Morbidity compression or expansion? A temporal analysis of the age at onset of non-communicable diseases in India

While there is evidence of morbidity compression in many countries, temporal patterns of non-communicable diseases (NCDs) in developing countries, such as India, are less clear. Age at onset of disease offers insights to understanding epidemiologic trends and is a key input for public health programs. Changes in age at onset and duration of major NCDs were estimated for 2004 (n = 38,044) and 2018 (n = 43,239) using health surveys from the India National Sample Survey (NSS). Survival regression models were used to compare trends by sociodemographic characteristics. Comparing 2004 to 2018, there were reductions in age at onset and increases in duration for overall and cause-specific NCDs. Median age at onset decreased for NCDs overall (57 to 53 years) and for diabetes, hypertension, heart disease, asthma, mental diseases, eye disease, and bone disease in the range of 2–7 years and increased for cancer, neurological disorders, some genitourinary disorders, and injuries/accidents in the range of 2–14 years. Hazards of NCDs were higher among females for cancers (HR 1.51, 95% CI 1.19–1.90) and neurological disorders (HR 1.18, 95% CI 1.06–1.32) but lower for heart diseases (HR 0.88, 95% CI 0.79–0.97) and injuries/accidents (HR 0.87, 95% CI 0.77–0.99). Hazards were greater among those with lower educational attainment at younger ages and higher educational attainment later in life. Unlike many countries, chronic disease morbidity may be expanding in India for many chronic diseases, indicating excess strain on the health system. Public health programs should focus on early diagnosis and prevention of NCDs.

     

Patterns in child stunting by age: A cross-sectional study of 94 low- and middle-income countries

Child stunting prevalence is primarily used as an indicator of impeded physical growth due to undernutrition and infections, which also increases the risk of mortality, morbidity and cognitive problems, particularly when occurring during the 1000 days from conception to age 2 years. This paper estimated the relationship between stunting prevalence and age for children 0–59 months old in 94 low- and middle-income countries. The overall stunting prevalence was 32%. We found higher stunting prevalence among older children until around 28 months of age—presumably from longer exposure times and accumulation of adverse exposures to undernutrition and infections. In most countries, the stunting prevalence was lower for older children after around 28 months—presumably mostly due to further adverse exposures being less detrimental for older children, and catch-up growth. The age for which stunting prevalence was the highest was fairly consistent across countries. Stunting prevalence and gradient of the rise in stunting prevalence by age varied across world regions, countries, living standards and sex. Poorer countries and households had a higher prevalence at all ages and a sharper positive age gradient before age 2. Boys had higher stunting prevalence but had peak stunting prevalence at lower ages than girls. Stunting prevalence was similar for boys and girls after around age 45 months. These results suggest that programmes to prevent undernutrition and infections should focus on younger children to optimise impact in reducing stunting prevalence. Importantly, however, since some catch-up growth may be achieved after age 2, screening around this time can be beneficial.     

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.     

Precision mapping child undernutrition for nearly 600,000 inhabited census villages in India

There are emerging opportunities to assess health indicators at truly small areas with increasing availability of data geocoded to micro geographic units and advanced modeling techniques. The utility of such fine-grained data can be fully leveraged if linked to local governance units that are accountable for implementation of programs and interventions. We used data from the 2011 Indian Census for village-level demographic and amenities features and the 2016 Indian Demographic and Health Survey in a bias-corrected semisupervised regression framework to predict child anthropometric failures for all villages in India. Of the total geographic variation in predicted child anthropometric failure estimates, 54.2 to 72.3% were attributed to the village level followed by 20.6 to 39.5% to the state level. The mean predicted stunting was 37.9% (SD: 10.1%; IQR: 31.2 to 44.7%), and substantial variation was found across villages ranging from less than 5% for 691 villages to over 70% in 453 villages. Estimates at the village level can potentially shift the paradigm of policy discussion in India by enabling more informed prioritization and precise targeting. The proposed methodology can be adapted and applied to diverse population health indicators, and in other contexts, to reveal spatial heterogeneity at a finer geographic scale and identify local areas with the greatest needs and with direct implications for actions to take place.

National trends in population health and development are now routinely available to guide policymaking even for most low- and middle-income countries (1). More recently, there has been an increasing recognition that national averages are inadequate given the substantial heterogeneity in patterns of disease and risk factors within any given country (1–3). As a consequence, there is a great interest for disaggregated data on population health and well-being to be provided and analyzed at subnational levels (1–3). Most studies that investigate subnational levels, however, are largely confined to macro geographies, such as states or districts in India (4, 5) or provinces in China (6), despite recent studies emphasizing more variation at finer geographic resolutions as small as villages or communities (7, 8). With increasing availability and accessibility of data geocoded to smaller geographic units and with varying degrees of precision along with the use of advanced modeling techniques, there are emerging opportunities to assess health and developmental indicators at truly small areas (9, 10). The future iterations of the Global Burden of Diseases, Injuries, and Risk Factors study are expected to feature maps of the different burden at a finer spatial resolution (5 × 5 km) (11). Geospatial analysis of estimates by 5 × 5 km grids has been presented for child mortality (12), child growth failure (13), childhood diarrheal morbidity and mortality (14), and women’s educational attainment (15) in Africa, and they revealed striking inequities at the local level.While this interest toward a focus on finer geographic resolution is a welcoming step toward precision public health (2, 3, 9, 10, 16), small area estimates with no explicit link to political or administrative jurisdiction have limited practical implications in terms of guiding efficient and equitable interventions. To enable immediate attention and action to take place, the unit of analysis and inferential target in empirical studies need to align with the local governance unit, often within districts or cities, that are accountable for implementation of programs and interventions (3, 16, 17). Such fine-grained data are critical to identify and target areas with the greatest needs for prioritization, incorporate specific local needs and resource base for plan formulation, and appropriately evaluate the successes and failures of programs and policies at the local level (18).With this conceptual motivation, and to aid the current movement toward decentralized planning in India to achieve global and national targets for population health and development, we present comprehensive estimates of child anthropometric failure for nearly 600,000 villages in rural India.     

Patterning of individual heterogeneity in body mass index: evidence from 57 low- and middle-income countries

Modeling variation at population level has become increasingly valued, but no clear application exists for modeling differential variation in health between individuals within a given population. We applied Goldstein’s method (in: Everrit, Howell (eds) Encyclopedia of statistics in behavioral science, Wiley, Hoboken, 2005) to model individual heterogeneity in body mass index (BMI) as a function of basic sociodemographic characteristics, each independently and jointly. Our analytic sample consisted of 643,315 non-pregnant women aged 15–49 years pooled from the latest Demographic Health Surveys (rounds V, VI, or VII; years 2005–2014) across 57 low- and middle-income countries. Individual variability in BMI ranged from 9.8 (95% CI: 9.8, 9.9) for the youngest to 23.2 (95% CI: 22.9, 23.5) for the oldest age group; 14.2 (95% CI: 14.1, 14.3) for those with no formal education to 19.7 (95% CI: 19.5, 19.9) for those who have completed higher education; and 13.6 (95% CI: 13.5, 13.7) for the poorest quintile to 20.1 (95% CI: 20.0, 20.2) for the wealthiest quintile group. Moreover, variability in BMI by age was also different for different socioeconomic groups. Empirically testing the fundamental assumption of constant variance and identifying groups with systematically large differentials in health experiences have important implications for reducing health disparity.