Household wealth, women’s empowerment, and food insecurity in conflict-affected Southeast Myanmar

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Methods A stratified cluster survey in 74 villages was designed to evaluate a program to deliver nutrition-sensitive interventions in areas of armed conflict. Primary outcomes included the Food Insecurity Experience Scale (FIES) and standard indicators of infant and young child feeding (IYCF). We quantified inequities according to several axes of vulnerability including household wealth, maternal education, and women’s empowerment, considered individually and in concert, by estimating crude- and multiply adjusted logistic regression models that also account for distance to food markets. Crude and adjusted relative concentration indices were used to summarize inequities in nutrition indicators and program coverage. Analysis accounted for the complex survey design. Results Among 801 respondents 32% are illiterate and 59% reported household wealth in the poorest National quintile. One quarter (25.3%) of households reported moderate or severe food insecurity overall, with moderate to large inequities related to wealth (crude rCI − 0.19) and education (-0.17) though only education inequities remained significant in adjusted models. Inequities for child and maternal nutrition outcomes except for minimum meal frequency were larger for household wealth (range in rCI, 0.14 to 0.34) and smaller for education (0.07 to 0.22). Women in wealthier households were more likely to report high empowerment, though women’s empowerment was not associated with nutrition outcomes. Project coverage was concentrated among wealthier households. Conclusion Large inequities in food insecurity, dietary intake and women’s empowerment exist in remote areas of Myanmar experiencing active armed conflict. Nutrition-sensitive intervention coverage also was inequitably distributed, suggesting that the nutrition project may have exacerbated inequities in food and nutrition outcomes. Humanitarian agencies should routinely measure health inequities and identify delivery models that ameliorate them. nutrition food insecurity breastfeeding child feeding women’s empowerment health equity humanitarian health internally displaced persons Myanmar Burma Figures Figure 1 Background Malnutrition contributes to nearly half of child deaths worldwide ( 1 ), with distributions of chronic (stunting) and acute (wasting) malnutrition ( 2 – 4 ), as well as food insecurity and infant and young child feeding practices [IYCF] ( 5 , 6 ) demonstrating large inequities according to axes of power and advantage in society, including household wealth and women’s empowerment ( 7 – 9 ). The World Food Program identifies poverty and gender inequality as drivers of household hunger globally and highlights armed conflict as the most important driver ( 10 ). Although evidence from many large-scale conflicts demonstrate adverse impacts on malnutrition ( 11 , 12 ), food insecurity ( 13 ), and IYCF ( 14 ), the impact of conflict on food and nutrition inequities per se is poorly understood. Equitable delivery of humanitarian services is a central goal of agencies operating in areas of armed conflict. The imperative to understand and address health inequities arises from the human rights framework underlying the Humanitarian Charter and SPHERE standards ( 15 ). However, many agencies do not collect, analyze, or report data related to household wealth, educational attainment, women’s empowerment, or other axes of disadvantage that in more stable settings demonstrate large and graded associations with adverse outcomes such as child mortality ( 16 ), food insecurity ( 17 ), and lower coverage of evidence-based interventions ( 18 ). Efforts to harmonize nutrition assessment in emergencies, including SMART, have focused on precise estimation of overall nutritional status, mortality, and program coverage; many nutrition assessments remain naïve to the distribution of outcomes across more- and less-vulnerable subgroups. The dearth of information on nutrition inequities in emergencies impedes efforts to identify progressive interventions that would promote nutrition equity and concentrate beneficial effects among the most vulnerable households and individuals. In Myanmar, prior to the SARS-CoV2 pandemic and 2021 coup d’etat, undernutrition and food insecurity were common and inequitably distributed between, as well as within States and Regions. A 2019 survey conducted by Community Partners International (CPI) documented the prevalence of stunting (40.2%) and wasting (6.2%) malnutrition as two- to three-times higher in hard-to-reach remote areas of contested governance than in more stable rural areas ( 19 , 20 ). For example, minimum dietary diversity ranged from 12.6–42.6% in poor to wealthy households, respectively; minimum acceptable diet ranged from 3.3–19.4% ( 20 ). The 2021 coup d'etat in Myanmar resulted in widespread armed conflict, displaced 2.7 million within the country, and placed 17.6 million in need of humanitarian assistance ( 21 , 22 ). A national telephone survey suggests the concomitant economic and political crises in Myanmar have exacerbated household hunger, which is more common in households with low income, assets, and adult education ( 23 ). In 2021 Myanmar’s gender inequality index of 0.498 ranked 106th out of 146 countries ( 24 , 25 ), though changes in women’s empowerment since the Coup are poorly understood. In March 2023 CPI and local partners conducted an in-person household survey to evaluate food insecurity, IYCF, and nutrition-sensitive interventions delivered to internally displaced populations in Southeastern Myanmar. The present study had two aims. First, to document inequities in household food insecurity, child feeding practices, maternal diet indicators, and coverage of nutrition-sensitive interventions according to women’s empowerment, household wealth, and educational attainment. Second, to quantify inequities in food insecurity and other nutrition outcomes when women’s empowerment, wealth, and educational attainment are considered in concert. Our a priori hypotheses were that nutrition outcomes, women’s empowerment, and intervention coverage would be inequitably distributed in the population and that accounting jointly for household wealth, education, and women’s empowerment would attenuate but not eliminate the respective wealth-related inequities in nutrition outcomes. Methods Study area and design This population-based cross-sectional survey used a two-stage cluster design to collect village- and household-level data in seven townships in Kayin State targeted by a CPI project in Southeastern Myanmar. The project was a multi-interventional program aimed to improve the nutritional status of mothers and children within the first 1,000 days in conflict-affected, hard to reach areas of eastern Myanmar. CPI, in partnership with three local health organizations, implemented this project from 2020 until 2025. The 504 project villages are in a complex political and geographic landscape with constantly evolving relationships between governing bodies. The sampling frame consisted of five strata defined according to the three local partner organizations A, B and C: strata 1A − 1C included 357 relatively accessible villages; strata 2A and 2B included 78 The rCI can be defined by the equationvillages less accessible due to topographical and security concerns. The survey was designed to provide 80% power to detect a 10-percentage point difference in key infant and child feeding indicators compared to 2019 baseline study results ( 26 ), assuming a non-response-rate of 10% and design effect of 1.5 due to the complex survey design. A planned sample of n = 788 permits estimation of parameters for indicators with a prevalence of 20% within a precision of +/- 5 percentage points. The final projected sample size consisted of 788 households with children < 5yrs, distributed unevenly between the strata. In Stratum 1, a probability proportionate to size (PPS) approach was used to select 34 of 357 villages; within each village 10 households were randomly surveyed. The Stratum 2 sample included approximately half (40 of 78) the partner organizations' target villages, with a maximum of 12 households per village for data collection. An ordered list of replacement villages was randomly generated in case of inaccessibility. Data collection tools and methods The survey questionnaire, based on the project’s objectives and logical framework indicators, consists of 10 modules: Demographics; Child MUAC and IYCF; Child Illness and Health Seeking Behavior; Dietary Diversity Score for Women; Women Health-Seeking Behavior; Food Insecurity Access Scale; Depression Symptoms (PHQ9); Water, Sanitation, and Hygiene; Women’s Empowerment; and Program Exposure. Outcomes were defined using tools designed and validated for their respective measurement fields. IYCF practices including the minimum dietary diversity for children (MDD-C) < 2yrs were based on WHO 2021 guidelines ( 27 ). Measurement of women's dietary diversity followed the Food and Agriculture Organization (FAO) guideline on Minimum Dietary Diversity for Women (MDD-W) ( 28 ), with consumption of at least 5 of 10 food groups indicating achievement of minimum dietary diversity. Household food insecurity was assessed using the Food Insecurity Experiences Scale (FIES) developed by FAO ( 29 ); FIES raw scores > 3 indicated moderate or severe food insecurity ( 30 ). We used a Rasch model to calibrate our estimate of food insecurity prevalence in the project area, to facilitate comparison to other populations. Demographic Health Survey (DHS) and other published tools were used for household income and coping strategies; household wealth was measured using the Myanmar-specific EquityTool®, a parsimonious asset index used to classify households into national wealth quintiles ( 31 ). We used the DHS women’s empowerment index that consists of eight household decisions regarding children (feeding, health-seeking behavior, well-being); women’s healthcare (pregnancy as well as general health); household purchases and social visits; and control over women’s own earnings. The index ranges from − 1 to 1, where − 1 indicates decisions made solely by men, 1 indicates those made solely by women, and 0 represents joint decision-making. We normalized responses for each item and applied inverse covariance weighting to the z scores to generate the overall women empowerment index; the highest quartile ( > = 75 percentile) was used to define a dichotomous outcome of “high women empowerment.” Survey questionnaires were programmed using KoboToolbox; data was collected using KoboCollect on Android tablets supported with an external battery to reach areas lacking electricity. Data quality checks included skip-pattern control, input value range limits, and logical consistency checks; enumerators were trained to flag data entry issues. An in-person five-day surveyor training included pilot testing and iterative revision of the survey. Teams of four people - one supervisor and three interviewers – collected data from 12/2022 to 04/2023. High frequency checks (HFCs) were conducted to ensure data quality and consisted of completion check, data consistency, and enumerator performance. HFC results were cross-checked with partner data collection teams to address duplicate observations, outliers in enumerator performance, and survey start time and duration. Statistical Analysis Weighted means and proportions were calculated for household socio-demographics and women’s empowerment indicators. To summarize the inequitable distribution of nutrition-sensitive interventions (program coverage) and nutrition outcomes, we calculated the relative concentration index (rCI), ( 32 ), a health equity metric that allows all study participants to contribute to parameter estimates. The rCI is based on the concentration curve and provides a summary measure of relative inequity by ranking individuals according to socioeconomic status or power (e.g., household wealth, women’s empowerment) on the X-axis and plotting the cumulative share of health (e.g., food insecurity) on the Y-axis. The rCI is then defined as twice the area between the concentration curve and the 45-degree diagonal. In a hypothetical, equal world where health is distributed evenly across ranked groups, the concentration curve would overlap with the diagonal 45-degree line and the rCI would be zero. As relative inequality increases, the concentration curve strays from the diagonal and the rCI increases in magnitude. The rCI can be defined by the equation: where y is a measure of the i th person’s health based on the health- or nutrition outcome; µ is the mean of the outcome (e.g. FIES); R is the fractional rank of the individual according to wealth (or income or educational attainment). A negative rCI indicates that the outcome is concentrated among the less well off. The rCI can be interpreted as 1.33 times the percentage of redistribution of the health outcome from high- to low-ranked individuals (above and below median wealth, respectively) required to make wealth-related inequality equal to zero ( 33 ). A rCI of 0.33 for MDD-W, for example, indicates that among all women who ate a minimally diverse diet, 24.8% in households with above-median incomes would need to transfer their diverse diet to women in households in below-median incomes for there to be an equal distribution of dietary diversity across wealth groups. Our primary focus is on crude, or unadjusted measures of inequity that we believe are most valuable when targeting interventions and evaluating program success in a context where multiple axes of power and vulnerability frequently cluster in the same household. Nevertheless, to explore how inequities for nutrition outcomes related to women’s empowerment, respondent educational attainment, and household wealth would be affected when the respective rCIs accounted for additional axes of disadvantage, we also calculated adjusted rCIs that describe independent associations of the outcomes and ranking variables, after accounting for the two complementary variables. For example, the adjusted rCI of the FIES for household wealth summarizes wealth-related inequality of food insecurity, taking account of educational attainment and women’s empowerment. Wealth-related inequity was summarized using the continuous value of household wealth index as the ranking variable to calculate concentration indices; in sensitivity analyses we used Myanmar national quintiles provided by EquityTool® as well as empirical quintiles defined based on the distribution of household wealth in our survey. Bivariate and multivariate linear- and logistic regression models were estimated to examine associations of each nutrition outcome with women’s empowerment, educational attainment, and household wealth. Variables whose bivariate measures of association with the outcomes were significant at the p < 0.1 level in either the logistic model or rCI were included in the full logistic model and adjusted rCI calculations. All adjusted logistic models and adjusted rCIs included dummy variables for distance from food markets, implementing agency, and insecurity (stratum). Inverse probability weights were used to account for the probability of selecting clusters and households within clusters, and to account for the replacement of villages (clusters) where insecurity precluded data collection. Analyses accounted for the complex survey design using the svy suite of commands in Stata 17.0. The conindex command was used to calculate crude concentration indices ( 34 ); adjusted concentration indices were calculated using the indirect standardization approach. Results The survey team reached 803 households with children < 5yrs located in 74 villages: 37 villages in each stratum. One implementing partner was unable to collect data from 3 villages in stratum 2 due to security constraints; when no replacement villages were available in Stratum 2 these villages were replaced with villages from Stratum 1. Of these households, 801 households (99.8%) consented to participate in the survey and comprised the final analytic sample. Demographics: Sample sociodemographic characteristics appear in Table 1 . The mean age of respondents was 30.2 years, and the mean household size was 5.3. Nearly all respondents were married (97.3%) and reported the mother as the main household caregiver (95.2%); 8.8% were pregnant at the time of data collection. One-third (32%) of respondents were illiterate, though only 9.4% of households lacked a literate member. Mean monthly income was reported as 129,904 kyat (~ $ 2 per day). Six in ten households surveyed are in the lowest national wealth quintile among Myanmar households. Nine in ten households reported income had either decreased (47.7%) or remained the same (42.2%) since the 2021 coup. Table 1 Demographics of survey respondents Demographics % mean LB UB Main Caregiver (n = 801) Mother 95.2% 93.2% 96.7% Other main caregiver 4.8% 3.3% 6.8% Household Head (n = 801) Female 6.9% 5.0% 9.3% Marital Status (n = 801) Married 97.3% 95.6% 98.3% Unmarried 0.2% 0.1% 0.7% Widowed 2.2% 1.2% 3.9% Separated 0.4% 0.1% 1.2% Currently Pregnant (n = 801) 8.8% 6.1% 12.6% Access to Mobile Phone (HH) (n = 801) 66.8% 58.5% 74.2% Strata (n = 801) Organization A, stable (A1) 12.4% 9.3% 16.3% Organization A, unstable (A2) 34.9% 27.6% 43.0% Organization B, stable (B1) 9.3% 7.3% 11.7% Organization B, unstable (B2) 38.1% 29.5% 47.4% Organization C, stable (C1) 5.4% 4.0% 7.2% Socioeconomic Status Measure % mean LB UB Wealth (Myanmar National quintiles) (n = 801) Poorest 59.0% 52.7% 65.0% Poor 24.1% 19.5% 29.5% Medium 8.7% 6.4% 11.8% Wealthy 5.6% 3.9% 8.0% Wealthiest 2.6% 1.4% 4.6% Education (n = 800) Illiterate 32.0% 26.0% 38.8% Primary education (< 5th standard) 37.6% 31.9% 43.6% Secondary education (< 9th standard) 21.3% 17.1% 26.1% Higher education (through matriculation exam) 7.6% 4.1% 13.9% Vocational education 0.1% 0.0% 0.4% Graduate level (University/ College) 0.8% 0.4% 1.6% Post graduate level (University) 0.3% 0.1% 0.8% Monastery Education 0.1% 0.0% 0.4% Other 0.3% 0.1% 1.4% Monthly Income Average monthly income (kyat) (n = 801) 129,904 111,978 147,830 HH income lower than last year (n = 735) 47.7% 40.8% 54.7% HH income higher than last year (n = 735) 10.1% 7.8% 12.9% HH income the same as last year (n = 735) 42.2% 35.4% 49.3% Received cash transfer from NGO/Gov’t (n = 752) 11.0% 8.2% 14.6% Distance (travel time) to health facility (n = 760) Health facility in village 30.2% 18.8% 44.7% ≤ 1.5 hours 13.6% 6.4% 26.8% 1.6–3 hours 22.6% 11.7% 39.3% > 3 hours 33.6% 23.7% 45.1% Distance (travel time) to nearest food market (n = 760) Food market in village 10.9% 4.4% 24.5% ≤ 1.5 hours 17.8% 9.9% 29.8% 1.6–3 hours 34.6% 22.6% 49.0% > 3 hours 36.7% 26.8% 48.0% * p < 0.1, ** p < 0.05, *** p < 0.01 Women’s empowerment, program coverage, and household wealth Women reported the most decision-making authority in respect to child feeding (75.2%), and the lowest related to child well-being (24.4%) and family visits (27.9%). Women’s empowerment demonstrated large wealth-related inequities: women from the poorest households were more likely to have low decision-making authority (61.1%) than women from wealthy (34.3%) households; the rCI was − 0.25 (p < 0.01). Statistically significant wealth-related inequities were observed for seven of eight dimensions of household decision-making; only child feeding decision-making was similar among women across wealth quintiles. Women in the wealthiest households were three times more likely than women in the poorest to have control over household purchases and family visits (Table 2 ). Project Nourish interventions reached a minority of targeted households, and coverage was low and highly inequitable for SBCC sessions (rCI 0.36), home gardening (rCI 0.41), and mother support groups (rCI 0.61, p < 0.01 for each distribution). In order to explore whether this inequity might reflect selective targeting of more wealthy villages we conducted post-hoc analyses limited to households in villages reached by each respective intervention; although overall coverage was higher, household-level inequities were similarly large and significant (data not shown). Table 2 Wealth-related inequities in women’s empowerment and coverage of nutrition-sensitive interventions Overall mean or Proportion Poorest Poor Medium Wealthy Wealthiest Crude Concentration Index Women’s Empowerment Index -0.06 -0.30 -0.12 0.03 0.07 0.06 0.07*** High Women's Empowerment 49.3% 26.5% 44.1% 58.4% 61.8% 61.0% -0.27*** Decision-making authority (Women’s empowerment components) Overall Proportion Poorest Poor Medium Wealthy Wealthiest Crude Concentration Index Child feeding 75.2% 71.8% 72.7% 77.2% 76.2% 81.8% 0.12 Child healthcare 45.4% 31.1% 43.9% 40.4% 53.7% 63.1% 0.23*** Child well-being 24.4% 10.3% 19.3% 29.1% 23.0% 43.8% 0.29*** Household purchase 40.3% 18.3% 29.0% 43.8% 55.6% 62.6% 0.37*** Control over own/woman’s earnings 65.2% 39.6% 55.2% 81.9% 80.8% 75.5% 0.34*** Family visits 27.9% 8.6% 19.6% 29.9% 42.1% 46.0% 0.37*** Women’s healthcare 56.5% 38.8% 45.7% 42.1% 54.8% 62.5% 0.17** Maternal/pregnancy-related healthcare 47.8% 36.9% 63.8% 58.3% 62.7% 62.9% 0.16** Project Nourish Intervention Coverage Overall Proportion Poorest Poor Medium Wealthy Wealthiest Crude Concentration Index Project Nourish - Overall 48.9% 39.4% 50.4% 45.9% 55.6% 53.1% 0.13 MUAC screening 36.7% 35.1% 38.1% 36.7% 35.5% 25.5% 0.03 Food basket/Cash for food 23.1% 28.2% 13.2% 27.9% 27.4% 22.2% 0.02 SBCC session 7.8% 0.2% 5.4% 8.2% 15.0% 12.7% 0.36*** Home gardening 6.6% 0.0% 5.2% 7.1% 6.2% 15.9% 0.41*** Mother support group 3.7% 0.0% 2.2% 0.3% 4.7% 13.1% 0.61*** WASH infrastructure support 3.1% 3.5% 2.4% 1.4% 3.9% 4.9% 0.15 * p < 0.1, ** p < 0.05, *** p < 0.01 Food Insecurity The prevalence of moderate or severe food insecurity, using a Rasch model to facilitate comparison with other populations, was 25.3%. A similar overall prevalence (23.5%) was estimated when using a common cutoff of 4 or greater to define moderate and severe food insecurity, and this dichotomous outcome was used in subsequent analyses to quantify inequities (see Table S1 in Additional file 1). Food insecurity was more common in lowest wealth quintile households (27.8%) and those whose respondent was illiterate (33.6%) than in highest wealth quintile households (2.1%) or with a respondent who passed their matriculation exam (9.3%); wealth- and education-related crude concentration indices for moderate/severe food insecurity were − 0.19 (p < 0.1) and − 0.17 (p < 0.05), respectively. Wealth- and education-related inequalities remained significant for at least one category in adjusted logistic models (see Table S1 in Additional file 1), though the summary measure of household inequity that considers the distribution across the entire spectrum of disadvantage (rCI) remained significant only for education in adjusted models that accounted for education, wealth, and distance from food markets (Table 3 ). Table 3 Crude and adjusted relative concentration indices to summarize inequities in food insecurity, child feeding practices, and maternal diet indicators, according to household wealth, education, and women's empowerment Household Wealth-Related Inequality (relative concentration index) Education-Related Inequality (relative concentration index) Women Empowerment-Related Inequality (relative concentration index) Househol d Crude Adjusted Crude Adjusted Crude Adjusted Household FIES raw score -0.12** -0.06 -0.09** -0.09** 0.01 0.05* Moderate / Severe Food Insecurity -0.19* -0.04 -0.17** -0.18** 0.01 0.05 Child EBF Exclusive Breastfeeding 0.23** 0.2 0.22** 0.17 0.02 -0.04 C-DDS Child Diet Diversity Score 0.15*** 0.11** 0.11** 0.02 0.05 0.02 MDD Minimum Dietary Diversity 0.34*** 0.23*** 0.09 0.04 0.1 -0.01 MMF Minimum Meal Frequency -0.04 -0.09 -0.15 0.05 0.04 -0.08 MAD (child 6–23 months) Minimum acceptable diet 0.27*** 0.22** 0.09 0.08 0.09 0.04 Maternal MDD-W Minimum Dietary Diversity for Women (dichotomous) 0.33*** 0.25*** 0.08 0.05 0.1 -0.003 MDD-W score (continuous0 0.14*** 0.10** 0.07*** 0.04 0.05** 0.02 * p < 0.1, ** p < 0.05, *** p < 0.01 Adjusted concentration indices are adjusted for distance from food markets, implementing agency, and insecurity (stratum)indirectly standardized for the two complementary variables among educational attainment, household wealth and women’s empowerment. Infant and Young Child Feeding and Maternal Diet Only 31.2% of respondents reported exclusive breastfeeding (EBF) for the first six months. EBF was concentrated in households with higher incomes (rCI adj 0.2) and more educated respondents (rCI adj 0.17), though these inequities were no longer significant in adjusted models. Evidence was lacking to support an association between EBF and women’s empowerment (Table 3 ). Children’s dietary diversity score (C-DDS) was low, with a mean of 3.7 out of 8 food groups consumed; fewer than one third (31.4%) of children achieved minimal dietary diversity (MDD-C). Both measures of dietary diversity were inequitably distributed according to wealth, education, and women’s empowerment, though the associations with education and women’s empowerment were substantially diminished and no longer statistically significant in adjusted models (Table 3 , and see Tables S1 and S2 in Additional file 1). Approximately two-thirds (64.2%) of children 6–23 months received the minimum meal frequency (MMF) overall, with similar MMF according to wealth, education, and women’s empowerment. Minimum acceptable diet (MAD), a composite outcome that includes minimum dietary diversity and meal frequency, was low (25.1%), and demonstrated patterns of inequity similar to MDD: MAD was associated with wealth, education, and women’s empowerment, though only the association with wealth remained significant in adjusted models. Both MMF (73.7% vs 50.5%) and MAD (36.0% vs 11.8%) were more common among children who continued to breastfeed from 6 to 23 months than among children who did not (see Table S3 in Additional file 1). Only one-third (36.8%) of women met the MDD-W indicating consumption of at least 5 of 10 food groups over a 24-hour period; the mean MDD-W score was 4.4. MDD-W was highly concentrated among households that were wealthier in both crude (rCI 0.33) and adjusted models (rCI adj 0.25, p < 0.01). MDD-W demonstrated modest associations with education and women’s empowerment; neither inequity remained significant in adjusted models. In a post-hoc analysis we found that food groups eaten by infants were strikingly different from foods eaten by their mothers. While 35.5% of mothers reported eating pulses, and 9.7% nuts and seeds, only 17.2% of children (6–23 months) ate pulses/nuts/seeds. Although 65.6% of mothers reported eating meat/poultry/fish, those same foods were only given to 12.4% of children (6–23 months). Dairy, on the other hand, is consumed by 17% of children (6–23 months) and 10.5% of mothers (Table S4). Discussion Our cross-sectional household survey of food insecurity, child feeding practices, and maternal diet in poor, remote areas of Southeast Myanmar experiencing armed conflict produced evidence that supports our a priori hypotheses that nutrition-related outcomes, women’s empowerment, and intervention coverage are inequitably distributed, and that accounting jointly for household wealth, education, and women’s empowerment would attenuate, but not eliminate, wealth-related inequities in nutrition outcomes. The magnitude of most inequalities documented in our study is large, and comparable to those documented in highly inequitable societies, including Myanmar prior to the Coup ( 35 ). Our findings are consistent with prior studies that found large inequities in coverage of key maternal and child health and nutrition interventions, and that the magnitude of inequities was larger in countries affected by armed conflict than in politically more stable countries ( 36 ). We extend these findings to a specific nutrition program implemented in an area experiencing active armed conflict. Below we place inequities for each indicator in context and make a case for incorporating health equity metrics into routine monitoring and evaluation frameworks to ensure that nutrition-sensitive interventions do not exacerbate the inequities they are designed to ameliorate. Food insecurity We found one in four households (25.3%) is moderately or severely food insecure as measured by the FIES, which according to the FAO ( 28 ) is lower than the prevalence of food insecurity among for households in rural areas of LMICs globally (42.7%), and roughly between estimates reported for Myanmar by the WFP (20%), and Humanitarian Needs and Response Plan Overview for Myanmar (28%). Prevalence of moderate-to-severe food insecurity in the poorest households (27.8%) was over ten times higher than in the wealthiest households (2.1%; Figure and Table S1 in Additional file 1). Poverty is a well established driver of food insecurity ( 2 – 5 ) and the association between poverty and food insecurity is not novel. However, many food and nutrition programs in emergencies do not routinely monitor the impact of their interventions on vulnerability, and whether their efforts ameliorate or exacerbate these inequity. Methods employed in this study to quantify nutrition inequities can be used to track inequities over time, and when incorporated into rigorously designed program evaluations can be used to identify interventions that reduce inequities. Infant and Young Child Feeding Exclusive breastfeeding is associated with a 44–45% reduction in neonatal mortality ( 37 ) and is protective against malnutrition ( 38 ). Overall rates of EBF were low (31%) and inversely associated with household wealth and maternal education. Our results are consistent with a review that found that in countries affected by armed conflict EBF was less common in households that were poor or less well educated than in more wealthy and educated households ( 36 ). These pro-wealth inequities contrast with evidence from countries not affected by conflict, where EBF tends to be more common among less well off households. Additional study is necessary to understand drivers of EBF inequities in this rural and predominantly agrarian economy affected by armed conflict. The large and unexpected discrepancies in dietary intake between mothers and young children, overall and within the same household, we observed in our post-hoc analysis requires confirmation, though it suggests there may be an opportunity to improve the diet of some children by the promotion of foods already available in the household. Women’s Empowerment The term “women’s empowerment” is context-specific; this limits comparison to other settings ( 39 , 40 ), though decision-making authority is commonly used to measure women’s empowerment ( 41 ). In Myanmar, “having more knowledge,” and “being able to support family” were concepts most associated with women’s empowerment ( 42 ). Although wealth-related inequities in women’s empowerment were anticipated, the magnitude of inequity documented in our study is exceptionally large. A working paper on women’s empowerment in Myanmar used data from the 2015–2016 DHS to document that wealthy women were 1.49 times more likely than poor women to have a higher empowerment level when controlling for other factors ( 43 ). Using a similar dichotomous outcome, we found that households in the highest wealth quintile were more than twice as likely (61.0%) to include women with an empowerment index above the median compared to women from the lowest quintile (26.5%). Although our study found that wealth demonstrated stronger independent associations with nutrition indicators than women’s empowerment, women’s empowerment remains an important target for nutrition interventions. Interventions specifically focused on women’s empowerment have been shown to be effective at addressing malnutrition in women and children ( 44 – 48 ), and these may be easier to implement and yield more timely results than interventions targeting wealth, education and/or physical accessibility of households to markets, especially in the context of active conflict. Empowerment of women is itself socially desirable and may yield benefits that extend beyond food and nutrition, such as healthcare access in Myanmar ( 49 ). Women's/mother’s groups are often utilized as an intervention to improve women’s empowerment ( 50 ) and health outcomes ( 51 , 52 ), but groups may exclude adolescents ( 44 ), nulliparous women, and men ( 53 – 58 ). While these groups have been shown to be effective at improving infant feeding practices and child nutrition ( 59 ), overall evidence is mixed ( 60 , 61 ), and these groups may be more effective when combined with a cash incentive ( 62 ). Results of our midterm evaluation were used to develop a qualitative study to explore factors contributing to low attendance at women’s groups, and why women from poor households were less likely to attend. Inequitable Intervention Coverage We documented large inequities in coverage of most key nutrition-sensitive interventions that may paradoxically exacerbate the outcome inequities they were designed to ameliorate. Low overall program coverage, which this project experienced, makes it less likely to observe an impact on nutrition/diet indicators. However, we can note that distribution of interventions most likely to impact nutrition and food insecurity outcomes were either highly unequal (SBCC, mother support, home gardening) or not statistically different from an equal distribution (food basket/cash); none were progressively distributed with higher coverage among the poorest households. This suggests that without conscientious effort to understand and address coverage inequities there is a risk that Project Nourish could perpetuate or exacerbate large nutrition-related inequities. Strengths Our stratified design took account of a complex political and security landscape and selected, a priori, a list of replacement villages perceived to be at similar risk of acute conflict. We applied health equity metrics developed for stable settings to quantify inequities in nutrition-related outcomes, women’s empowerment and coverage of interventions in areas experiencing active armed conflict. Our survey used electronic tablets with skip pattern and consistency checks to enhance data quality. We conducted an in-person household survey that likely generated nutrition indicators more representative of population parameters than phone-based surveys that attempt to weight results using data on phone ownership and utilization collected during relatively stable political periods ( 63 ). Since 2021 cellular service coverage and internet blackouts have oscillated and at times services appear to be deliberately suppressed in areas of active conflict ( 64 ). These new drivers of selection bias likely threaten the validity of using phone surveys to estimate nutrition indicators in conflict-affected populations in Myanmar. Although it is beyond the scope of the current manuscript to formally compare the accuracy of findings from our in-person survey to results from phone-based methods, it is notable that our estimates of MDD-W (36.6%) and other indicators are substantially lower than those generated by a phone survey conducted by Project Nourish staff at the inception of the program (adequate MDD-W 66.7%). Limitations We used the empowerment measure from DHS to facilitate comparison with previous surveys in Myanmar. Measures developed specifically for nutrition-related outcomes such as the Women’s Empowerment in Nutrition Index (WENI) and Women’s Empowerment in Agriculture index (WEIA) may have produced alternative results though they include a relatively large number of items ( 15 – 30 ) and lack of validation in rural Myanmar ( 65 – 67 ). We did not calculate a multi-dimensional index of inequality, nor did we conduct a formal decomposition analysis ( 68 ) to examine major drivers of food insecurity, both of which may provide additional nuance to our findings. We did not analyze the association between income loss and nutrition. In 2020, 83% of households reported their income was lower than usual, with the decline averaging 43% and being concentrated in poorer households ( 69 ). Income loss may translate to worsening nutrition in the form of less food, or less nutritious food intake ( 69 – 71 ). The on-going SARS-CoV2 pandemic and heightened political instability in project areas forced some survey activities to be conducted remotely. The use of tablets and high frequency data checks likely improved data quality, but the impact of remote format for several training and supervision activities is unknown. Three villages were inaccessible to survey teams due to insecurity and weather events, and their replacement with more secure villages may have led us to underestimate food insecurity in this region. However, most security concerns were temporary and our stratified design and a priori selection of replacement villages perceived to be at similar risk of insecurity likely minimized this bias. The initial design of Project Nourish was built on the expectation that the government of Myanmar would implement a MCCT program to provide cash payments to mothers during the first thousand days from conception through the first 2 years of life. The suspension of the MCCT program caused implementation challenges for Project Nourish and likely affected overall outcomes. Conclusions Our cross-sectional household survey documented large inequities in food insecurity, dietary intake and women’s empowerment in remote areas of Myanmar experiencing active armed conflict. Furthermore, we found that poor, less educated households with less empowered women were less likely to utilize nutrition-sensitive interventions. This suggests that the LIFT Pooled Fund for Livelihoods, whose best practices and recommendations this project followed, may exacerbate inequities in food and nutrition outcomes. Whether or not other donors’ nutrition projects adversely impact nutrition inequities can only be determined if the distributions of services and outcomes are measured and analyzed. Our application of health equity metrics to quantify inequities in intervention coverage and outcomes provides an approach to track health inequities over time and to identify interventions that ameliorate – or at least do not exacerbate – inequities in other health services and outcomes in other humanitarian emergencies. Abbreviations C-DDS - Child Diet Diversity Score CPI - Community Partners International DHS - Demographic Health Survey EBF - Exclusive Breast Feeding FAO - Food and Agriculture Organization FIES - Food Insecurity Experience Scale HFCs - High Frequency Checks IYVF - Infant and Young Child Feeding LIFT - Livelihoods and Food Security Fund LMIC - Lower Middle Income Country MAD - Minimum Acceptable Diet MCCT - Maternal and Child Cash Transfer MDD - Minimum Dietary Diversity MDD-C - Minimum Dietary Diversity for Children MDD-W - Minimum Dietary Diversity for Women MMF - Minimum Meal Frequency MUAC - Mid-Upper Arm Circumference PHQ9 - Patient Health Questionnaire 9 PPS - Probability Proportionate to Size rCI - relative Concentration Index SBCC - Social and Behavioral Change Communication WEIA - Women’s Empowerment in Agriculture Index WENI - Women's Empowerment in Nutrition Index WFP - World Food Program Declarations Ethics approval The study involving human participants was reviewed and approved by the Community Ethics Advisory Board (CEAB) of Mae Tao Clinic; the Institutional Review Board of The George Washington University deemed the secondary analysis of de-identified project data to be exempt from ethical review. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements, but informed verbal consent was obtained at the time of survey response. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests Dr. Richards is Senior Technical Advisor to CPI and is an advisor to a fund at The Tides Foundation that has funded CPI for other work; Dr. Richards did not receive compensation from The Tides Foundation for this or any other study. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding Project Nourish and its M&E activities are funded by The Livelihoods and Food Security Fund (LIFT). LIFT is a multi-donor program that aims to tackle the issue of poverty and hunger directly in Myanmar's rural communities. Donors include UKAID, USAID, Australian Aid, The European Union, Canada AiD, New Zealand Aid, the Norwegian Ministry of Foreign Affairs, and the Swiss Agency for Development and Cooperation. Consent for publication Not applicable Author Contributions KTZ and NTZ contributed equally as first authors. NTZ, TT, and AR developed the study design. ANE and HMW managed data collection. NTZ and AR analyzed data. KTZ contributed to data analysis and wrote the literature review and manuscript draft. TT acted as principal investigator of the study. HH acted as program manager. All authors were involved in the writing of the manuscript and have approved the final version for publication. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7322766","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499862611,"identity":"0bf0a122-52c0-4943-954c-fca33e4c08dc","order_by":0,"name":"Kate Teela Zeichner","email":"","orcid":"","institution":"Community Partners International","correspondingAuthor":false,"prefix":"","firstName":"Kate","middleName":"Teela","lastName":"Zeichner","suffix":""},{"id":499862613,"identity":"71bc5081-88ce-4ba5-a9e8-75eceb757bad","order_by":1,"name":"Nicholus Tint Zaw","email":"","orcid":"","institution":"Community Partners International","correspondingAuthor":false,"prefix":"","firstName":"Nicholus","middleName":"Tint","lastName":"Zaw","suffix":""},{"id":499862614,"identity":"eefb434a-6358-4f13-a870-bc42a115d2de","order_by":2,"name":"Hnin Hnin Tha Myint","email":"","orcid":"","institution":"Community Partners International","correspondingAuthor":false,"prefix":"","firstName":"Hnin","middleName":"Hnin Tha","lastName":"Myint","suffix":""},{"id":499862615,"identity":"175724ad-8d19-45b1-834d-78fd5dc734eb","order_by":3,"name":"Aye Nyein Ei","email":"","orcid":"","institution":"Community Partners International","correspondingAuthor":false,"prefix":"","firstName":"Aye","middleName":"Nyein","lastName":"Ei","suffix":""},{"id":499862616,"identity":"090b5565-edb6-44cd-bf19-5830bd02e464","order_by":4,"name":"Hay Mar Wai","email":"","orcid":"","institution":"Community Partners International","correspondingAuthor":false,"prefix":"","firstName":"Hay","middleName":"Mar","lastName":"Wai","suffix":""},{"id":499862617,"identity":"c6a28f21-7591-40af-982c-fd7892e612d4","order_by":5,"name":"Tom Traill","email":"","orcid":"","institution":"Community Partners International","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"","lastName":"Traill","suffix":""},{"id":499862618,"identity":"5d74645b-5152-4fbb-9efd-a84ded980d81","order_by":6,"name":"Adam Richards","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYFCCBCA2gDAPPAAy+MGCBcRqSQAyJBtAggaEtKBoP8CAMAQb4G9PfrqBoaCOgV/s8MMDCQV37DafX534AehCeX6xA1i1SJx5ZnaDweAwg+TsNAOgw54lb7vxdrME0GGGM2cnYNXCcCMBpOUAg8HtBJCWw8lmN85uAGlJAIpg1SF/I/0bUEsdg/3t9A9gLcYzzm7+gU+LwY0ckC3MDAbSOWBb7Az4e7fhtcXwzJuyG0CVPBK3cwpAWhIkbvBus0gwkMDpF7nj6dtufPhTJ8c/O33zhw9/Dtvz95/dfPNHhY08vzQO74MAUIoHxk5skACrlMCtHB3YM/AfIF71KBgFo2AUjAgAAOAuZ+SpmuJWAAAAAElFTkSuQmCC","orcid":"","institution":"Mahidol University","correspondingAuthor":true,"prefix":"","firstName":"Adam","middleName":"","lastName":"Richards","suffix":""}],"badges":[],"createdAt":"2025-08-08 02:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7322766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7322766/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89272221,"identity":"8a1a9eb1-5293-4d48-8326-d30f302c7f4c","added_by":"auto","created_at":"2025-08-18 09:03:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124683,"visible":true,"origin":"","legend":"\u003cp\u003eWealth-related inequities in women’s empowerment, intervention coverage, food insecurity and diet outcomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e Equiplots for household wealth-related inequities in women’s empowerment indicators (Panel A), coverage of Project Nourish interventions (Panel B) and Food and nutrition outcomes (Panel C). Household wealth quintiles were defined using National cutoffs provided by the EquityTool asset index. Women’s empowerment index is comprised of eight items from Demographic and Health Surveys related to decision-making authority in eight domains: 1 indicates those made solely by women, -1 represent decisions made solely by men, and 0 represents joint decision-making. A dichotomous indicator was defined for each domain if the woman was the sole decision-maker. Responses for each item were normalized, and applied inverse covariance weighting was applied to the z scores to generate the overall women empowerment index; the highest quartile (\u0026gt;= 75 percentile) was used to define a dichotomous outcome of “high women empowerment.” Food insecurity is defined as ≥4 on the Food Insecurity Index Scale (FIES). Infant and young child feeding (IYCF) indicators apply standard definitions – please see full text.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7322766/v1/c3116cd87df23ec9be47cee7.jpg"},{"id":89275975,"identity":"e89fb641-a9fb-4ccb-9db9-996e3740d195","added_by":"auto","created_at":"2025-08-18 09:27:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1444977,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7322766/v1/8007cc22-c549-4d16-84f6-c44549e7f292.pdf"},{"id":89272224,"identity":"78215478-e897-47b2-9a91-067247bc5884","added_by":"auto","created_at":"2025-08-18 09:03:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":47633,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7322766/v1/c62631af2a8b582b0fba633e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Household wealth, women’s empowerment, and food insecurity in conflict-affected Southeast Myanmar","fulltext":[{"header":"Background","content":"\u003cp\u003eMalnutrition contributes to nearly half of child deaths worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), with distributions of chronic (stunting) and acute (wasting) malnutrition (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), as well as food insecurity and infant and young child feeding practices [IYCF] (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) demonstrating large inequities according to axes of power and advantage in society, including household wealth and women\u0026rsquo;s empowerment (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The World Food Program identifies poverty and gender inequality as drivers of household hunger globally and highlights armed conflict as the most important driver (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Although evidence from many large-scale conflicts demonstrate adverse impacts on malnutrition (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), food insecurity (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and IYCF (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), the impact of conflict on food and nutrition inequities \u003cem\u003eper se\u003c/em\u003e is poorly understood.\u003c/p\u003e\u003cp\u003eEquitable delivery of humanitarian services is a central goal of agencies operating in areas of armed conflict. The imperative to understand and address health inequities arises from the human rights framework underlying the Humanitarian Charter and SPHERE standards (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, many agencies do not collect, analyze, or report data related to household wealth, educational attainment, women\u0026rsquo;s empowerment, or other axes of disadvantage that in more stable settings demonstrate large and graded associations with adverse outcomes such as child mortality (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), food insecurity (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and lower coverage of evidence-based interventions (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Efforts to harmonize nutrition assessment in emergencies, including SMART, have focused on precise estimation of overall nutritional status, mortality, and program coverage; many nutrition assessments remain na\u0026iuml;ve to the distribution of outcomes across more- and less-vulnerable subgroups. The dearth of information on nutrition inequities in emergencies impedes efforts to identify progressive interventions that would promote nutrition equity and concentrate beneficial effects among the most vulnerable households and individuals.\u003c/p\u003e\u003cp\u003eIn Myanmar, prior to the SARS-CoV2 pandemic and 2021 coup d\u0026rsquo;etat, undernutrition and food insecurity were common and inequitably distributed between, as well as within States and Regions. A 2019 survey conducted by Community Partners International (CPI) documented the prevalence of stunting (40.2%) and wasting (6.2%) malnutrition as two- to three-times higher in hard-to-reach remote areas of contested governance than in more stable rural areas (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). For example, minimum dietary diversity ranged from 12.6\u0026ndash;42.6% in poor to wealthy households, respectively; minimum acceptable diet ranged from 3.3\u0026ndash;19.4% (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe 2021 coup d'etat in Myanmar resulted in widespread armed conflict, displaced 2.7\u0026nbsp;million within the country, and placed 17.6\u0026nbsp;million in need of humanitarian assistance (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). A national telephone survey suggests the concomitant economic and political crises in Myanmar have exacerbated household hunger, which is more common in households with low income, assets, and adult education (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In 2021 Myanmar\u0026rsquo;s gender inequality index of 0.498 ranked 106th out of 146 countries (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), though changes in women\u0026rsquo;s empowerment since the Coup are poorly understood.\u003c/p\u003e\u003cp\u003eIn March 2023 CPI and local partners conducted an in-person household survey to evaluate food insecurity, IYCF, and nutrition-sensitive interventions delivered to internally displaced populations in Southeastern Myanmar. The present study had two aims. First, to document inequities in household food insecurity, child feeding practices, maternal diet indicators, and coverage of nutrition-sensitive interventions according to women\u0026rsquo;s empowerment, household wealth, and educational attainment. Second, to quantify inequities in food insecurity and other nutrition outcomes when women\u0026rsquo;s empowerment, wealth, and educational attainment are considered in concert. Our \u003cem\u003ea priori\u003c/em\u003e hypotheses were that nutrition outcomes, women\u0026rsquo;s empowerment, and intervention coverage would be inequitably distributed in the population and that accounting jointly for household wealth, education, and women\u0026rsquo;s empowerment would attenuate but not eliminate the respective wealth-related inequities in nutrition outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area and design\u003c/h2\u003e\u003cp\u003eThis population-based cross-sectional survey used a two-stage cluster design to collect village- and household-level data in seven townships in Kayin State targeted by a CPI project in Southeastern Myanmar. The project was a multi-interventional program aimed to improve the nutritional status of mothers and children within the first 1,000 days in conflict-affected, hard to reach areas of eastern Myanmar. CPI, in partnership with three local health organizations, implemented this project from 2020 until 2025. The 504 project villages are in a complex political and geographic landscape with constantly evolving relationships between governing bodies.\u003c/p\u003e\u003cp\u003eThe sampling frame consisted of five strata defined according to the three local partner organizations A, B and C: strata 1A \u0026minus;\u0026thinsp;1C included 357 relatively accessible villages; strata 2A and 2B included 78 The rCI can be defined by the equationvillages less accessible due to topographical and security concerns.\u003c/p\u003e\u003cp\u003eThe survey was designed to provide 80% power to detect a 10-percentage point difference in key infant and child feeding indicators compared to 2019 baseline study results (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), assuming a non-response-rate of 10% and design effect of 1.5 due to the complex survey design. A planned sample of n\u0026thinsp;=\u0026thinsp;788 permits estimation of parameters for indicators with a prevalence of 20% within a precision of +/- 5 percentage points.\u003c/p\u003e\u003cp\u003eThe final projected sample size consisted of 788 households with children\u0026thinsp;\u0026lt;\u0026thinsp;5yrs, distributed unevenly between the strata. In Stratum 1, a probability proportionate to size (PPS) approach was used to select 34 of 357 villages; within each village 10 households were randomly surveyed. The Stratum 2 sample included approximately half (40 of 78) the partner organizations' target villages, with a maximum of 12 households per village for data collection. An ordered list of replacement villages was randomly generated in case of inaccessibility.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection tools and methods\u003c/h3\u003e\n\u003cp\u003eThe survey questionnaire, based on the project\u0026rsquo;s objectives and logical framework indicators, consists of 10 modules: Demographics; Child MUAC and IYCF; Child Illness and Health Seeking Behavior; Dietary Diversity Score for Women; Women Health-Seeking Behavior; Food Insecurity Access Scale; Depression Symptoms (PHQ9); Water, Sanitation, and Hygiene; Women\u0026rsquo;s Empowerment; and Program Exposure.\u003c/p\u003e\u003cp\u003eOutcomes were defined using tools designed and validated for their respective measurement fields. IYCF practices including the minimum dietary diversity for children (MDD-C)\u0026thinsp;\u0026lt;\u0026thinsp;2yrs were based on WHO 2021 guidelines (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Measurement of women's dietary diversity followed the Food and Agriculture Organization (FAO) guideline on Minimum Dietary Diversity for Women (MDD-W) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), with consumption of at least 5 of 10 food groups indicating achievement of minimum dietary diversity. Household food insecurity was assessed using the Food Insecurity Experiences Scale (FIES) developed by FAO (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e); FIES raw scores\u0026thinsp;\u0026gt;\u0026thinsp;3 indicated moderate or severe food insecurity (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). We used a Rasch model to calibrate our estimate of food insecurity prevalence in the project area, to facilitate comparison to other populations. Demographic Health Survey (DHS) and other published tools were used for household income and coping strategies; household wealth was measured using the Myanmar-specific EquityTool\u0026reg;, a parsimonious asset index used to classify households into national wealth quintiles (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe used the DHS women\u0026rsquo;s empowerment index that consists of eight household decisions regarding children (feeding, health-seeking behavior, well-being); women\u0026rsquo;s healthcare (pregnancy as well as general health); household purchases and social visits; and control over women\u0026rsquo;s own earnings. The index ranges from \u0026minus;\u0026thinsp;1 to 1, where \u0026minus;\u0026thinsp;1 indicates decisions made solely by men, 1 indicates those made solely by women, and 0 represents joint decision-making. We normalized responses for each item and applied inverse covariance weighting to the z scores to generate the overall women empowerment index; the highest quartile (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;75 percentile) was used to define a dichotomous outcome of \u0026ldquo;high women empowerment.\u0026rdquo;\u003c/p\u003e\u003cp\u003eSurvey questionnaires were programmed using KoboToolbox; data was collected using KoboCollect on Android tablets supported with an external battery to reach areas lacking electricity. Data quality checks included skip-pattern control, input value range limits, and logical consistency checks; enumerators were trained to flag data entry issues. An in-person five-day surveyor training included pilot testing and iterative revision of the survey.\u003c/p\u003e\u003cp\u003eTeams of four people - one supervisor and three interviewers \u0026ndash; collected data from 12/2022 to 04/2023. High frequency checks (HFCs) were conducted to ensure data quality and consisted of completion check, data consistency, and enumerator performance. HFC results were cross-checked with partner data collection teams to address duplicate observations, outliers in enumerator performance, and survey start time and duration.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWeighted means and proportions were calculated for household socio-demographics and women\u0026rsquo;s empowerment indicators. To summarize the inequitable distribution of nutrition-sensitive interventions (program coverage) and nutrition outcomes, we calculated the relative concentration index (rCI), (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), a health equity metric that allows all study participants to contribute to parameter estimates. The rCI is based on the concentration curve and provides a summary measure of relative inequity by ranking individuals according to socioeconomic status or power (e.g., household wealth, women\u0026rsquo;s empowerment) on the X-axis and plotting the cumulative share of health (e.g., food insecurity) on the Y-axis. The rCI is then defined as twice the area between the concentration curve and the 45-degree diagonal. In a hypothetical, equal world where health is distributed evenly across ranked groups, the concentration curve would overlap with the diagonal 45-degree line and the rCI would be zero. As relative inequality increases, the concentration curve strays from the diagonal and the rCI increases in magnitude.\u003c/p\u003e\u003cp\u003eThe rCI can be defined by the equation:\u003c/p\u003e\u003cp\u003e\u003cimg 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\" style=\"width: 286px; height: 81.6266px;\" width=\"286\" height=\"81.6266\"\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ey\u003c/em\u003e is a measure of the \u003cem\u003ei\u003c/em\u003eth person\u0026rsquo;s health based on the health- or nutrition outcome; \u0026micro; is the mean of the outcome (e.g. FIES); R is the fractional rank of the individual according to wealth (or income or educational attainment). A negative rCI indicates that the outcome is concentrated among the less well off.\u003c/p\u003e\u003cp\u003eThe rCI can be interpreted as 1.33 times the percentage of redistribution of the health outcome from high- to low-ranked individuals (above and below median wealth, respectively) required to make wealth-related inequality equal to zero (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). A rCI of 0.33 for MDD-W, for example, indicates that among all women who ate a minimally diverse diet, 24.8% in households with above-median incomes would need to transfer their diverse diet to women in households in below-median incomes for there to be an equal distribution of dietary diversity across wealth groups.\u003c/p\u003e\u003cp\u003eOur primary focus is on crude, or unadjusted measures of inequity that we believe are most valuable when targeting interventions and evaluating program success in a context where multiple axes of power and vulnerability frequently cluster in the same household. Nevertheless, to explore how inequities for nutrition outcomes related to women\u0026rsquo;s empowerment, respondent educational attainment, and household wealth would be affected when the respective rCIs accounted for additional axes of disadvantage, we also calculated adjusted rCIs that describe independent associations of the outcomes and ranking variables, after accounting for the two complementary variables. For example, the adjusted rCI of the FIES for household wealth summarizes wealth-related inequality of food insecurity, taking account of educational attainment and women\u0026rsquo;s empowerment. Wealth-related inequity was summarized using the continuous value of household wealth index as the ranking variable to calculate concentration indices; in sensitivity analyses we used Myanmar national quintiles provided by EquityTool\u0026reg; as well as empirical quintiles defined based on the distribution of household wealth in our survey.\u003c/p\u003e\u003cp\u003eBivariate and multivariate linear- and logistic regression models were estimated to examine associations of each nutrition outcome with women\u0026rsquo;s empowerment, educational attainment, and household wealth. Variables whose bivariate measures of association with the outcomes were significant at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 level in either the logistic model or rCI were included in the full logistic model and adjusted rCI calculations. All adjusted logistic models and adjusted rCIs included dummy variables for distance from food markets, implementing agency, and insecurity (stratum).\u003c/p\u003e\u003cp\u003eInverse probability weights were used to account for the probability of selecting clusters and households within clusters, and to account for the replacement of villages (clusters) where insecurity precluded data collection. Analyses accounted for the complex survey design using the svy suite of commands in Stata 17.0. The conindex command was used to calculate crude concentration indices (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e); adjusted concentration indices were calculated using the indirect standardization approach.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe survey team reached 803 households with children\u0026thinsp;\u0026lt;\u0026thinsp;5yrs located in 74 villages: 37 villages in each stratum. One implementing partner was unable to collect data from 3 villages in stratum 2 due to security constraints; when no replacement villages were available in Stratum 2 these villages were replaced with villages from Stratum 1. Of these households, 801 households (99.8%) consented to participate in the survey and comprised the final analytic sample.\u003c/p\u003e\n\u003ch3\u003eDemographics:\u003c/h3\u003e\n\u003cp\u003eSample sociodemographic characteristics appear in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of respondents was 30.2 years, and the mean household size was 5.3. Nearly all respondents were married (97.3%) and reported the mother as the main household caregiver (95.2%); 8.8% were pregnant at the time of data collection. One-third (32%) of respondents were illiterate, though only 9.4% of households lacked a literate member. Mean monthly income was reported as 129,904 kyat (~\u003cspan\u003e$\u003c/span\u003e2 per day). Six in ten households surveyed are in the lowest national wealth quintile among Myanmar households. Nine in ten households reported income had either decreased (47.7%) or remained the same (42.2%) since the 2021 coup.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographics of survey respondents\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUB\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMain Caregiver (n\u0026thinsp;=\u0026thinsp;801)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther main caregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Head (n\u0026thinsp;=\u0026thinsp;801)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status (n\u0026thinsp;=\u0026thinsp;801)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeparated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrently Pregnant (n\u0026thinsp;=\u0026thinsp;801)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAccess to Mobile Phone (HH)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;801)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStrata (n\u0026thinsp;=\u0026thinsp;801)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganization A, stable (A1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganization A, unstable (A2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganization B, stable (B1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganization B, unstable (B2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganization C, stable (C1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocioeconomic Status Measure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e% mean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eUB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWealth (Myanmar National quintiles)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;801)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWealthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWealthiest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation (n\u0026thinsp;=\u0026thinsp;800)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary education (\u0026lt;\u0026thinsp;5th standard)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary education (\u0026lt;\u0026thinsp;9th standard)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigher education (through matriculation exam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVocational education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGraduate level (University/ College)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost graduate level (University)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonastery Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonthly Income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage monthly income (kyat) (n\u0026thinsp;=\u0026thinsp;801)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129,904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111,978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e147,830\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHH income lower than last year (n\u0026thinsp;=\u0026thinsp;735)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHH income higher than last year (n\u0026thinsp;=\u0026thinsp;735)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHH income the same as last year (n\u0026thinsp;=\u0026thinsp;735)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReceived cash transfer from NGO/Gov\u0026rsquo;t\u0026nbsp; (n\u0026thinsp;=\u0026thinsp;752)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance (travel time) to health facility\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;760)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth facility in village\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026le;\u0026thinsp;1.5 hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1.6\u0026ndash;3 hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;3 hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance (travel time) to nearest food market\u0026nbsp;\u0026nbsp;(n\u0026thinsp;=\u0026thinsp;760)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFood market in village\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026le;\u0026thinsp;1.5 hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1.6\u0026ndash;3 hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;3 hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eWomen\u0026rsquo;s empowerment, program coverage, and household wealth\u003c/h2\u003e\u003cp\u003eWomen reported the most decision-making authority in respect to child feeding (75.2%), and the lowest related to child well-being (24.4%) and family visits (27.9%). Women\u0026rsquo;s empowerment demonstrated large wealth-related inequities: women from the poorest households were more likely to have low decision-making authority (61.1%) than women from wealthy (34.3%) households; the rCI was \u0026minus;\u0026thinsp;0.25 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Statistically significant wealth-related inequities were observed for seven of eight dimensions of household decision-making; only child feeding decision-making was similar among women across wealth quintiles. Women in the wealthiest households were three times more likely than women in the poorest to have control over household purchases and family visits (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProject Nourish interventions reached a minority of targeted households, and coverage was low and highly inequitable for SBCC sessions (rCI 0.36), home gardening (rCI 0.41), and mother support groups (rCI 0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for each distribution). In order to explore whether this inequity might reflect selective targeting of more wealthy villages we conducted \u003cem\u003epost-hoc\u003c/em\u003e analyses limited to households in villages reached by each respective intervention; although overall coverage was higher, household-level inequities were similarly large and significant (data not shown).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWealth-related inequities in women\u0026rsquo;s empowerment and coverage of nutrition-sensitive interventions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall mean or Proportion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWealthy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWealthiest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCrude Concentration Index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u0026rsquo;s Empowerment Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.07***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Women's Empowerment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e61.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.27***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecision-making authority\u003c/p\u003e\u003cp\u003e(Women\u0026rsquo;s empowerment components)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall Proportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWealthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWealthiest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCrude Concentration Index\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild feeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e81.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild healthcare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.23***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.29***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold purchase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.37***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl over own/woman\u0026rsquo;s earnings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e75.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.34***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily visits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e46.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.37***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u0026rsquo;s healthcare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.17**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal/pregnancy-related healthcare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.16**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProject Nourish Intervention Coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall Proportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWealthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWealthiest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCrude Concentration Index\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProject Nourish - Overall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMUAC screening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood basket/Cash for food\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBCC session\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.36***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome gardening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.41***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother support group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.61***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWASH infrastructure support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003e* p \u003c 0.1, ** p \u003c 0.05, *** p \u003c 0.01\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFood Insecurity\u003c/h2\u003e\u003cp\u003eThe prevalence of moderate or severe food insecurity, using a Rasch model to facilitate comparison with other populations, was 25.3%. A similar overall prevalence (23.5%) was estimated when using a common cutoff of 4 or greater to define moderate and severe food insecurity, and this dichotomous outcome was used in subsequent analyses to quantify inequities (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in Additional file 1).\u003c/p\u003e\u003cp\u003eFood insecurity was more common in lowest wealth quintile households (27.8%) and those whose respondent was illiterate (33.6%) than in highest wealth quintile households (2.1%) or with a respondent who passed their matriculation exam (9.3%); wealth- and education-related crude concentration indices for moderate/severe food insecurity were \u0026minus;\u0026thinsp;0.19 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and \u0026minus;\u0026thinsp;0.17 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively. Wealth- and education-related inequalities remained significant for at least one category in adjusted logistic models (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in Additional file 1), though the summary measure of household inequity that considers the distribution across the entire spectrum of disadvantage (rCI) remained significant only for education in adjusted models that accounted for education, wealth, and distance from food markets (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCrude and adjusted relative concentration indices to summarize inequities in food insecurity, child feeding practices, and maternal diet indicators, according to household wealth, education, and women's empowerment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHousehold Wealth-Related Inequality\u003c/p\u003e\u003cp\u003e(relative concentration index)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eEducation-Related Inequality\u003c/p\u003e\u003cp\u003e(relative concentration index)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eWomen Empowerment-Related Inequality (relative concentration index)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehol\u003c/b\u003ed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCrude\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eAdjusted\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCrude\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eAdjusted\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003eCrude\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eAdjusted\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold FIES\u003c/p\u003e\u003cp\u003eraw score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.12**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.09**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.09**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.05*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate / Severe Food Insecurity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.19*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.17**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.18**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEBF\u003c/p\u003e\u003cp\u003eExclusive Breastfeeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.23**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-DDS\u003c/p\u003e\u003cp\u003eChild Diet Diversity Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD\u003c/p\u003e\u003cp\u003eMinimum Dietary Diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.34***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.23***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMF\u003c/p\u003e\u003cp\u003eMinimum Meal Frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAD (child 6\u0026ndash;23 months) Minimum acceptable diet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.22**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD-W\u003c/p\u003e\u003cp\u003eMinimum Dietary Diversity\u003c/p\u003e\u003cp\u003efor Women (dichotomous)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD-W score (continuous0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.05**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/h2\u003e\u003cp\u003eAdjusted concentration indices are adjusted for distance from food markets, implementing agency, and insecurity (stratum)indirectly standardized for the two complementary variables among educational attainment, household wealth and women\u0026rsquo;s empowerment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eInfant and Young Child Feeding and Maternal Diet\u003c/h2\u003e\u003cp\u003eOnly 31.2% of respondents reported exclusive breastfeeding (EBF) for the first six months. EBF was concentrated in households with higher incomes (rCI\u003csub\u003eadj\u003c/sub\u003e 0.2) and more educated respondents (rCI\u003csub\u003eadj\u003c/sub\u003e 0.17), though these inequities were no longer significant in adjusted models. Evidence was lacking to support an association between EBF and women\u0026rsquo;s empowerment (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eChildren\u0026rsquo;s dietary diversity score (C-DDS) was low, with a mean of 3.7 out of 8 food groups consumed; fewer than one third (31.4%) of children achieved minimal dietary diversity (MDD-C). Both measures of dietary diversity were inequitably distributed according to wealth, education, and women\u0026rsquo;s empowerment, though the associations with education and women\u0026rsquo;s empowerment were substantially diminished and no longer statistically significant in adjusted models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and see Tables S1 and S2 in Additional file 1).\u003c/p\u003e\u003cp\u003eApproximately two-thirds (64.2%) of children 6\u0026ndash;23 months received the minimum meal frequency (MMF) overall, with similar MMF according to wealth, education, and women\u0026rsquo;s empowerment. Minimum acceptable diet (MAD), a composite outcome that includes minimum dietary diversity and meal frequency, was low (25.1%), and demonstrated patterns of inequity similar to MDD: MAD was associated with wealth, education, and women\u0026rsquo;s empowerment, though only the association with wealth remained significant in adjusted models. Both MMF (73.7% vs 50.5%) and MAD (36.0% vs 11.8%) were more common among children who continued to breastfeed from 6 to 23 months than among children who did not (see Table S3 in Additional file 1).\u003c/p\u003e\u003cp\u003eOnly one-third (36.8%) of women met the MDD-W indicating consumption of at least 5 of 10 food groups over a 24-hour period; the mean MDD-W score was 4.4. MDD-W was highly concentrated among households that were wealthier in both crude (rCI 0.33) and adjusted models (rCI\u003csub\u003eadj\u003c/sub\u003e 0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). MDD-W demonstrated modest associations with education and women\u0026rsquo;s empowerment; neither inequity remained significant in adjusted models.\u003c/p\u003e\u003cp\u003eIn a \u003cem\u003epost-hoc\u003c/em\u003e analysis we found that food groups eaten by infants were strikingly different from foods eaten by their mothers. While 35.5% of mothers reported eating pulses, and 9.7% nuts and seeds, only 17.2% of children (6\u0026ndash;23 months) ate pulses/nuts/seeds. Although 65.6% of mothers reported eating meat/poultry/fish, those same foods were only given to 12.4% of children (6\u0026ndash;23 months). Dairy, on the other hand, is consumed by 17% of children (6\u0026ndash;23 months) and 10.5% of mothers (Table S4).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur cross-sectional household survey of food insecurity, child feeding practices, and maternal diet in poor, remote areas of Southeast Myanmar experiencing armed conflict produced evidence that supports our \u003cem\u003ea priori\u003c/em\u003e hypotheses that nutrition-related outcomes, women\u0026rsquo;s empowerment, and intervention coverage are inequitably distributed, and that accounting jointly for household wealth, education, and women\u0026rsquo;s empowerment would attenuate, but not eliminate, wealth-related inequities in nutrition outcomes.\u003c/p\u003e\u003cp\u003eThe magnitude of most inequalities documented in our study is large, and comparable to those documented in highly inequitable societies, including Myanmar prior to the Coup (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Our findings are consistent with prior studies that found large inequities in coverage of key maternal and child health and nutrition interventions, and that the magnitude of inequities was larger in countries affected by armed conflict than in politically more stable countries (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). We extend these findings to a specific nutrition program implemented in an area experiencing active armed conflict. Below we place inequities for each indicator in context and make a case for incorporating health equity metrics into routine monitoring and evaluation frameworks to ensure that nutrition-sensitive interventions do not exacerbate the inequities they are designed to ameliorate.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eFood insecurity\u003c/h2\u003e\u003cp\u003eWe found one in four households (25.3%) is moderately or severely food insecure as measured by the FIES, which according to the FAO (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) is lower than the prevalence of food insecurity among for households in rural areas of LMICs globally (42.7%), and roughly between estimates reported for Myanmar by the WFP (20%), and Humanitarian Needs and Response Plan Overview for Myanmar (28%).\u003c/p\u003e\u003cp\u003ePrevalence of moderate-to-severe food insecurity in the poorest households (27.8%) was over ten times higher than in the wealthiest households (2.1%; Figure and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in Additional file 1). Poverty is a well established driver of food insecurity (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and the association between poverty and food insecurity is not novel. However, many food and nutrition programs in emergencies do not routinely monitor the impact of their interventions on vulnerability, and whether their efforts ameliorate or exacerbate these inequity. Methods employed in this study to quantify nutrition inequities can be used to track inequities over time, and when incorporated into rigorously designed program evaluations can be used to identify interventions that reduce inequities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eInfant and Young Child Feeding\u003c/h2\u003e\u003cp\u003eExclusive breastfeeding is associated with a 44\u0026ndash;45% reduction in neonatal mortality (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and is protective against malnutrition (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Overall rates of EBF were low (31%) and inversely associated with household wealth and maternal education. Our results are consistent with a review that found that in countries affected by armed conflict EBF was less common in households that were poor or less well educated than in more wealthy and educated households (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These pro-wealth inequities contrast with evidence from countries not affected by conflict, where EBF tends to be more common among less well off households. Additional study is necessary to understand drivers of EBF inequities in this rural and predominantly agrarian economy affected by armed conflict.\u003c/p\u003e\u003cp\u003eThe large and unexpected discrepancies in dietary intake between mothers and young children, overall and within the same household, we observed in our \u003cem\u003epost-hoc\u003c/em\u003e analysis requires confirmation, though it suggests there may be an opportunity to improve the diet of some children by the promotion of foods already available in the household.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eWomen\u0026rsquo;s Empowerment\u003c/h2\u003e\u003cp\u003eThe term \u0026ldquo;women\u0026rsquo;s empowerment\u0026rdquo; is context-specific; this limits comparison to other settings (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), though decision-making authority is commonly used to measure women\u0026rsquo;s empowerment (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). In Myanmar, \u0026ldquo;having more knowledge,\u0026rdquo; and \u0026ldquo;being able to support family\u0026rdquo; were concepts most associated with women\u0026rsquo;s empowerment (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough wealth-related inequities in women\u0026rsquo;s empowerment were anticipated, the magnitude of inequity documented in our study is exceptionally large. A working paper on women\u0026rsquo;s empowerment in Myanmar used data from the 2015\u0026ndash;2016 DHS to document that wealthy women were 1.49 times more likely than poor women to have a higher empowerment level when controlling for other factors (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Using a similar dichotomous outcome, we found that households in the highest wealth quintile were more than twice as likely (61.0%) to include women with an empowerment index above the median compared to women from the lowest quintile (26.5%).\u003c/p\u003e\u003cp\u003eAlthough our study found that wealth demonstrated stronger independent associations with nutrition indicators than women\u0026rsquo;s empowerment, women\u0026rsquo;s empowerment remains an important target for nutrition interventions. Interventions specifically focused on women\u0026rsquo;s empowerment have been shown to be effective at addressing malnutrition in women and children (\u003cspan additionalcitationids=\"CR45 CR46 CR47\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), and these may be easier to implement and yield more timely results than interventions targeting wealth, education and/or physical accessibility of households to markets, especially in the context of active conflict. Empowerment of women is itself socially desirable and may yield benefits that extend beyond food and nutrition, such as healthcare access in Myanmar (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWomen's/mother\u0026rsquo;s groups are often utilized as an intervention to improve women\u0026rsquo;s empowerment (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) and health outcomes (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), but groups may exclude adolescents (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), nulliparous women, and men (\u003cspan additionalcitationids=\"CR54 CR55 CR56 CR57\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). While these groups have been shown to be effective at improving infant feeding practices and child nutrition (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), overall evidence is mixed (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e), and these groups may be more effective when combined with a cash incentive (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Results of our midterm evaluation were used to develop a qualitative study to explore factors contributing to low attendance at women\u0026rsquo;s groups, and why women from poor households were less likely to attend.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eInequitable Intervention Coverage\u003c/h2\u003e\u003cp\u003eWe documented large inequities in coverage of most key nutrition-sensitive interventions that may paradoxically exacerbate the outcome inequities they were designed to ameliorate. Low overall program coverage, which this project experienced, makes it less likely to observe an impact on nutrition/diet indicators. However, we can note that distribution of interventions most likely to impact nutrition and food insecurity outcomes were either highly unequal (SBCC, mother support, home gardening) or not statistically different from an equal distribution (food basket/cash); none were progressively distributed with higher coverage among the poorest households. This suggests that without conscientious effort to understand and address coverage inequities there is a risk that Project Nourish could perpetuate or exacerbate large nutrition-related inequities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStrengths\u003c/h2\u003e\u003cp\u003eOur stratified design took account of a complex political and security landscape and selected, a priori, a list of replacement villages perceived to be at similar risk of acute conflict. We applied health equity metrics developed for stable settings to quantify inequities in nutrition-related outcomes, women\u0026rsquo;s empowerment and coverage of interventions in areas experiencing active armed conflict. Our survey used electronic tablets with skip pattern and consistency checks to enhance data quality. We conducted an in-person household survey that likely generated nutrition indicators more representative of population parameters than phone-based surveys that attempt to weight results using data on phone ownership and utilization collected during relatively stable political periods (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Since 2021 cellular service coverage and internet blackouts have oscillated and at times services appear to be deliberately suppressed in areas of active conflict (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). These new drivers of selection bias likely threaten the validity of using phone surveys to estimate nutrition indicators in conflict-affected populations in Myanmar. Although it is beyond the scope of the current manuscript to formally compare the accuracy of findings from our in-person survey to results from phone-based methods, it is notable that our estimates of MDD-W (36.6%) and other indicators are substantially lower than those generated by a phone survey conducted by Project Nourish staff at the inception of the program (adequate MDD-W 66.7%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eWe used the empowerment measure from DHS to facilitate comparison with previous surveys in Myanmar. Measures developed specifically for nutrition-related outcomes such as the Women\u0026rsquo;s Empowerment in Nutrition Index (WENI) and Women\u0026rsquo;s Empowerment in Agriculture index (WEIA) may have produced alternative results though they include a relatively large number of items (\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and lack of validation in rural Myanmar (\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe did not calculate a multi-dimensional index of inequality, nor did we conduct a formal decomposition analysis (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) to examine major drivers of food insecurity, both of which may provide additional nuance to our findings.\u003c/p\u003e\u003cp\u003eWe did not analyze the association between income loss and nutrition. In 2020, 83% of households reported their income was lower than usual, with the decline averaging 43% and being concentrated in poorer households (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Income loss may translate to worsening nutrition in the form of less food, or less nutritious food intake (\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe on-going SARS-CoV2 pandemic and heightened political instability in project areas forced some survey activities to be conducted remotely. The use of tablets and high frequency data checks likely improved data quality, but the impact of remote format for several training and supervision activities is unknown.\u003c/p\u003e\u003cp\u003eThree villages were inaccessible to survey teams due to insecurity and weather events, and their replacement with more secure villages may have led us to underestimate food insecurity in this region. However, most security concerns were temporary and our stratified design and \u003cem\u003ea priori\u003c/em\u003e selection of replacement villages perceived to be at similar risk of insecurity likely minimized this bias.\u003c/p\u003e\u003cp\u003eThe initial design of Project Nourish was built on the expectation that the government of Myanmar would implement a MCCT program to provide cash payments to mothers during the first thousand days from conception through the first 2 years of life. The suspension of the MCCT program caused implementation challenges for Project Nourish and likely affected overall outcomes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur cross-sectional household survey documented large inequities in food insecurity, dietary intake and women\u0026rsquo;s empowerment in remote areas of Myanmar experiencing active armed conflict. Furthermore, we found that poor, less educated households with less empowered women were less likely to utilize nutrition-sensitive interventions. This suggests that the LIFT Pooled Fund for Livelihoods, whose best practices and recommendations this project followed, may exacerbate inequities in food and nutrition outcomes. Whether or not other donors\u0026rsquo; nutrition projects adversely impact nutrition inequities can only be determined if the distributions of services and outcomes are measured and analyzed. Our application of health equity metrics to quantify inequities in intervention coverage and outcomes provides an approach to track health inequities over time and to identify interventions that ameliorate \u0026ndash; or at least do not exacerbate \u0026ndash; inequities in other health services and outcomes in other humanitarian emergencies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eC-DDS - Child Diet Diversity Score\u003c/p\u003e\n\u003cp\u003eCPI - Community Partners International\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDHS - Demographic Health Survey\u003c/p\u003e\n\u003cp\u003eEBF - Exclusive Breast Feeding\u003c/p\u003e\n\u003cp\u003eFAO - Food and Agriculture Organization\u003c/p\u003e\n\u003cp\u003eFIES - Food Insecurity Experience Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHFCs - High Frequency Checks\u003c/p\u003e\n\u003cp\u003eIYVF - Infant and Young Child Feeding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLIFT - Livelihoods and Food Security Fund\u003c/p\u003e\n\u003cp\u003eLMIC - Lower Middle Income Country\u003c/p\u003e\n\u003cp\u003eMAD - Minimum Acceptable Diet\u003c/p\u003e\n\u003cp\u003eMCCT - Maternal and Child Cash Transfer\u003c/p\u003e\n\u003cp\u003eMDD - Minimum Dietary Diversity\u003c/p\u003e\n\u003cp\u003eMDD-C - Minimum Dietary Diversity for Children\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMDD-W - Minimum Dietary Diversity for Women\u003c/p\u003e\n\u003cp\u003eMMF - Minimum Meal Frequency\u003c/p\u003e\n\u003cp\u003eMUAC - Mid-Upper Arm Circumference\u003c/p\u003e\n\u003cp\u003ePHQ9 - Patient Health Questionnaire 9\u003c/p\u003e\n\u003cp\u003ePPS - Probability Proportionate to Size\u003c/p\u003e\n\u003cp\u003erCI - relative Concentration Index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSBCC - Social and Behavioral Change Communication\u003c/p\u003e\n\u003cp\u003eWEIA - Women\u0026rsquo;s Empowerment in Agriculture Index\u003c/p\u003e\n\u003cp\u003eWENI - Women\u0026apos;s Empowerment in Nutrition Index\u003c/p\u003e\n\u003cp\u003eWFP - World Food Program\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study involving human participants was reviewed and approved by the Community Ethics Advisory Board (CEAB) of Mae Tao Clinic; the Institutional Review Board of The George Washington University deemed the secondary analysis of de-identified project data to be exempt from ethical review. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements, but informed verbal consent was obtained at the time of survey response.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eDr. Richards is Senior Technical Advisor to CPI and is an advisor to a fund at The Tides Foundation that has funded CPI for other work; Dr. Richards did not receive compensation from The Tides Foundation for this or any other study. \u0026nbsp;The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eProject Nourish and its M\u0026amp;E activities are funded by The Livelihoods and Food Security Fund (LIFT). LIFT is a multi-donor program that aims to tackle the issue of poverty and hunger directly in Myanmar\u0026apos;s rural communities. Donors include UKAID, USAID, Australian Aid, The European Union, Canada AiD, New Zealand Aid, the Norwegian Ministry of Foreign Affairs, and the Swiss Agency for Development and Cooperation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot\u0026nbsp;applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKTZ and NTZ contributed equally as first authors.\u0026nbsp;\u003cbr\u003e\u0026nbsp;NTZ, TT, and AR developed the study design.\u0026nbsp;\u003cbr\u003e\u0026nbsp;ANE and HMW managed data collection.\u003cbr\u003e\u0026nbsp;NTZ and AR analyzed data.\u003cbr\u003e\u0026nbsp;KTZ contributed to data analysis and wrote the literature review and manuscript draft.\u0026nbsp;\u003cbr\u003e\u0026nbsp;TT acted as principal investigator of the study.\u003cbr\u003e\u0026nbsp;HH acted as program manager.\u0026nbsp;\u003cbr\u003e\u0026nbsp;All authors were involved in the writing of the manuscript and have approved the final version for publication.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors thank and acknowledge significant effort from the data collection team, project partners, and survey respondents, who face significant challenges from conflict daily yet made the effort to contribute to this research project. The authors also thank the people of Kayin state, and Burma at large.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMalnutrition in Children - UNICEF DATA [Internet]. [cited 2023 Sep 10]. Available from: https://data.unicef.org/topic/nutrition/malnutrition/\u003c/li\u003e\n\u003cli\u003eShirisha P, Muraleedharan VR, Vaidyanathan G. Wealth related inequality in women and children malnutrition in the state of Chhattisgarh and Tamil Nadu. BMC Nutr. 2022 Dec 1;8(1). \u003c/li\u003e\n\u003cli\u003eAkombi BJ, Agho KE, Renzaho AM, Hall JJ, Merom DR. Trends in socioeconomic inequalities in child undernutrition: Evidence from Nigeria demographic and health survey (2003 \u0026ndash; 2013). PLoS One. 2019 Feb 1;14(2). \u003c/li\u003e\n\u003cli\u003eNovignon J, Aboagye E, Agyemang OS, Aryeetey G. Socioeconomic-related inequalities in child malnutrition: evidence from the Ghana multiple indicator cluster survey. 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Matern Child Nutr. 2021 Oct 1;17(4). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"nutrition, food insecurity, breastfeeding, child feeding, women’s empowerment, health equity, humanitarian health, internally displaced persons, Myanmar, Burma","lastPublishedDoi":"10.21203/rs.3.rs-7322766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7322766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo quantify inequities in food security, maternal diet, child feeding and nutrition intervention coverage in conflict zones of Southeast Myanmar.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA stratified cluster survey in 74 villages was designed to evaluate a program to deliver nutrition-sensitive interventions in areas of armed conflict. Primary outcomes included the Food Insecurity Experience Scale (FIES) and standard indicators of infant and young child feeding (IYCF). We quantified inequities according to several axes of vulnerability including household wealth, maternal education, and women\u0026rsquo;s empowerment, considered individually and in concert, by estimating crude- and multiply adjusted logistic regression models that also account for distance to food markets. Crude and adjusted relative concentration indices were used to summarize inequities in nutrition indicators and program coverage. Analysis accounted for the complex survey design.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 801 respondents 32% are illiterate and 59% reported household wealth in the poorest National quintile. One quarter (25.3%) of households reported moderate or severe food insecurity overall, with moderate to large inequities related to wealth (crude rCI \u0026minus;\u0026thinsp;0.19) and education (-0.17) though only education inequities remained significant in adjusted models. Inequities for child and maternal nutrition outcomes except for minimum meal frequency were larger for household wealth (range in rCI, 0.14 to 0.34) and smaller for education (0.07 to 0.22). Women in wealthier households were more likely to report high empowerment, though women\u0026rsquo;s empowerment was not associated with nutrition outcomes. Project coverage was concentrated among wealthier households.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eLarge inequities in food insecurity, dietary intake and women\u0026rsquo;s empowerment exist in remote areas of Myanmar experiencing active armed conflict. Nutrition-sensitive intervention coverage also was inequitably distributed, suggesting that the nutrition project may have exacerbated inequities in food and nutrition outcomes. Humanitarian agencies should routinely measure health inequities and identify delivery models that ameliorate them.\u003c/p\u003e","manuscriptTitle":"Household wealth, women’s empowerment, and food insecurity in conflict-affected Southeast Myanmar","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 09:03:33","doi":"10.21203/rs.3.rs-7322766/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-15T13:51:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T16:43:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80163632539800727111304416885353254587","date":"2025-10-03T00:00:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T10:43:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257336270775386618144176807347947783409","date":"2025-08-13T09:24:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-10T23:29:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-10T08:31:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-10T08:18:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Health, Population and Nutrition","date":"2025-08-08T02:34:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f83a0570-a057-4fd6-b22c-1191d6f3be59","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T19:08:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-18 09:03:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7322766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7322766","identity":"rs-7322766","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}