Back

Analyzing Surgical Volumes, Rates, and Need in Rural India

Zadey, S.; Nickenig Vissoci, J. R.

2021-11-04 surgery
10.1101/2021.11.03.21265903 medRxiv
Show abstract

BackgroundGlobally, 5 billion people lack timely access to safe and affordable surgical care, with over a fifth of them living in India. Solving Indias surgical access issues can have high returns on investment. While healthcare access and unaffordability problems are well-known in India particularly among its rural people, research on surgical volumes and need is scant. This study attempts to fill the research gap through high-resolution nationwide estimates that have direct implications for Indias national surgical plan. MethodsSecondary data analysis with a diverse geospatial and statistical toolbox was used to create the national, state, and district-level estimates for surgical rates and c- section proportions and their corresponding met need w.r.t. to the globally prescribed thresholds - 5000 major surgeries (those requiring anesthesia) per 100,000 (Lancet Commission on Global Surgery) and 10-15% of all institutional deliveries (World Health Organization). ResultsNationally, only 6.81% of need for major surgical operations was met for rural India. 13.6% of the institutional deliveries were c-sections falling within the WHO- prescribed range of 10-15%. There were marked variations at state and district-levels and significant rural-urban differences for surgical rates and c-section proportions. We validate our estimates based on data from Health Management and Information System against existing sources that are commonly used in academic and policy research. ConclusionsOur methodological workflow has high translational value for global surgery research in low-and-middle-income countries. For India, these are the first such nationwide findings that can direct the development of a National Surgical, Obstetric, and Anesthesia Plan (NSOAP).

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.