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The Data Challenges Before Ayushman Bharat

The launch of Ayushman Bharat-Pradhan Mantri Jan Aarogya Yojana (AB-PMJAY) has generated an intense debate on the scheme’s ability to offer respite to India’s poor and vulnerable. The scheme’s performance may well depend on an oft-ignored ingredient in Indian policy making: data. What will make or mar the AB-PMJAY scheme is the pricing of various procedures that the scheme covers for. And that will depend on a regular flow of credible and granular data. To calculate the odds of patients opting for a certain procedure in a certain district, for instance, the insurer must know the disease burden in that district. The government too must know this to be able to regulate insurers and service providers effectively. However, such data simply does not exist. Even state-level data on ailments, reported by the National Sample Survey Office (NSSO), can’t be taken at face value as it suffers from gross under-reporting of illnesses in poorer and resource-poor states.

An analysis of the 2013-14 data from the last NSSO health survey shows that the proportion of people reporting ailments is higher in states where the infant mortality rate (IMR) is low, and where the per capita health expenditure is high. This suggests that the reported rate of ailments corresponds more to health awareness and ability to access healthcare than to one’s actual state of health. A look at the quintile-wise distribution of ailing people confirms this. Poor families are much less likely to report illnesses compared with richer ones. Data on medical attention before deaths also leads us to similar conclusions. As the first part of this series showed, deaths in the poorest income classes are much less likely to get medical attention, compared with those among elites. The gross under-reporting of illnesses among the poor means that the reporting rate as well as hospitalization rate among these sections is likely to increase if Ayushman Bharat takes off in a big way. But in the absence of adequate data on the health profile of the beneficiaries, regulators will find it difficult to gauge whether the spike in a certain procedure in a district reflects actual rise in coverage or is a case of insurance fraud.

Even the limited data that is available from the NSSO surveys can be used only to derive state-level estimates, and is not suited for district-level analysis. The Health and Management Information System (HMIS), launched under the National Rural Health Mission (NRHM) to digitize health records in 2008, should have been of help. But the HMIS suffers from poor quality and data gaps. A 2017 Comptroller and Auditor General (CAG) report showed that 18 percent of health facilities did not even report basic infrastructure data in the HMIS portal in 2015-16. CAG also found wide-ranging discrepancies between what the HMIS system reported and the physical records. For instance, the number of infant deaths recorded by the HMIS in Jharkhand was substantially lower than the number in the physical records. The lack of capacity and accountability has meant that India squandered an opportunity to collect rich granular data through the HMIS over the past decade. The poor state of health statistics means that India under-reports even common diseases such as malaria and TB, data from the World Health Organization (WHO) shows.

Ideally, the Ayushman Bharat scheme should have been preceded by an independently conducted large-scale morbidity survey to assess the state of health across Indian districts and to evaluate the costs associated with health shocks across districts and demographic groups. That would have helped generate credible baseline estimates and also provided a solid yardstick to measure the scheme’s impact over the next few years. But perhaps that is too much to expect from a country that can’t even measure its gross domestic product (GDP) satisfactorily. – Livemint