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Explaining Within- vs Between-Population Variation in Child Anthropometry and Hemoglobin Measures in India: A Multilevel Analysis of the National Family Health Survey 2015-2016.

ABSTRACT: BACKGROUND:The complex etiology of child growth failure and anemia-commonly used indicators of child undernutrition-involving proximate and distal risk factors at multiple levels is generally recognized. However, their independent and joint effects are often assessed with no clear conceptualization of inferential targets. METHODS:We utilized hierarchical linear modeling and a nationally representative sample of 139,116 children aged 6-59 months from India (2015-2016) to estimate the extent to which a comprehensive set of 27 covariates explained the within- and between-population variation in height-for-age, weight-for-age, weight-for-height, and hemoglobin level. RESULTS:Most of the variation in child anthropometry and hemoglobin measures was attributable to within-population differences (80-85%), whereas between-population differences (including communities, districts, and states) accounted for only 15-20%. The proximate and distal covariates explained 0.2-7.5% of within-population variation and 2.1-34.0% of between-population variation, depending on the indicator of interest. Substantial heterogeneity was observed in the magnitude of within-population variation, and the fraction explained, in child anthropometry and hemoglobin measures across the 36 states/union territories of India. CONCLUSIONS:Policies and interventions aimed at reducing between-population inequalities in child undernutrition may require a different set of components than those concerned with within-population inequalities. Both are needed to promote the health of the general population, as well as that of high-risk children.

SUBMITTER: Rodgers J

PROVIDER: S-EPMC7557174 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Explaining Within- vs Between-Population Variation in Child Anthropometry and Hemoglobin Measures in India: A Multilevel Analysis of the National Family Health Survey 2015-2016.

Rodgers Justin J Kim Rockli R Subramanian S V SV

Journal of epidemiology 20191012 11

<h4>Background</h4>The complex etiology of child growth failure and anemia-commonly used indicators of child undernutrition-involving proximate and distal risk factors at multiple levels is generally recognized. However, their independent and joint effects are often assessed with no clear conceptualization of inferential targets.<h4>Methods</h4>We utilized hierarchical linear modeling and a nationally representative sample of 139,116 children aged 6-59 months from India (2015-2016) to estimate t ...[more]

PMID: 31611523

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Project description:BACKGROUND:India has launched the malaria elimination initiative in February 2016. Studies suggest that estimates of malaria are useful to rationalize interventions and track their impact. Hence, a national study was launched to estimate burden of malaria in India in 2015. METHODS:For sampling, all 624 districts of India were grouped in three Annual Parasite Incidence (cases per thousand population) categories, < two (low); two-five (moderate) and > five (high) API. Using probability proportional to size (PPS) method, two districts from each stratum were selected covering randomly 200,000 persons per district. Active surveillance was strengthened with 40 trained workers per study district. Data on malaria cases and deaths was collated from all health care providers i.e. pathological laboratories, private practitioners and hospitals in private and public health sectors and was used for analysis and burden estimation. RESULTS:Out of 1215,114 population under surveillance, 198,612 (16.3%) tests were performed and 19,386 (9.7%) malaria cases were detected. The malaria cases estimated in India were 3875,078 (95% confidence interval 3792,018-3958,137) with API of 3.05 (2.99-3.12) including 2789,483 (2740,577-2838,389) Plasmodium falciparum with Annual Falciparum Incidence of 2.2 (2.16-2.24). Out of 8025 deaths investigated, 102 (1.27%) were attributed to malaria. The estimated deaths in India were 29,341 (23,354-35,327) including 19,067 (13,665-24,470) confirmed and 10,274 (7694-12,853) suspected deaths in 2015-2016. CONCLUSIONS:Estimated malaria incidence was about four folds greater than one million reported by the national programme, but three folds lesser than thirteen million estimated by the World Health Organization (WHO). However, the estimated deaths were 93 folds more than average 313 deaths reported by the national malaria programme in 2015-2016. The 29,341 deaths were comparable with 24,000 deaths in 2015 and 22,786 deaths in 2016 estimated by the WHO for India. These malaria estimates can serve as a benchmark for tracking the success of malaria elimination campaign in India.

Project description:BackgroundMultilevel analyses are ideally suited to assess the effects of ecological (higher level) and individual (lower level) exposure variables simultaneously. In applying such analyses to measures of ecologies in epidemiological studies, individual variables are usually aggregated into the higher level unit. Typically, the aggregated measure includes responses of every individual belonging to that group (i.e. it constitutes a self-included measure). More recently, researchers have developed an aggregate measure which excludes the response of the individual to whom the aggregate measure is linked (i.e. a self-excluded measure). In this study, we clarify the substantive and technical properties of these two measures when they are used as exposures in multilevel models.MethodsAlthough the differences between the two aggregated measures are mathematically subtle, distinguishing between them is important in terms of the specific scientific questions to be addressed. We then show how these measures can be used in two distinct types of multilevel models-self-included model and self-excluded model-and interpret the parameters in each model by imposing hypothetical interventions. The concept is tested on empirical data of workplace social capital and employees' systolic blood pressure.ResultsResearchers assume group-level interventions when using a self-included model, and individual-level interventions when using a self-excluded model. Analytical re-parameterizations of these two models highlight their differences in parameter interpretation. Cluster-mean centered self-included models enable researchers to decompose the collective effect into its within- and between-group components. The benefit of cluster-mean centering procedure is further discussed in terms of hypothetical interventions.ConclusionsWhen investigating the potential roles of aggregated variables, researchers should carefully explore which type of model-self-included or self-excluded-is suitable for a given situation, particularly when group sizes are relatively small.

Project description:BackgroundChildhood vaccinations are a vital preventive measure to reduce disease incidence and deaths among children. As a result, immunisation coverage against measles was a key indicator for monitoring the fourth Millennium Development Goal (MDG), aimed at reducing child mortality. India was among the list of countries that missed the target of this MDG. Immunisation targets continue to be included in the post-2015 Sustainable Development Goals (SDG), and are a monitoring tool for the Indian health care system. The SDGs also strongly emphasise reducing inequalities; even where immunisation coverage improves, there is a further imperative to safeguard against inequalities in immunisation outcomes. This study aims to document whether socioeconomic inequalities in immunisation coverage exist among children aged 12-59 months in India.MethodsData for this observational study came from the fourth round of the National Family Health Survey (2015-16). We used the concentration index to assess inequalities in whether children were fully, partially or never immunised. Where children were partially immunised, we also examined immunisation intensity. Decomposition analysis was applied to examine the underlying factors associated with inequality across these categories of childhood immunisation.ResultsWe found that in India, only 37% of children are fully immunised, 56% are partially immunised, and 7% have never been immunised. There is a disproportionate concentration of immunised children in higher wealth quintiles, demonstrating a socioeconomic gradient in immunisation. The data also confirm this pattern of socioeconomic inequality across regions. Factors such as mother's literacy, institutional delivery, place of residence, geographical location, and socioeconomic status explain the disparities in immunisation coverage.ConclusionsIn India, there are considerable inequalities in immunisation coverage among children. It is essential to ensure an improvement in immunisation coverage and to understand underlying factors that affect poor uptake and disparities in immunisation coverage in India in order to improve child health and survival and meet the SDGs.

Dataset Information

Explaining Within- vs Between-Population Variation in Child Anthropometry and Hemoglobin Measures in India: A Multilevel Analysis of the National Family Health Survey 2015-2016.

Publications

Explaining Within- vs Between-Population Variation in Child Anthropometry and Hemoglobin Measures in India: A Multilevel Analysis of the National Family Health Survey 2015-2016.

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