Determinants of Health in India: Income and Geography
by Gayatri Dewan
The most important sources of variation in health in India are age, income, and location. The relationships with income and location diverge from traditional beliefs. Once these three factors are taken into account, many things that were thought to matter - education, caste, religion, and gender - are relatively unimportant. These are findings of a new research paper by Ila Patnaik, Renuka Sane, Ajay Shah, and S. V. Subramaniam, Distribution of Self-Reported Health in India: The Role of Income and Geography.
Using 6 waves of nationally representative, longitudinal household data from the Consumer Pyramids Household Survey (CPHS), the researchers looked at the self-reported health status for over 736,900 individuals to create an ill-health rate. They employed data from 2018-2019 to eliminate the influence of independent incidents of ill-health such as those arising from natural disasters/seasonal factors and to create a baseline for the characteristics of health in India.
This work is non-causal; it establishes facts and correlations. A key finding concerns the role of income. We may think the rich are likely to be healthier than the poor. The paper finds that this is only true for about half of India. For 35 percent of the regions, income had no impact, and at the other extreme, 16 percent of the regions showed higher ill-health for richer households, indicating significant geographical heterogeneity. The authors conjecture that this may be owing to a possible survivorship bias: higher mortality rates for the poor could result in more ill-health in affluent survivors.
Looking at their second source of variation, geography, the authors find that predicted health is particularly dependent on region. First examining the question without controls, the authors found strong location effects, with the North-East and East having significantly higher than average ill-health ratios, and the converse being true for West, South, and Central regions.
To eliminate the demographic and socio-economic effects on health and look at geography in isolation, the authors studied the predicted ill-health rate across all regions for the modal individual in the dataset, namely a Hindu male from the SC/ST community, in the 35-49 age category, educated till the 12th class.
Across both the models with and without socio-economic controls, the regions which exhibited higher ill-health rates stayed the same, indicating that there is something about the regions themselves which is producing greater ill-health, as opposed to the age or income profile of the population.
The paper sheds new light on epidemiology at an all-India scale. Epidemiology requires such large-scale datasets, where unusual pockets of ill-health merit examination, and can possibly result in improvements in public health that can decisively change the sources of ill-health. The authors find there are pockets of high ill-health rates in Uttarakhand, West Haryana, East Uttar Pradesh, Bengal, Assam, Telangana, Andhra Pradesh, and Kerala. They wonder whether the poor health in eastern Uttar Pradesh, Assam, and Bengal could be a result of arsenic contamination of water in the area. Intriguingly, though uniformly poor health outcomes were expected across Uttar Pradesh and Bihar due to high levels of poverty, particularly high ill health was only found in Eastern UP.
Simple summary data can be misleading. On average, ill health rates by gender, religion, caste, and education indicate poorer health amongst rural women and Muslims, higher ill health for upper caste communities in the 35-39 age category, and lower than average health for low-educated households. Yet, when controls for income, age, and region are present, the effects of caste, religion, and education diminish1. Only gender continues to be important, displaying higher ill-health rates for women. Overall, the authors estimate the ill-health rate to be 3.25 percent across India, implying that approximately 44.4 million individuals are unwell on any given day.
The authors conclude by urging policy makers and researchers to look beyond simplistic North-South binaries given the regional heterogeneity in ill-health across the country. The prior of most people in health would be that Kerala is healthy and Bihar is unhealthy; the evidence is more complex. Additionally, they distinguish between regions experiencing high ill-health, and regions where the rich are healthier than the poor, characterizing them as distinct phenomena requiring further research.
At present, the mainstream work on health in India is derived from the NSSO, NFHS, IHDS, datasets at the level of one health care facility, and small experiments. The CMIE-CPHS constitutes the first large-scale longitudinal dataset where health research can commence. It makes possible research on an array of health-related questions. New vistas of health research open up based on these facts that are being systematically recorded. This paper is a first examination of self-reported health, which is one health outcome measure. The identical data is available to all researchers, who can replicate these findings, and build more sophisticated projects that analyse the causes and consequences of health outcomes.
1 With the exception of one category under education