Socioeconomic and geographic inequalities in antenatal and postnatal care components in India, 2016-2021.

Despite the well-known importance of high-quality care before and after delivery, not every mother and newborn in India receive appropriate antenatal and postnatal care (ANC/PNC). Using India's National Family Health Surveys (2015-2016 and 2019-2021), we quantified the socioeconomic and geographic inequalities in the utilization of ANC/PNC among women aged 15-49 years and their newborns (N = 161,225 in 2016; N = 150,611 in 2021). For each of the eighteen ANC/PNC components, we assessed absolute and relative inequalities by household wealth (poorest vs. richest), maternal education (no education vs. higher than secondary), and type of place of residence (rural vs. urban) and evaluated state-level heterogeneity. In 2021, the national prevalence of ANC/PNC components ranged from 19.8% for 8 + ANC visits to 91.6% for maternal weight measurement. Absolute inequalities were greatest for ultrasound test (33.3%-points by wealth, 30.3%-points by education) and 8 + ANC visits (13.2%-points by residence). Relative inequalities were greatest for 8 + ANC visits (1.8 ~ 4.4 times). All inequalities declined over time. State-specific estimates were overall consistent with national results. Socioeconomic and geographic inequalities in ANC/PNC varied significantly across components and by states. To optimize maternal and newborn health in India, future interventions should aim to achieve universal coverage of all ANC/PNC components.

Women's empowerment and child anthropometric failures across 28 sub-Saharan African countries: A cross-level interaction by Gender Inequality Index.

Background: Child undernutrition remains a major global health issue, particularly in sub-Saharan Africa (SSA). Given the important role mothers play in early childhood health and development, we examined how individual-level women's empowerment and country-level Gender Inequality Index (GII) are jointly related with child undernutrition in SSA. Methods: We pooled recent Demographic and Health Surveys from 28 SSA countries. For 137,699 children <5 years old, undernutrition was defined using anthropometric failures (stunting, underweight, wasting). Women's empowerment was assessed using three domains of Survey-based Women's EmPowERment (SWPER) index: attitude to violence, social independence, and decision-making; and country-level gender inequality was measured using GII from United Nations Development Programme. Three-level logistic regression was conducted to examine the joint associations of SWPER and GII as well as their interactions with child anthropometric failures, after adjusting for sociodemographic covariates. Results: Overall, 32.85% of children were stunted, 17.63% were underweight, and 6.68% had wasting. Children of mothers with low-level of empowerment for all domains of SWPER had higher odds of stunting (attitude to violence: OR=1.15; 95% CI, 1.11-1.19; social independence: OR=1.21; 95% CI, 1.17-1.25; decision-making: OR=1.16; 95% CI, 1.12-1.20), and consistent results were found for underweight and wasting. Independent of women's empowerment, country-level GII increased the probability of underweight (ranging ORs=1.46; 95% CI, 1.15-1.85 to 1.50; 95% CI, 1.18-1.90) and wasting (ranging ORs=1.56; 95% CI, 1.24-1.97 to 1.61; 95% CI, 1.27-2.03). Significant interaction was found between women's empowerment and country-level GII for stunting and underweight (p<0.05). Conclusions: In SSA countries with greater gender inequality, improving women's social independence and decision-making power in particular can reduce their children's risk of anthropometric failures. Policies and interventions targeted at strengthening women's empowerment should consider the degree of gender inequality in each country.

Prevalence, trend, and inequality of prolonged exclusive breastfeeding among children aged 6-23 months old in India from 1992-2021: A cross-sectional study of nationally representative, individual-level data.

Background: Prolonged exclusive breastfeeding (PEB) for children older than six months old is a threat to appropriate complementary feeding practices. This study aims to examine the trend of PEB among children aged 6-23 months in India. Methods: We adopted five waves of National Family Health Survey (NFHS) data between 1992-93 and 2019-21. PEB was defined as children aged six months and above currently consuming breastmilk as the only source of energy, protein and micronutrients. We generated descriptive statistics and a series of multivariable logistic regressions to estimate the prevalence and trend in the PEB rate. Moreover, we assessed how child age and socioeconomic factors (i.e. child gender and age, place of residence, household wealth, and maternal education) were related with PEB using mutually and single-adjusted model. Results: There were 184 891 Indian children aged 6-23 months old included in this study with 48.0% being female. We found that the proportion of PEB increased from 4.3% in 1992 to 7.7% in 2021, of which the rate for children aged six-eight months rose from 14.0 to 20.1%. Our results showed that children who were from poorer households or with lower-educated mothers were more likely to experience prolonged exclusive breastfed. Take the year of 2019-21 as an example, compared to the households of the richest quintile, children from households of the poorer quintile were significantly more likely to experience PEB, with odds ratio (OR) of 1.33 (95% confidence interval (CI) = 1.09-1.61). Moreover, children with illiterate mothers had 21% higher odds of having prolonged exclusively breastfeeding (OR = 1.21; 95% CI = 1.01-1.44) compared with children with mothers who have college and above education. Conclusions: PEB among children over six months old is prevalent in India, particularly among children from disadvantaged households. Poverty reduction and maternal education are of great potential importance for policymakers to promote appropriate complementary feeding practice.

An environmental justice analysis of air pollution in India.

Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 µg/m3 (95% CI 0.062 µg/m3, 0.192 µg/m3) and 0.199 µg/m3 (95% CI 0.116 µg/m3, 0.283 µg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters.

Socio-economic distribution of modifiable risk factors for cardiovascular diseases: An analysis of the national longitudinal ageing study in India.

The association of socioeconomic status (SES) with modifiable risk factors for cardiovascular diseases (CVDs) is unclear in developing nations. We studied SES variations in major risk factors and their percentage distribution for adults aged 45 years or above in India. Using individual records of 59,672 individuals aged 45 years or above from the Longitudinal Ageing Study in India Wave 1 (cross-sectional study design), 2017-18, we chart age-and-sex-adjusted prevalence of clinical risk factors such as measured high blood pressure, hypertension, overweight, obesity, central adiposity and self-reported high blood glucose; and lifestyle risk factors such as excessive use of alcohol, current use of smoking and smokeless tobacco and physical inactivity across SES variables of education, quintiles of mean per capita expenditure and social caste. Multivariable analysis was used to explore the SES gradient of risk factors. The sample used in the study is predominantly rural (69.9%), illiterate (50.7%), has more females (54.2%), and belongs to other backward classes (45.6%). Prevalence of high blood pressure, overweight, obesity, central adiposity, high blood glucose, and physical inactivity increased; and excessive alcohol consumption and current use of smoking/smokeless tobacco decreased with income, education, and social caste. However, no significant income gradient was noted for lifestyle risk factors except the use of smokeless tobacco. The income gradient was largest for central adiposity (waist-circumference) with a difference of 23.4 percentage points as it increased from 38.7% among the poorest to 62.1% among the richest. The major burden of CVDs risk factors among older adults aged 45+ years falls among high SES.

Explaining the poor-rich gap in anthropometric failure among children in India: An econometric analysis of the NFHS, 2021 and 2016.

Wealth inequality in anthropometric failure is a persistent concern for policymakers in India. This necessitates a comprehensive analysis and identification of various risk factors that can explain the poor-rich gap in anthropometric failure among children in India. We analyze the fifth and fourth rounds of the Indian National Family Health Survey collected from June 2019 to April 2021 and January 2015 to December 2016, respectively. Two samples of children aged 0-59 and 6-23 months old with singleton birth, alive at the time of the survey with non-pregnant mothers, and with valid data on stunting, severe stunting, underweight, severely underweight, wasting, and severe wasting are included in the analytical samples from both rounds. We estimate the wealth gradients and distribution of wealth among children with anthropometric failure. Wealth gap in anthropometric failure is identified using logistic regression analysis. The contribution of risk factors in explaining the poor-rich gap in AF is estimated by the multivariate decomposition analysis. We observe a negative wealth gradient for each measure of anthropometric failure. Wealth distributions indicate that at least 60% of the population burden of anthropometric failure is among the poor and poorest wealth groups. Even among children with similar modifiable risk factors, children from poor and poorest backgrounds have a higher prevalence of anthropometric failure compared to children from the richest backgrounds. Maternal BMI, exposure to mass media, and access to sanitary facility are the most significant risk factors that explain the poor-rich gap in anthropometric failure. This evidence suggests that the burden of anthropometric failure and its risk factors are unevenly distributed in India. The policy interventions focusing on maternal and child health, implemented with a targeted approach prioritizing the vulnerable groups, can only partially bridge the poor-rich gap in anthropometric failure. The role of anti-poverty programs and growth is essential to narrow this gap in anthropometric failure.

Socioeconomic and geographic variation in coverage of health insurance across India.

Introduction: In India, regular monitoring of health insurance at district levels (the most essential administrative unit) is important for its effective uptake to contain the high out of pocket health expenditures. Given that the last individual data on health insurance coverage at district levels in India was in 2016, we update the evidence using the latest round of the National Family Health Survey conducted in 2019-2021. Methods: We use the unit records of households from the latest round (2021) of the nationally representative National Family Health Survey to calculate the weighted percentage (and 95% CI) of households with at least one member covered by any form of health insurance and its types across socio-economic characteristics and geographies of India. Further, we used a random intercept logistic regression to measure the variation in coverage across communities, district and state. Such household level study of coverage is helpful as it represents awareness and outreach for at least one member, which can percolate easily to the entire household with further interventions. Results: We found that only 2/5th of households in India had insurance coverage for at least one of its members, with vast geographic variation emphasizing need for aggressive expansion. About 15.5% were covered by national schemes, 47.1% by state health scheme, 13.2% by employer provided health insurance, 3.3% had purchased health insurance privately and 25.6% were covered by other health insurance schemes (not covered above). About 30.5% of the total variation in coverage was attributable to state, 2.7% to districts and 9.5% to clusters. Household size, gender, marital status and education of household head show weak gradient for coverage under "any" insurance. Discussion: Despite substantial increase in population eligible for state sponsored health insurance and rise in private health insurance companies, nearly 60% of families do not have a single person covered under any health insurance scheme. Further, the existing coverage is fragmented, with significant rural/urban and geographic variation within districts. It is essential to consider these disparities and adopt rigorous place-based interventions for improving health insurance coverage.

Progress on Sustainable Development Goal indicators in 707 districts of India: a quantitative mid-line assessment using the National Family Health Surveys, 2016 and 2021.

Background: India has committed itself to accomplishing the Sustainable Development Goals (SDGs) by 2030. Meeting these goals would require prioritizing and targeting specific areas within India. We provide a mid-line assessment of the progress across 707 districts of India for 33 SDG indicators related to health and social determinants of health. Methods: We used data collected on children and adults from two rounds of the National Family Health Survey (NFHS) conducted in 2016 and 2021. We identified 33 indicators that cover 9 of the 17 official SDGs. We used the goals and targets outlined by the Global Indicator Framework, Government of India and World Health Organization (WHO) to determine SDG targets to be met by 2030. Using precision-weighted multilevel models, we estimated district mean for 2016 and 2021, and using these values, computed the Annual Absolute Change (AAC) for each indicator. Using the AAC and targets, we classified India and each district as: Achieved-I, Achieved-II, On-Target and Off-Target. Further, when a district was Off-Target on a given indicator, we further identified the calendar year in which the target will be met post-2030. Findings: India is not On-Target for 19 of the 33 SDGs indicators. The critical Off-Target indicators include Access to Basic Services, Wasting and Overweight Children, Anaemia, Child Marriage, Partner Violence, Tobacco Use, and Modern Contraceptive Use. For these indicators, more than 75% of the districts were Off-Target. Because of a worsening trend observed between 2016 and 2021, and assuming no course correction occurs, many districts will never meet the targets on the SDGs even well after 2030. These Off-Target districts are concentrated in the states of Madhya Pradesh, Chhattisgarh, Jharkhand, Bihar, and Odisha. Finally, it does not appear that Aspirational Districts, on average, are performing better in meeting the SDG targets than other districts on majority of the indicators. Interpretation: A mid-line assessment of districts' progress on SDGs suggests an urgent need to increase the pace and momentum on four SDG goals: No Poverty (SDG 1), Zero Hunger (SDG 2), Good Health and Well-Being (SDG 3) and Gender Equality (SDG 5). Developing a strategic roadmap at this time will help India ensure success with regards to meeting the SDGs. India's emergence and sustenance as a leading economic power depends on meeting some of the more basic health and social determinants of health-related SDGs in an immediate and equitable manner. Funding: This work was funded by the Bill and Melinda Gates Foundation, INV-002992.

Prevalence of Zero-Food among infants and young children in India: patterns of change across the States and Union Territories of India, 1993-2021.

Background: The extent of food deprivation and insecurity among infants and young children-a critical phase for children's current and future health and well-being-in India is unknown. We estimate the prevalence of food deprivation among infants and young children in India and describe its evolution over time at sub-national levels. Methods: Data from five National Family Health Surveys (NFHS) conducted in 1993, 1999, 2006, 2016 and 2021 for the 36 states/Union Territories (UTs) of India were used. The study population consisted of the most recent children (6-23 months) born to mothers (aged 15-49 years), who were alive and living with the mother at the time of survey (n = 175,614 after excluding observations that had no responses to the food question). Food deprivation was defined based on the mother's reporting of the child having not eaten any food of substantial calorific content (i.e., any solid/semi-solid/soft/mushy food types, infant formula and powdered/tinned/fresh milk) in the past 24 hours (h), which we labelled as "Zero-Food". In this study, we analyzed Zero-Food in terms of percent prevalence as well as population headcount burden. We calculated the Absolute Change (AC) to quantify the change in the percentage points of Zero-Food across time periods for all-India and by states/UTs. Findings: The prevalence of Zero-Food in India marginally declined from 20.0% (95% CI: 19.3%-20.7%) in 1993 to 17.8% (95% CI: 17.5%-18.1%) in 2021. There were considerable differences in the trajectories of change in the prevalence of Zero-Food across states. Chhattisgarh, Mizoram, and Jammu and Kashmir experienced high increase in the prevalence of Zero-Food over this time period, while Nagaland, Odisha, Rajasthan and Madhya Pradesh witnessed a significant decline. In 2021, Uttar Pradesh (27.4%), Chhattisgarh (24.6%), Jharkhand (21%), Rajasthan (19.8%) and Assam (19.4%) were states with the highest prevalence of Zero-Food. As of 2021, the estimated number of Zero-Food children in India was 5,998,138, with the states of Uttar Pradesh (28.4%), Bihar (14.2%), Maharashtra (7.1%), Rajasthan (6.5%), and Madhya Pradesh (6%) accounting for nearly two-thirds of the total Zero-Food children in India. Zero-Food in 2021 was concerningly high among children aged 6-11 months (30.6%) and substantial even among children aged 18-23 months (8.5%). Overall, socioeconomically advantaged groups had lower prevalence of Zero-Food than disadvantaged groups. Interpretation: Concerted efforts at the national and state levels are required to further strengthen existing policies, and design and develop new ones to provide affordable food to children in a timely and equitable manner to ensure food security among infants and young children. Funding: This study was supported by a grant from the Bill & Melinda Gates Foundation INV-002992.

Small area variations in four measures of poverty among Indian households: Econometric analysis of National Family Health Survey 2019-2021.

India has seen enormous reductions in poverty in the past few decades. However, much of this progress has been unequal throughout the country. This paper examined the 2019-2021 National Family Health Survey to examine small area variations in four measures of household poverty. Overall, the results show that clusters and states were the largest sources of variation for the four measures of poverty. These findings also show persistent within-district inequality when examining the bottom 10th wealth percentile, bottom 20th wealth percentile, and multidimensional poverty. Thus, these findings pinpoint the precise districts where between-cluster inequality in poverty is most prevalent. This can help guide policy makers in terms of targeting policies aimed at reducing poverty.

Small Area Variation in the Quality of Maternal and Newborn Care in India.

Importance: In India, the district serves as the primary policy unit for implementing and allocating resources for various programs aimed at improving key developmental and health indicators. Recent evidence highlights that high-quality care for mothers and newborns is critical to reduce preventable mortality. However, the geographic variation in maternal and newborn health service quality has never been investigated. Objective: To examine the variation between smaller areas within districts in the quality of maternal and newborn care in India. Design, Setting, and Participants: This cross-sectional study assessed data from women aged 15 to 49 years on the most recent birth (singleton or multiples) in the 5 years that preceded the fifth National Family Health Survey (June 17, 2019, to April 30, 2021). Exposures: Maternal and newborn care in 36 states and union territories (UTs), 707 districts, and 28 113 clusters (small areas) in India. Main Outcomes and Measures: The composite quality score of maternal and newborn care was defined as the proportion of components of care received of the total 11 essential components of antenatal and postnatal care. Four-level logistic and linear regression was used for analyses of individual components of care and composite score, respectively. Precision-weighted prevalence of each component of care and mean composite score across districts as well as their between-small area SD were calculated. Results: The final analytic sample for the composite score was composed of 123 257 births nested in 28 113 small areas, 707 districts, and 36 states/UTs. For the composite score, 58.3% of the total geographic variance was attributable to small areas, 29.3% to states and UTs, and 12.4% to districts. Of 11 individual components of care, the small areas accounted for the largest proportion of geographic variation for 6 individual components of care (ranging from 42.3% for blood pressure taken to 73.0% for tetanus injection), and the state/UT was the largest contributor for 4 components of care (ranging from 41.7% for being weighed to 52.3% for ultrasound test taken). District-level composite score and prevalence of individual care components and their variation across small areas within the districts showed a consistently strong negative correlation (Spearman rank correlation ρ = -0.981 to -0.886). Low-quality scores and large between-small area disparities were not necessarily concentrated in aspirational districts (mean district composite score [SD within districts], 92.7% [2.1%] among aspirational districts and 93.7% [1.8%] among nonaspirational districts). Conclusions and Relevance: The findings of this cross-sectional study suggest that the policy around maternal and child health care needs to be designed more precisely to consider district mean and between-small area heterogeneity in India. This study may have implications for other low- and middle-income countries seeking to improve maternal and newborn outcomes, particularly for large countries with geographic heterogeneity.

The relative importance of macro versus micro geographical scale in explaining suicide variation in Seoul, South Korea 2014-2016.

BACKGROUND: As ecological factors are getting attention as important determinants of suicide, it is important to identify the unit at which the largest variation exists for more tailed strategy to prevent suicide. We examined the relative importance of two administrative levels for geographic variation in the suicide rate between 2014-2016 in Seoul, the capital city of Korea. METHODS: Two-level linear regression with Dongs (level 1) nested within Gus (level 2) was performed based on suicide death data aggregated at the Dong-level. We performed pooled analyses and then year-stratified analyses. Dong-level socioeconomic status and environmental characteristics were included as control variables. RESULTS: The overall age- and sex- standardized suicide rate across all Dongs decreased over time from 24.9 deaths per 100,000 in 2014 to 23.7 deaths in 2016. When Dong and Gu units were simultaneously considered in a multilevel analysis, most of the variation in suicide rate was attributed to within-Gu, between-Dong differences with a contribution of Gu-level being small and decreasing over time in year (Variance partitioning coefficient of Gu = 5.3% in 2014, <0.1% in 2015 and 2016). The number of divorce cases per 100,000 explained a large fraction of variation in suicide rate at the Dong-level. CONCLUSIONS: Findings from this study suggest that ecological micro-area unit is more important in reducing the geographic variation in the suicide rate. More diverse ecological-level data needs to be collected for targeted area-based suicide prevention policies in Korea.

Assessment of the predictive power of a causal variable: An application to the Head Start impact study.

In a study attempting to estimate a causal effect of a causal variable, an assessment of the predictive power of the causal variable can shed light on the heterogeneity around its average effect. Using data from the Head Start Impact Study, a randomized controlled trial of the Head Start, a nation-wide early childhood education program in the United States, we provide a parallel comparison between measures of average effect and predictive power of the Head Start on five cognitive outcomes. We observed that one year of the Head Start increased scores for all five outcomes, with effect sizes ranging from 0.12 to 0.19 standard deviations. Percent variation explained by the Head Start ranged from 0.56 to 1.62%. For binary versions of the outcomes, the overall pattern remained; the Head Start on average improved the outcomes by meaningful magnitudes. In contrast, in a fully adjusted model, the Head Start only improved area under the curve (AUC) by less than 1% and its influence on the variance of predicted probabilities was negligible. The Head-Start-only model only achieved AUC ranging from 50.22 to 55.24%. Negligible predictive power despite the significant average effect suggests that the heterogeneity in effects may be large. The average effect estimates may not generalize well to different populations or different Head Start program settings. Assessment of the predictive power of a causal variable in randomized data should be a routine practice as it can provide helpful information on the causal effect and especially its heterogeneity.

Components of Out-of-Pocket Expenditure and Their Relative Contribution to Economic Burden of Diseases in India.

Importance: High out-of-pocket expenditure (OOPE) on health in India may limit achieving universal health coverage. A clear insight on the components of health expenditure may be necessary to make allocative decisions to reduce OOPE, and such details by sociodemographic group and state have not been studied in India. Objective: To analyze the relative contribution of drugs, diagnostic tests, doctor and surgeon fees, and expenditure on other medical services and nonmedical health-related services, such as transport, lodging, and food, by sociodemographic characteristics of patients, geography, and type of illness. Design, Setting, and Participants: A population-based cross-sectional health consumption survey conducted by the National Sample Survey Organisation in 2018 was analyzed in this cross-sectional study. Respondents who provided complete information on costs of medicine, doctors, diagnostics tests, other medical costs, and nonmedical costs were selected. Data were analyzed from August through September 2021. Main Outcomes and Measures: Mean and median share of components (ie, medicine, diagnostic tests, doctor fees, other medical costs, and nonmedical costs) in total health care expenditure and income were calculated. Bivariate survey-weighted mean (with 95% CI) and median (IQR) expenditures were calculated for each component across sociodemographic characteristics. The proportion of total expenditure and income contributed by each cost was calculated for each individual. Mean and median were then used to summarize such proportions at the population level. The association between state net domestic product per capita and component share of each health care service was graphically explored. Results: Health expenditure details were analyzed for 43 781 individuals for inpatient costs (27 272 [64.3%] women; 26 830 individuals aged 25-64 years [59.9%]) and 8914 individuals for outpatient costs (4176 [48.2%] women; 4901 individuals aged 25-64 years [54.2%]); most individuals were rural residents (24 106 inpatients [67.0]; 4591 outpatients [63.9%]). Medicines accounted for a mean of 29.1% (95% CI, 28.9%-29.2%) of OOPE among inpatients and 60.3% (95% CI, 59.7%-60.9%) of OOPE among outpatients. Doctor consultation charges were a mean of 15.3% (95% CI, 15.1%-15.4%) of OOPE among inpatients and 12.4% (95% CI, 12.1%-12.6%) of OOPE among outpatients. Diagnostic tests accounted for a mean of 12.3% (95% CI, 12.2%-12.4%) of OOPE for inpatient and 9.2% (95% CI, 8.9%-9.5%) of OOPE for outpatient services. Nonmedical costs accounted for a mean of 23.6% (95% CI, 23.3%-23.8%) of OOPE among inpatients and 14.6% (95% CI, 14.1%-15.1%) of OOPE among outpatients. Mean share of OOPE from doctor consultations and diagnostic test charges increased with socioeconomic status. For example, for the lowest vs highest monthly per capita income quintile among inpatients, doctor consultations accounted for 11.5% (95% CI, 11.1%-11.8%) vs 21.2% (95% CI, 20.8%-21.6%), and diagnostic test charges accounted for 10.9% (95% CI, 10.6%-11.1%) vs 14.3% (95% CI, 14.0%-14.5%). The proportion of mean annual health expenditure from mean annual income was $299 of $1918 (15.6%) for inpatient and $391 of $1788 (21.9%) for outpatient services. Conclusions and Relevance: This study found that nonmedical costs were significant, share of total health care OOPE from doctor consultation and diagnostic test charges increased with socioeconomic status, and annual cost as a proportion of annual income was lower for inpatient than outpatient services.

Small area variations in low birth weight and small size of births in India.

The states and districts are the primary focal points for policy formulation and programme intervention in India. The within-districts variation of key health indicators is not well understood and consequently underemphasised. This study aims to partition geographic variation in low birthweight (LBW) and small birth size (SBS) in India and geovisualize the distribution of small area estimates. Applying a four-level logistic regression model to the latest round of the National Family Health Survey (2015-2016) covering 640 districts within 36 states and union territories of India, the variance partitioning coefficient and precision-weighted prevalence of LBW (<2.5 kg) and SBS (mother's self-report) were estimated. For each outcome, the spatial distribution by districts of mean prevalence and small area variation (as measured by standard deviation) and the correlation between them were computed. Of the total valid sample, 17.6% (out of 193,345 children) had LBW and 12.4% (out of 253,213 children) had SBS. The small areas contributed the highest share of total geographic variance in LBW (52%) and SBS (78%). The variance of LBW attributed to small areas was unevenly distributed across the regions of India. While a strong correlation between district-wide percent and within-district standard deviation was identified in both LBW (r = 0.88) and SBS (r = 0.87), they were not necessarily concentrated in the aspirational districts. We find the necessity of precise policy attention specifically to the small areas in the districts of India with a high prevalence of LBW and SBS in programme formulation and intervention that may be beneficial to improve childbirth outcomes.

Assessment of heterogeneous Head Start treatment effects on cognitive and social-emotional outcomes.

Head Start is a federally funded, nation-wide program in the U.S. for enhancing school readiness of children aged 3-5 from low-income families. Understanding heterogeneity in treatment effects (HTE) is an important task when evaluating programs, but most attempts to explore HTE in Head Start have been limited to subgroup analyses that rely on average treatment effects by subgroups. This study applies an extension of multilevel modelling, complex variance modelling, to data from a randomized controlled trial of Head Start, Head Start Impact Study (HSIS). The treatment effects on the variance, in addition to the mean, of nine cognitive and social-emotional outcomes were assessed for 4,442 children aged 3-4 years who were followed until their 3rd grade year. Head Start had positive short-term effects on the means of multiple cognitive outcomes while having no effect on the means of social-emotional outcomes. Head Start reduced the variances of multiple cognitive and one social-emotional outcomes, meaning that substantial HTE exists. In particular, the increased mean and decreased variance reflect the ability of Head Start to improve the outcomes and reduce their variability. Exploratory secondary analyses suggested that larger benefits for children with Spanish as a primary language and low parental educational level partly explained the reduced variability, but the HTE remained and the variability was reduced even within these subgroups. Routinely monitoring the treatment effects on the variance, in addition to the mean, would lead to a more comprehensive program evaluation that describes how a program performs on average and on the entire distribution.

Life expectancies across congressional districts in the United States.

Examining data on the congressional district level may better align with the interests of Members of Congress who have the power to implement federal health policies. Despite this importance, measurement of health indicators at the congressional district level remains widely understudied. In this study, we estimated overall life expectancy and variation within each congressional district by computing standard deviations of census tract-level age-specific life expectancies for 2010-2015. We found smaller standard deviations in congressional districts with higher overall life expectancy at younger ages, but the pattern was reversed at older ages.

Associations of single versus multiple anthropometric failure with mortality in children under 5 years: A prospective cohort study.

BACKGROUND/OBJECTIVES: Stunting, underweight, and wasting are used to monitor nutritional status in children, but they do not identify children with concurrent anthropometric failures (AF). Our study estimates the association between AF and mortality in children with single versus multiple failures, then calculates the percentage of child deaths attributable to AF. SUBJECTS/METHODS: Using data from a prospective, longitudinal study of 3605 children from age 1 to age 5 years in Ethiopia and India, we estimate the association between AF and mortality using conventional definitions (stunting, underweight, and wasting) and the mutually exclusive categories of stunted only underweight only, wasted only, stunted and underweight (SU), underweight and wasted, and stunted, underweight, and wasted (SUW), adjusting for socioeconomic status and other demographic variables. Last, we calculate the population attributable fraction. RESULTS: Children who were SU and SUW had 3.20 (95% CI: 1.69, 6.06; p < 0.001) and 5.52 (95% CI: 2.25, 13.56; p < 0.001) times the odds of death in fully adjusted models by Round 2 compared to children with no failure, while no increased mortality risk was found among children with other categories of failure. We estimate that 42.69% of child deaths can be attributed to children who are SUW (17.02%) or SU (25.67%), accounting for nearly 80% of child deaths from AF. CONCLUSIONS: This study provides new insight to programs and policy to better identify children most at risk of malnutrition-related mortality.

Estimating the Burden of Child Undernutrition for Smaller Electoral Units in India.

Importance: Geographic targeting of public health interventions is needed in resource-constrained developing countries. Objective: To develop methods for estimating health and development indicators across micropolicy units, using assembly constituencies (ACs) in India as an example. Design, Setting, and Participants: This cross-sectional study included children younger than 5 years who participated in the fourth National Family and Health Survey (NFHS-4), conducted between January 2015 and December 2016. Participants lived in 36 states and union territories and 640 districts in India. Children who had valid weight and height measures were selected for stunting, underweight, and wasting analysis, and children between age 6 and 59 months with valid blood hemoglobin concentration levels were included in the anemia analysis sample. The analysis was performed between February 1 and August 15, 2020. Exposures: A total of 3940 ACs were identified from the geographic location of primary sampling units in which the children's households were surveyed in NFHS-4. Main Outcomes and Measures: Stunting, underweight, and wasting were defined according to the World Health Organization Child Growth Standards. Anemia was defined as blood hemoglobin concentration less than 11.0 g/dL. Results: The main analytic sample included 222 172 children (mean [SD] age, 30.03 [17.01] months; 114 902 [51.72%] boys) from 3940 ACs in the stunting, underweight, and wasting analysis and 215 593 children (mean [SD] age, 32.63 [15.47] months; 112 259 [52.07%] boys) from 3941 ACs in the anemia analysis. The burden of child undernutrition varied substantially across ACs: from 18.02% to 60.94% for stunting, with a median (IQR) of 35.56% (29.82%-42.42%); from 10.40% to 63.24% for underweight, with a median (IQR) of 32.82% (25.50%-40.96%); from 5.56% to 39.91% for wasting, with a median (IQR) of 19.91% (15.70%-24.27%); and from 18.63% to 83.05% for anemia, with a median (IQR) of 55.74% (48.41%-63.01%). The degree of inequality within states varied across states; those with high stunting, underweight, and wasting prevalence tended to have high levels of inequality. For example, Uttar Pradesh, Jharkhand, and Karnataka had high mean AC-level prevalence of child stunting (Uttar Pradesh, 45.29%; Jharkhand, 43.76%; Karnataka, 39.77%) and also large SDs (Uttar Pradesh, 6.90; Jharkhand, 6.02; Karnataka, 6.72). The Moran I indices ranged from 0.25 to 0.80, indicating varying levels of spatial autocorrelation in child undernutrition across the states in India. No substantial difference in AC-level child undernutrition prevalence was found after adjusting for possible random displacement of geographic location data. Conclusions and Relevance: In this cross-sectional study, substantial inequality in child undernutrition was found across ACs in India, suggesting the importance of considering local electoral units in designing targeted interventions. The methods presented in this paper can be further applied to measuring health and development indicators in small electoral units for enhanced geographic precision of public health data in developing countries.

Assessment of Undernutrition Among Children in 55 Low- and Middle-Income Countries Using Dietary and Anthropometric Measures.

Importance: Evidence on the suitability of anthropometric failure (ie, stunting, underweight, and wasting) as a stand-alone measure of child undernutrition can inform global and national nutrition and health agendas. Objective: To provide a comprehensive estimate of the prevalence of child undernutrition by evaluating both dietary and anthropometric measures simultaneously across 55 low- and middle-income countries. Design, Setting, and Participants: This was a cross-sectional study that used Demographic and Health Surveys program data from July 2009 to January 2019, to allocate children into dietary and anthropometric failure categories. Nationally representative household surveys were conducted in 55 low- and middle-income countries. Participants included children aged 6 to 23 months who were born singleton and had valid anthropometric measures as well as available 24-hour food intake recollection. Data analysis was conducted from August 23 to October 22, 2020. Exposures: Two factors were considered to allocate children into the respective categories. Dietary failure was based on the World Health Organization standards for minimum dietary diversity. Anthropometric failure was constructed using the World Health Organization child growth reference standard z score for stunted growth, muscle wasting, and less than average weight for age. Main Outcomes and Measures: Dietary and anthropometric failures were cross-tabulated, which yielded 4 potential outcomes: dietary failure only, anthropometric failure only, both failures, and neither failure. Total child populations for each category were extrapolated from United Nations population estimates. Results: Of the 162 589 children (median age [range], 14 months [6-23 months]; 83 467 boys [51.3%]; 78 894 Asian children [48.5%]) in our sample, 42.9% of children had dietary failure according to the standard World Health Organization definition without being identified as having anthropometric failures. In all, 34.7% had both failures, 42.9% had dietary failure only, 8.3% had anthropometric failure only, and 14.1% had neither failure. Dietary and anthropometric measures were discordant for 51.2% of children; these children had nutritional needs identified by only 1 of the 2 measures. Dietary failure doubled the proportion of children in need of dietary interventions compared with anthropometry alone (43%). A total of 45.3 million additional children who experienced undernutrition in these 55 countries were not captured through the evaluation of anthropometric failures only. These results were consistent across geographic regions. Conclusions and Relevance: The results of this cross-sectional study suggest that the current standard of measuring child undernutrition by estimating the prevalence of anthropometric failure should be complemented with dietary and food-based measures. Anthropometry alone may fail to identify many children who have insufficient dietary intake.