Changing Determinants of Child Wasting: Insights from the Prevalence of Stunting, Wasting, and Malnutrition and Their Determinants — An Analysis of BDHS 2022 Data

Changing Determinants of Child Wasting: Insights from the Prevalence of Stunting, Wasting, and Malnutrition and Their Determinants — An Analysis of BDHS 2022 Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Changing Determinants of Child Wasting: Insights from the Prevalence of Stunting, Wasting, and Malnutrition and Their Determinants — An Analysis of BDHS 2022 Data Md Towhidur Rahman, Shompa Akter This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6737252/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Malnutrition remains a critical public health challenge in Bangladesh, particularly among children under five years. This study examines the prevalence and determinants of stunting, wasting, and underweight using nationally representative data from the 2022 Bangladesh Demographic and Health Survey (BDHS), with an emphasis on post-pandemic trends and socioeconomic disparities. Methods: We conducted a cross-sectional analysis of 4,118 children aged 0–59 months with valid anthropometric data. Multivariable logistic regression was performed on a complete-case subsample of 1,216 children to identify associations between malnutrition outcomes and socioeconomic, maternal and child health, and environmental factors. Outcomes were defined using WHO 2006 Child Growth Standards. Results: Among 4,118 children under five, the prevalence of stunting, wasting, and underweight was 23.5%, 11.7%, and 22.8%, respectively. Stunting and underweight showed strong associations with wealth and maternal education, while wasting was less influenced by long-term socioeconomic factors. Higher wealth (AOR for Richest vs. Poorest: 0.38) and maternal education (AOR for Higher vs. None: 0.33) significantly reduced the odds of stunting. Low birth weight, fewer ANC visits, and lower maternal BMI increased the risk of malnutrition—particularly for wasting and underweight. Wasting appeared more sensitive to acute shocks, likely reflecting the impact of the COVID-19 pandemic. Environmental factors like unimproved sanitation and older child age also increased the odds of stunting and underweight. Other factors, including child sex and delivery characteristics, were not significant in adjusted models. Conclusion: Child malnutrition in Bangladesh is strongly influenced by socioeconomic and maternal health factors, but wasting may reflect more immediate shocks. Targeted interventions focusing on poverty reduction, maternal health, and prenatal care are critical to addressing persistent and emerging forms of malnutrition. Malnutrition Bangladesh Covid-19 BDHS Wasting Figures Figure 1 Figure 2 Background Child malnutrition remains a pervasive public health challenge in many low- and middle-income countries, including Bangladesh. Despite consistent investments in health and nutrition over the past two decades, undernutrition continues to affect a substantial proportion of children under five years of age, contributing to impaired growth, developmental delays, and increased vulnerability to illness and mortality (Hossain et al., 2017 ; Rahman et al., 2020 ). The three key indicators of child malnutrition—stunting (low height-for-age), wasting (low weight-for-height), and underweight (low weight-for-age)—capture different dimensions of nutritional deficits, each with distinct causes and policy implications. Nationally representative data from the Bangladesh Demographic and Health Surveys (BDHS) have documented a steady decline in stunting, from 36% in 2014 to 24% in 2022. However, underweight prevalence has plateaued at 22%, and the prevalence of wasting has increased from 8% in 2017–18 to 11% in 2022 (NIPORT, 2024 , p. 185). This stagnation and recent reversal in progress—particularly in acute malnutrition—suggest emerging vulnerabilities, possibly influenced by socioeconomic disruptions, natural disasters, and most recently, the COVID-19 pandemic. Studies conducted during and after the pandemic have raised concerns over food insecurity, income loss, and deteriorating child health, especially in economically and environmentally vulnerable areas such as coastal and saline-prone regions (Della et al., 2025 ; Morshed et al., 2025 ; Rahman et al., 2022 ) Prior research has consistently emphasized the central role of socioeconomic status in determining child nutrition outcomes. Wealthier households tend to have greater access to diverse diets, healthcare, clean water, and sanitation, which together reduce the risk of undernutrition (Chowdhury et al., 2021 ; Chowdhury et al., 2022 ; Hossain et al., 2024 ; Peña & Bacallao, 2002 ). A decomposition analysis of BDHS data from 1996 to 2014 attributed nearly half of the inequality in child dietary diversity to household wealth and a quarter to maternal education (Kundu et al., 2022 ). Numerous studies have also highlighted maternal education as a powerful protective factor—educated mothers are more likely to practice appropriate feeding, hygiene, and healthcare-seeking behaviors (Abdulla et al., 2023 ; Hossain & Khan, 2018 ). Maternal and child health characteristics, such as maternal BMI, antenatal care (ANC) attendance, and birth weight, are also key predictors of child nutritional status. Low birth weight (LBW), often a result of poor maternal nutrition or inadequate prenatal care, is closely linked with higher risk of stunting, wasting, and underweight (Islam et al., 2018 ; Rahman et al., 2016 ; Sanin et al., 2023 ). Maternal BMI has both biological and behavioral implications for child health, as better-nourished mothers tend to have better pregnancy outcomes and are more likely to provide nutritionally adequate care at home (Duggal & Petri Jr, 2018 ; Silveira et al., 2010 ). Similarly, ANC visits serve as important touchpoints for nutritional counselling and early detection of maternal and fetal complications, and some studies suggest they may play a protective role against child malnutrition (Rahman et al., 2016 ; Tamanna et al., 2025 ; Toma et al., 2018 ). Environmental factors such as sanitation and water quality continue to shape child nutrition outcomes, particularly in areas where exposure to fecal pathogens and enteric infections leads to nutrient loss and poor absorption (Chowdhury et al., 2020 ; Hasan et al., 2023 ). Children in households lacking improved sanitation facilities are more likely to suffer from repeated infections, which can compound undernutrition even in the presence of adequate caloric intake. Despite a robust body of literature on malnutrition in Bangladesh, few studies have analyzed national-level data in the context of the COVID-19 pandemic. Emerging evidence suggests that wasting, in particular, may be more sensitive to short-term shocks—such as sudden food shortages or income loss—than to long-term socioeconomic status (Hossain et al., 2024 ; Talukder, 2021 ; Talukder et al., 2021 ). This distinction is critical for designing effective interventions in times of crisis. Moreover, gender-based and regional disparities in malnutrition continue to surface in sub-national studies, with some areas reporting significantly higher rates of undernutrition among girls, particularly in socially marginalized or disaster-prone communities (Khanam & Haque, 2021 ; Sanin et al., 2023 ). In light of these evolving dynamics, there is an urgent need to re-examine the determinants of child malnutrition using the most recent nationally representative data. The BDHS 2022 provides a unique opportunity to assess the post-pandemic landscape of child nutrition in Bangladesh, understand whether traditional risk factors remain significant, and identify any emerging patterns that demand tailored policy responses. Objectives of the Study is i) To estimate the current prevalence of stunting, wasting, and underweight among children under five in Bangladesh using BDHS 2022 data, ii) To identify key socioeconomic, maternal and child health, demographic, and environmental determinants of these malnutrition outcomes, and iii) To assess whether the determinants of malnutrition, particularly wasting, have shifted in the wake of the COVID-19 pandemic, and how these findings compare to prior studies. Methods Study Design and Data Source This cross-sectional study utilized nationally representative data from the 2022 Bangladesh Demographic and Health Survey (BDHS), the ninth in a series of surveys conducted under the global Demographic and Health Surveys (DHS) Program (USAID, 2022 ). The 2022 BDHS was implemented by Mitra and Associates under the authority of the National Institute of Population Research and Training (NIPORT), with technical assistance from ICF and financial support from the United States Agency for International Development (USAID). Data collection was conducted between June and December 2022. The BDHS (NIPORT, 2024 ) employed a two-stage stratified sampling design to ensure national representativeness across all eight administrative divisions of Bangladesh. A total of 30,330 households were selected, and 30,018 were successfully interviewed, yielding a household response rate of 99.6% overall. The response rate for eligible women aged 15–49 was 99.1% across all questionnaire types. Study Population Anthropometric data were collected for children aged 0–59 months in the selected households using standardized procedures. Weight was measured using SECA 874U digital scales, while length or height was measured using ShorrBoards®, depending on the child's age and ability to stand. Based on these measurements, height-for-age (HAZ), weight-for-height (WHZ), and weight-for-age (WAZ) Z-scores were calculated using the WHO 2006 Child Growth Standards. Children with Z-scores outside the biologically plausible range (HAZ + 6, WHZ + 5, WAZ + 5) were excluded from the analysis in accordance with WHO flagging criteria (WHO, 2006 ). After cleaning the data, a total of 4,118 children with valid anthropometric measurements were retained for descriptive analysis. For the multivariable regression analysis, complete-case analysis was conducted, resulting in a final analytical sample of 1,216 children with complete data across all relevant covariates. Outcome Measures The primary outcomes of interest in this study were the binary indicators of stunting, wasting, and underweight. A child was considered stunted if their HAZ was less than − 2 standard deviations (SD) from the WHO median, wasted if their WHZ was below − 2 SD, and underweight if their WAZ was below − 2 SD. Each of these outcomes was coded as a binary variable, with 1 indicating the presence of the condition and 0 otherwise. Explanatory Variables A wide range of explanatory variables was included based on prior literature and the conceptual relevance to child nutrition. These included socioeconomic, maternal and child health, and environmental factors. Socioeconomic characteristics encompassed household wealth index (divided into quintiles: poorest, poorer, middle, richer, and richest), maternal education (categorized as none, primary, secondary, or higher), household access to electricity, and maternal media exposure (defined as exposure to at least one of television, radio, or newspapers). Maternal and child health-related variables included maternal body mass index (BMI), number of antenatal care (ANC) visits, place of delivery (home, public facility, or private facility), mode of delivery (cesarean or vaginal), and birth weight in kilograms, with missing values handled for special DHS codes. Demographic and environmental variables comprised child’s sex and age (in months), household size, and household-level water source and sanitation facilities. Water and sanitation were classified as “improved” or “unimproved” according to WHO/UNICEF Joint Monitoring Programme definitions. All variable transformations and coding were implemented in accordance with DHS guidelines and confirmed via review of the BDHS 2022 final report and dataset codebook. Statistical Analysis Descriptive statistics, including frequencies and percentages, were used to summarize the distribution of key child, maternal, household, and environmental characteristics. The prevalence of stunting, wasting, and underweight was computed overall and across categories of key explanatory variables such as child sex, maternal education, household wealth, residence, religion, and media exposure. Prevalence was calculated using the formula: (number of affected children / total number of children) × 100. To identify factors associated with the three nutritional outcomes, multivariable logistic regression models were constructed. These models were estimated using the glm() function in R with a binomial family and logit link. Three sets of thematic models were developed to examine the role of distinct covariate blocks: a socioeconomic model including wealth, maternal education, electricity, and media exposure; a maternal and child health model including BMI, ANC visits, place and mode of delivery, and birth weight; and a demographic and environmental model including child sex and age, household size, water source, and sanitation. Each of these models was separately estimated for the outcomes of stunting, wasting, and underweight. A final combined model included all covariates simultaneously to adjust for potential confounding and to examine the relative strength of each predictor in a multivariable context. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported to summarize the magnitude and direction of associations. Statistical significance was evaluated at the 5%, 1%, and 0.1% levels, and key findings were interpreted accordingly. Across models, birth weight emerged as a strong and consistent predictor of all three forms of malnutrition, with lower birth weights significantly increasing the likelihood of stunting, wasting, and underweight. Additional associations were observed with socioeconomic disadvantage, unimproved sanitation, and lack of maternal education. All statistical analyses were conducted using R version 4.3.2. Results The general description of the overall sample is provided in the Table 1. The study sample comprised 4,118 children under five years, with a balanced sex distribution (51.5% male, 48.5% female). Wealth was evenly distributed across quintiles (21.5% Poorest, 19.7% Poorer, 20.1% Middle, 19.2% Richer, 19.5% Richest), and maternal education levels were 6.2% None, 22.8% Primary, 52.1% Secondary, and 18.9% Higher. Most children resided in rural areas (68.0%), and 91.5% were from Muslim households. Access to electricity was prevalent (88.5%), with 86.4% of households using improved water sources and 72.5% having improved sanitation. Missing data were substantial for birth weight (61.1%), delivery place (41.3%), and caesarean delivery (39.3%). Mean maternal BMI was 23.1 kg/m² (SD 4.20), mean child age was 29.0 months (SD 17.6), mean household size was 5.78 (SD 2.37), mean ANC visits were 3.31 (SD 2.33), and mean birth weight was 2.98 kg (SD 0.652). Table 1. Descriptive statistics of the sample Variable Category/Statistic N=4118 N (%) / Value Child Sex Male 2119 (51.5%) Female 1999 (48.5%) Wealth Quintile Poorest 884 (21.5%) Poorer 812 (19.7%) Middle 829 (20.1%) Richer 791 (19.2%) Richest 802 (19.5%) Mother’s Education None 255 (6.2%) Primary 940 (22.8%) Secondary 2146 (52.1%) Higher 777 (18.9%) Residence Urban 1317 (32.0%) Rural 2801 (68.0%) Religion Muslim 3767 (91.5%) Hindu 317 (7.7%) Other 0 (0.0%) Missing 34 (0.8%) BMI (kg/m²) Mean (SD) 23.1 (4.20) Median [Min, Max] 22.8 [13.0, 43.7] Missing 2 (0.0%) Child Age (months) Mean (SD) 29.0 (17.6) Median [Min, Max] 29.0 [0, 59.0] HH Size Mean (SD) 5.78 (2.37) Median [Min, Max] 5.00 [2.00, 19.0] Electricity No 64 (1.6%) Yes 3646 (88.5%) Missing 408 (9.9%) ANC Visits Mean (SD) 3.31 (2.33) Median [Min, Max] 3.00 [0, 20.0] Missing 1727 (41.9%) Place of Delivery Home 895 (21.7%) Public Facility 389 (9.4%) Private Facility 1133 (27.5%) Missing 1701 (41.3%) Caesarean Delivery No 1363 (33.1%) Yes 1136 (27.6%) Missing 1619 (39.3%) Birth Weight (kg) Mean (SD) 2.98 (0.652) Median [Min, Max] 3.00 [0.500, 5.50] Missing 2515 (61.1%) Media Exposure Not Exposed 1818 (44.1%) Exposed 2300 (55.9%) Water Source Improved 3557 (86.4%) Unimproved 112 (2.7%) Missing 449 (10.9%) Sanitation Improved 2986 (72.5%) Unimproved 643 (15.6%) Missing 489 (11.9%) The Table 2 presents the prevalence of malnutrition by both numbers and their percentage. This prevalence analyses revealed significant disparities. Stunting prevalence was highest in the Poorest quintile (35.1%) compared to the Richest (12.6%), with a clear gradient across wealth levels (Poorer: 26.8%, Middle: 23.3%, Richer: 18.2%). Underweight followed a similar pattern (Poorest: 33.8%, Richest: 13.6%), while wasting showed a less pronounced gradient (Poorest: 14.0%, Richest: 10.8%). Maternal education strongly influenced outcomes: children of mothers with no education had the highest prevalence (stunting: 41.2%, wasting: 20.4%, underweight: 43.9%), compared to those with higher education (stunting: 12.4%, wasting: 11.5%, underweight: 14.0%). Rural children exhibited higher stunting (24.8%) and underweight (24.0%) than urban children (20.5% and 20.2%), while wasting prevalence was similar (11.4% rural vs. 12.4% urban). Households with unimproved sanitation had higher stunting (22.7%) and underweight (20.7%) compared to those with improved sanitation (15.6% for both). Lack of media exposure was associated with higher stunting (27.4%) and underweight (26.7%) compared to exposed children (20.3% and 19.7%). Religion showed modest differences, with Hindu children having slightly lower stunting (20.2%) than Muslim children (23.8%). Child sex showed minimal differences (stunting: 23.6% male vs. 23.3% female; wasting: 11.5% male vs. 12.0% female; underweight: 22.6% male vs. 23.0% female). Table 2. prevalence of malnutrition by number and percentage and their distribution according to variables Variable Stunted (n) Stunting (%) Wasted (n) Wasting (%) Underweight (n) Underweight (%) Total N= 4118 966 23.45 482 11.70 937 22.75 Child Sex Male 500 23.59 243 11.46 478 22.56 Female 466 23.31 239 11.95 459 22.96 Wealth i ndex Poorest 310 35.08 124 14.02 299 33.83 Poorer 218 26.85 99 12.20 209 25.74 Middle 193 23.28 92 11.10 186 22.44 Richer 144 18.20 80 10.11 134 16.94 Richest 101 12.59 87 10.85 109 13.59 Mother’s Education None 105 41.18 52 20.39 112 43.92 Primary 285 30.32 107 11.38 266 28.30 Secondary 480 22.36 234 10.91 450 20.96 Higher 96 12.35 89 11.45 109 14.03 Residence Urban 270 20.50 163 12.37 266 20.19 Rural 696 24.84 319 11.39 671 23.96 Religion Muslim 898 23.83 437 11.60 861 22.85 Hindu 64 20.19 40 12.62 68 21.45 Other 0 0.00 0 0.00 0 0.00 Missing 4 11.76 5 14.71 8 23.53 Electricity No 22 34.38 10 15.63 21 32.81 Yes 850 23.32 416 11.41 828 22.71 Missing 94 23.04 56 13.73 88 21.57 Media Exposure Not Exposed 499 27.45 220 12.10 485 26.67 Exposed 467 20.30 262 11.39 452 19.65 Water Source Improved 832 23.39 407 11.44 807 22.69 Unimproved 32 28.57 14 12.50 34 30.36 Missing 102 22.72 61 13.59 96 21.38 Sanitation Improved 634 21.23 338 11.32 637 21.34 Unimproved 219 34.06 80 12.44 194 30.17 Missing 113 23.11 64 13.09 106 21.67 Figure 1 visualises the overall prevalence of malnutrition, as well as the prevalence of stunting, wasting, and underweight, by residence, sex, sanitation status, maternal education level, and wealth index. Fig 1. (a) Overall prevalence of malnutrition and prevalence of malnutrition by (b)Residence, (c) child sex, (d) sanitation facilities, (e) Maternal education and, (f) wealth index In the socio-economic models (Table 3), higher wealth quintiles were protective against stunting (Poorer vs. Poorest: OR 0.57, p=0.007; Richest vs. Poorest: OR 0.31, p<0.001) and underweight (Poorer: OR 0.52, p=0.014; Richest: OR 0.40, p=0.001), but not significantly for wasting (Richest: OR 1.13, p=0.752). Higher maternal education reduced the odds of all outcomes (Higher vs. None: stunting OR 0.31, p=0.004; wasting OR 0.34, p=0.020; underweight OR 0.35, p=0.009). Electricity and media exposure were not significant predictors. Table 3. Logistic Regression Results of Socio-Economic Factors on Child Malnutrition Stunting Wasting Underweight Predictor Estimate (SE) p-value Estimate (SE) p-value Estimate (SE) p-value (Intercept) -0.188 (1.22) 0.877 -0.115 (1.23) 0.926 -0.079 (1.22) 0.949 Wealth Poorer -0.661** (0.246) 0.00718 0.217 (0.361) 0.547 -0.654* (0.265) 0.0135 Middle -0.583* (0.237) 0.0140 0.178 (0.354) 0.614 -0.418 (0.248) 0.0925 Richer -0.814*** (0.242) 0.000775 -0.156 (0.372) 0.676 -0.847** (0.262) 0.00123 Richest -1.170*** (0.274) 0.0000196 0.119 (0.377) 0.752 -0.904** (0.282) 0.00132 Education Primary -0.663 (0.394) 0.0925 -0.945* (0.466) 0.0425 -0.779* (0.396) 0.0491 Secondary -0.549 ( 0.371) 0.139 -1.180** (0.433) 0.00663 -0.917* (0.373) 0.0138 Higher -1.180** (0.411) 0.00423 -1.090* (0.471) 0.0204 -1.060** (0.407) 0.00928 Electricity (Y) 0.049 (1.17) 0.967 -1.170 (1.18) 0.321 -0.084 (1.18) 0.943 Media exposure (Y) 0.140 (0.160) 0.381 -0.034 (0.218) 0.875 0.069 (0.170) 0.686 Table 3. Associations between household and maternal characteristics and three forms of child malnutrition: stunting, wasting, and underweight. Each cell reports the regression coefficient (Estimate) followed by its standard error in parentheses. Statistical significance is indicated as follows: p < 0.05 ( ), p < 0.01 (), and p < 0.001 ( ). The reference group for the household wealth variable is “Poorest”. The reference group for maternal education is “No Education”. Binary variables (Electricity and Media exposure) are coded as 1 = Yes, 0 = No. In the maternal and child health models (Table 4), higher birth weight was protective against stunting (OR 0.73, p=0.005) and underweight (OR 0.60, p<0.001), but not wasting (OR 0.81, p=0.163). Higher maternal BMI reduced the odds of wasting (OR 0.93, p=0.007) and underweight (OR 0.94, p=0.003), as did more ANC visits (wasting: OR 0.89, p=0.029; underweight: OR 0.88, p=0.002). Delivery place and caesarean delivery were not significant. Table 4: Logistic Regression Results of Maternal and Child Health Factors on Child Malnutrition Stunting Wasting Underweight Predictor Estimate (SE) p-value Estimate (SE) p-value Estimate (SE) p-value (Intercept) 0.658 (0.540) 0.223 -0.088 (0.782) 0.911 1.940*** (0.589) 0.00095 Maternal BMI -0.024 (0.018) 0.191 -0.070** (0.026) 0.00725 -0.060** (0.020) 0.00293 ANC visits -0.064 (0.035) 0.069 -0.115* (0.053) 0.0293 -0.124** (0.041) 0.00235 Delivery place (Public) -0.225 (0.282) 0.425 0.463 (0.454) 0.308 -0.183 (0.303) 0.545 Delivery place (Private) -0.467 (0.294) 0.112 0.258 (0.470) 0.583 -0.538 (0.318) 0.0908 C-Section delivery (Y) 0.007 (0.193) 0.972 0.206 (0.265) 0.436 0.209 (0.210) 0.320 Child weight at Birth -0.314** (0.111) 0.00452 -0.213 (0.153) 0.163 -0.506*** (0.120) 0.00003 Table 4. Association between maternal and child health service factors and malnutrition outcomes. Values are estimates with standard errors in parentheses. p < 0.05 (), p < 0.01 (), p < 0.001 (). Reference category for delivery place is home. C-section is coded as 1 = Yes, 0 = No. In the demographic and environmental models (Table 5), older child age increased the odds of stunting (OR 1.03, p<0.001) and underweight (OR 1.02, p=0.002), while unimproved sanitation increased stunting (OR 1.80, p=0.003) and underweight (OR 1.58, p=0.030). Child sex, household size, and water source were not significant. Table 5: Logistic Regression Results of Demographic and Environmental Factors on Child Malnutrition Stunting Wasting Underweight Predictor Estimate (SE) p-value Estimate (SE) p-value Estimate (SE) p-value (Intercept) -1.70*** (0.264) 1.15e-10 -1.87*** (0.348) 7.62e-08 -2.01*** (0.276) 3.25e-13 Child sex -0.218 (0.148) 0.139 -0.190 (0.201) 0.344 -0.165 (0.156) 0.289 Child Age 0.028*** (0.007) 1.12e-04 -0.008 (0.010) 0.398 0.023** (0.008) 0.00221 Household size -0.032 (0.035) 0.355 -0.029 (0.047) 0.540 0.002 (0.035) 0.953 Water source (unimproved) 0.154 (0.436) 0.724 -1.24 (1.020) 0.225 0.331 (0.435) 0.447 Sanitation (unimproved) 0.589** (0.198) 0.00287 -0.123 (0.309) 0.690 0.458* (0.211) 0.0302 Table 5. Associations between child and household characteristics and malnutrition outcomes. Values are estimates with standard errors in parentheses. p < 0.05 (), p < 0.01 (), p < 0.001 (). Child sex is coded as 1 = Male, 0 = Female. Reference categories: improved water source and improved sanitation. The combined model (Table 6) reinforced these findings. Higher wealth quintiles significantly reduced stunting risk (Poorer: OR 0.57, 95% CI 0.35–0.94, p=0.028; Richest: OR 0.38, 95% CI 0.21–0.69, p=0.002) and showed a protective trend for underweight (Poorer: OR 0.58, 95% CI 0.34–0.99, p=0.045). Maternal education remained protective across all outcomes (Higher vs. None: stunting OR 0.33, 95% CI 0.14–0.74, p=0.008; wasting OR 0.31, 95% CI 0.12–0.81, p=0.017; underweight OR 0.34, 95% CI 0.15–0.77, p=0.010). Higher birth weight reduced stunting (OR 0.72, 95% CI 0.58–0.89, p=0.002) and underweight (OR 0.60, 95% CI 0.48–0.76, p<0.001). Maternal BMI (wasting: OR 0.93, 95% CI 0.88–0.98, p=0.006; underweight: OR 0.94, 95% CI 0.90–0.98, p=0.003) and ANC visits (wasting: OR 0.88, 95% CI 0.79–0.98, p=0.024; underweight: OR 0.91, 95% CI 0.84–0.99, p=0.026) were protective for wasting and underweight. Older child age increased stunting (OR 1.03, 95% CI 1.01–1.04, p<0.001) and underweight (OR 1.03, 95% CI 1.01–1.04, p=0.001), visualized in the figure 2. Child sex, household size, water source, sanitation, delivery place, caesarean delivery, and electricity were not significant in the combined model. Visualizations confirmed these trends, with bar plots showing clear gradients in prevalence by wealth and education, and a smoothed LOESS curve indicating stunting prevalence peaking at 30–40 months of age. These findings highlight the critical roles of socio-economic status, maternal education, and early-life health factors in mitigating child malnutrition in Bangladesh, with wealth and education showing consistent protective effects across models. Table 6: Logistic regression of the combined model Variable Stunted OR (95% CI) P-value Wasted OR (95% CI) P-value Underweight OR (95% CI) P-value (Intercept) 1.74 (0.12–25.2) 0.686 11.5 (0.58–226) 0.109 11.1 (0.72–173) 0.085 Child sex (Female) 0.83 (0.62–1.12) 0.220 0.84 (0.56–1.26) 0.406 0.86 (0.63–1.19) 0.366 Wealth Poorer 0.57 (0.35–0.94) 0.028* 1.28 (0.62–2.63) 0.509 0.58 (0.34–0.99) 0.045* Middle 0.66 (0.41–1.09) 0.102 1.37 (0.66–2.84) 0.393 0.84 (0.50–1.41) 0.502 Richer 0.55 (0.33–0.93) 0.026* 1.11 (0.51–2.42) 0.796 0.64 (0.36–1.12) 0.116 Richest 0.38 (0.21–0.69) 0.0015** 1.72 (0.76–3.92) 0.195 0.63 (0.34–1.19) 0.153 Maternal Education Primary 0.53 (0.24–1.17) 0.115 0.37 (0.15–0.95) 0.039* 0.44 (0.20–0.99) 0.046* Secondary 0.60 (0.29–1.25) 0.174 0.29 (0.12–0.69) 0.0055** 0.37 (0.17–0.79) 0.010* Higher 0.33 (0.14–0.74) 0.0076** 0.31 (0.12–0.81) 0.017* 0.34 (0.15–0.77) 0.010* Electricity (Y) 1.34 (0.13–13.7) 0.807 0.34 (0.03–3.60) 0.372 1.13 (0.11–11.7) 0.920 Media exposure (Y) 1.10 (0.80–1.52) 0.553 1.02 (0.66–1.57) 0.934 1.07 (0.76–1.51) 0.703 Maternal BMI 0.98 (0.95–1.02) 0.367 0.93 (0.88–0.98) 0.0061** 0.94 (0.90–0.98) 0.0030** ANC visits 0.99 (0.92–1.07) 0.856 0.88 (0.79–0.98) 0.024* 0.91 (0.84–0.99) 0.026* Delivery place (Public) 0.92 (0.52–1.63) 0.774 1.48 (0.60–3.65) 0.398 0.85 (0.46–1.57) 0.610 Delivery place (Private) 0.70 (0.38–1.27) 0.235 1.23 (0.49–3.14) 0.659 0.58 (0.31–1.11) 0.100 C-Section Deliver (Y) 1.15 (0.77–1.71) 0.487 1.22 (0.72–2.08) 0.467 1.34 (0.87–2.05) 0.184 Child weight at Birth 0.72 (0.58–0.89) 0.0025** 0.80 (0.59–1.08) 0.151 0.60 (0.48–0.76) 0.000026*** Child age 1.03 (1.01–1.04) 0.000089*** 0.99 (0.97–1.01) 0.422 1.03 (1.01–1.04) 0.0012** Household size 1.00 (0.93–1.07) 0.930 0.98 (0.89–1.07) 0.636 1.03 (0.96–1.10) 0.469 Water source (unimproved) 1.12 (0.47–2.68) 0.802 0.29 (0.04–2.20) 0.233 1.43 (0.59–3.48) 0.427 Sanitation (unimproved) 1.24 (0.81–1.91) 0.322 0.82 (0.43–1.59) 0.564 1.18 (0.74–1.89) 0.477 Table 6. Odds ratios (ORs) with 95% confidence intervals (CIs) and p-values for predictors of child malnutrition outcomes. p < 0.05 (), p < 0.01 (), p < 0.001 (). Reference categories: Male child (for sex), "Poorest" (for wealth), "No education" (for maternal education), "No electricity", "No media exposure", "Unimproved water source", and "Improved sanitation". OR < 1 indicates reduced odds of the outcome. Discussion This study investigated the prevalence and determinants of malnutrition (stunting, wasting, and underweight) among children under five years in Bangladesh using the Bangladesh Demographic and Health Survey (BDHS) 2022 dataset. The findings reveal a prevalence of 23.5% for stunting, 11.7% for wasting, and 22.8% for underweight, with significant associations identified with socio-economic factors (wealth index, maternal education), maternal and child health factors (birth weight, maternal BMI, antenatal care visits), and demographic and environmental factors (child age, sanitation). Some of these findings echoed previous results, whereas others showed differences, especially in the case of wasting. Prevalence of malnutrition The prevalence of stunting (23.5%), wasting (11.7%), and underweight (22.8%) in this study aligns with the BDHS 2022 report, which reported stunting at 24%, wasting at 11%, and underweight at 22% (NIPORT, 2024 ). This consistency reflects the appropriateness of the coding and statistical analysis used in this study. Comparing these findings with earlier BDHS data, the 2014 report showed higher rates of stunting (36%), wasting (14%), and underweight (33%), while the 2017–18 report indicated reduced rates of 31%, 8%, and 22%, respectively (NIPORT, 2024 ). These trends suggest that from 2014 to 2018, the prevalence of stunting and underweight declined significantly. However, from 2018 to 2022, the prevalence of wasting has shown an increasing trend, and in the case of stunting and underweight, no further decrease has occurred. Since the BDHS data were collected during the COVID-19 pandemic (from June to December 2022), the increasing trend in wasting and the non-decreasing trends in stunting and underweight reflect the assumption of rising poverty and reduced food security due to the pandemic (Rahman et al., 2022 ; Shuvo et al., 2022 ; Zaman et al., 2025 ). In support of this prediction, studies conducted during the onset of the pandemic reported the prevalence of stunting, wasting, and underweight as 40%, 32%, and 44% in the coastal region (Morshed et al., 2025 ), and 28.6%, 20.7%, and 24.8%, respectively, in a saline-prone region of Bangladesh (Della et al., 2025 ). Although these studies involved relatively small sample sizes and focused on localized areas, the findings are still alarming. Socio-economic determinants: Our findings confirm the strong protective effects of higher wealth and maternal education against child malnutrition. In the combined model, children in the Poorer and Richest wealth quintiles had significantly lower odds of stunting compared to those in the Poorest quintile, with similar patterns observed for underweight. Maternal education also emerged as a consistent protective factor across all outcomes. These results align with major previous studies that identified household wealth and maternal education as key determinants of malnutrition in Bangladesh. (Abdulla et al., 2023 ; Chowdhury et al., 2021 ; Chowdhury et al., 2022 ; Hossain & Khan, 2018 ; S. J. Rahman et al., 2021 ). The pronounced socio-economic gradient in our study, with stunting prevalence dropping from 35.1% in the Poorest quintile to 12.6% in the Richest, mirrors findings from a decomposition analysis of BDHS data (1996–2014), which attributed 49.47% of inequality in minimum dietary diversity (MDD) to wealth status and 25.06% to maternal education (Kundu et al., 2022 ). These consistent associations highlight the role of socio-economic disparities in shaping nutritional outcomes, as wealthier households likely have better access to food, healthcare, and sanitation, while maternal education enhances nutritional knowledge and caregiving practices. However, the lack of statistical significance for wealth in the wasting models contrasts with findings from some previous studies (Chowdhury et al., 2020 ; Hossain et al., 2024 ; Hossain et al., 2022 ; Jubayer et al., 2022 ; M. A. Rahman et al., 2021 ; Talukder, 2021 ), which identified wealth as a significant determinant of wasting. This discrepancy could be attributed to the timing of the survey, which coincided with the COVID-19 pandemic. Wasting, defined as low weight-for-height, reflects acute malnutrition and is sensitive to short-term shocks such as sudden declines in income, food access, or healthcare. During the pandemic, widespread economic disruptions increased poverty and reduced food and nutritional security (Hossain et al., 2024 ; Rahman et al., 2022 ; Shuvo et al., 2022 ). In this context, although the wealth index captured long-term household assets or status, it may not have reflected the immediate income losses or food insecurity that more directly influenced wasting during the pandemic. Therefore, the observed non-significance of wealth in this model may be a reflection of these acute, short-term economic shocks rather than a contradiction of previous evidence. Maternal and child health factors Our study identified higher birth weight, maternal BMI, and antenatal care (ANC) visits as protective against malnutrition, particularly for wasting and underweight. Higher birth weight reduced the odds of stunting and underweight and the protective effect of maternal BMI is observed on wasting and underweight previous studies (Rahman et al., 2016 ; Sanin et al., 2023 ). Again, this insight is valuable for understanding the determinants of wasting. While higher birth weight may offer some protection, it is less likely to shield children during sudden economic shocks. In contrast, maternal BMI, has two possible effects i) maternal height has positive associated with children health (Silveira et al., 2010 ), and ii) mothers with higher BMI may shape home environments (like diet, physical activity norms, or feeding practices), which in turn influence the child’s BMI (Duggal & Petri Jr, 2018 ). Additionally, a higher number of antenatal care (ANC) visits may reflect greater health awareness. These factors could have motivated parents to prioritise child nutrition, even if it meant sacrificing other household necessities (Toma et al., 2018 ). A novel contribution of our study is the significant protective effect of ANC visits on wasting and underweight which was less consistently reported in prior studies. For example, Hossain et al. ( 2022 ) found no significant association between ANC visits and wasting, possibly due to differences in model specifications or sample size. Our finding suggests that ANC may mitigate acute malnutrition by improving maternal health practices during pregnancy, a hypothesis warranting further investigation in longitudinal studies. The lack of significance for delivery place and caesarean delivery in our models contrasts with some literature (Das et al., 2019 ), which found institutional deliveries protective due to better access to skilled care. This discrepancy may stem from our reduced sample size or the high missingness in delivery-related variables (41.3% for delivery place, 39.3% for caesarean delivery), potentially masking effects. These results emphasize the critical role of maternal health in early-life nutrition, as LBW and low maternal BMI may reflect intrauterine growth restriction and maternal undernutrition, respectively, while ANC visits facilitate early interventions like nutritional counselling. Demographic and environmental factors Child age was a significant predictor of stunting and underweight, consistent with previous studies (Abdulla et al., 2023 ; Das & Gulshan, 2017 ; Hossain et al., 2017 ; Islam et al., 2018 ). Most of these studies reported the highest stunting and underweight prevalence at 18–23 months. In our combined model we found no significant effect of age on wasting which a different from few other studies (Hossain et al., 2024 ; Hossain et al., 2022 ; S. J. Rahman et al., 2021 ). This may also reflect the impact of a sudden nutritional shock caused by the COVID-19 pandemic. The observed cases of wasting in the dataset were not age-related but instead resulted from acute malnutrition triggered by the pandemic. Unimproved sanitation increased stunting and underweight in our demographic and environmental models, aligning with Hasan et al. ( 2023 ) and Chowdhury et al. ( 2020 ), who linked poor sanitation to higher malnutrition. However, sanitation was not significant in our combined model, possibly due to collinearity with socio-economic factors like wealth, which often correlate with access to improved facilities. The lack of association between child sex and malnutrition in our study (stunting: 23.6% male vs. 23.3% female; wasting: 11.5% male vs. 12.0% female) contrasts with some regional findings. For instance, a previous study reported higher prevalence among girls (39% stunting, 54% wasting, 45% underweight) than boys in haor areas, attributing this to gender-based discrimination in food allocation (Khanam & Haque, 2021 ). Our nationally representative data may mask such regional or cultural variations, suggesting a need for sub-group analyses to explore gender differences further. Similarly, the non-significance of water source and household size in our models differs from studies like Hasan et al. ( 2023 ), which identified water quality as a determinant in Sylhet. This may reflect the high prevalence of improved water sources (86.4%) in our sample, reducing variability. The high missingness in key variables (birth weight, delivery place, caesarean delivery) is a critical limitation, reducing our regression sample to 1,216 and potentially introducing selection bias. This issue, also noted in prior studies (Islam et al., 2022 ), highlights the challenge of incomplete data in DHS surveys, particularly for retrospective variables like birth weight. Our sensitivity analyses confirmed consistent trends, but the reduced sample size may have attenuated some associations, such as those for delivery place or water source. Compared to machine learning approaches (S. J. Rahman et al., 2021 ; Tamanna et al., 2025 ), which achieved higher predictive accuracy for calculating malnutrition using BDHS data, our logistic regression models provide interpretable odds ratios, facilitating policy translation. Conclusion This study reaffirms that the determinants of malnutrition include the wealth index, maternal education, child’s age, birth weight, maternal BMI, and antenatal care (ANC) visits. However, the wealth index is not the primary contributing factor to wasting, due to the acute nature of this form of malnutrition. The post-COVID data suggest that the wealth index does not adequately capture the sudden economic shock experienced by the population during the COVID-19 pandemic. Nonetheless, this temporary economic crisis adversely impacted food and nutritional security, thereby affecting child nourishment and contributing to the prevalence of wasting. In this context, maternal BMI, education, and ANC visits emerge as key protective factors against wasting. When designing government safety nets to address sudden economic crises, these factors should be emphasized to effectively reduce acute malnutrition among children. Declarations Disclaimers We confirm that this work is original and has not been published elsewhere, nor it is currently under consideration for publication elsewhere. Source(s) of support/funding This study did not receive any funding or financial support from any funding agency, institution, or organisation. Conclusion Child malnutrition in Bangladesh is strongly influenced by socioeconomic and maternal health factors, but wasting may reflect more immediate shocks. Targeted interventions focusing on poverty reduction, maternal health, and prenatal care are critical to addressing persistent and emerging forms of malnutrition. Author Contribution Md Towhidur Rahman: Conception and design, Acquisition of data, Analysis and interpretation of data, drafting of the manuscript, statistical analysis, Administrative, technical or material supportShompa Akter: Coding, Interpreting the statistical outputs, Reviewing and Editing Data Availability Data used in this manuscript is available at the website of the DHS program at the following linkhttps://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=0 References Abdulla F, Rahman A, Hossain MM. Prevalence and risk predictors of childhood stunting in Bangladesh. PLoS ONE. 2023;18(1):e0279901. https://doi.org/10.1371/journal.pone.0279901 . Chowdhury MRK, Khan HT, Rashid M, Kabir R, Islam S, Islam MS, Kader M. Differences in risk factors associated with single and multiple concurrent forms of undernutrition (stunting, wasting or underweight) among children under 5 in Bangladesh: a nationally representative cross-sectional study. BMJ open. 2021;11(12):e052814. https://doi.org/10.1136/bmjopen-2021-052814 . Chowdhury MRK, Rahman MS, Billah B, Kabir R, Perera NK, Kader M. The prevalence and socio-demographic risk factors of coexistence of stunting, wasting, and underweight among children under five years in Bangladesh: a cross-sectional study. BMC Nutr. 2022;8(1):84. https://doi.org/10.1186/s40795-022-00584-x . Chowdhury TR, Chakrabarty S, Rakib M, Afrin S, Saltmarsh S, Winn S. Factors associated with stunting and wasting in children under 2 years in Bangladesh. Heliyon. 2020;6(9). https://doi.org/10.1016/j.heliyon.2020.e04849 . External Link. Das S, Fahim SM, Islam MS, Biswas T, Mahfuz M, Ahmed T. Prevalence and sociodemographic determinants of household-level double burden of malnutrition in Bangladesh. Public Health Nutr. 2019;22(8):1425–32. https://doi.org/10.1017/S1368980018003580 . Das S, Gulshan J. Different forms of malnutrition among under five children in Bangladesh: a cross sectional study on prevalence and determinants. BMC Nutr. 2017;3(1):1. https://doi.org/10.1186/s40795-016-0122-2 . Della NA, Moslem S, Junhai GR, Topu MH, Joty RB, Ferdaus F. Assessing Nutritional Status and Health Outcomes of Children in Saline-Prone Areas: A Comprehensive Study. Asia Pac J Surg Adv. 2025;2(1):26–32. https://doi.org/10.70818/apjsa.2025.v02i01.016 . Duggal P, Petri WA Jr. Does malnutrition have a genetic component? Annu Rev Genom Hum Genet. 2018;19(1):247–62. https://doi.org/10.1146/annurev-genom-083117-021340 . Hasan MM, Asif A, Barua CA, Banerjee A, Kalam A, Kader MA, Wahed A, Noman T, M. W., Talukder A. Association of access to water, sanitation and handwashing facilities with undernutrition of children below 5 years of age in Bangladesh: evidence from two population-based, nationally representative surveys. BMJ open. 2023;13(6):e065330. https://doi.org/10.1136/bmjopen-2022-065330 . Hossain M, Choudhury N, Abdullah KAB, Mondal P, Jackson AA, Walson J, Ahmed T. Evidence-based approaches to childhood stunting in low and middle income countries: a systematic review. Arch Dis Child. 2017;102(10):903–9. https://doi.org/10.1136/archdischild-2016-311050 . Hossain MB, Khan MHR. Role of parental education in reduction of prevalence of childhood undernutrition in Bangladesh. Public Health Nutr. 2018;21(10):1845–54. https://doi.org/10.1017/S1368980018000162 . Hossain MI, Huq S, Islam MM, Mahfuz M, Sari M, Khan GM, Sharmin F, Bulbul MMI, Rahman SM, Mustaphi P. Risk or associated factors of wasting among under-five children in Bangladesh: A systematic review. Asia Pac J Clin Nutr. 2024;33(4):457. https://doi.org/10.6133/apjcn.202412_33(4).0001 . Hossain MM, Abdulla F, Rahman A. Prevalence and determinants of wasting of under-5 children in Bangladesh: Quantile regression approach. PLoS ONE. 2022;17(11):e0278097. https://doi.org/10.1371/journal.pone.0278097 . Islam MM, Islam MT, Noor FM. Determinants of Malnutrition among Under-Five Children in Bangladesh: Evidence from Multiple Indicator Cluster Survey, 2019 Data. Fortune J Health Sci. 2022;5(2):284–95. https://doi.org/10.26502/fjhs.061 . Islam MM, Sanin KI, Mahfuz M, Ahmed AS, Mondal D, Haque R, Ahmed T. Risk factors of stunting among children living in an urban slum of Bangladesh: findings of a prospective cohort study. BMC Public Health. 2018;18:1–13. https://doi.org/10.1186/s12889-018-5101-x . Jubayer A, Islam MH, Nayan MM. Malnutrition among under-five children in St. Martin’s Island, Bangladesh: A cross-sectional study on prevalence and associated factors. SAGE Open Med. 2022;10:20503121221116246. https://doi.org/10.1177/20503121221116246 . Khanam SJ, Haque MA. Prevalence and determinants of malnutrition among primary school going children in the haor areas of Kishoreganj district of Bangladesh. Heliyon. 2021;7(9). https://doi.org/10.1016/j.heliyon.2021.e08077 . Kundu S, Das P, Rahman MA, Al Banna MH, Fatema K, Islam MA, Srivastava S, Muhammad T, Dey R, Hossain A. Socio-economic inequalities in minimum dietary diversity among Bangladeshi children aged 6–23 months: a decomposition analysis. Sci Rep. 2022;12(1):21712. https://doi.org/10.1038/s41598-022-26305-9 . Morshed R, Mahmud MR, Islam J, Islam A, Ahammed E, Tasnim F, Ferdaus F. Rising Threat of Malnutrition in Coastal Communities of Southern Bangladesh. Asia Pac J Med Innovations. 2025;2(1):31–8. NIPORT. (2024). Demographic and Health Survey 2022 . https://dhsprogram.com/pubs/pdf/FR386/FR386.pdf Peña M, Bacallao J. Malnutrition and poverty. Annu Rev Nutr. 2002;22(1):241–53. https://doi.org/10.1146/annurev.nutr.22.120701.141104 . Rahman MA, Halder HR, Rahman MS, Parvez M. Poverty and childhood malnutrition: Evidence-based on a nationally representative survey of Bangladesh. PLoS ONE. 2021;16(8):e0256235. https://doi.org/10.1371/journal.pone.0256235 . Rahman MA, Rahman MS, Shakur SM, Howlader MH, Ashikuzzaman M, Husna AU, Khan B. Risk factors of chronic childhood malnutrition: an analysis of the Bangladesh demographic and health survey 2014 data. J Public Health. 2020;1–13. https://doi.org/10.1007/s10389-020-01281-4 . Rahman MS, Howlader T, Masud MS, Rahman ML. Association of Low-Birth Weight with Malnutrition in Children under Five Years in Bangladesh: Do Mother’s Education, Socio-Economic Status, and Birth Interval Matter? PLoS ONE. 2016;11(6):e0157814. https://doi.org/10.1371/journal.pone.0157814 . Rahman MT, Akter S, Rana MR, Sabuz AA, Jubayer MF. How COVID-19 pandemic is affecting achieved food security in Bangladesh: A perspective with required policy interventions. J Agric food Res. 2022;7:100258. https://doi.org/10.1016/j.jafr.2021.100258 . Rahman SJ, Ahmed NF, Abedin MM, Ahammed B, Ali M, Rahman MJ, Maniruzzaman M. Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach. PLoS ONE. 2021;16(6):e0253172. https://doi.org/10.1371/journal.pone.0253172 . Sanin KI, Khanam M, Rita RS, Haque MA, Ahmed T. Common factors influencing childhood undernutrition and their comparison between Sylhet, the most vulnerable region, and other parts of Bangladesh: Evidence from BDHS 2007–18 rounds. Front Nutr. 2023;9:999520. https://doi.org/10.3389/fnut.2022.999520 . Shuvo SD, Hossain MS, Riazuddin M, Mazumdar S, Roy D. Factors influencing low-income households’ food insecurity in Bangladesh during the COVID-19 lockdown. PLoS ONE. 2022;17(5):e0267488. https://doi.org/10.1371/journal.pone.0267488 . Silveira KB, Alves JF, Ferreira HS, Sawaya AL, Florêncio TM. Association between malnutrition in children living in favelas, maternal nutritional status, and environmental factors. Jornal de pediatria. 2010;86:215–20. https://doi.org/10.1590/S0021-75572010000300009 . Talukder A. Risk factors associated with wasting among under-5 children residing in urban areas of Bangladesh: a multilevel modelling approach. J Public Health. 2021;29:525–31. https://doi.org/10.1007/s10389-019-01163-4 . Talukder B, Hipel KW, Orbinski J. COVID-19's implications on agri-food systems and human health in Bangladesh. Curr Res Environ Sustain. 2021;3:100033. https://doi.org/10.1016/j.crsust.2021.100033 . Tamanna T, Mahmud S, Salma N, Hossain MM, Karim MR. Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study. Sci Rep. 2025;15(1):14336. https://doi.org/10.1038/s41598-025-99288-y . Toma A, Talukder A, Shirin Khan S, Razu SR. An assessment of the association between antenatal care and child malnutrition in Bangladesh. Family Med Prim Care Rev. 2018;4373–8. https://doi.org/10.5114/fmpcr.2018.79350 . USAID. (2022). Bangladesh: Standard DHS, 2022 . https://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=1 WHO. (2006). WHO child growth standards: length/height-for-age, weight-for-age, weight-for length, weight-for-height and body mass index-for-age: methods and development . https://www.who.int/publications/i/item/924154693X Zaman S, Ahammed T, Hasan MA, Huque ME. COVID-19 effect on food security, livelihood, and mental health in affected households of Jashore, Bangladesh. Dialogues Health. 2025;6:100217. https://doi.org/https://doi.org/10.1016/j.dialog.2025.100217 . Additional Declarations No competing interests reported. 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13:55:54","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8868,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6737252/v1/9abf19a13f384757657708b5.png"},{"id":94397833,"identity":"c56641fc-f4a1-4972-9646-8c9404dddc58","added_by":"auto","created_at":"2025-10-27 13:56:50","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166925,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6737252/v1/7d9eed50289747fd3a1e95eb.html"},{"id":94396337,"identity":"ed0ed20b-edfb-4d3c-b417-b26ec47bbcc1","added_by":"auto","created_at":"2025-10-27 13:55:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98324,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Overall prevalence of malnutrition and prevalence of malnutrition by (b)Residence, (c) child sex, (d) sanitation facilities, (e) Maternal education and, (f) wealth index\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6737252/v1/ff471105d38b79d9d29dad29.png"},{"id":94399316,"identity":"7e7594ac-7e2a-4465-bcae-3271d91c4e4b","added_by":"auto","created_at":"2025-10-27 13:57:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23171,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of age on stunting\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6737252/v1/e6a4eb88d3fed75027cf5f9c.png"},{"id":94459491,"identity":"456a12f8-a5b4-4c29-86fe-68c48e141b4e","added_by":"auto","created_at":"2025-10-27 14:53:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1457092,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6737252/v1/92b82827-1c9b-44e0-942b-5b4171b112b1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Changing Determinants of Child Wasting: Insights from the Prevalence of Stunting, Wasting, and Malnutrition and Their Determinants — An Analysis of BDHS 2022 Data","fulltext":[{"header":"Background","content":"\u003cp\u003eChild malnutrition remains a pervasive public health challenge in many low- and middle-income countries, including Bangladesh. Despite consistent investments in health and nutrition over the past two decades, undernutrition continues to affect a substantial proportion of children under five years of age, contributing to impaired growth, developmental delays, and increased vulnerability to illness and mortality (Hossain et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The three key indicators of child malnutrition\u0026mdash;stunting (low height-for-age), wasting (low weight-for-height), and underweight (low weight-for-age)\u0026mdash;capture different dimensions of nutritional deficits, each with distinct causes and policy implications.\u003c/p\u003e \u003cp\u003eNationally representative data from the Bangladesh Demographic and Health Surveys (BDHS) have documented a steady decline in stunting, from 36% in 2014 to 24% in 2022. However, underweight prevalence has plateaued at 22%, and the prevalence of wasting has increased from 8% in 2017\u0026ndash;18 to 11% in 2022 (NIPORT, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p. 185). This stagnation and recent reversal in progress\u0026mdash;particularly in acute malnutrition\u0026mdash;suggest emerging vulnerabilities, possibly influenced by socioeconomic disruptions, natural disasters, and most recently, the COVID-19 pandemic. Studies conducted during and after the pandemic have raised concerns over food insecurity, income loss, and deteriorating child health, especially in economically and environmentally vulnerable areas such as coastal and saline-prone regions (Della et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Morshed et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ePrior research has consistently emphasized the central role of socioeconomic status in determining child nutrition outcomes. Wealthier households tend to have greater access to diverse diets, healthcare, clean water, and sanitation, which together reduce the risk of undernutrition (Chowdhury et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chowdhury et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pe\u0026ntilde;a \u0026amp; Bacallao, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). A decomposition analysis of BDHS data from 1996 to 2014 attributed nearly half of the inequality in child dietary diversity to household wealth and a quarter to maternal education (Kundu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Numerous studies have also highlighted maternal education as a powerful protective factor\u0026mdash;educated mothers are more likely to practice appropriate feeding, hygiene, and healthcare-seeking behaviors (Abdulla et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hossain \u0026amp; Khan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMaternal and child health characteristics, such as maternal BMI, antenatal care (ANC) attendance, and birth weight, are also key predictors of child nutritional status. Low birth weight (LBW), often a result of poor maternal nutrition or inadequate prenatal care, is closely linked with higher risk of stunting, wasting, and underweight (Islam et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sanin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Maternal BMI has both biological and behavioral implications for child health, as better-nourished mothers tend to have better pregnancy outcomes and are more likely to provide nutritionally adequate care at home (Duggal \u0026amp; Petri Jr, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Silveira et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Similarly, ANC visits serve as important touchpoints for nutritional counselling and early detection of maternal and fetal complications, and some studies suggest they may play a protective role against child malnutrition (Rahman et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tamanna et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Toma et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEnvironmental factors such as sanitation and water quality continue to shape child nutrition outcomes, particularly in areas where exposure to fecal pathogens and enteric infections leads to nutrient loss and poor absorption (Chowdhury et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hasan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Children in households lacking improved sanitation facilities are more likely to suffer from repeated infections, which can compound undernutrition even in the presence of adequate caloric intake.\u003c/p\u003e \u003cp\u003eDespite a robust body of literature on malnutrition in Bangladesh, few studies have analyzed national-level data in the context of the COVID-19 pandemic. Emerging evidence suggests that wasting, in particular, may be more sensitive to short-term shocks\u0026mdash;such as sudden food shortages or income loss\u0026mdash;than to long-term socioeconomic status (Hossain et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Talukder, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Talukder et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This distinction is critical for designing effective interventions in times of crisis. Moreover, gender-based and regional disparities in malnutrition continue to surface in sub-national studies, with some areas reporting significantly higher rates of undernutrition among girls, particularly in socially marginalized or disaster-prone communities (Khanam \u0026amp; Haque, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sanin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn light of these evolving dynamics, there is an urgent need to re-examine the determinants of child malnutrition using the most recent nationally representative data. The BDHS 2022 provides a unique opportunity to assess the post-pandemic landscape of child nutrition in Bangladesh, understand whether traditional risk factors remain significant, and identify any emerging patterns that demand tailored policy responses.\u003c/p\u003e \u003cp\u003eObjectives of the Study is i) To estimate the current prevalence of stunting, wasting, and underweight among children under five in Bangladesh using BDHS 2022 data, ii) To identify key socioeconomic, maternal and child health, demographic, and environmental determinants of these malnutrition outcomes, and iii) To assess whether the determinants of malnutrition, particularly wasting, have shifted in the wake of the COVID-19 pandemic, and how these findings compare to prior studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Source\u003c/h2\u003e \u003cp\u003eThis cross-sectional study utilized nationally representative data from the 2022 Bangladesh Demographic and Health Survey (BDHS), the ninth in a series of surveys conducted under the global Demographic and Health Surveys (DHS) Program (USAID, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The 2022 BDHS was implemented by Mitra and Associates under the authority of the National Institute of Population Research and Training (NIPORT), with technical assistance from ICF and financial support from the United States Agency for International Development (USAID). Data collection was conducted between June and December 2022.\u003c/p\u003e \u003cp\u003eThe BDHS (NIPORT, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) employed a two-stage stratified sampling design to ensure national representativeness across all eight administrative divisions of Bangladesh. A total of 30,330 households were selected, and 30,018 were successfully interviewed, yielding a household response rate of 99.6% overall. The response rate for eligible women aged 15\u0026ndash;49 was 99.1% across all questionnaire types.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eAnthropometric data were collected for children aged 0\u0026ndash;59 months in the selected households using standardized procedures. Weight was measured using SECA 874U digital scales, while length or height was measured using ShorrBoards\u0026reg;, depending on the child's age and ability to stand. Based on these measurements, height-for-age (HAZ), weight-for-height (WHZ), and weight-for-age (WAZ) Z-scores were calculated using the WHO \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e Child Growth Standards. Children with Z-scores outside the biologically plausible range (HAZ \u0026lt; -6 or \u0026gt;\u0026thinsp;+\u0026thinsp;6, WHZ \u0026lt; -5 or \u0026gt;\u0026thinsp;+\u0026thinsp;5, WAZ \u0026lt; -6 or \u0026gt;\u0026thinsp;+\u0026thinsp;5) were excluded from the analysis in accordance with WHO flagging criteria (WHO, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). After cleaning the data, a total of 4,118 children with valid anthropometric measurements were retained for descriptive analysis. For the multivariable regression analysis, complete-case analysis was conducted, resulting in a final analytical sample of 1,216 children with complete data across all relevant covariates.\u003c/p\u003e\n\u003ch3\u003eOutcome Measures\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes of interest in this study were the binary indicators of stunting, wasting, and underweight. A child was considered stunted if their HAZ was less than \u0026minus;\u0026thinsp;2 standard deviations (SD) from the WHO median, wasted if their WHZ was below \u0026minus;\u0026thinsp;2 SD, and underweight if their WAZ was below \u0026minus;\u0026thinsp;2 SD. Each of these outcomes was coded as a binary variable, with 1 indicating the presence of the condition and 0 otherwise.\u003c/p\u003e\n\u003ch3\u003eExplanatory Variables\u003c/h3\u003e\n\u003cp\u003eA wide range of explanatory variables was included based on prior literature and the conceptual relevance to child nutrition. These included socioeconomic, maternal and child health, and environmental factors. Socioeconomic characteristics encompassed household wealth index (divided into quintiles: poorest, poorer, middle, richer, and richest), maternal education (categorized as none, primary, secondary, or higher), household access to electricity, and maternal media exposure (defined as exposure to at least one of television, radio, or newspapers). Maternal and child health-related variables included maternal body mass index (BMI), number of antenatal care (ANC) visits, place of delivery (home, public facility, or private facility), mode of delivery (cesarean or vaginal), and birth weight in kilograms, with missing values handled for special DHS codes. Demographic and environmental variables comprised child\u0026rsquo;s sex and age (in months), household size, and household-level water source and sanitation facilities. Water and sanitation were classified as \u0026ldquo;improved\u0026rdquo; or \u0026ldquo;unimproved\u0026rdquo; according to WHO/UNICEF Joint Monitoring Programme definitions. All variable transformations and coding were implemented in accordance with DHS guidelines and confirmed via review of the BDHS 2022 final report and dataset codebook.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics, including frequencies and percentages, were used to summarize the distribution of key child, maternal, household, and environmental characteristics. The prevalence of stunting, wasting, and underweight was computed overall and across categories of key explanatory variables such as child sex, maternal education, household wealth, residence, religion, and media exposure. Prevalence was calculated using the formula: (number of affected children / total number of children) \u0026times; 100.\u003c/p\u003e \u003cp\u003eTo identify factors associated with the three nutritional outcomes, multivariable logistic regression models were constructed. These models were estimated using the \u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003eglm()\u003c/span\u003e function in R with a binomial family and logit link. Three sets of thematic models were developed to examine the role of distinct covariate blocks: a socioeconomic model including wealth, maternal education, electricity, and media exposure; a maternal and child health model including BMI, ANC visits, place and mode of delivery, and birth weight; and a demographic and environmental model including child sex and age, household size, water source, and sanitation. Each of these models was separately estimated for the outcomes of stunting, wasting, and underweight. A final combined model included all covariates simultaneously to adjust for potential confounding and to examine the relative strength of each predictor in a multivariable context.\u003c/p\u003e \u003cp\u003eAdjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported to summarize the magnitude and direction of associations. Statistical significance was evaluated at the 5%, 1%, and 0.1% levels, and key findings were interpreted accordingly. Across models, birth weight emerged as a strong and consistent predictor of all three forms of malnutrition, with lower birth weights significantly increasing the likelihood of stunting, wasting, and underweight. Additional associations were observed with socioeconomic disadvantage, unimproved sanitation, and lack of maternal education. All statistical analyses were conducted using R version 4.3.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eThe general description of the overall sample is provided in the Table 1. The study sample comprised 4,118 children under five years, with a balanced sex distribution (51.5% male, 48.5% female). Wealth was evenly distributed across quintiles (21.5% Poorest, 19.7% Poorer, 20.1% Middle, 19.2% Richer, 19.5% Richest), and maternal education levels were 6.2% None, 22.8% Primary, 52.1% Secondary, and 18.9% Higher. Most children resided in rural areas (68.0%), and 91.5% were from Muslim households. Access to electricity was prevalent (88.5%), with 86.4% of households using improved water sources and 72.5% having improved sanitation. Missing data were substantial for birth weight (61.1%), delivery place (41.3%), and caesarean delivery (39.3%). Mean maternal BMI was 23.1 kg/m\u0026sup2; (SD 4.20), mean child age was 29.0 months (SD 17.6), mean household size was 5.78 (SD 2.37), mean ANC visits were 3.31 (SD 2.33), and mean birth weight was 2.98 kg (SD 0.652).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eTable 1. Descriptive statistics of the sample\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eVariable\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eCategory/Statistic\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eN=4118\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eN (%) / Value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eChild Sex\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e2119 (51.5%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eFemale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1999 (48.5%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eWealth Quintile\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePoorest\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e884 (21.5%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePoorer\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e812 (19.7%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMiddle\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e829 (20.1%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRicher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e791 (19.2%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRichest\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e802 (19.5%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMother\u0026rsquo;s Education\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNone\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e255 (6.2%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePrimary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e940 (22.8%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eSecondary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e2146 (52.1%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHigher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e777 (18.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eResidence\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eUrban\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1317 (32.0%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRural\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e2801 (68.0%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eReligion\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMuslim\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e3767 (91.5%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHindu\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e317 (7.7%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eOther\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0 (0.0%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e34 (0.8%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eBMI (kg/m\u0026sup2;)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMean (SD)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.1 (4.20)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMedian [Min, Max]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22.8 [13.0, 43.7]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e2 (0.0%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eChild Age (months)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMean (SD)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e29.0 (17.6)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMedian [Min, Max]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e29.0 [0, 59.0]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHH Size\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMean (SD)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e5.78 (2.37)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMedian [Min, Max]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e5.00 [2.00, 19.0]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eElectricity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e64 (1.6%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e3646 (88.5%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e408 (9.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eANC Visits\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMean (SD)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e3.31 (2.33)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMedian [Min, Max]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e3.00 [0, 20.0]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1727 (41.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePlace of Delivery\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHome\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e895 (21.7%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePublic Facility\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e389 (9.4%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePrivate Facility\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1133 (27.5%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1701 (41.3%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eCaesarean Delivery\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1363 (33.1%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1136 (27.6%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1619 (39.3%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eBirth Weight (kg)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMean (SD)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e2.98 (0.652)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMedian [Min, Max]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e3.00 [0.500, 5.50]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e2515 (61.1%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMedia Exposure\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNot Exposed\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1818 (44.1%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eExposed\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e2300 (55.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eWater Source\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eImproved\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e3557 (86.4%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eUnimproved\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e112 (2.7%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e449 (10.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eSanitation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eImproved\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e2986 (72.5%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eUnimproved\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e643 (15.6%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e489 (11.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eThe Table 2 presents the prevalence of malnutrition by both numbers and their percentage. This prevalence analyses revealed significant disparities. Stunting prevalence was highest in the Poorest quintile (35.1%) compared to the Richest (12.6%), with a clear gradient across wealth levels (Poorer: 26.8%, Middle: 23.3%, Richer: 18.2%). Underweight followed a similar pattern (Poorest: 33.8%, Richest: 13.6%), while wasting showed a less pronounced gradient (Poorest: 14.0%, Richest: 10.8%). Maternal education strongly influenced outcomes: children of mothers with no education had the highest prevalence (stunting: 41.2%, wasting: 20.4%, underweight: 43.9%), compared to those with higher education (stunting: 12.4%, wasting: 11.5%, underweight: 14.0%). Rural children exhibited higher stunting (24.8%) and underweight (24.0%) than urban children (20.5% and 20.2%), while wasting prevalence was similar (11.4% rural vs. 12.4% urban). Households with unimproved sanitation had higher stunting (22.7%) and underweight (20.7%) compared to those with improved sanitation (15.6% for both). Lack of media exposure was associated with higher stunting (27.4%) and underweight (26.7%) compared to exposed children (20.3% and 19.7%). Religion showed modest differences, with Hindu children having slightly lower stunting (20.2%) than Muslim children (23.8%). Child sex showed minimal differences (stunting: 23.6% male vs. 23.3% female; wasting: 11.5% male vs. 12.0% female; underweight: 22.6% male vs. 23.0% female).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eTable 2. prevalence of malnutrition by number and percentage and their distribution according to variables\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eVariable\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eStunted (n)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eStunting (%)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eWasted (n)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eWasting (%)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eUnderweight (n)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eUnderweight (%)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eTotal N= 4118\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e966\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e23.45\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e482\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.70\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e937\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22.75\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eChild Sex\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e500\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.59\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e243\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e478\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22.56\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eFemale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e466\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e239\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.95\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e459\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22.96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eWealth i\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003endex\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;Poorest\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e310\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e35.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e124\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e14.02\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e299\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e33.83\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePoorer\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e218\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e26.85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e99\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e12.20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e209\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e25.74\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMiddle\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e193\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.28\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e92\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e186\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22.44\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRicher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e144\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e18.20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e80\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e10.11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e134\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e16.94\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRichest\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e101\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e12.59\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e87\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e10.85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e109\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e13.59\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eMother\u0026rsquo;s Education\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNone\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e105\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e41.18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e52\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e20.39\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e112\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e43.92\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePrimary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e285\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e30.32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e107\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.38\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e266\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e28.30\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eSecondary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e480\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22.36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e234\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e10.91\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e450\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e20.96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHigher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e12.35\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.45\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e109\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e14.03\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eResidence\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eUrban\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e270\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e20.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e163\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e12.37\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e266\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e20.19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRural\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e696\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e24.84\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e319\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.39\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e671\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eReligion\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMuslim\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e898\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.83\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e437\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.60\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e861\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22.85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHindu\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e64\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e20.19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e12.62\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e68\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e21.45\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eOther\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.76\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e14.71\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.53\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eElectricity\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e34.38\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e15.63\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e32.81\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e850\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e416\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.41\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e828\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22.71\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e94\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.04\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e56\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e13.73\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e88\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e21.57\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eMedia Exposure\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNot Exposed\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e499\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e27.45\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e220\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e12.10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e485\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e26.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eExposed\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e467\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e20.30\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e262\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.39\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e452\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e19.65\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eWater Source\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eImproved\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e832\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.39\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e407\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.44\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e807\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22.69\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eUnimproved\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e28.57\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e14\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e12.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e30.36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e102\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e22.72\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e61\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e13.59\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e21.38\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eSanitation\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eImproved\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e634\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e21.23\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e338\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e637\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e21.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eUnimproved\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e219\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e34.06\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e80\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e12.44\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e194\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e30.17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMissing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e113\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e23.11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e64\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e13.09\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e106\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e21.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure 1 visualises the overall prevalence of malnutrition, as well as the prevalence of stunting, wasting, and underweight, by residence, sex, sanitation status, maternal education level, and wealth index.\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eFig 1. (a) Overall prevalence of malnutrition and prevalence of malnutrition by (b)Residence, (c) child sex, (d) sanitation facilities, (e) Maternal education and, (f) wealth index\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eIn the socio-economic models (Table 3), higher wealth quintiles were protective against stunting (Poorer vs. Poorest: OR 0.57, p=0.007; Richest vs. Poorest: OR 0.31, p\u0026lt;0.001) and underweight (Poorer: OR 0.52, p=0.014; Richest: OR 0.40, p=0.001), but not significantly for wasting (Richest: OR 1.13, p=0.752). Higher maternal education reduced the odds of all outcomes (Higher vs. None: stunting OR 0.31, p=0.004; wasting OR 0.34, p=0.020; underweight OR 0.35, p=0.009). Electricity and media exposure were not significant predictors.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eTable 3. Logistic Regression Results of Socio-Economic Factors on Child Malnutrition\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eStunting\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eWasting\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eUnderweight\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ePredictor\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eEstimate (SE)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eEstimate (SE)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eEstimate (SE)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(Intercept)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.188 (1.22)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.877\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.115\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(1.23)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.926\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.079\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(1.22)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.949\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eWealth\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePoorer\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.661**\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.246)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00718\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.217\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.361)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.547\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.654*\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.265)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0135\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMiddle\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.583*\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.237)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0140\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.178\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.354)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.614\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.418\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.248)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0925\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRicher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.814*** (0.242)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.000775\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.156\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.372)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.676\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.847**\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.262)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00123\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRichest\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-1.170*** (0.274)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0000196\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.119\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.377)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.752\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.904**\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.282)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00132\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eEducation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePrimary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.663\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.394)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0925\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.945*\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.466)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0425\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.779*\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.396)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0491\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eSecondary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.549 (\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.371)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.139\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-1.180**\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.433)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00663\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.917*\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.373)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0138\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHigher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-1.180**\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.411)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00423\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-1.090*\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.471)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0204\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-1.060**\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.407)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00928\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eElectricity (Y)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.049\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(1.17)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.967\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-1.170\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(1.18)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.321\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.084\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(1.18)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.943\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMedia exposure (Y)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.140\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.160)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.381\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.034\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.218)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.875\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.069\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.170)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.686\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eTable 3. Associations between household and maternal characteristics and three forms of child malnutrition: stunting, wasting, and underweight. Each cell reports the regression coefficient (Estimate) followed by its standard error in parentheses. Statistical significance is indicated as follows: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (\u003cem\u003e),\u0026nbsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (), and\u0026nbsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (\u003c/em\u003e). The\u0026nbsp;reference group\u0026nbsp;for the household wealth variable is\u0026nbsp;\u0026ldquo;Poorest\u0026rdquo;. The\u0026nbsp;reference group\u0026nbsp;for maternal education is\u0026nbsp;\u0026ldquo;No Education\u0026rdquo;. Binary variables (Electricity and Media exposure) are coded as 1 = Yes, 0 = No.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eIn the maternal and child health models (Table 4), higher birth weight was protective against stunting (OR 0.73, p=0.005) and underweight (OR 0.60, p\u0026lt;0.001), but not wasting (OR 0.81, p=0.163). Higher maternal BMI reduced the odds of wasting (OR 0.93, p=0.007) and underweight (OR 0.94, p=0.003), as did more ANC visits (wasting: OR 0.89, p=0.029; underweight: OR 0.88, p=0.002). Delivery place and caesarean delivery were not significant.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTable 4: Logistic Regression Results of Maternal and Child Health Factors on Child Malnutrition\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eStunting\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eWasting\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eUnderweight\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ePredictor\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eEstimate (SE)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eEstimate (SE)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eEstimate (SE)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(Intercept)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.658\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.540)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.223\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.088\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.782)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.911\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.940***\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.589)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00095\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMaternal BMI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.024\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.018)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.191\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.070** (0.026)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00725\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.060**\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.020)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00293\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eANC visits\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.064\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.035)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.069\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.115*\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.053)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0293\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.124**\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.041)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00235\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eDelivery place (Public)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.225\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.282)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.425\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.463\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.454)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.308\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.183\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.303)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.545\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eDelivery place (Private)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.467\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.294)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.112\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.258\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.470)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.583\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.538\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.318)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0908\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eC-Section delivery (Y)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.007\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.193)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.972\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.206\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.265)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.436\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.209\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.210)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.320\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eChild weight at Birth\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.314**\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.111)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00452\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.213\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.153)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.163\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.506***\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.120)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00003\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eTable 4. Association between maternal and child health service factors and malnutrition outcomes. Values are estimates with standard errors in parentheses. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (), p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (). Reference category for delivery place is home. C-section is coded as 1 = Yes, 0 = No.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eIn the demographic and environmental models (Table 5), older child age increased the odds of stunting (OR 1.03, p\u0026lt;0.001) and underweight (OR 1.02, p=0.002), while unimproved sanitation increased stunting (OR 1.80, p=0.003) and underweight (OR 1.58, p=0.030). Child sex, household size, and water source were not significant.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTable 5: Logistic Regression Results of Demographic and Environmental Factors on Child Malnutrition\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eStunting\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eWasting\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eUnderweight\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ePredictor\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eEstimate (SE)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eEstimate (SE)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eEstimate (SE)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(Intercept)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-1.70***\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.264)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.15e-10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-1.87***\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.348)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e7.62e-08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-2.01***\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.276)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e3.25e-13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eChild sex\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.218\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.148)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.139\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.190\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.201)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.344\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.165\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.156)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.289\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eChild Age\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.028***\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.007)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.12e-04\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.008\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.010)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.398\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.023**\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.008)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00221\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHousehold size\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.032\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.035)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.355\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.029\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.047)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.540\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.002\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.035)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.953\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eWater source\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(unimproved)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.154\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.436)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.724\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-1.24\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(1.020)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.225\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.331\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.435)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.447\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eSanitation\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(unimproved)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.589**\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.198)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.00287\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e-0.123\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.309)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.690\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.458*\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.211)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0302\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eTable 5. Associations between child and household characteristics and malnutrition outcomes. Values are estimates with standard errors in parentheses. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (), p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (). Child sex is coded as 1 = Male, 0 = Female. Reference categories: improved water source and improved sanitation.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThe combined model (Table 6) reinforced these findings. Higher wealth quintiles significantly reduced stunting risk (Poorer: OR 0.57, 95% CI 0.35\u0026ndash;0.94, p=0.028; Richest: OR 0.38, 95% CI 0.21\u0026ndash;0.69, p=0.002) and showed a protective trend for underweight (Poorer: OR 0.58, 95% CI 0.34\u0026ndash;0.99, p=0.045). Maternal education remained protective across all outcomes (Higher vs. None: stunting OR 0.33, 95% CI 0.14\u0026ndash;0.74, p=0.008; wasting OR 0.31, 95% CI 0.12\u0026ndash;0.81, p=0.017; underweight OR 0.34, 95% CI 0.15\u0026ndash;0.77, p=0.010). Higher birth weight reduced stunting (OR 0.72, 95% CI 0.58\u0026ndash;0.89, p=0.002) and underweight (OR 0.60, 95% CI 0.48\u0026ndash;0.76, p\u0026lt;0.001). Maternal BMI (wasting: OR 0.93, 95% CI 0.88\u0026ndash;0.98, p=0.006; underweight: OR 0.94, 95% CI 0.90\u0026ndash;0.98, p=0.003) and ANC visits (wasting: OR 0.88, 95% CI 0.79\u0026ndash;0.98, p=0.024; underweight: OR 0.91, 95% CI 0.84\u0026ndash;0.99, p=0.026) were protective for wasting and underweight. Older child age increased stunting (OR 1.03, 95% CI 1.01\u0026ndash;1.04, p\u0026lt;0.001) and underweight (OR 1.03, 95% CI 1.01\u0026ndash;1.04, p=0.001), visualized in the figure 2. Child sex, household size, water source, sanitation, delivery place, caesarean delivery, and electricity were not significant in the combined model. Visualizations confirmed these trends, with bar plots showing clear gradients in prevalence by wealth and education, and a smoothed LOESS curve indicating stunting prevalence peaking at 30\u0026ndash;40 months of age. These findings highlight the critical roles of socio-economic status, maternal education, and early-life health factors in mitigating child malnutrition in Bangladesh, with wealth and education showing consistent protective effects across models.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eTable 6: Logistic regression of the combined model\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eVariable\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eStunted OR (95% CI)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eP-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eWasted OR (95% CI)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eP-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eUnderweight OR (95% CI)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eP-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(Intercept)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.74\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.12\u0026ndash;25.2)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.686\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.5\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.58\u0026ndash;226)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.109\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.1\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.72\u0026ndash;173)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.085\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eChild sex (Female)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.83\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.62\u0026ndash;1.12)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.220\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.84\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.56\u0026ndash;1.26)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.406\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.86\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.63\u0026ndash;1.19)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.366\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eWealth\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePoorer\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.57\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.35\u0026ndash;0.94)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.028*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.28\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.62\u0026ndash;2.63)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.509\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.58\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.34\u0026ndash;0.99)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.045*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMiddle\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.66\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.41\u0026ndash;1.09)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.102\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.37\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.66\u0026ndash;2.84)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.393\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.84\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.50\u0026ndash;1.41)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.502\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRicher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.55\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.33\u0026ndash;0.93)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.026*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.11\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.51\u0026ndash;2.42)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.796\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.64\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.36\u0026ndash;1.12)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.116\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRichest\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.38\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.21\u0026ndash;0.69)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0015**\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.72\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.76\u0026ndash;3.92)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.195\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.63\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.34\u0026ndash;1.19)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.153\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMaternal Education\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePrimary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.53\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.24\u0026ndash;1.17)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.115\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.37\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.15\u0026ndash;0.95)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.039*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.44\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.20\u0026ndash;0.99)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.046*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eSecondary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.60\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.29\u0026ndash;1.25)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.174\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.29\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.12\u0026ndash;0.69)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0055**\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.37\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.17\u0026ndash;0.79)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.010*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHigher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.33\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.14\u0026ndash;0.74)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0076**\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.31\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.12\u0026ndash;0.81)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.017*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.34\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.15\u0026ndash;0.77)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.010*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eElectricity (Y)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.34\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.13\u0026ndash;13.7)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.807\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.34\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.03\u0026ndash;3.60)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.372\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.13\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.11\u0026ndash;11.7)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.920\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMedia exposure (Y)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.10\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.80\u0026ndash;1.52)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.553\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.02\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.66\u0026ndash;1.57)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.934\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.07\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.76\u0026ndash;1.51)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.703\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMaternal BMI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.98\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.95\u0026ndash;1.02)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.367\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.93\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.88\u0026ndash;0.98)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0061**\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.94\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.90\u0026ndash;0.98)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0030**\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eANC visits\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.99\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.92\u0026ndash;1.07)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.856\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.88\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.79\u0026ndash;0.98)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.024*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.91\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.84\u0026ndash;0.99)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.026*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eDelivery place (Public)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.92\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.52\u0026ndash;1.63)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.774\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.48\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.60\u0026ndash;3.65)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.398\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.85\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.46\u0026ndash;1.57)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.610\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eDelivery place (Private)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.70\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.38\u0026ndash;1.27)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.235\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.23\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.49\u0026ndash;3.14)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.659\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.58\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.31\u0026ndash;1.11)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.100\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eC-Section Deliver (Y)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.15\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.77\u0026ndash;1.71)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.487\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.22\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.72\u0026ndash;2.08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.467\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.34\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.87\u0026ndash;2.05)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.184\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eChild weight at Birth\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.72\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.58\u0026ndash;0.89)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0025**\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.80\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.59\u0026ndash;1.08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.151\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e0.60\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(0.48\u0026ndash;0.76)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.000026***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eChild age\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e1.03\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(1.01\u0026ndash;1.04)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.000089***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.99\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.97\u0026ndash;1.01)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.422\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e1.03\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e(1.01\u0026ndash;1.04)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.0012**\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHousehold size\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.00\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.93\u0026ndash;1.07)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.930\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.98\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.89\u0026ndash;1.07)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.636\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.03\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.96\u0026ndash;1.10)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.469\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eWater source\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(unimproved)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.12\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.47\u0026ndash;2.68)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.802\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.29\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.04\u0026ndash;2.20)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.233\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.43\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.59\u0026ndash;3.48)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.427\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eSanitation\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(unimproved)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.24\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.81\u0026ndash;1.91)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.322\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.82\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.43\u0026ndash;1.59)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.564\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e1.18\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(0.74\u0026ndash;1.89)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.477\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan lang=\"EN-AU\"\u003eTable 6. Odds ratios (ORs) with 95% confidence intervals (CIs) and p-values for predictors of child malnutrition outcomes. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (), p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (). Reference categories: Male child (for sex), \u0026quot;Poorest\u0026quot; (for wealth), \u0026quot;No education\u0026quot; (for maternal education), \u0026quot;No electricity\u0026quot;, \u0026quot;No media exposure\u0026quot;, \u0026quot;Unimproved water source\u0026quot;, and \u0026quot;Improved sanitation\u0026quot;. OR \u0026lt; 1 indicates reduced odds of the outcome.\u003c/span\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the prevalence and determinants of malnutrition (stunting, wasting, and underweight) among children under five years in Bangladesh using the Bangladesh Demographic and Health Survey (BDHS) 2022 dataset. The findings reveal a prevalence of 23.5% for stunting, 11.7% for wasting, and 22.8% for underweight, with significant associations identified with socio-economic factors (wealth index, maternal education), maternal and child health factors (birth weight, maternal BMI, antenatal care visits), and demographic and environmental factors (child age, sanitation). Some of these findings echoed previous results, whereas others showed differences, especially in the case of wasting.\u003c/p\u003e\n\u003ch3\u003ePrevalence of malnutrition\u003c/h3\u003e\n\u003cp\u003eThe prevalence of stunting (23.5%), wasting (11.7%), and underweight (22.8%) in this study aligns with the BDHS 2022 report, which reported stunting at 24%, wasting at 11%, and underweight at 22% (NIPORT, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This consistency reflects the appropriateness of the coding and statistical analysis used in this study. Comparing these findings with earlier BDHS data, the 2014 report showed higher rates of stunting (36%), wasting (14%), and underweight (33%), while the 2017\u0026ndash;18 report indicated reduced rates of 31%, 8%, and 22%, respectively (NIPORT, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These trends suggest that from 2014 to 2018, the prevalence of stunting and underweight declined significantly. However, from 2018 to 2022, the prevalence of wasting has shown an increasing trend, and in the case of stunting and underweight, no further decrease has occurred. Since the BDHS data were collected during the COVID-19 pandemic (from June to December 2022), the increasing trend in wasting and the non-decreasing trends in stunting and underweight reflect the assumption of rising poverty and reduced food security due to the pandemic (Rahman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shuvo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zaman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In support of this prediction, studies conducted during the onset of the pandemic reported the prevalence of stunting, wasting, and underweight as 40%, 32%, and 44% in the coastal region (Morshed et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and 28.6%, 20.7%, and 24.8%, respectively, in a saline-prone region of Bangladesh (Della et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although these studies involved relatively small sample sizes and focused on localized areas, the findings are still alarming.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSocio-economic determinants:\u003c/h2\u003e \u003cp\u003eOur findings confirm the strong protective effects of higher wealth and maternal education against child malnutrition. In the combined model, children in the Poorer and Richest wealth quintiles had significantly lower odds of stunting compared to those in the Poorest quintile, with similar patterns observed for underweight. Maternal education also emerged as a consistent protective factor across all outcomes. These results align with major previous studies that identified household wealth and maternal education as key determinants of malnutrition in Bangladesh. (Abdulla et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chowdhury et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chowdhury et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hossain \u0026amp; Khan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; S. J. Rahman et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pronounced socio-economic gradient in our study, with stunting prevalence dropping from 35.1% in the Poorest quintile to 12.6% in the Richest, mirrors findings from a decomposition analysis of BDHS data (1996\u0026ndash;2014), which attributed 49.47% of inequality in minimum dietary diversity (MDD) to wealth status and 25.06% to maternal education (Kundu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These consistent associations highlight the role of socio-economic disparities in shaping nutritional outcomes, as wealthier households likely have better access to food, healthcare, and sanitation, while maternal education enhances nutritional knowledge and caregiving practices.\u003c/p\u003e \u003cp\u003eHowever, the lack of statistical significance for wealth in the wasting models contrasts with findings from some previous studies (Chowdhury et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jubayer et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; M. A. Rahman et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Talukder, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which identified wealth as a significant determinant of wasting. This discrepancy could be attributed to the timing of the survey, which coincided with the COVID-19 pandemic. Wasting, defined as low weight-for-height, reflects acute malnutrition and is sensitive to short-term shocks such as sudden declines in income, food access, or healthcare. During the pandemic, widespread economic disruptions increased poverty and reduced food and nutritional security (Hossain et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shuvo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this context, although the wealth index captured long-term household assets or status, it may not have reflected the immediate income losses or food insecurity that more directly influenced wasting during the pandemic. Therefore, the observed non-significance of wealth in this model may be a reflection of these acute, short-term economic shocks rather than a contradiction of previous evidence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMaternal and child health factors\u003c/h2\u003e \u003cp\u003eOur study identified higher birth weight, maternal BMI, and antenatal care (ANC) visits as protective against malnutrition, particularly for wasting and underweight. Higher birth weight reduced the odds of stunting and underweight and the protective effect of maternal BMI is observed on wasting and underweight previous studies (Rahman et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sanin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Again, this insight is valuable for understanding the determinants of wasting. While higher birth weight may offer some protection, it is less likely to shield children during sudden economic shocks. In contrast, maternal BMI, has two possible effects i) maternal height has positive associated with children health (Silveira et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and ii) mothers with higher BMI may shape home environments (like diet, physical activity norms, or feeding practices), which in turn influence the child\u0026rsquo;s BMI (Duggal \u0026amp; Petri Jr, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, a higher number of antenatal care (ANC) visits may reflect greater health awareness. These factors could have motivated parents to prioritise child nutrition, even if it meant sacrificing other household necessities (Toma et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A novel contribution of our study is the significant protective effect of ANC visits on wasting and underweight which was less consistently reported in prior studies. For example, Hossain et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found no significant association between ANC visits and wasting, possibly due to differences in model specifications or sample size. Our finding suggests that ANC may mitigate acute malnutrition by improving maternal health practices during pregnancy, a hypothesis warranting further investigation in longitudinal studies.\u003c/p\u003e \u003cp\u003eThe lack of significance for delivery place and caesarean delivery in our models contrasts with some literature (Das et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which found institutional deliveries protective due to better access to skilled care. This discrepancy may stem from our reduced sample size or the high missingness in delivery-related variables (41.3% for delivery place, 39.3% for caesarean delivery), potentially masking effects. These results emphasize the critical role of maternal health in early-life nutrition, as LBW and low maternal BMI may reflect intrauterine growth restriction and maternal undernutrition, respectively, while ANC visits facilitate early interventions like nutritional counselling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and environmental factors\u003c/h2\u003e \u003cp\u003eChild age was a significant predictor of stunting and underweight, consistent with previous studies (Abdulla et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Das \u0026amp; Gulshan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Most of these studies reported the highest stunting and underweight prevalence at 18\u0026ndash;23 months. In our combined model we found no significant effect of age on wasting which a different from few other studies (Hossain et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; S. J. Rahman et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This may also reflect the impact of a sudden nutritional shock caused by the COVID-19 pandemic. The observed cases of wasting in the dataset were not age-related but instead resulted from acute malnutrition triggered by the pandemic.\u003c/p\u003e \u003cp\u003eUnimproved sanitation increased stunting and underweight in our demographic and environmental models, aligning with Hasan et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Chowdhury et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who linked poor sanitation to higher malnutrition. However, sanitation was not significant in our combined model, possibly due to collinearity with socio-economic factors like wealth, which often correlate with access to improved facilities.\u003c/p\u003e \u003cp\u003eThe lack of association between child sex and malnutrition in our study (stunting: 23.6% male vs. 23.3% female; wasting: 11.5% male vs. 12.0% female) contrasts with some regional findings. For instance, a previous study reported higher prevalence among girls (39% stunting, 54% wasting, 45% underweight) than boys in haor areas, attributing this to gender-based discrimination in food allocation (Khanam \u0026amp; Haque, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our nationally representative data may mask such regional or cultural variations, suggesting a need for sub-group analyses to explore gender differences further. Similarly, the non-significance of water source and household size in our models differs from studies like Hasan et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which identified water quality as a determinant in Sylhet. This may reflect the high prevalence of improved water sources (86.4%) in our sample, reducing variability.\u003c/p\u003e \u003cp\u003eThe high missingness in key variables (birth weight, delivery place, caesarean delivery) is a critical limitation, reducing our regression sample to 1,216 and potentially introducing selection bias. This issue, also noted in prior studies (Islam et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), highlights the challenge of incomplete data in DHS surveys, particularly for retrospective variables like birth weight. Our sensitivity analyses confirmed consistent trends, but the reduced sample size may have attenuated some associations, such as those for delivery place or water source. Compared to machine learning approaches (S. J. Rahman et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tamanna et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which achieved higher predictive accuracy for calculating malnutrition using BDHS data, our logistic regression models provide interpretable odds ratios, facilitating policy translation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConclusion\u003c/strong\u003e \u003cp\u003eThis study reaffirms that the determinants of malnutrition include the wealth index, maternal education, child\u0026rsquo;s age, birth weight, maternal BMI, and antenatal care (ANC) visits. However, the wealth index is not the primary contributing factor to wasting, due to the acute nature of this form of malnutrition. The post-COVID data suggest that the wealth index does not adequately capture the sudden economic shock experienced by the population during the COVID-19 pandemic. Nonetheless, this temporary economic crisis adversely impacted food and nutritional security, thereby affecting child nourishment and contributing to the prevalence of wasting. In this context, maternal BMI, education, and ANC visits emerge as key protective factors against wasting. When designing government safety nets to address sudden economic crises, these factors should be emphasized to effectively reduce acute malnutrition among children.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDisclaimers\u003c/h2\u003e \u003cp\u003eWe confirm that this work is original and has not been published elsewhere, nor it is currently under consideration for publication elsewhere.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource(s) of support/funding\u003c/strong\u003e \u003cp\u003eThis study did not receive any funding or financial support from any funding agency, institution, or organisation.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eChild malnutrition in Bangladesh is strongly influenced by socioeconomic and maternal health factors, but wasting may reflect more immediate shocks. Targeted interventions focusing on poverty reduction, maternal health, and prenatal care are critical to addressing persistent and emerging forms of malnutrition.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMd Towhidur Rahman: Conception and design, Acquisition of data, Analysis and interpretation of data, drafting of the manuscript, statistical analysis, Administrative, technical or material supportShompa Akter: Coding, Interpreting the statistical outputs, Reviewing and Editing\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData used in this manuscript is available at the website of the DHS program at the following linkhttps://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=0\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdulla F, Rahman A, Hossain MM. Prevalence and risk predictors of childhood stunting in Bangladesh. PLoS ONE. 2023;18(1):e0279901. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0279901\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0279901\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury MRK, Khan HT, Rashid M, Kabir R, Islam S, Islam MS, Kader M. Differences in risk factors associated with single and multiple concurrent forms of undernutrition (stunting, wasting or underweight) among children under 5 in Bangladesh: a nationally representative cross-sectional study. BMJ open. 2021;11(12):e052814. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2021-052814\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2021-052814\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury MRK, Rahman MS, Billah B, Kabir R, Perera NK, Kader M. The prevalence and socio-demographic risk factors of coexistence of stunting, wasting, and underweight among children under five years in Bangladesh: a cross-sectional study. BMC Nutr. 2022;8(1):84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40795-022-00584-x\u003c/span\u003e\u003cspan address=\"10.1186/s40795-022-00584-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury TR, Chakrabarty S, Rakib M, Afrin S, Saltmarsh S, Winn S. Factors associated with stunting and wasting in children under 2 years in Bangladesh. Heliyon. 2020;6(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2020.e04849\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2020.e04849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. External Link.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas S, Fahim SM, Islam MS, Biswas T, Mahfuz M, Ahmed T. Prevalence and sociodemographic determinants of household-level double burden of malnutrition in Bangladesh. Public Health Nutr. 2019;22(8):1425\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1368980018003580\u003c/span\u003e\u003cspan address=\"10.1017/S1368980018003580\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas S, Gulshan J. Different forms of malnutrition among under five children in Bangladesh: a cross sectional study on prevalence and determinants. BMC Nutr. 2017;3(1):1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40795-016-0122-2\u003c/span\u003e\u003cspan address=\"10.1186/s40795-016-0122-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDella NA, Moslem S, Junhai GR, Topu MH, Joty RB, Ferdaus F. Assessing Nutritional Status and Health Outcomes of Children in Saline-Prone Areas: A Comprehensive Study. Asia Pac J Surg Adv. 2025;2(1):26\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.70818/apjsa.2025.v02i01.016\u003c/span\u003e\u003cspan address=\"10.70818/apjsa.2025.v02i01.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuggal P, Petri WA Jr. Does malnutrition have a genetic component? Annu Rev Genom Hum Genet. 2018;19(1):247\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-genom-083117-021340\u003c/span\u003e\u003cspan address=\"10.1146/annurev-genom-083117-021340\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasan MM, Asif A, Barua CA, Banerjee A, Kalam A, Kader MA, Wahed A, Noman T, M. W., Talukder A. Association of access to water, sanitation and handwashing facilities with undernutrition of children below 5 years of age in Bangladesh: evidence from two population-based, nationally representative surveys. BMJ open. 2023;13(6):e065330. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2022-065330\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2022-065330\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain M, Choudhury N, Abdullah KAB, Mondal P, Jackson AA, Walson J, Ahmed T. Evidence-based approaches to childhood stunting in low and middle income countries: a systematic review. Arch Dis Child. 2017;102(10):903\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/archdischild-2016-311050\u003c/span\u003e\u003cspan address=\"10.1136/archdischild-2016-311050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain MB, Khan MHR. Role of parental education in reduction of prevalence of childhood undernutrition in Bangladesh. Public Health Nutr. 2018;21(10):1845\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1368980018000162\u003c/span\u003e\u003cspan address=\"10.1017/S1368980018000162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain MI, Huq S, Islam MM, Mahfuz M, Sari M, Khan GM, Sharmin F, Bulbul MMI, Rahman SM, Mustaphi P. Risk or associated factors of wasting among under-five children in Bangladesh: A systematic review. Asia Pac J Clin Nutr. 2024;33(4):457. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6133/apjcn.202412_33(4).0001\u003c/span\u003e\u003cspan address=\"10.6133/apjcn.202412_33(4).0001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain MM, Abdulla F, Rahman A. Prevalence and determinants of wasting of under-5 children in Bangladesh: Quantile regression approach. PLoS ONE. 2022;17(11):e0278097. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0278097\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0278097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MM, Islam MT, Noor FM. Determinants of Malnutrition among Under-Five Children in Bangladesh: Evidence from Multiple Indicator Cluster Survey, 2019 Data. Fortune J Health Sci. 2022;5(2):284\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26502/fjhs.061\u003c/span\u003e\u003cspan address=\"10.26502/fjhs.061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MM, Sanin KI, Mahfuz M, Ahmed AS, Mondal D, Haque R, Ahmed T. Risk factors of stunting among children living in an urban slum of Bangladesh: findings of a prospective cohort study. BMC Public Health. 2018;18:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-018-5101-x\u003c/span\u003e\u003cspan address=\"10.1186/s12889-018-5101-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJubayer A, Islam MH, Nayan MM. Malnutrition among under-five children in St. Martin\u0026rsquo;s Island, Bangladesh: A cross-sectional study on prevalence and associated factors. SAGE Open Med. 2022;10:20503121221116246. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20503121221116246\u003c/span\u003e\u003cspan address=\"10.1177/20503121221116246\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanam SJ, Haque MA. Prevalence and determinants of malnutrition among primary school going children in the haor areas of Kishoreganj district of Bangladesh. Heliyon. 2021;7(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2021.e08077\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2021.e08077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKundu S, Das P, Rahman MA, Al Banna MH, Fatema K, Islam MA, Srivastava S, Muhammad T, Dey R, Hossain A. Socio-economic inequalities in minimum dietary diversity among Bangladeshi children aged 6\u0026ndash;23 months: a decomposition analysis. Sci Rep. 2022;12(1):21712. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-022-26305-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-26305-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorshed R, Mahmud MR, Islam J, Islam A, Ahammed E, Tasnim F, Ferdaus F. Rising Threat of Malnutrition in Coastal Communities of Southern Bangladesh. Asia Pac J Med Innovations. 2025;2(1):31\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNIPORT. (2024). \u003cem\u003eDemographic and Health Survey 2022\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/pubs/pdf/FR386/FR386.pdf\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/pubs/pdf/FR386/FR386.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePe\u0026ntilde;a M, Bacallao J. Malnutrition and poverty. Annu Rev Nutr. 2002;22(1):241\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.nutr.22.120701.141104\u003c/span\u003e\u003cspan address=\"10.1146/annurev.nutr.22.120701.141104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MA, Halder HR, Rahman MS, Parvez M. Poverty and childhood malnutrition: Evidence-based on a nationally representative survey of Bangladesh. PLoS ONE. 2021;16(8):e0256235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0256235\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0256235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MA, Rahman MS, Shakur SM, Howlader MH, Ashikuzzaman M, Husna AU, Khan B. Risk factors of chronic childhood malnutrition: an analysis of the Bangladesh demographic and health survey 2014 data. J Public Health. 2020;1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10389-020-01281-4\u003c/span\u003e\u003cspan address=\"10.1007/s10389-020-01281-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MS, Howlader T, Masud MS, Rahman ML. Association of Low-Birth Weight with Malnutrition in Children under Five Years in Bangladesh: Do Mother\u0026rsquo;s Education, Socio-Economic Status, and Birth Interval Matter? PLoS ONE. 2016;11(6):e0157814. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0157814\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0157814\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MT, Akter S, Rana MR, Sabuz AA, Jubayer MF. How COVID-19 pandemic is affecting achieved food security in Bangladesh: A perspective with required policy interventions. J Agric food Res. 2022;7:100258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jafr.2021.100258\u003c/span\u003e\u003cspan address=\"10.1016/j.jafr.2021.100258\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman SJ, Ahmed NF, Abedin MM, Ahammed B, Ali M, Rahman MJ, Maniruzzaman M. Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach. PLoS ONE. 2021;16(6):e0253172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0253172\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0253172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanin KI, Khanam M, Rita RS, Haque MA, Ahmed T. Common factors influencing childhood undernutrition and their comparison between Sylhet, the most vulnerable region, and other parts of Bangladesh: Evidence from BDHS 2007\u0026ndash;18 rounds. Front Nutr. 2023;9:999520. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2022.999520\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2022.999520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShuvo SD, Hossain MS, Riazuddin M, Mazumdar S, Roy D. Factors influencing low-income households\u0026rsquo; food insecurity in Bangladesh during the COVID-19 lockdown. PLoS ONE. 2022;17(5):e0267488. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0267488\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0267488\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilveira KB, Alves JF, Ferreira HS, Sawaya AL, Flor\u0026ecirc;ncio TM. Association between malnutrition in children living in favelas, maternal nutritional status, and environmental factors. Jornal de pediatria. 2010;86:215\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/S0021-75572010000300009\u003c/span\u003e\u003cspan address=\"10.1590/S0021-75572010000300009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalukder A. Risk factors associated with wasting among under-5 children residing in urban areas of Bangladesh: a multilevel modelling approach. J Public Health. 2021;29:525\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10389-019-01163-4\u003c/span\u003e\u003cspan address=\"10.1007/s10389-019-01163-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalukder B, Hipel KW, Orbinski J. COVID-19's implications on agri-food systems and human health in Bangladesh. Curr Res Environ Sustain. 2021;3:100033. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.crsust.2021.100033\u003c/span\u003e\u003cspan address=\"10.1016/j.crsust.2021.100033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamanna T, Mahmud S, Salma N, Hossain MM, Karim MR. Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study. Sci Rep. 2025;15(1):14336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-99288-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-99288-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToma A, Talukder A, Shirin Khan S, Razu SR. An assessment of the association between antenatal care and child malnutrition in Bangladesh. Family Med Prim Care Rev. 2018;4373\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5114/fmpcr.2018.79350\u003c/span\u003e\u003cspan address=\"10.5114/fmpcr.2018.79350\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSAID. (2022). \u003cem\u003eBangladesh: Standard DHS, 2022\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=1\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. (2006). \u003cem\u003eWHO child growth standards: length/height-for-age, weight-for-age, weight-for length, weight-for-height and body mass index-for-age: methods and development\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/924154693X\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/924154693X\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaman S, Ahammed T, Hasan MA, Huque ME. COVID-19 effect on food security, livelihood, and mental health in affected households of Jashore, Bangladesh. Dialogues Health. 2025;6:100217. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.dialog.2025.100217\u003c/span\u003e\u003cspan address=\"10.1016/j.dialog.2025.100217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Malnutrition, Bangladesh, Covid-19, BDHS, Wasting","lastPublishedDoi":"10.21203/rs.3.rs-6737252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6737252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Malnutrition remains a critical public health challenge in Bangladesh, particularly among children under five years. This study examines the prevalence and determinants of stunting, wasting, and underweight using nationally representative data from the 2022 Bangladesh Demographic and Health Survey (BDHS), with an emphasis on post-pandemic trends and socioeconomic disparities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a cross-sectional analysis of 4,118 children aged 0–59 months with valid anthropometric data. Multivariable logistic regression was performed on a complete-case subsample of 1,216 children to identify associations between malnutrition outcomes and socioeconomic, maternal and child health, and environmental factors. Outcomes were defined using WHO 2006 Child Growth Standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 4,118 children under five, the prevalence of stunting, wasting, and underweight was 23.5%, 11.7%, and 22.8%, respectively. Stunting and underweight showed strong associations with wealth and maternal education, while wasting was less influenced by long-term socioeconomic factors. Higher wealth (AOR for Richest vs. Poorest: 0.38) and maternal education (AOR for Higher vs. None: 0.33) significantly reduced the odds of stunting.\u003c/p\u003e\n\u003cp\u003eLow birth weight, fewer ANC visits, and lower maternal BMI increased the risk of malnutrition—particularly for wasting and underweight. Wasting appeared more sensitive to acute shocks, likely reflecting the impact of the COVID-19 pandemic. Environmental factors like unimproved sanitation and older child age also increased the odds of stunting and underweight. Other factors, including child sex and delivery characteristics, were not significant in adjusted models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Child malnutrition in Bangladesh is strongly influenced by socioeconomic and maternal health factors, but wasting may reflect more immediate shocks. Targeted interventions focusing on poverty reduction, maternal health, and prenatal care are critical to addressing persistent and emerging forms of malnutrition.\u003c/p\u003e","manuscriptTitle":"Changing Determinants of Child Wasting: Insights from the Prevalence of Stunting, Wasting, and Malnutrition and Their Determinants — An Analysis of BDHS 2022 Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 00:26:39","doi":"10.21203/rs.3.rs-6737252/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"44cadf95-7d47-46fc-9cea-d779e659dc95","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-26T00:26:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-26 00:26:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6737252","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6737252","identity":"rs-6737252","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}