Multilevel analysis of geographic variation among correlates of child undernutrition in India

Reproductive and child health transition among selected empowered action groups states of India: A district-level analysis

BACKGROUND

Health is an inseparable part of life and central to all life supporting systems. The reproductive and child health shares a major portion of public health cases that is crucial for socio-economic development. Studies on reproductive and child health have traditionally been focused on demographic aspects using socio-economic parameters. Given the emphasis of Sustainable Development Goal (SDG)-3 on health and well-being, it is imperative to understand the geo-spatial dimension with the visible transition of key health indicators of fertility, maternal and infant/child health in the high burdened districts within these high focus Empowered Action Group (EAG) states of Rajasthan, Madhya Pradesh, Uttar Pradesh and Bihar that make up nearly 40% of India's population with relatively laggard health status.

METHODOLOGY

This paper aims to understand the status and trend of key reproductive and child health indicators and vital statistics based on the recent representative demographic surveys. We intend to undertake a district level spatio-temporal analysis by developing District Composite Health Profile (DCHP) using Composite Index Method on selected 13 equally weighted key reproductive and child health indicators. The study has been carried out using data from National Family Health Survey-4 (2015-16) and National Family Health Survey-5 (2019-21) survey rounds. We employed geo-spatial techniques i.e. Moran's-I, and univariate LISA to comprehend the geographical clustering of high and low health burden districts and their heterogeneities at the district level.

RESULTS/CONCLUSIONS

The study highlights emerging inter-districts, and inter-state disparities over survey periods. With consistent improvement in the selected EAG states over time, the overall reproductive and child health status through DCHP along with each indicator was relatively better in the states of Rajasthan and worse in Bihar. Districts along the Terai belt in Uttar Pradesh and Bihar consistently performed sluggish during survey rounds. The geo-spatial clustering follows the political boundary of states, albeit with intra-state variations. Monitoring of key health indicators using composite index method provides a useful leverage for identifying priority districts/regions for universal health access that should also consider geographical space as an important policy dimension.

Burden of undernutrition among under-five Bengali children and its determinants: Findings from Demographic and Health Surveys of Bangladesh and India

BACKGROUND

Globally, undernutrition is the leading cause of mortality among under-five children. Bangladesh and India were in the top ten countries in the world for under-five mortality. The aim of the study was to investigate the nutritional status of Bengali under-five children.

METHODS

Data on 25938 under-five children were retrieved from the Bangladesh Demographic and Health Survey 2017-18 (BDHS) and the National Family Health Survey of India 2015-16 (NFHS-4). Stunting, wasting, underweight and thinness were considered to understand the nutritional status of under-five children. Binary logistic regression was used to identify associated factors of undernutrition among children.

RESULTS

Over one-quarter of Bengali under-five children were found to be suffering from the problem of stunting (31.9%) and underweight (28.1%), while other nutritional indicators raised serious concern and revealed inter-country disparities. In the cases of wasting, underweight and thinness, the mean z-scores and frequency differences between Bangladesh and India were significant. The nutritional status of Bengali under-five children appeared to have improved in Bangladesh compared to India. Child undernutrition had significant relations with maternal undernutrition in both countries. Girls in Bangladesh had slightly better nutritional status than boys. In Bangladesh, lack of formal education among mothers was a leading cause of child undernutrition. Stunting and underweight coexist with low household wealth index in both counties.

CONCLUSIONS

The research revealed that various factors were associated with child undernutrition in Bengalis. It has been proposed that programmes promoting maternal education and nutrition, along with household wealth index be prioritised. The study recommends that the Governments of Bangladesh and India should increase the budget for health of children so as to reach the sustainable development goals.

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.

Quality of anthropometric data in India's National Family Health Survey: Disentangling interviewer and area effect using a cross-classified multilevel model

India has adopted a target-based approach to reduce the scourge of child malnourishment. Because the monitoring and evaluation required by this approach relies primarily on large-scale data, a data quality assessment is essential. As field teams are the primary mode of data collection in large-scale surveys, this study attempts to understand their contribution to variations in child anthropometric measures. This research can help disentangle the confounding effects of regions/districts and field teams on the quality of child anthropometric data. The anthropometric z-scores of 2,25,002 children below five years were obtained from the fourth round of India's National Family and Health Survey (NFHS-4), 2015–16. Unadjusted and adjusted standard deviations (SD) of the anthropometric measures were estimated to assess the variations in measurements. In addition, a cross-classified multilevel model (CCMM) approach was adopted to estimate the contribution of geographical regions/districts and teams to variations in anthropometric measures. The unadjusted SDs of the measures of stunting, wasting, and underweight were 1.7, 1.4, and 1.2, respectively. The SD of stunting was above the World Health Organisation threshold (0.8–1.2), as well as the Demographic and Health Survey mark. After adjusting for team-level characteristics, the SDs of all three measures reduced marginally, indicating that team-level workload had a marginal but significant role in explaining the variations in anthropometric z-scores. The CCMM showed that the maximum contribution to variations in anthropometric z-scores came from community-level (Primary Sampling Unit (PSU)) characteristics. Team-level characteristics had a higher contribution to variations in anthropometric z-scores than district-level attributes. Variations in measurement were higher for child height than weight. The present study decomposes the effects of district- and team-level factors and highlights the nuances of introducing teams as a level of analysis in multilevel modelling. Population size, density, and terrain variations between PSUs should be considered when allocating field teams in large-scale surveys.

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Unadjusted standard deviation for child malnourishment indicators are above the recommended level of DHS data quality standards.

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Variation in stunting is directly proportional to workload measured by number of eligible children in the PSUs.

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Cross-classified multilevel models show significant team-level contribution in explaining variations in anthropometric scores.

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Team-level contribution to explaining variations in child anthropometric measures is larger than district-level factors.

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The number of days assigned to gather anthropometric measurements should be dependent on the number of eligible respondents in a PSU, which may be identified at the time of mapping & listing, rather than being a fixed number of days across all the states of India.

Small Area Variations in Dietary Diversity Among Children in India: A Multilevel Analysis of 6-23-Month-Old Children

Dietary diversity is an important indicator of child malnutrition. However, little is known about the geographic variation of diet indicators across India, particularly within districts and across states. As such, the purpose of this paper was to elucidate the small area variations in diet indicators between clusters within districts of India. Overall, we found that clusters were the largest source of variation for children not eating grains, roots, and tubers, legumes and nuts, dairy, vitamin A-rich vegetables and fruits, and other vegetables and fruits. We also found positive correlations between the district percent and cluster standard deviations of children not breastfeeding or eating grains, roots, and tubers, but negative correlations between the district percent and cluster standard deviation for the remaining seven outcomes. These findings underscore the importance of targeting clusters to improve child dietary diversity.

Geographic and Age Variations in Low Body Mass Index Among Community-Dwelling Older People in Xinjiang: A Cross-Sectional Study

Background: Studies have shown an association between undernutrition and increased adverse outcome, as well as substantial geographic and age variations in undernutrition. Body mass index (BMI), a core indicator of undernutrition, is easy to measure and reflects the nutritional and health status of the human body. It is a simple and suitable tool for epidemiological investigations in large sample populations. Herein, we provide the first description of geographic and age variations in the prevalence of low BMI among community-dwelling older people in Xinjiang.

Methods: From January 2019 to December 2019, using a multi-stage random sampling method, we conducted a cross-sectional epidemiological survey of the community-dwelling older people in Xinjiang at different latitudes. Of the 87,000 participants, the statistical analyses included 86,514 participants with complete data.

Results: In Xinjiang, the prevalence of low BMI was 7.7% in the community-dwelling older people. The BMI gradually decreased with increasing age and gradually increased with latitude. The prevalence of low BMI in northern Xinjiang was 5.3%, which was significantly lower than that in eastern (7.7%) and southern (9.3%) Xinjiang. In the 60–69-, 70–79-, 80–89-, and ≥90-year age groups, the prevalence rates of low BMI were 5.8, 7.9, 10.0, and 13.9%, respectively. After adjusting for confounding factors (sex, ethnic group, hypertension, diabetes, hyperlipemia, smoking, and drinking), multivariate logistic regression analysis showed that the odds ratios (95% CI) for low BMI in eastern and southern Xinjiang were 1.165 (1.056–1.285) and 1.400 (1.274–1.538), respectively, compared to northern Xinjiang. The adjusted odds ratios (95% CI) for low BMI in the 70–79-, 80–89-, and ≥90-year age groups were 1.511 (1.39–1.635), 2.233 (2.030–2.456), and 3.003 (2.439–3.696), respectively, compared to the 60–69-year age group.

Conclusion: The results of this study revealed geographic and age variations in the prevalence of low BMI in the community-dwelling older people in Xinjiang. The prevalence of low BMI gradually increased as the latitude decreased and as age increased.

Multilevel analysis of geographic variation among correlates of child undernutrition in India

Prior research has identified a number of risk factors ranging from inadequate household sanitation to maternal characteristics as important determinants of child malnutrition and health in India. What is less known is the extent to which these individual‐level risk factors are geographically distributed. Assessing the geographic distribution, especially at multiple levels, matters as it can inform where, and at what level, interventions should be targeted. The three levels of significance in the Indian context are villages, districts, and states. Thus, the purpose of this paper was to (a) examine what proportion of the variation in 21 risk factors is attributable to villages, districts, and states in India and (b) elucidate the specific states where these risk factors are clustered within India. Using the fourth National Family Health Survey dataset, from 2015 to 2016, we found that the proportion of variation attributable to villages ranged from 14% to 63%, 10% to 29% for districts and 17% to 62% for states. Furthermore, we found that Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh were in the highest risk quintile for more than 10 of the risk factors included in our study. This is an indication of geographic clustering of risk factors. The risk factors that are clustered in states such as Bihar, Jharkhand, Madhya Pradesh and Uttar Pradesh underscore the need for policies and interventions that address a broader set of child malnutrition determinants beyond those that are nutrition specific.