{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:50:27Z","timestamp":1787237427357,"version":"build-2736575974"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The COVID-19 pandemic significantly affected individuals, society, and the national economy. However, there is limited information on the socioeconomic dimension of COVID-19-related health impacts from nationally representative large datasets using an integrated approach. Such information is crucial for tailored policy responses and national preparedness planning. Our study aimed to assess the state-level health and economic impacts of COVID-19 in India.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The COVID-19 data on age and gender distribution were collated from the Registrar General of India, state dashboards, and the National COVID Clinical Registry. The working population information was obtained from the Periodic Labour Force Survey. We calculated age and gender wise discounted Disability Adjusted Life Years for each state by combining population projections with recovery time and severity percentages. The cost of productivity loss for the working-age population were calculated using each state\u2019s per capita income, to compare the economic impact of COVID-19 and productivity loss disparities across states.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>From March 2020 to September 2021, India recorded 33.6 million COVID-19 cases and 450 thousand deaths with higher proportion of male (65%), indicating a gender disparity in COVID-19 susceptibility. Simultaneously, India incurred a loss of 195.05 billion INR due to mortality and 268.13 billion INR due to absenteeism from work during this period.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>The pandemic had serious economic consequences, particularly for the working-age population, resulting in lost productivity from illness or death. To combat future pandemics and reduce the spread of infections and their socioeconomic consequences, national preparedness planning is critical, which includes integrating available nationally representative datasets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1710029","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T13:27:28Z","timestamp":1784640448000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A state wise analysis of the socioeconomic and health impacts of the COVID-19 pandemic in India: lessons for future health system preparedness"],"prefix":"10.3389","volume":"14","author":[{"given":"Geetha R.","family":"Menon","sequence":"first","affiliation":[{"name":"Indian Council of Medical Research","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"U.","family":"Venkatesh","sequence":"additional","affiliation":[{"name":"Department of Community Medicine & Family Medicine, All India Institute of Medical Sciences","place":["Gorakhpur, Uttar Pradesh, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeetendra","family":"Yadav","sequence":"additional","affiliation":[{"name":"ICMR-National Institute for Research in Digital Health and Data Science, (Formerly, ICMR-National Institute of Medical Statistics), Medical Enclave Ansari Nagar","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krushna Chandra","family":"Sahoo","sequence":"additional","affiliation":[{"name":"Health Technology Assessment in India (HTAIn), Department of Health Research, Ministry of Health & Family Welfare, Govt. of India","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanu","family":"Anand","sequence":"additional","affiliation":[{"name":"Clinical Studies & Trials Unit (CSTU), Division of Development Research, ICMR","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aparna","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Clinical Studies & Trials Unit (CSTU), Division of Development Research, ICMR","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gunjan","family":"Kumar","sequence":"additional","affiliation":[{"name":"Clinical Studies & Trials Unit (CSTU), Division of Development Research, ICMR","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alka","family":"Turuk","sequence":"additional","affiliation":[{"name":"Clinical Studies & Trials Unit (CSTU), Division of Development Research, ICMR","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashoo","family":"Grover","sequence":"additional","affiliation":[{"name":"Division of Delivery, ICMR","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saurabh","family":"Sharma","sequence":"additional","affiliation":[{"name":"Division of Delivery, ICMR","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandhya","family":"Singh","sequence":"additional","affiliation":[{"name":"Social Statistics Division, National Statistical Office, Ministry of Statistics and PI, Govt of India","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firoz","family":"Khan","sequence":"additional","affiliation":[{"name":"ICMR-National Institute for Research in Digital Health and Data Science, (Formerly, ICMR-National Institute of Medical Statistics), Medical Enclave Ansari Nagar","place":["New Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"3181","DOI":"10.1111\/tbed.14732","article-title":"The origins of COVID-19 pandemic: a brief overview","volume":"69","author":"Hao","year":"2022","journal-title":"Transbound Emerg Dis"},{"key":"ref2","year":"2021"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.pulmoe.2023.02.013","article-title":"Mortality among elderly patients with COVID-19 ARDS\u2014age still does matter","volume":"29","author":"Schultz","year":"2023","journal-title":"Pulmonology"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1590\/1414-431x2021e11711","article-title":"Why do men have worse COVID-19-related outcomes? 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