Working Paper | Missing female patients: An analysis of gender ratios from a tertiary care hospital in New Delhi, India

Content from the Brookings Institution India Center is now archived. After seven years of an impactful partnership, as of September 11, 2020, Brookings India is now the Centre for Social and Economic Progress, an independent public policy institution based in India.


The issue of missing women, which is excess mortality of females as seen in low population ratio of women to men, in developing countries was first highlighted in a landmark mark paper by Sen in 1990 and again in 2003. Anderson and Ray estimate suggests that among the stock of women alive today, over 25 million women are missing in India. Subsequent research by Anderson and Ray has shown that this excess mortality was not limited to newborns (due to foeticide or infanticide) but in fact was pervasive over older age groups too. While sharp estimates exist on the magnitude of missing women, it has been difficult to explain its causality given the paucity of data. The causes for the same are understandably hypothesized to be multitude of factors, with health care being one of the important reason.

Several empirical studies have documented gender biases in healthcare in women in both developing and developed countries, more so in the former. One of the important aspects is the health care access, which is obviously very important since it has multiplicative downstream beneficial effects. Though gender gaps in access have been studied in India it has largely been in selective patient groups or selected medical conditions and in small studies. In this paper, we assess gender gap in the health care access to a large referral hospital catering to a large population of North India by contrasting the sex ratio of patients across all departments of the hospital, excluding obstetrics and gynaecology. This is further analysed across age groups and states of residence, with growing distance from the hospital, to assess impacts of these on the bias. Based on these ratios the number of missing female patients is then calculated.

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