Distribution of self-reported health in India: The role of income and geography

by Ila Patnaik, Renuka Sane, Ajay Shah and S. V. Subramaniam.

In health research, we study the causes and consequences of health at the individual level. This requires measurement of the health status of individuals. One simple path lies in asking a person: "Are you feeling well today?". This `self-reported health' (SRH) is a measure that is easy to implement, and has limitations in that psychological factors are present. A significant global literature has emerged, which draws on this measure to explore the causes and consequences of health.

The CMIE CPHS is an important new dataset which has longitudinal data for about 170,000 households, measured three times a year. They measure SRH for each individual in each wave. This measurement of SRH, alongside a rich array of household characteristics, makes possible many interesting research projects. In a new paper, Distribution of self-reported health in India: The role of income and geography, we discern some new facts and phenomena about health in India, through this data.

We use data for calendar 2018 and 2019, which works out to 3.5 million observation of a person in a wave. These years were chosen in order to obtain a baseline description of health in India, while avoiding the pandemic of 2020 and the possible impact of demonetisation in 2017.

What do we find? On average, ill health is observed in 3.25% of the records. On average, people in India are unwell for about 12 days a year. There is a U-shaped curve in age, with higher ill health rates for the young and the old.

We get a nice map of the variation of the ill-health rate across the country. This is interesting, in and of itself, as it shows us something about health care requirements. However, some of this variation reflects geographical heterogeneity in income and age structure.

We estimate logit models which explore correlations between standard socio-economic measures and the ill-health rate. The important sources of variation turn out to be age, income and location.

We then focus on an approximately modal person. Model-based predictions for the ill-health probability are constructed for this individual. This yields a map of the predicted ill-health rate --  

 

This shows the variation of ill-health in the 102 `homogeneous regions' (HRs), after controlling for income, age structure and other standard socioeconomic characteristics. It is an interesting and new map. These results do not conform with the standard stereotypes of north vs. south. Epidemiological research is required in understanding what is at work in each of the difficult HRs. Major gains in the health of the people could potentially be obtained by focusing on these hot spots and finding the right public health interventions.

We then ask: are rich people healthier than poor people? As the rich fare better on nutrition, housing quality, knowledge and access to health care, we expect there would be such a correlation. This is indeed the case in the overall aggregate data. However, there is strong geographical variation in this correlation. Ill health and poverty are positively correlated in only half of the country. There are even HRs where the relationship is reverse -- where poor people report better health than the rich. Further, the two maps (the map of ill health of the modal person, and the map of the places where ill health is not positively correlated with income) show different patterns. They are distinct phenomena that invite further exploration.