.384, 2.318) 48–59 months 1.318** (1.014, 1.715) 1.312** (1.008, 1.706) 1.276* (0.982, 1.659) Child's birth size Large 1.00 1.00 1.00 Average 1.256** (1.044, 1.512) 1.259** (1.046, 1.515) 1.26** (1.049, 1.514) Small 1.805*** (1.428, 2.282) 1.809*** (1.431, 2.288) 1.79*** (1.419, 2.26) Preceding birth interval None 1.00 1.00 1.00 8–24 months 1.313*** (1.092, 1.579) 1.32*** (1.098, 1.587) 1.311*** (1.091, 1.575) 25–35 months 1.015 (0.85, 1.212) 1.019 (0.854, 1.217) 1.015 (0.85, 1.211) 36–59 months 0.839* (0.699, 1.008) 0.844* (0.703, 1.014) 0.841* (0.701, 1.011) 60 + months 0.753** (0.591, 0.96) 0.758** (0.594, 0.966) 0.757** (0.594, 0.964) Took Iron supplements No 1.00 1.00 1.00 Yes 1.096 (0.948, 1.268) 1.099 (0.95, 1.27) 1.13* (0.979, 1.304) Duration of breastfeeding Ever breastfed, not currently breastfeeding 1.00 1.00 1.00 Never breastfed 1.169 (0.762, 1.793) 1.155 (0.753, 1.771) 1.185 (0.773, 1.815) Still breastfeeding 0.996 (0.819, 1.211) 0.988 (0.813, 1.202) 0.974 (0.801, 1.184) Malaria RTD status Negative 1.00 1.00 1.00 Positive 1.078 (0.956, 1.216) 1.07 (0.949, 1.207) 1.071 (0.95, 1.207) Anaemia status Not anaemic 1.00 1.00 1.00 Anaemic 1.33*** (1.176, 1.505) 1.325*** (1.172, 1.499) 1.332*** (1.178, 1.506) Child had cough in last 2 weeks before the survey No 1.00 1.00 1.00 Yes 0.926 (0.801, 1.07) 0.932 (0.807, 1.077) 0.899 (0.779, 1.039) Child had diarrheal in last 2 weeks before the survey No 1.00 1.00 1.00 Yes 1.267*** (1.078, 1.49) 1.276*** (1.086, 1.501) 1.267*** (1.08, 1.488) Place of child's delivery Home 1.00 1.00 1.00 Public facility 0.925 (0.801, 1.069) 0.933 (0.807, 1.079) 0.934 (0.809, 1.078) Private facility 0.843* (0.689, 1.032) 0.843* (0.689, 1.033) 0.853 (0.698, 1.042) Elsewhere 0.722 (0.475, 1.098) 0.717 (0.472, 1.091) 0.718 (0.473, 1.09) Mother/Caregiver currently working No 1.00 1.00 1.00 Yes 1.134* (0.997, 1.289) 1.128* (0.992, 1.282) 1.14** (1.005, 1.294) Maternal/caregiver highest educational level No education 1.00 1.00 1.00 Primary 0.94 (0.786, 1.123) 0.938 (0.783, 1.123) 0.972 (0.812, 1.163) Secondary 0.713*** (0.591, 0.859) 0.702*** (0.576, 0.854) 0.709*** (0.583, 0.863) Higher 0.52*** (0.386, 0.701) 0.512*** (0.377, 0.695) 0.513*** (0.378, 0.696) Mother's religious status Catholic 1.00 1.00 1.00 Other Christian 1.076 (0.861, 1.345) 1.093 (0.874, 1.367) 1.107 (0.891, 1.375) Islam 1.206 (0.912, 1.594) 1.247 (0.942, 1.649) 1.203 (0.919, 1.575) Traditionalist & others 0.868 (0.455, 1.656) 0.881 (0.462, 1.679) 1.029 (0.539, 1.966) Maternal ethnicity Hausa/Fulani/Kanuri/Seribiri 1.00 1.00 1.00 Ibos 0.542*** (0.382, 0.768) 0.544*** (0.383, 0.771) 0.633** (0.415, 0.966) Yoruba 0.879 (0.644, 1.201) 0.889 (0.651, 1.212) 1.187 (0.853, 1.651) Others 0.737*** (0.599, 0.908) 0.733*** (0.595, 0.902) 0.831* (0.677, 1.021) Mother's Anaemia status Not Anaemic 1.00 1.00 1.00 Anaemic 1.091 (0.977, 1.218) 1.088 (0.975, 1.215) 1.096 (0.983, 1.223) Maternal body weight status Normal 1.00 1.00 1.00 Underweight 1.267*** (1.064, 1.508) 1.267*** (1.064, 1.509) 1.266*** (1.063, 1.506) Overweight 0.706*** (0.607, 0.82) 0.707*** (0.609, 0.822) 0.71*** (0.611, 0.824) Obese 0.599*** (0.484, 0.741) 0.604*** (0.488, 0.748) 0.596*** (0.481, 0.737) Partner education status No education 1.00 1.00 1.00 Primary education 1.002 (0.829, 1.21) 1.003 (0.83, 1.213) 1.017 (0.842, 1.228) Secondary education 0.96 (0.806, 1.143) 0.968 (0.812, 1.154) 0.965 (0.81, 1.148) Tertiary education 0.789** (0.629, 0.989) 0.797* (0.636, 1) 0.78** (0.623, 0.977) Household wealth index Poorest 1.00 1.00 1.00 Poorer 0.895 (0.751, 1.067) 0.923 (0.773, 1.102) 0.943 (0.792, 1.122) Middle 0.846* (0.698, 1.025) 0.952 (0.77, 1.177) 0.97 (0.787, 1.195) Richer 0.639*** (0.516, 0.791) 0.764 (0.592, 0.986) 0.797* (0.62, 1.026) Richest 0.571*** (0.438, 0.743) 0.687** (0.508, 0.93) 0.732** (0.543, 0.986) Household Head age group Less 34 years 1.00 1.00 1.00 35–44 years 0.945 (0.82, 1.089) 0.945 (0.819, 1.089) 0.946 (0.821, 1.091) 45–55 years 0.857* (0.72, 1.019) 0.861* (0.723, 1.024) 0.863* (0.725, 1.026) 56 years+ 0.899 (0.736, 1.099) 0.913 (0.746, 1.116) 0.922 (0.755, 1.127) Number of bedrooms in household One-room 1.00 1.00 1.00 Two rooms 1.215** (1.049, 1.408) 1.204** (1.038, 1.395) 1.191** (1.028, 1.38) Three rooms 1.286** (1.073, 1.541) 1.264** (1.054, 1.515) 1.227** (1.024, 1.469) Four rooms 1.381*** (1.103, 1.729) 1.347** (1.075, 1.689) 1.336** (1.068, 1.673) Five + rooms 1.207 (0.938, 1.553) 1.179 (0.916, 1.519) 1.158 (0.9, 1.49) Number of people in household 0–3 1.00 1.00 1.00 4–6 0.836 (0.674, 1.036) 0.837 (0.675, 1.038) 0.84 (0.678, 1.041) 7–9 0.921 (0.718, 1.181) 0.921 (0.718, 1.182) 0.928 (0.724, 1.189) 10+ 1.009 (0.761, 1.337) 1.013 (0.764, 1.344) 1.008 (0.761, 1.335) Community wealth level Low 1.00 1.00 High 0.797** (0.657, 0.967) 0.845* (0.696, 1.026) Proportion of community distance to health facility is no big problem Low 1.00 1.00 High 0.918 (0.8, 1.054) 0.867** (0.761, 0.988) Proportion of community maternal education level Low 1.00 1.00 High 1.103 (0.906, 1.344) 1.135 (0.933, 1.381) Proportion of community households with no bed net Low 1.00 1.00 High 0.935 (0.813, 1.076) 0.972 (0.848, 1.113) Gender inequality index by state (GII ) Lowest GII 1.00 Low GII 1.454*** (1.144, 1.849) Average GII 1.066 (0.819, 1.388) High GII 0.814* (0.647, 1.023) Highest GII 1.042 (0.783, 1.387) Region of residence North-central 1.00 North-east 2.272*** (1.773, 2.912) North-west 3.111*** (2.339, 4.138) South-east 1.106 (0.714, 1.712) South-south 0.979 (0.734, 1.306) South-west 1.093 (0.807, 1.481) Type of place of residence Urban 1.00 Rural 1.01 (0.869, 1.173) Intercept 0.527 (0.319, 0.872) 0.547 (0.329, 0.91) 0.316*** (0.182, 0.547) Random effect Community-level variance 0.166 0.098, 0.283 0.165 0.096, 0.282 0.155 0.089, 0.270 State-level variance 0112 0.057, 0.220 0.109 0.054, 0.219 0.006 0.000, 0.159 VPC: community-level 0.047 0.046 0.045 VPC: state-level 0.031 0.031 0.002 ICC: community-level 0.078 0.077 0.047 ICC: state-level 0.031 0.031 0.002 MOR: community 1.475 1.348, 1.661 1.473 1.344, 1.659 1.456 1.329, 1.641 MOR: state 1.376 1.256, 1.564 1.370 1.248, 1.563 1.077 1.014, 1.463 Model fit statistic Log-likelihood -4520.5 -4516.16 -4490.25 AIC 9150.996 9150.317 9118.499 BIC 9533.688 9560.841 9598.603 ***p < 0.001, **p < 0.05, *p < 0.10 AOR: Adjusted Odds Ratios, ICC: Intraclass Correlation Coefficient, VPC: Variance Partition Coefficient, AIC: Akaike Information Criterion (Given a set of candidate models for the data, the preferred model is the one with the minimum AIC value) Similarly, children of working-class mothers have increased odds (AOR = 1.14, 95% CI: 1.01–1.29) of being poorly nourished compared with children whose mothers do not work. The educational levels attained by mothers and fathers are inversely proportional to the odds of their children being poorly nourished. Children of mothers with secondary education (AOR = 0.71, 95% CI: 0.58–0.86), tertiary education (AOR = 0.51, 95% CI: 0.38–0.70), and fathers with tertiary education (AOR = 0.78, 95% CI: 0.62–0.98), were respectively, 29%, 49%, and 22% reduced odds of being poorly nourished compared with children whose mothers and fathers do not have any formal education. The results also show that the richer the household, the less likely the children will be poorly nourished compared with their counterparts in the poorest household. Also, the odds of children residing in a 3-bedroom household (AOR = 1.23, 95% CI: 1.02–1.47) are 1.23 times more likely to be poorly nourished than children residing in a one-bedroom household. Furthermore, among the community-related variables, children from a community where the proportion of the community distance to the nearest health centre is not a big problem are high; there are 17% reduced odds of being poorly nourished compared with children from a community with a low proportion. Moreover, from the area-related variables, the odds of children from a state where the gender inequality index is low (AOR = 1.45, 95% CI: 1.14–1.85) is significantly 1.45 times more likely to be poorly nourished when compared with children from the state with lowest gender inequality index. The odds of children from the North-east (AOR = 2.27, 95% CI: 1.77–2.94), and the North-west (AOR = 3.11, 95% CI: 2.40–4.14), are 127% and 211% significantly more likely to be poorly nourished respectively. Discussion In the random effect analysis, the intrastate correlation coefficient of 0.142 and the intracommunity correlation coefficient (ICC) of 0.218 were used to assess the variations in malnutrition status among children aged 6–59 months in Nigeria due to the three-level factors. These results showed that 14.2% and 21.8% of the total variation in the odds of malnutrition were due to state and community levels, respectively. In addition, the ICC at the state level also decreased from 14.1–0.1%, which was statistically significant. Furthermore, the ICC at the community level in the null model decreased from 21.8–4.6% in the choice model, meaning the correlation between two children/individuals (unit of analysis) within the same community and the same state is 0.046. Similarly, model 4's median odds ratio (MOR) calculated for states was 1.077, indicating that if a child travels from one state to another where there is a greater chance of 7.7% of increased risk of being poorly nourished. In addition, moving to a community with a higher risk of malnutrition increases a child's probability of being poorly nourished by 46%. In the fixed effect analysis, the study found that female children have considerably decreased odds of being poorly nourished than their male counterparts. The same findings were reported in previous studies on malnutrition [ 17 – 30 ], The risks of children aged 24–35 months cohabiting malnutrition were 2.22 times higher than those of children aged 6–11 months. Similar conclusions were reached in previous studies that an increase in the age of the children has a harmful effect on poor nourishment for the children [ 31 – 43 ]. The possible explanation for this difference is that as the child grows older, less attention is paid to their welfare compared with the youngest child. Also, children who had diarrhoea two weeks before the survey, were more likely to be poorly nourished than children who did not have the illnesses. This finding is supported by previous studies [ 32 , 33 , 43 – 46 ]. Similarly, children of working-class mothers are more likely to be poorly nourished than children whose mothers do not work. This agrees with another study [ 47 ]. The possible reason for this is that working class mothers do not breast feed their children much longer compared to mothers who do not work. The odds of being poorly nourished are inversely proportional to the educational status that mothers and fathers have obtained. Other studies substantiated the result on maternal education [ 31 – 34 , 36 , 38 – 40 , 42 , 44 – 46 , 48 – 52 ], and for paternal educational attainment was supported in [ 33 , 39 , 51 ]. The results also show that the richer the household, the less likely the children from such a home will be poorly nourished compared with their counterparts from the poorest household. This finding agrees with other studies [ 31 – 34 , 36 – 40 , 42 – 46 , 48 , 50 – 54 ], and contrary finding with [ 55 ]. In addition, children from communities where many residents do not consider distance to the nearest health facility to be 'no big problem' are 17% less likely to be poorly nourished than children from communities with a low proportion do exist. The odds of children from a state with a low gender inequality index being poorly nourished are significantly 1.45 times higher than those with the highest gender inequality index. The odds of children from the North-east, and the North-west, are 127% and 211%, significantly more likely to be poorly nourished, respectively. Children living in urban areas are more protected from being malnourished than children from rural areas. This result agrees with other previous studies [ 36 , 38 , 42 , 44 , 46 , 48 , 50 , 51 ]. Strengths and limitations of the study This study is based on the analysis of the merged data sets whereby contextual variables from 2018 NHDR were incorporated into the 2018 NDHS. Moreover, the study took into consideration the hierarchical nature of the data set and conducted a 3-state multilevel mixed effect logistic analysis to account for individua, community, and state variabilities. In addition, the study recognised overweight as part of the indicators (stunting, wasting, and underweight) that formed the proxies for malnutrition. There are several restrictions on this study. First, because the survey was cross-sectional, the study could only look at the relationships between factors. Consequently, it was impossible to determine causality. It's also important to note that the random coefficient model in a three-level model was not examined in this study to see if it was superior to the variance component and random intercept model. Additionally, only the individual level of weighting was considered. These are appropriate for further research. Conclusion In general, the Northern Nigerian states of Kebbi, Zamfara, Katsina, Kano, Jigawa, Bauchi, and Gombe had the highest proportions of poorly malnourished children aged 6–59 months. Most of these areas are highly affected by high rates of constant insecurities that have distorted farming activities. Efforts should be made to end these age long insecurities in these areas. The results reported female children are well-nourished compared to their male counterparts. Therefore, gender-sensitive policies and community mobilisation against gender-based bias are required to overcome the discrepancies in malnutrition status between male and female child. Declarations Author Contributions: The conceptualization of this study was done by P.E.O. and K.K.; the formal drafting of the manuscript was carried out by P.E.O., while S.J.W., R.J. and K.K. reviewed, revised, and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This study is an integral part of P.E.O.’s doctoral study at the School of Health and Related Research of the University of Sheffield, United Kingdom. The funding for the doctoral study was granted by TETFUND (Nigeria). Institutional Review Board Statement: The ethical approval to carry out this research study had been granted by the School of Health and Related Research (ScHARR) Ethics Committee of the University of Sheffield (Reference Number: 031534). This study is a secondary analysis of two nationally representative samples. Permission to use the data sets (2018 Nigeria Demographic and Health Survey and 2018 National Human Development Report) had been obtained from two organizations: Inner City Fund (ICF)-International, and United Nations Development Program (UNDP-Nigeria). All methods were carried out in accordance with relevant guidelines and regulations. Informed Consent Statement: Informed consent had been obtained by the original owners of the data sets (MeasureDHS and UNDP-Nigeria). Data Availability Statement: The data set used in this study is available in MeasureDHS https: //dhsprogram.com (accessed on 28 January 2020) and UNDP-Nigeria http://hdr.undp.org/sites/ default/files/hdr_2018_nigeria_finalfinalx3.pdf (accessed on 3 March 2020). Acknowledgments: The authors acknowledged the contributions received from ScHARR community. Phillips appreciate the Rector and the management staff of Niger State Polytechnic, Nigeria, for nominating him for the TETFUND (Nigeria) sponsorship of his doctoral program. Conflicts of Interest: The authors declare no conflict of interest. Consent for publication: Not applicable References World Health Organisation. Fact sheets - Malnutrition, https://www.who.int/news-room/fact-sheets/detail/malnutrition (2020, accessed 31 May 2020). Endris N, Asefa H, Dube L. Prevalence of Malnutrition and Associated Factors among Children in Rural Ethiopia. Biomed Res Int 2017; 2017: 6587853. Global Nutrition Report. The burden of malnutrition, https://globalnutritionreport.org/reports/global-nutrition-report-2018/burden-malnutrition/ (2020, accessed 25 June 2020). Jude CK, Chukwunedum AU, Egbuna KO. Under-five malnutrition in a South-Eastern Nigeria metropolitan city. Afr Health Sci 2019; 19: 3078–3084. National Population Commission (NPC), ICF International. Nigeria Demographic and Health Survey 2013. Federal Republic of Nigeria and MeasureDHS , June 2014. Babatunde RO, Olagunju FI, Fakayode SB, et al. Prevalence and Determinants of Malnutrition among Under-five Children of Farming Households in Kwara State, Nigeria. JAS 2011; 3: p173. National Population C, I. C. F. International. Nigeria Demographic and Health Survey 2003. Federal Republic of Nigeria and MeasureDHS . Kwansa AL, Akparibo R, Cecil JE, et al. Risk Factors for Overweight and Obesity within the Home Environment of Preschool Children in Sub-Saharan Africa: A Systematic Review. Nutrients 2022; 14: 1706. Kandala NB, Lang S, Klasen S, et al. Semiparametric Analysis of the Socio-Demographic and Spatial Determinants of Undernutrition in Two African Countries. LMU 2011; 18. National Population C, I. C. F. International. Nigeria Demographic and Health Survey 2018 . Abuja, Nigeria, and Rockville, Maryland, USA: NPC and ICF, 2019. National Population Commission (NPC)[Nigeria], ICF. Nigeria Demographic and Health Survey 2018. Bamiwuye SO, Wet ND, Adedini SA. Linkages between autonomy, poverty and contraceptive use in two sub-Saharan African countries. African Population Studies 2013; 27: 164–173. Nandy S, Daoud A, Gordon D. Examining the changing profile of undernutrition in the context of food price rises and greater inequality. Social Science & Medicine 2016; 149: 153–163. Nandy S, Jaime Miranda J. Overlooking undernutrition? Using a composite index of anthropometric failure to assess how underweight misses and misleads the assessment of undernutrition in young children. Soc Sci Med 2008; 66: 1963–1966. Obasohan PE, Walters SJ, Jacques R, et al. Risk Factors Associated with Malnutrition among Children Under-Five Years in Sub-Saharan African Countries: A Scoping Review. International Journal of Environmental Research and Public Health 2020; 17: 8782. Obasohan PE, Walters SJ, Jacques R, et al. Individual and Contextual Factors Associated with Malaria among Children 6–59 Months in Nigeria: A Multilevel Mixed Effect Logistic Model Approach. International Journal of Environmental Research and Public Health 2021; 18: 11234. Adekanmbi VT, Uthman OA, Mudasiru OM. Exploring variations in childhood stunting in Nigeria using league table, control chart and spatial analysis. BMC Public Health 2013; 13: 361. Akombi BJ, Agho KE, Hall JJ, et al. Stunting and severe stunting among children under-5 years in Nigeria: A multilevel analysis. BMC Pediatr 2017; 17: 15. Akombi BJ, Agho KE, Renzaho AM, et al. Trends in socioeconomic inequalities in child undernutrition: Evidence from Nigeria Demographic and Health Survey (2003 - 2013). PLoS ONE 2019; 14: e0211883. Amaral MM, Herrin WE, Gulere GB. Using the Uganda National Panel Survey to analyze the effect of staple food consumption on undernourishment in Ugandan children. BMC Public Health 2017; 18: 32. 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. Globalization and Health ; 15. Epub ahead of print 2019. DOI: 10.1186/s12992-019-0505-7. Fantay Gebru K, Mekonnen Haileselassie W, Haftom Temesgen A, et al. Determinants of stunting among under-five children in Ethiopia: A multilevel mixed-effects analysis of 2016 Ethiopian demographic and health survey data. BMC Pediatrics ; 19. Epub ahead of print 2019. DOI: 10.1186/s12887-019-1545-0. Hv D, S N-S. Trends and Determinants of Child Growth Indicators in Malawi and Implications for the Sustainable Development Goals. AIMS Public Health 2017; 4: 590–614. Machisa M, Wichmann J, Nyasulu PS. Biomass fuel use for household cooking in Swaziland: is there an association with anaemia and stunting in children aged 6-36 months? Trans R Soc Trop Med Hyg 2013; 107: 535–44. Magadi MA. Household and community HIV/AIDS status and child malnutrition in sub-Saharan Africa: Evidence from the demographic and health surveys. Soc Sci Med 2011; 73: 436–446. Miller CM, Gruskin S, Subramanian SV, et al. Emerging health disparities in Botswana: examining the situation of orphans during the AIDS epidemic. Social Science & Medicine 2007; 64: 2476–2486. Nankinga O, Kwagala B, Walakira E. Maternal employment and child nutritional status in Uganda, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6922416/ (2019, accessed 11 August 2020). Nshimyiryo A, Hedt-Gauthier B, Mutaganzwa C, et al. Risk factors for stunting among children under five years: a cross-sectional population-based study in Rwanda using the 2015 Demographic and Health Survey. BMC Public Health 2019; 19: 175. Takele K, Zewotir T, Ndanguza D. Understanding correlates of child stunting in Ethiopia using generalized linear mixed models. BMC Public Health 2019; 19: 626. Ukwuani FA, Suchindran CM. Implications of women’s work for child nutritional status in sub-Saharan Africa: a case study of Nigeria. Soc Sci Med 2003; 56: 2109–2121. Aboagye RG, Seidu AA, Ahinkorah BO, et al. Dietary Diversity and Undernutrition in Children Aged 6-23 Months in Sub-Saharan Africa. Nutrients ; 13. Epub ahead of print 28 September 2021. DOI: 10.3390/nu13103431. Adam Birhan N, Bitew Belay D. Associated risk factors of underweight among under-five children in Ethiopia using multilevel ordinal logistic regression model. Afr H Sci 2021; 21: 362–72. Bekele SA, Fetene MZ. Modeling non-Gaussian data analysis on determinants of underweight among under five children in rural Ethiopia: Ethiopian demographic and health survey 2016 evidences. PLoS ONE 2021; 16: e0251239. Kassie GW, Workie DL. Determinants of under-nutrition among children under five years of age in Ethiopia. BMC Public Health 2020; 20: 399. Kebede D, Aynalem A. Prevalence of undernutrition and potential risk factors among children below five years of age in Somali region, Ethiopia: evidence from 2016 Ethiopian demographic and health survey. BMC Nutr 2021; 7: 56. Khamis AG, Mwanri AW, Kreppel K, et al. The burden and correlates of childhood undernutrition in Tanzania according to composite index of anthropometric failure. BMC Nutr 2020; 6: 39. Masibo PK, Humwa F, Macharia TN. The double burden of overnutrition and undernutrition in mother−child dyads in Kenya: demographic and health survey data, 2014. J Nutr Sci 2020; 9: e5. Muche A, Dewau R. Severe stunting and its associated factors among children aged 6-59 months in Ethiopia; multilevel ordinal logistic regression model. Ital J Pediatr 2021; 47: 161. Rutayisire R, Kanazayire C, Tuyisenge G, et al. Trends in the Prevalence and Associated Contributing Factors of Stunting in Children Under Five Years of Age. Secondary Data Analysis of 2005, 2010 and 2014-2015 Rwanda Demographic and Health Surveys. RJMHS 2020; 3: 71–85. Simelane MS, Chemhaka GB, Zwane E. A multilevel analysis of individual, household and community level factors on stunting among children aged 6-59 months in Eswatini: A secondary analysis of the Eswatini 2010 and 2014 Multiple Indicator Cluster Surveys. PLoS One 2020; 15: e0241548. Sserwanja Q, Mutisya LM, Olal E, et al. Factors associated with childhood overweight and obesity in Uganda: a national survey. Bmc Public Health ; 21. Epub ahead of print August 2021. DOI: 10.1186/s12889-021-11567-1. Tesema GA, Yeshaw Y, Worku MG, et al. Pooled prevalence and associated factors of chronic undernutrition among under-five children in East Africa: A multilevel analysis. PLoS One 2021; 16: e0248637. Uwiringiyimana V, Osei F, Amer S, et al. Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden. BMC Public Health 2022; 22: 159. Chikako TU, Seidu A-A, Hagan JE, et al. Complex Multilevel Modelling of the Individual, Household and Regional Level Variability in Predictors of Undernutrition among Children Aged 6–59 Months in Ethiopia. Nutrients 2021; 13: 3018. Muche A, Gezie LD, Baraki AG, et al. Predictors of stunting among children age 6-59 months in Ethiopia using Bayesian multi-level analysis. Sci Rep 2021; 11: 3759. Tesfaw LM, Fenta HM. Multivariate logistic regression analysis on the association between anthropometric indicators of under-five children in Nigeria: NDHS 2018. BMC Pediatr 2021; 21: 193. Amegbor PM, Yankey O, Sabel CE. Examining the Effect of Geographic Region of Residence on Childhood Malnutrition in Uganda. Journal of Tropical Pediatrics 2020; 66: 598–611. Adedokun ST, Yaya S. Factors associated with adverse nutritional status of children in sub-Saharan Africa: Evidence from the Demographic and Health Surveys from 31 countries. Matern Child Nutr 2021; 17: e13198. Amaha ND, Woldeamanuel BT. Maternal factors associated with moderate and severe stunting in Ethiopian children: analysis of some environmental factors based on 2016 demographic health survey. Nutr J 2021; 20: 18. Amoako Johnson F. Spatiotemporal clustering and correlates of childhood stunting in Ghana: Analysis of the fixed and nonlinear associative effects of socio-demographic and socio-ecological factors. PLoS ONE 2022; 17: e0263726. Fenta HM, Zewotir T, Muluneh EK. Spatial data analysis of malnutrition among children under-five years in Ethiopia. Bmc Medical Research Methodology ; 21. Epub ahead of print October 2021. DOI: 10.1186/s12874-021-01391-x. Hailu BA, Bogale GG, Beyene J. Spatial heterogeneity and factors influencing stunting and severe stunting among under-5 children in Ethiopia: spatial and multilevel analysis. Sci Rep 2020; 10: 16427. Amegbor PM, Zhang Z, Dalgaard R, et al. Multilevel and spatial analyses of childhood malnutrition in Uganda: examining individual and contextual factors. Sci Rep 2020; 10: 20019. Musuka GN, Dzinamarira T, Cuadros DF, et al. Mothers’ HIV status and their children’s nutritional status: Insights from secondary analysis of the Zimbabwe Demographic and Health Survey data (2015–2016). Food Sci Nutr 2021; 9: 5509–5516. Wondimu H, Dejene K. Determinants of under-five malnutrition, significant changes, and policy implications in the Ethiopian Demographic Health Survey, 2019. Discov Sustain 2022; 3: 16. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2024 Read the published version in BMC Nutrition → Version 1 posted Editorial decision: Major revision 25 Jul, 2023 Editor assigned by journal 17 Jul, 2023 Submission checks completed at journal 16 Jul, 2023 First submitted to journal 10 Jul, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-3157817","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":218936424,"identity":"ab2929f5-c783-4ec9-af35-43b91a4fc719","order_by":0,"name":"Phillips Edomwonyi Obasohan","email":"data:image/png;base64,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","orcid":"","institution":"University of Sheffield","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Phillips","middleName":"Edomwonyi","lastName":"Obasohan","suffix":""},{"id":218936425,"identity":"e8ce2d34-181c-45a5-b9ba-8fa4f2cf63f3","order_by":1,"name":"Stephen J. Walters","email":"","orcid":"","institution":"University of Sheffield","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"J.","lastName":"Walters","suffix":""},{"id":218936426,"identity":"ce31e198-6cb3-4194-8c4c-8dbd6a8df629","order_by":2,"name":"Richard Jacques","email":"","orcid":"","institution":"University of Sheffield","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Jacques","suffix":""},{"id":218936427,"identity":"27fced56-210c-4908-baba-9adcb800f34a","order_by":3,"name":"Khaled Khatab","email":"","orcid":"","institution":"Sheffield Hallam University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Khaled","middleName":"","lastName":"Khatab","suffix":""}],"badges":[],"createdAt":"2023-07-10 19:14:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3157817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3157817/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40795-023-00813-x","type":"published","date":"2024-01-02T15:00:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":40260762,"identity":"91a390da-c758-4ed0-baff-d71d134cf0df","added_by":"auto","created_at":"2023-07-19 14:40:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100816,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of malnutrition status by indicators\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3157817/v1/51208deefb26a74064372ba3.png"},{"id":40260761,"identity":"2b2d98c6-1740-43ec-8887-a6dd1cd9f89f","added_by":"auto","created_at":"2023-07-19 14:40:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213243,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial maps describing the proportions of poorly nourished children by states \u0026amp; FCT\u003c/p\u003e\n\u003cp\u003eSource: Data computed from Nigeria DHS 2018\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3157817/v1/cf5c328c3c2396341a04c831.png"},{"id":49315337,"identity":"6ec386af-1e68-47e2-9fc4-dcd27d301194","added_by":"auto","created_at":"2024-01-08 15:03:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1603324,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3157817/v1/6a070602-25f0-4801-90df-aa338c9b78f5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Socio-economic, Demographic, and Contextual Predictors of Malnutrition among Children aged 6-59 months in Nigeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalnutrition which \u0026ldquo;refers to deficiencies, excesses, or imbalances in a person\u0026rsquo;s intake of energy and nutrients\u0026rdquo; [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], has continued to be a public health concern world over and especially in developing countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Over 200\u0026nbsp;million children under-five are either undernourished or overweight [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Undernutrition in children is contributing about 45% of under-five mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The World Health Organisation (WHO) member states recently ratified a commitment to nine global targets by 2025, including a 40% decrease in childhood stunting, a prevalence of childhood wasting of less than 5%, a guarantee that the number of overweight children will not rise, and the elimination of all forms of malnutrition by 2030 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. With less than three years till the goal date, the development has remained minimal, and none of the nine targets have been fully attained by any nation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Prevalence of malnutrition in under-five years is highest by age classification [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, in the last two decades, stunting in children has witnessed significant reduction, while overweight is on the increase [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Nigeria, the 2013 Nigeria Demographic and Health Survey (NDHS) reported that 37%, 18% and 29% of the children under-five years are stunted, wasted and underweight, respectively [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Understanding how different socio-economic, demographic, and contextual factors determine child malnutrition is a great way to improve the distribution of scare resources for the desired interventions. Variations also exist in the indicators of malnutrition as per the place of residence where urban children are likely to be more nourished than children in rural areas [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. To reach the nine targets, set forth by the WHO on malnutrition by 2025, researchers have committed to investigating malnutrition (particularly in children under the age of five). Effective interventions cannot be created until a solid evidence base is built [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This study aims to assess the prevalence, and its individual and contextual predictors of malnutrition among children aged 6\u0026ndash;59 months in Nigeria\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSource of data\u003c/h2\u003e \u003cp\u003eThis study is a secondary analysis of data from two separate, independently collected, nationally representative cross-sectional surveys. We took the Gender Inequality Index (GII), Human Development Index (HDI) and Multi-dimensional Poverty Index (MPI) out of the National Human Development Report (NHDR 2018) to use as the state variables and were included in the 2018 Nigeria Demographic and Health Survey (2018 NDHS), which is the primary data set. Using a two-stage stratified cluster design on each stratum obtained from the demarcation of the identified enumeration in the 2006 census, samples were independently chosen from each stratum in the NDHS. 1,400 Enumeration Areas (EAs) were chosen as sampling units at the first stage. At the second stage equal probability sampling was used to select 30 households at random from each EA, yielding a target sample size for the survey of 42,000 (30 x 1400) households.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVariables definition\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eOutcome or dependent variable\u003c/h2\u003e \u003cp\u003eIn NDHS, the malnutrition status (MNS) of children under-five years was determined through the measurement of anthropometric indices expressed as (i) Stunting: measured as height-for-age Z-score (HAZ) of less than minus two standard deviations from the median of the reference population. It is an indicator of growth retardation. (ii) Wasting: a measure of weight-for-height Z-score (WHZ) of less than minus two standard deviations from the median of the reference population. (iii) Underweight: a measure of weight-for-age Z-score (WAZ) of less than minus two standard deviations from the median of the reference population [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; these are measures of undernutrition. (iv) Overweight is also a measure of weight-for-height Z-score (WHZ) of more than plus (+) two standard deviations above the median of the reference population [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], considered as a measure of 'overnutrition\u0026rsquo;. The measure of malnutrition was considered in this study as the outcome of either undernutrition or overnutrition indicators. A recumbent length for children less than two years old was taken lying down, while children two years and above had their height standing up using Shorr Board measuring instrument. On the other hand, the weights of the children were measured with the SECA scale (model 878U) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this study, the composite index for malnutrition was computed using the four indicators (stunting, wasting, underweight, and overweight) as a measure of the overall description of malnutrition status among children aged 6\u0026ndash;59 months in Nigeria. Children with no trace of anthropometric failure were classified as '0', labelled as 'well nourished', and those that have at least one of the four indicators were classified as '1', labelled as 'poorly nourished' [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIndependent variables\u003c/h2\u003e \u003cp\u003eThe predictor variables considered for this study along with full descriptions and classifications have previously been reported [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These variables were grouped into children/individual-related variables to include, child\u0026rsquo;s sex, age, perceived birth size, order, malaria status, nutritional status, fever, acute respiratory infection status, had diarrhoea, duration of breastfeeding, deworming, iron pills/syrup, and child took vitamin A; parental-related to include, place of delivery; preceding birth interval, maternal religious status, age group, age at first birth, educational status, working status, body mass index, anaemia status, autonomy level, marital status, ante-natal care visit, maternal ethnicity, religious status, maternal iron supplement during pregnancy, and paternal education status and work status; others are household-related to include, household socioeconomic status (wealth index), household size, number rooms for sleeping, number of under-five in the household, age and sex of household head, under-five slept under bed net last; and community-related variables to include, household region of residence, place of residence, community distance to health facility; and area-related variables to include, the state multidimensional poverty index, and the state human development index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAt the first level of analysis, charts and spatial map were used to describe the prevalence of malnutrition. At the second level, multivariate multilevel logistic regression models were fitted to determine the predictors of malnutrition status among 6\u0026ndash;59 months of age in Nigeria due to the complex/hierarchical nature of the data sets, where children/individuals (at level 1) are nested in communities/clusters (at level 2) and nested in states (at level 3). The 3-level model was found to be more appropriate than the 2-level model and single-level model using a likelihood ratio test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003ePrevalence of malnutrition\u003c/h2\u003e\n \u003cp\u003eFour indicators were used to establish the nutrition status of the children: stunting, wasting, underweight, and overweight. The \u0026apos;composite of anthropometric failure\u0026apos; was used to classify the children as poorly nourished if the child had any indicators and represented as \u0026apos;1\u0026apos;, and well-nourished otherwise. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e indicates that 56.6% (5931/10481), 95%CI (55.63\u0026ndash;57.53) of the children is well nourished. In addition, about 38% (3942/10481), 95%CI (36.7\u0026ndash;38.5) of the children aged 6\u0026ndash;59 months in Nigeria are stunted, 7% (709/10481), 95%CI (6.30\u0026ndash;7.26) are wasted, 22% (2296/10481), 95%CI (21.12\u0026ndash;22.71) are underweight, while about 2% (172/10481), 95%CI (1.42\u0026ndash;1.91) are overweight.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the spatial variations in the proportion of children aged 6\u0026ndash;59 months in Nigeria that were poorly nourished by states and FCT. The proportions of poorly nourished children were generally highest in the Northern Nigeria- Kebbi, Zamfara, Katsina, Kano, Jigawa, Bauchi and Gombe states. Edo and Lagos states are among the states with the least poorly nourished children.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eMultilevel analysis of malnutrition status\u003c/h2\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003eMultilevel multivariate models of predictors of malnutrition status\u003c/h2\u003e\n \u003cp\u003eIn the first instance, a multicollinearity test was conducted to check for highly correlated predictors. The outcome has already been reported elsewhere [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. The backward and forward stepwise procedures were used to test all 48 variables that scaled through the multicollinearity checks. The removal was by p\u0026thinsp;\u0026ge;\u0026thinsp;0.20 for backward, and entry was \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.20 for stepwise forward methods, and then combined the outcomes of both forward and backward stepwise methods. Furthermore, the goodness of fits (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) from these three methods using AIC and BIC shows that the combination of the backward and forward stepwise variables yielded the least AIC (9124.917).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEvaluation of goodness of fit for variables selection methods in malnutrition\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGoodness of fit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBackward stepwise\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eForward stepwise\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBackward\u0026thinsp;+\u0026thinsp;forward\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9262.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9154.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9124.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9729.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9635.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9639.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eGiven above, 31 potential variables (child\u0026apos;s age, sex, birth size, preceding birth interval, child took iron syrup in the last six months before the survey, duration of breastfeeding, malaria status, anaemia status, cough status, diarrhoeal status, and child\u0026apos;s place of delivery, mother lives with partner, maternal education status, maternal ethnic group, maternal religious status, paternal education status, maternal body weight status, maternal anaemia status, household wealth, household head age group, number of bedrooms, low cluster wealth level, cluster distance to a health facility is no big problem, low cluster maternal education level, low cluster household with bed net, state human development index, state gender inequality index, region of residence, and place of residence), were finally retained for the multilevel model building.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eModel Set-up\u003c/h2\u003e\n \u003cp\u003eGiven the complex/hierarchical nature of the data sets, such that children/parental/household in individual units at Level-1 (since children from the same parent, and household tend to be more similar than children from other households because they share the same characteristics) [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], are nested in communities/clusters at Level-2 and nested in states at Level-3, multivariate multilevel logistic regression models were fitted to determine the predictors of malnutrition status among 6\u0026ndash;59 months of age in Nigeria. Furthermore, a likelihood ratio test was carried out to establish that the three-level model was more appropriate than the two-level model (the likelihood-ratio test is LR \u0026chi;2\u0026thinsp;=\u0026thinsp;448.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for Level-2 nested in Level-3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eModel Building\u003c/h2\u003e\n \u003cp\u003eThe analysis built four multilevel logistic models. Model 1 is a null model (or empty model) with no predictors. The essence is to measure the variations across the communities and the states. Model 2 (level-1 factors only. Model 3 contains level-1 and level-2 factors only; that is, community-related variables were added to Model 2; Model 4 (full model) was derived for all the selected variables, including the area-related variables (level-3 variables). Intra-class correlation coefficients (both the communities and the states), variance partition coefficients (both for the communities and states), and median odds ratios (MOR) were the post-estimation techniques used to evaluate the models. The goodness of fit was determined using Log-likelihood (LLH), Akaike\u0026apos;s information criteria (AIC) and Bayesian information criteria (BIC), such that the model with the highest LLH and lowest AIC and/or BIC was chosen as the best fit [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eMultilevel Model Results\u003c/h2\u003e\n \u003cp\u003eThis section presents the results from the analysis using multilevel mixed-effects logistic regression. A weighted total sample of 7770 children were analysed. These result into 1361 communities (level 2) with an average of 6 persons per community, nested in 37 states (level 3) with the number of children ranging between 92 and 373 and on average of 210 children per state.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eA Measure of Variation (Random Effects)\u003c/h2\u003e\n \u003cp\u003eModel 1 is the null model (no predictors) with the random effect showing that the proportions\u003c/p\u003e\n \u003cp\u003eof the total variations due to differences in the communities and the states were, respectively, 0.322 (95%CI: 0.234 to 0.443) and 0.595 (95%CI: 0.367 to 0.966), while the variance due to individual level is 3.29 (\u0026pi;\u003csup\u003e2\u003c/sup\u003e/3), which is fixed for logit. Therefore, the variations in the prevalence of malnutrition status among children aged 6\u0026ndash;59 months in Nigeria due to the three-level factors were assessed through an intrastate correlation coefficient of 0.1415 (95% CI: 0.092\u0026ndash;0.210) and intracommunity correlation coefficient of 0.218 (95% CI: 0.165\u0026ndash;0.281), indicating that 14.15% and 21.8% of the total variation in the odds of malnutrition were respectively due to state and community levels. Therefore, the variance partition coefficient (VPC) at the state level corresponds with the ICC at the state level. However, the VPC at the community level is 0.076, meaning that 7.6% of the total variance is collectively attributed to both the state and community levels.\u003c/p\u003e\n \u003cp\u003eThe performance of models was established using AIC and likelihood ratio. Improvements in model fit were achieved at Model 4 (full model), with AIC\u0026thinsp;=\u0026thinsp;9118.5 and log-likelihood\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4490.3. Therefore, this study interpreted the results of model 4. From this chosen model (Model 4) in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the ICC at the community level in the null model has dropped from 21.8\u0026ndash;4.6% (95% CI:17\u0026ndash;25%), meaning the correlation between two children/individuals (unit of analysis) within the same community and the same state is 0.046, and the ICC at state-level also dropped from 14.1\u0026ndash;0.1% (95% CI: 0.1\u0026ndash;4.4%), both had remained significant.\u003c/p\u003e\n \u003cp\u003eThe variance partition coefficient (VPC) is the same as the ICC at the state level (0.002). Nevertheless, the VPC at the community level is 0.045, meaning that 4.5% of the total variance is collectively attributed to both the state and the community levels.\u003c/p\u003e\n \u003cp\u003eSimilarly, in model 4, the median odds ratio (MOR) computed for states was 1.077, signifying that there exists a mean difference of the risk of being poorly nourished for two children with the same level-1 characteristics and picked randomly from two states. It means there is a 7.7% increased risk of a child being poorly nourished if he/she moves from one state to another state of increased risk of poorly nourished. Additionally, there is a 46% increased risk of a child being poorly nourished if he/she moves to another community with a higher risk of being poorly nourished.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasures of Association (Fixed Effects)\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the adjusted odds ratios (AOR) for the variables used in the analysis after adjusting for the other variables in the model. Model 4 represented the model with the best fit and contained all the variables from the three levels (individual variables at level 1, community variables at level 2 and state/area variables at level 3). Variables at level-1 are child\u0026apos;s age, sex, birth size, preceding birth interval; the child took iron syrup in the last six months before the survey, anaemia status, the child had diarrhoea in the last two weeks before the survey, mother currently works, maternal education status, maternal ethnicity, paternal education status, maternal body weight status, household wealth, and several bedrooms in the household were statistically significant predictors of malnutrition among children 6\u0026ndash;59 months of age in Nigeria. Among the community-related variables (level 2-related), cluster distance to a health facility is no big problem and was a statistically significant predictor of malnutrition. Gender inequality index and region of residence among the area variables were also significant predictors of malnutrition. On the other hand, the child who took iron syrup in the last six months before the survey, the child\u0026apos;s duration of breast-feeding status, malaria RDT status, the child had cough two weeks before the survey, and place of delivery were not statistically significant predictors of malnutrition. Also, maternal religious status, maternal anaemia status, household head age group, number of people in the household, community wealth status, the proportion of community maternal education status, proportion of community households with no net, and type of place of residence, were not statistically significant predictors of malnutrition among children aged 6\u0026ndash;59 months in Nigeria.\u003c/p\u003e\n \u003cp\u003eThe odds of female children being poorly nourished (AOR\u0026thinsp;=\u0026thinsp;0.74, 95% CI 0.67\u0026ndash;0.82) are significantly lower than their male counterparts. Also, the odds of children aged 24\u0026ndash;35 months developing malnutrition were 2.22 times the odds of children 6\u0026ndash;11 months of age (AOR\u0026thinsp;=\u0026thinsp;2.22, 95% CI: 1.72\u0026ndash;2.86). The smaller the birth size of the children is, the more likely they will be poorly nourished. Children who were born with average birth size (AOR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.05\u0026ndash;1.51), and born with small birth size (AOR\u0026thinsp;=\u0026thinsp;1.79, 95% CI: 1.45\u0026ndash;2.26), had 26% and 79% increased odds of contracting malnutrition when compared with children born with large birth size. Anaemic children (AOR\u0026thinsp;=\u0026thinsp;1.33, 95% CI: 1.18\u0026ndash;1.51) and those who had diarrhoeal (AOR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 1.08\u0026ndash;1.49) two weeks preceding the survey were more prone to being poorly nourished compared with children who do not have any of these conditions.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultilevel multivariate logistic models of predictors of malnutrition with adjusted odds ratios (AOR) among children 6\u0026ndash;59 months in Nigeria\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2 (N\u0026thinsp;=\u0026thinsp;7770): Level-1 variables (child, parental, and households)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3 (N\u0026thinsp;=\u0026thinsp;7770): Level-1 \u0026amp; 2 variables (Model 2\u0026thinsp;+\u0026thinsp;community-related)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 4 (N\u0026thinsp;=\u0026thinsp;7770): Level 1, 2, \u0026amp; 3 variables (Model 3\u0026thinsp;+\u0026thinsp;area-related)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChild\u0026rsquo;s sex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.742***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.669, 0.823)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.743***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.669, 0.825)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.742***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.669, 0.824)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild\u0026apos;s age in group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026ndash;11 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u0026ndash;23 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.402, 2.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.688***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.4, 2.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.664***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.381, 2.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026ndash;35 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.282***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.771, 2.941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.268***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.759, 2.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.219***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.722, 2.858)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u0026ndash;47 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.848***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.428, 2.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.847***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.426, 2.391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.791***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.384, 2.318)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u0026ndash;59 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.318**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.014, 1.715)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.312**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.008, 1.706)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.276*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.982, 1.659)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild\u0026apos;s birth size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.256**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.044, 1.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.259**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.046, 1.515)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.049, 1.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.805***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.428, 2.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.809***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.431, 2.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.419, 2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreceding birth interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;24 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.313***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.092, 1.579)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.32***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.098, 1.587)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.311***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.091, 1.575)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;35 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.85, 1.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.854, 1.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.85, 1.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u0026ndash;59 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.839*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.699, 1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.844*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.703, 1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.841*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.701, 1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026thinsp;+\u0026thinsp;months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.753**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.591, 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.758**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.594, 0.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.757**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.594, 0.964)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTook Iron supplements\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.948, 1.268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.95, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.979, 1.304)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of breastfeeding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEver breastfed, not currently breastfeeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever breastfed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.762, 1.793)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.753, 1.771)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.773, 1.815)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStill breastfeeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.819, 1.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.813, 1.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.801, 1.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalaria RTD status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.956, 1.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.949, 1.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.95, 1.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnaemia status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot anaemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnaemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.176, 1.505)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.325***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.172, 1.499)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.332***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.178, 1.506)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild had cough in last 2 weeks before the survey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.801, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.807, 1.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.779, 1.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild had diarrheal in last 2 weeks before the survey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.267***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.078, 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.276***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.086, 1.501)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.267***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.08, 1.488)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of child\u0026apos;s delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.801, 1.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.807, 1.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.809, 1.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.843*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.689, 1.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.843*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.689, 1.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.698, 1.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElsewhere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.475, 1.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.472, 1.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.473, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMother/Caregiver currently working\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.134*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.997, 1.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.128*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.992, 1.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.005, 1.294)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal/caregiver highest educational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.786, 1.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.783, 1.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.812, 1.163)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.713***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.591, 0.859)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.702***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.576, 0.854)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.709***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.583, 0.863)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.386, 0.701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.512***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.377, 0.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.513***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.378, 0.696)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMother\u0026apos;s religious status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCatholic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Christian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.861, 1.345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.874, 1.367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.891, 1.375)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIslam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.912, 1.594)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.942, 1.649)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.919, 1.575)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraditionalist \u0026amp; others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.455, 1.656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.462, 1.679)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.539, 1.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHausa/Fulani/Kanuri/Seribiri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIbos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.542***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.382, 0.768)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.544***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.383, 0.771)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.633**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.415, 0.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYoruba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.644, 1.201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.651, 1.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.853, 1.651)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.737***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.599, 0.908)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.733***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.595, 0.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.677, 1.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMother\u0026apos;s Anaemia status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Anaemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnaemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.977, 1.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.975, 1.215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.983, 1.223)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal body weight status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.267***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.064, 1.508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.267***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.064, 1.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.266***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.063, 1.506)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.706***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.607, 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.707***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.609, 0.822)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.611, 0.824)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.599***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.484, 0.741)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.604***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.488, 0.748)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.596***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.481, 0.737)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartner education status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.829, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.83, 1.213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.842, 1.228)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.806, 1.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.812, 1.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.81, 1.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.789**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.629, 0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.797*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.636, 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.623, 0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold wealth index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.751, 1.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.773, 1.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.792, 1.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.846*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.698, 1.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.77, 1.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.787, 1.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.639***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.516, 0.791)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.592, 0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.797*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.62, 1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.571***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.438, 0.743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.687**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.508, 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.732**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.543, 0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Head age group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess 34 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.82, 1.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.819, 1.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.821, 1.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.857*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.72, 1.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.861*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.723, 1.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.863*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.725, 1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 years+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.736, 1.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.746, 1.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.755, 1.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of bedrooms in household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo rooms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.215**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.049, 1.408)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.204**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.038, 1.395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.191**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.028, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThree rooms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.286**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.073, 1.541)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.264**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.054, 1.515)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.227**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.024, 1.469)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFour rooms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.381***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.103, 1.729)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.347**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.075, 1.689)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.336**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.068, 1.673)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFive\u0026thinsp;+\u0026thinsp;rooms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.938, 1.553)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.916, 1.519)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.9, 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of people in household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026ndash;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.674, 1.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.675, 1.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.678, 1.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.718, 1.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.718, 1.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.724, 1.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.761, 1.337)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.764, 1.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.761, 1.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity wealth level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.797**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.657, 0.967)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.845*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.696, 1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of community distance to health facility is no big problem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.8, 1.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.867**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.761, 0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of community maternal education level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.906, 1.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.933, 1.381)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of community households with no bed net\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.813, 1.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.848, 1.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender inequality index by state (GII\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLowest GII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow GII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.454***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.144, 1.849)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage GII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.819, 1.388)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh GII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.814*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.647, 1.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighest GII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.783, 1.387)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth-central\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth-east\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.272***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.773, 2.912)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth-west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.111***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(2.339, 4.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth-east\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.714, 1.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth-south\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.734, 1.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth-west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.807, 1.481)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of place of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.869, 1.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.319, 0.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.329, 0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.316***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.182, 0.547)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRandom effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity-level variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098, 0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.096, 0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089, 0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState-level variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057, 0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054, 0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000, 0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVPC: community-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVPC: state-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICC: community-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICC: state-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMOR: community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.348, 1.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.344, 1.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.329, 1.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMOR: state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.256, 1.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.248, 1.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.014, 1.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel fit statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog-likelihood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4520.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4516.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4490.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9150.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9150.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9118.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9533.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9560.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9598.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eAOR: Adjusted Odds Ratios, ICC: Intraclass Correlation Coefficient, VPC: Variance Partition Coefficient, AIC: Akaike Information Criterion (Given a set of candidate models for the data, the preferred model is the one with the minimum AIC value)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eSimilarly, children of working-class mothers have increased odds (AOR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 1.01\u0026ndash;1.29) of being poorly nourished compared with children whose mothers do not work. The educational levels attained by mothers and fathers are inversely proportional to the odds of their children being poorly nourished. Children of mothers with secondary education (AOR\u0026thinsp;=\u0026thinsp;0.71, 95% CI: 0.58\u0026ndash;0.86), tertiary education (AOR\u0026thinsp;=\u0026thinsp;0.51, 95% CI: 0.38\u0026ndash;0.70), and fathers with tertiary education (AOR\u0026thinsp;=\u0026thinsp;0.78, 95% CI: 0.62\u0026ndash;0.98), were respectively, 29%, 49%, and 22% reduced odds of being poorly nourished compared with children whose mothers and fathers do not have any formal education. The results also show that the richer the household, the less likely the children will be poorly nourished compared with their counterparts in the poorest household. Also, the odds of children residing in a 3-bedroom household (AOR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.02\u0026ndash;1.47) are 1.23 times more likely to be poorly nourished than children residing in a one-bedroom household. Furthermore, among the community-related variables, children from a community where the proportion of the community distance to the nearest health centre is not a big problem are high; there are 17% reduced odds of being poorly nourished compared with children from a community with a low proportion. Moreover, from the area-related variables, the odds of children from a state where the gender inequality index is low (AOR\u0026thinsp;=\u0026thinsp;1.45, 95% CI: 1.14\u0026ndash;1.85) is significantly 1.45 times more likely to be poorly nourished when compared with children from the state with lowest gender inequality index. The odds of children from the North-east (AOR\u0026thinsp;=\u0026thinsp;2.27, 95% CI: 1.77\u0026ndash;2.94), and the North-west (AOR\u0026thinsp;=\u0026thinsp;3.11, 95% CI: 2.40\u0026ndash;4.14), are 127% and 211% significantly more likely to be poorly nourished respectively.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the random effect analysis, the intrastate correlation coefficient of 0.142 and the intracommunity correlation coefficient (ICC) of 0.218 were used to assess the variations in malnutrition status among children aged 6\u0026ndash;59 months in Nigeria due to the three-level factors. These results showed that 14.2% and 21.8% of the total variation in the odds of malnutrition were due to state and community levels, respectively. In addition, the ICC at the state level also decreased from 14.1\u0026ndash;0.1%, which was statistically significant. Furthermore, the ICC at the community level in the null model decreased from 21.8\u0026ndash;4.6% in the choice model, meaning the correlation between two children/individuals (unit of analysis) within the same community and the same state is 0.046. Similarly, model 4's median odds ratio (MOR) calculated for states was 1.077, indicating that if a child travels from one state to another where there is a greater chance of 7.7% of increased risk of being poorly nourished. In addition, moving to a community with a higher risk of malnutrition increases a child's probability of being poorly nourished by 46%.\u003c/p\u003e \u003cp\u003eIn the fixed effect analysis, the study found that female children have considerably decreased odds of being poorly nourished than their male counterparts. The same findings were reported in previous studies on malnutrition [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], The risks of children aged 24\u0026ndash;35 months cohabiting malnutrition were 2.22 times higher than those of children aged 6\u0026ndash;11 months. Similar conclusions were reached in previous studies that an increase in the age of the children has a harmful effect on poor nourishment for the children [\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The possible explanation for this difference is that as the child grows older, less attention is paid to their welfare compared with the youngest child. Also, children who had diarrhoea two weeks before the survey, were more likely to be poorly nourished than children who did not have the illnesses. This finding is supported by previous studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Similarly, children of working-class mothers are more likely to be poorly nourished than children whose mothers do not work. This agrees with another study [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The possible reason for this is that working class mothers do not breast feed their children much longer compared to mothers who do not work. The odds of being poorly nourished are inversely proportional to the educational status that mothers and fathers have obtained. Other studies substantiated the result on maternal education [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan additionalcitationids=\"CR49 CR50 CR51\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and for paternal educational attainment was supported in [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The results also show that the richer the household, the less likely the children from such a home will be poorly nourished compared with their counterparts from the poorest household. This finding agrees with other studies [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37 CR38 CR39\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan additionalcitationids=\"CR43 CR44 CR45\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan additionalcitationids=\"CR51 CR52 CR53\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and contrary finding with [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, children from communities where many residents do not consider distance to the nearest health facility to be 'no big problem' are 17% less likely to be poorly nourished than children from communities with a low proportion do exist. The odds of children from a state with a low gender inequality index being poorly nourished are significantly 1.45 times higher than those with the highest gender inequality index. The odds of children from the North-east, and the North-west, are 127% and 211%, significantly more likely to be poorly nourished, respectively. Children living in urban areas are more protected from being malnourished than children from rural areas. This result agrees with other previous studies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations of the study\u003c/h2\u003e \u003cp\u003eThis study is based on the analysis of the merged data sets whereby contextual variables from 2018 NHDR were incorporated into the 2018 NDHS. Moreover, the study took into consideration the hierarchical nature of the data set and conducted a 3-state multilevel mixed effect logistic analysis to account for individua, community, and state variabilities. In addition, the study recognised overweight as part of the indicators (stunting, wasting, and underweight) that formed the proxies for malnutrition.\u003c/p\u003e \u003cp\u003eThere are several restrictions on this study. First, because the survey was cross-sectional, the study could only look at the relationships between factors. Consequently, it was impossible to determine causality. It's also important to note that the random coefficient model in a three-level model was not examined in this study to see if it was superior to the variance component and random intercept model. Additionally, only the individual level of weighting was considered. These are appropriate for further research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn general, the Northern Nigerian states of Kebbi, Zamfara, Katsina, Kano, Jigawa, Bauchi, and Gombe had the highest proportions of poorly malnourished children aged 6\u0026ndash;59 months. Most of these areas are highly affected by high rates of constant insecurities that have distorted farming activities. Efforts should be made to end these age long insecurities in these areas. The results reported female children are well-nourished compared to their male counterparts. Therefore, gender-sensitive policies and community mobilisation against gender-based bias are required to overcome the discrepancies in malnutrition status between male and female child.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eThe conceptualization of this study was done by P.E.O. and K.K.; the formal drafting of the manuscript was carried out by P.E.O., while S.J.W., R.J. and K.K. reviewed, revised, and edited the manuscript. All authors have read and agreed to the published version of the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study is an integral part of P.E.O.\u0026rsquo;s doctoral study at the School of Health and Related Research of the University of Sheffield, United Kingdom. The funding for the doctoral study was granted by TETFUND (Nigeria).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e The ethical approval to carry out this research study had been granted by the School of Health and Related Research (ScHARR) Ethics Committee of the University of Sheffield (Reference Number: 031534). This study is a secondary analysis of two nationally representative samples. Permission to use the data sets (2018 Nigeria Demographic and Health Survey and 2018 National Human Development Report) had been obtained from two organizations: Inner City Fund (ICF)-International, and United Nations Development Program (UNDP-Nigeria).\u0026nbsp;All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Informed consent had been obtained by the original owners of the data sets (MeasureDHS and UNDP-Nigeria).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data set used in this study is available in MeasureDHS https: //dhsprogram.com (accessed on 28 January 2020) and UNDP-Nigeria http://hdr.undp.org/sites/ default/files/hdr_2018_nigeria_finalfinalx3.pdf (accessed on 3 March 2020). Acknowledgments: The authors acknowledged the contributions received from ScHARR community. Phillips appreciate the Rector and the management staff of Niger State Polytechnic, Nigeria, for nominating him for the TETFUND (Nigeria) sponsorship of his doctoral program.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003cbr\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organisation. Fact sheets - Malnutrition, https://www.who.int/news-room/fact-sheets/detail/malnutrition (2020, accessed 31 May 2020).\u003c/li\u003e\n\u003cli\u003eEndris N, Asefa H, Dube L. Prevalence of Malnutrition and Associated Factors among Children in Rural Ethiopia. \u003cem\u003eBiomed Res Int\u003c/em\u003e 2017; 2017: 6587853.\u003c/li\u003e\n\u003cli\u003eGlobal Nutrition Report. The burden of malnutrition, https://globalnutritionreport.org/reports/global-nutrition-report-2018/burden-malnutrition/ (2020, accessed 25 June 2020).\u003c/li\u003e\n\u003cli\u003eJude CK, Chukwunedum AU, Egbuna KO. Under-five malnutrition in a South-Eastern Nigeria metropolitan city. \u003cem\u003eAfr Health Sci\u003c/em\u003e 2019; 19: 3078\u0026ndash;3084.\u003c/li\u003e\n\u003cli\u003eNational Population Commission (NPC), ICF International. Nigeria Demographic and Health Survey 2013. \u003cem\u003eFederal Republic of Nigeria and MeasureDHS\u003c/em\u003e, June 2014.\u003c/li\u003e\n\u003cli\u003eBabatunde RO, Olagunju FI, Fakayode SB, et al. Prevalence and Determinants of Malnutrition among Under-five Children of Farming Households in Kwara State, Nigeria. \u003cem\u003eJAS\u003c/em\u003e 2011; 3: p173.\u003c/li\u003e\n\u003cli\u003eNational Population C, I. C. F. International. Nigeria Demographic and Health Survey 2003. \u003cem\u003eFederal Republic of Nigeria and MeasureDHS\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eKwansa AL, Akparibo R, Cecil JE, et al. Risk Factors for Overweight and Obesity within the Home Environment of Preschool Children in Sub-Saharan Africa: A Systematic Review. \u003cem\u003eNutrients\u003c/em\u003e 2022; 14: 1706.\u003c/li\u003e\n\u003cli\u003eKandala NB, Lang S, Klasen S, et al. Semiparametric Analysis of the Socio-Demographic and Spatial Determinants of Undernutrition in Two African Countries. \u003cem\u003eLMU\u003c/em\u003e 2011; 18.\u003c/li\u003e\n\u003cli\u003eNational Population C, I. C. F. International. \u003cem\u003eNigeria Demographic and Health Survey 2018\u003c/em\u003e. Abuja, Nigeria, and Rockville, Maryland, USA: NPC and ICF, 2019.\u003c/li\u003e\n\u003cli\u003eNational Population Commission (NPC)[Nigeria], ICF. Nigeria Demographic and Health Survey 2018.\u003c/li\u003e\n\u003cli\u003eBamiwuye SO, Wet ND, Adedini SA. Linkages between autonomy, poverty and contraceptive use in two sub-Saharan African countries. \u003cem\u003eAfrican Population Studies\u003c/em\u003e 2013; 27: 164\u0026ndash;173.\u003c/li\u003e\n\u003cli\u003eNandy S, Daoud A, Gordon D. Examining the changing profile of undernutrition in the context of food price rises and greater inequality. \u003cem\u003eSocial Science \u0026amp; Medicine\u003c/em\u003e 2016; 149: 153\u0026ndash;163.\u003c/li\u003e\n\u003cli\u003eNandy S, Jaime Miranda J. Overlooking undernutrition? Using a composite index of anthropometric failure to assess how underweight misses and misleads the assessment of undernutrition in young children. \u003cem\u003eSoc Sci Med\u003c/em\u003e 2008; 66: 1963\u0026ndash;1966.\u003c/li\u003e\n\u003cli\u003eObasohan PE, Walters SJ, Jacques R, et al. Risk Factors Associated with Malnutrition among Children Under-Five Years in Sub-Saharan African Countries: A Scoping Review. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e 2020; 17: 8782.\u003c/li\u003e\n\u003cli\u003eObasohan PE, Walters SJ, Jacques R, et al. Individual and Contextual Factors Associated with Malaria among Children 6\u0026ndash;59 Months in Nigeria: A Multilevel Mixed Effect Logistic Model Approach. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e 2021; 18: 11234.\u003c/li\u003e\n\u003cli\u003eAdekanmbi VT, Uthman OA, Mudasiru OM. Exploring variations in childhood stunting in Nigeria using league table, control chart and spatial analysis. \u003cem\u003eBMC Public Health\u003c/em\u003e 2013; 13: 361.\u003c/li\u003e\n\u003cli\u003eAkombi BJ, Agho KE, Hall JJ, et al. Stunting and severe stunting among children under-5 years in Nigeria: A multilevel analysis. \u003cem\u003eBMC Pediatr\u003c/em\u003e 2017; 17: 15.\u003c/li\u003e\n\u003cli\u003eAkombi BJ, Agho KE, Renzaho AM, et al. Trends in socioeconomic inequalities in child undernutrition: Evidence from Nigeria Demographic and Health Survey (2003 - 2013). \u003cem\u003ePLoS ONE\u003c/em\u003e 2019; 14: e0211883.\u003c/li\u003e\n\u003cli\u003eAmaral MM, Herrin WE, Gulere GB. Using the Uganda National Panel Survey to analyze the effect of staple food consumption on undernourishment in Ugandan children. \u003cem\u003eBMC Public Health\u003c/em\u003e 2017; 18: 32.\u003c/li\u003e\n\u003cli\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. \u003cem\u003eGlobalization and Health\u003c/em\u003e; 15. Epub ahead of print 2019. DOI: 10.1186/s12992-019-0505-7.\u003c/li\u003e\n\u003cli\u003eFantay Gebru K, Mekonnen Haileselassie W, Haftom Temesgen A, et al. Determinants of stunting among under-five children in Ethiopia: A multilevel mixed-effects analysis of 2016 Ethiopian demographic and health survey data. \u003cem\u003eBMC Pediatrics\u003c/em\u003e; 19. Epub ahead of print 2019. DOI: 10.1186/s12887-019-1545-0.\u003c/li\u003e\n\u003cli\u003eHv D, S N-S. Trends and Determinants of Child Growth Indicators in Malawi and Implications for the Sustainable Development Goals. \u003cem\u003eAIMS Public Health\u003c/em\u003e 2017; 4: 590\u0026ndash;614.\u003c/li\u003e\n\u003cli\u003eMachisa M, Wichmann J, Nyasulu PS. Biomass fuel use for household cooking in Swaziland: is there an association with anaemia and stunting in children aged 6-36 months? \u003cem\u003eTrans R Soc Trop Med Hyg\u003c/em\u003e 2013; 107: 535\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eMagadi MA. Household and community HIV/AIDS status and child malnutrition in sub-Saharan Africa: Evidence from the demographic and health surveys. \u003cem\u003eSoc Sci Med\u003c/em\u003e 2011; 73: 436\u0026ndash;446.\u003c/li\u003e\n\u003cli\u003eMiller CM, Gruskin S, Subramanian SV, et al. Emerging health disparities in Botswana: examining the situation of orphans during the AIDS epidemic. \u003cem\u003eSocial Science \u0026amp; Medicine\u003c/em\u003e 2007; 64: 2476\u0026ndash;2486.\u003c/li\u003e\n\u003cli\u003eNankinga O, Kwagala B, Walakira E. Maternal employment and child nutritional status in Uganda, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6922416/ (2019, accessed 11 August 2020).\u003c/li\u003e\n\u003cli\u003eNshimyiryo A, Hedt-Gauthier B, Mutaganzwa C, et al. Risk factors for stunting among children under five years: a cross-sectional population-based study in Rwanda using the 2015 Demographic and Health Survey. \u003cem\u003eBMC Public Health\u003c/em\u003e 2019; 19: 175.\u003c/li\u003e\n\u003cli\u003eTakele K, Zewotir T, Ndanguza D. Understanding correlates of child stunting in Ethiopia using generalized linear mixed models. \u003cem\u003eBMC Public Health\u003c/em\u003e 2019; 19: 626.\u003c/li\u003e\n\u003cli\u003eUkwuani FA, Suchindran CM. Implications of women\u0026rsquo;s work for child nutritional status in sub-Saharan Africa: a case study of Nigeria. \u003cem\u003eSoc Sci Med\u003c/em\u003e 2003; 56: 2109\u0026ndash;2121.\u003c/li\u003e\n\u003cli\u003eAboagye RG, Seidu AA, Ahinkorah BO, et al. Dietary Diversity and Undernutrition in Children Aged 6-23 Months in Sub-Saharan Africa. \u003cem\u003eNutrients\u003c/em\u003e; 13. Epub ahead of print 28 September 2021. DOI: 10.3390/nu13103431.\u003c/li\u003e\n\u003cli\u003eAdam Birhan N, Bitew Belay D. Associated risk factors of underweight among under-five children in Ethiopia using multilevel ordinal logistic regression model. \u003cem\u003eAfr H Sci\u003c/em\u003e 2021; 21: 362\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eBekele SA, Fetene MZ. Modeling non-Gaussian data analysis on determinants of underweight among under five children in rural Ethiopia: Ethiopian demographic and health survey 2016 evidences. \u003cem\u003ePLoS ONE\u003c/em\u003e 2021; 16: e0251239.\u003c/li\u003e\n\u003cli\u003eKassie GW, Workie DL. Determinants of under-nutrition among children under five years of age in Ethiopia. \u003cem\u003eBMC Public Health\u003c/em\u003e 2020; 20: 399.\u003c/li\u003e\n\u003cli\u003eKebede D, Aynalem A. Prevalence of undernutrition and potential risk factors among children below five years of age in Somali region, Ethiopia: evidence from 2016 Ethiopian demographic and health survey. \u003cem\u003eBMC Nutr\u003c/em\u003e 2021; 7: 56.\u003c/li\u003e\n\u003cli\u003eKhamis AG, Mwanri AW, Kreppel K, et al. The burden and correlates of childhood undernutrition in Tanzania according to composite index of anthropometric failure. \u003cem\u003eBMC Nutr\u003c/em\u003e 2020; 6: 39.\u003c/li\u003e\n\u003cli\u003eMasibo PK, Humwa F, Macharia TN. The double burden of overnutrition and undernutrition in mother\u0026minus;child dyads in Kenya: demographic and health survey data, 2014. \u003cem\u003eJ Nutr Sci\u003c/em\u003e 2020; 9: e5.\u003c/li\u003e\n\u003cli\u003eMuche A, Dewau R. Severe stunting and its associated factors among children aged 6-59\u0026thinsp;months in Ethiopia; multilevel ordinal logistic regression model. \u003cem\u003eItal J Pediatr\u003c/em\u003e 2021; 47: 161.\u003c/li\u003e\n\u003cli\u003eRutayisire R, Kanazayire C, Tuyisenge G, et al. Trends in the Prevalence and Associated Contributing Factors of Stunting in Children Under Five Years of Age. Secondary Data Analysis of 2005, 2010 and 2014-2015 Rwanda Demographic and Health Surveys. \u003cem\u003eRJMHS\u003c/em\u003e 2020; 3: 71\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eSimelane MS, Chemhaka GB, Zwane E. A multilevel analysis of individual, household and community level factors on stunting among children aged 6-59 months in Eswatini: A secondary analysis of the Eswatini 2010 and 2014 Multiple Indicator Cluster Surveys. \u003cem\u003ePLoS One\u003c/em\u003e 2020; 15: e0241548.\u003c/li\u003e\n\u003cli\u003eSserwanja Q, Mutisya LM, Olal E, et al. Factors associated with childhood overweight and obesity in Uganda: a national survey. \u003cem\u003eBmc Public Health\u003c/em\u003e; 21. Epub ahead of print August 2021. DOI: 10.1186/s12889-021-11567-1.\u003c/li\u003e\n\u003cli\u003eTesema GA, Yeshaw Y, Worku MG, et al. Pooled prevalence and associated factors of chronic undernutrition among under-five children in East Africa: A multilevel analysis. \u003cem\u003ePLoS One\u003c/em\u003e 2021; 16: e0248637.\u003c/li\u003e\n\u003cli\u003eUwiringiyimana V, Osei F, Amer S, et al. Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden. \u003cem\u003eBMC Public Health\u003c/em\u003e 2022; 22: 159.\u003c/li\u003e\n\u003cli\u003eChikako TU, Seidu A-A, Hagan JE, et al. Complex Multilevel Modelling of the Individual, Household and Regional Level Variability in Predictors of Undernutrition among Children Aged 6\u0026ndash;59 Months in Ethiopia. \u003cem\u003eNutrients\u003c/em\u003e 2021; 13: 3018.\u003c/li\u003e\n\u003cli\u003eMuche A, Gezie LD, Baraki AG, et al. Predictors of stunting among children age 6-59 months in Ethiopia using Bayesian multi-level analysis. \u003cem\u003eSci Rep\u003c/em\u003e 2021; 11: 3759.\u003c/li\u003e\n\u003cli\u003eTesfaw LM, Fenta HM. Multivariate logistic regression analysis on the association between anthropometric indicators of under-five children in Nigeria: NDHS 2018. \u003cem\u003eBMC Pediatr\u003c/em\u003e 2021; 21: 193.\u003c/li\u003e\n\u003cli\u003eAmegbor PM, Yankey O, Sabel CE. Examining the Effect of Geographic Region of Residence on Childhood Malnutrition in Uganda. \u003cem\u003eJournal of Tropical Pediatrics\u003c/em\u003e 2020; 66: 598\u0026ndash;611.\u003c/li\u003e\n\u003cli\u003eAdedokun ST, Yaya S. Factors associated with adverse nutritional status of children in sub-Saharan Africa: Evidence from the Demographic and Health Surveys from 31 countries. \u003cem\u003eMatern Child Nutr\u003c/em\u003e 2021; 17: e13198.\u003c/li\u003e\n\u003cli\u003eAmaha ND, Woldeamanuel BT. Maternal factors associated with moderate and severe stunting in Ethiopian children: analysis of some environmental factors based on 2016 demographic health survey. \u003cem\u003eNutr J\u003c/em\u003e 2021; 20: 18.\u003c/li\u003e\n\u003cli\u003eAmoako Johnson F. Spatiotemporal clustering and correlates of childhood stunting in Ghana: Analysis of the fixed and nonlinear associative effects of socio-demographic and socio-ecological factors. \u003cem\u003ePLoS ONE\u003c/em\u003e 2022; 17: e0263726.\u003c/li\u003e\n\u003cli\u003eFenta HM, Zewotir T, Muluneh EK. Spatial data analysis of malnutrition among children under-five years in Ethiopia. \u003cem\u003eBmc Medical Research Methodology\u003c/em\u003e; 21. Epub ahead of print October 2021. DOI: 10.1186/s12874-021-01391-x.\u003c/li\u003e\n\u003cli\u003eHailu BA, Bogale GG, Beyene J. Spatial heterogeneity and factors influencing stunting and severe stunting among under-5 children in Ethiopia: spatial and multilevel analysis. \u003cem\u003eSci Rep\u003c/em\u003e 2020; 10: 16427.\u003c/li\u003e\n\u003cli\u003eAmegbor PM, Zhang Z, Dalgaard R, et al. Multilevel and spatial analyses of childhood malnutrition in Uganda: examining individual and contextual factors. \u003cem\u003eSci Rep\u003c/em\u003e 2020; 10: 20019.\u003c/li\u003e\n\u003cli\u003eMusuka GN, Dzinamarira T, Cuadros DF, et al. Mothers\u0026rsquo; HIV status and their children\u0026rsquo;s nutritional status: Insights from secondary analysis of the Zimbabwe Demographic and Health Survey data (2015\u0026ndash;2016). \u003cem\u003eFood Sci Nutr\u003c/em\u003e 2021; 9: 5509\u0026ndash;5516.\u003c/li\u003e\n\u003cli\u003eWondimu H, Dejene K. Determinants of under-five malnutrition, significant changes, and policy implications in the Ethiopian Demographic Health Survey, 2019. \u003cem\u003eDiscov Sustain\u003c/em\u003e 2022; 3: 16. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3157817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3157817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMalnutrition has remained a global public health issue, particularly in low- and middle-income countries (LMICs). Researchers have committed to studying malnutrition (especially in children under the age of five) to address the nine malnutrition targets, set by the WHO to be achieved by 2025. With barely three years to the end of target date, there is so much yet undone to meet the target especially in developing countries. In fact, there can be no effective interventions without a strong evidence base. This study seeks to evaluate the prevalence of malnutrition among children aged 6\u0026ndash;59 months across Nigeria and its states, as well as its individual and contextual factors. Two separates, independently collected, nationally representative cross-sectional surveys, the National Human Development Report (NHDR 2018) and the 2018 Nigeria Demographic and Health Survey (2018 NDHS) were linked for the study. Spatial map was used to describe the prevalence of malnutrition, a 3-level multivariate multilevel logistic regression models were fitted where children/individuals (at level 1) were nested in communities/clusters (at level 2) and nested in states (at level 3). The results showed that an estimated 43.6% of children aged 6\u0026ndash;59 months are poorly nourished in Nigeria. The proportions of poorly nourished children were generally highest in the Northern Nigeria. Child\u0026rsquo;s gender, age, birth size, preceding birth order, anaemia status, maternal education, work status, body weight, household wealth status, number of bedrooms were among individual/household predictors of malnutrition. On the community level, being from community with high wealth index, distance to nearest health facilities is no big problem. Regional variations and gender inequality index were state level predictors of malnutrition among children in Nigeria. This study has shown that two-third of children aged 6\u0026ndash;59 months in Nigeria were poorly nourished. The high rates of constant insecurities that have distorted farming activities in the northern part of the country should be addressed urgently.\u003c/p\u003e","manuscriptTitle":"The Socio-economic, Demographic, and Contextual Predictors of Malnutrition among Children aged 6-59 months in Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-19 14:40:14","doi":"10.21203/rs.3.rs-3157817/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-07-25T15:45:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-17T08:18:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-07-16T12:10:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2023-07-10T19:03:31+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":"7f6b150a-5fe9-4213-9bdf-a90167f54ee5","owner":[],"postedDate":"July 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-01-08T15:02:13+00:00","versionOfRecord":{"articleIdentity":"rs-3157817","link":"https://doi.org/10.1186/s40795-023-00813-x","journal":{"identity":"bmc-nutrition","isVorOnly":false,"title":"BMC Nutrition"},"publishedOn":"2024-01-02 15:00:35","publishedOnDateReadable":"January 2nd, 2024"},"versionCreatedAt":"2023-07-19 14:40:14","video":"","vorDoi":"10.1186/s40795-023-00813-x","vorDoiUrl":"https://doi.org/10.1186/s40795-023-00813-x","workflowStages":[]},"version":"v1","identity":"rs-3157817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3157817","identity":"rs-3157817","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}