A Statewise Analysis of the Socioeconomic and Health Impacts of the COVID-19 Pandemic in India: Lessons for Future Health System Preparedness

A Statewise Analysis of the Socioeconomic and Health Impacts of the COVID-19 Pandemic in India: Lessons for Future Health System Preparedness | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Statewise Analysis of the Socioeconomic and Health Impacts of the COVID-19 Pandemic in India: Lessons for Future Health System Preparedness Geetha R. Menon, U Venkatesh, Jeetendra Yadav, Krushna Chandra Sahoo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6568384/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: 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. Methods: 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 were 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's per capita income, to compare the economic impact of COVID-19 and productivity loss disparities across states. Findings: 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 during this period. Interpretation: The pandemic has 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. COVID-19 Years of Potential Productive Life Lost (YPPLL) Disability-Adjusted Life Years (DALYs) Figures Figure 1 Figure 2 Figure 3 Introduction The coronavirus disease (COVID-19) discovered in Wuhan, China in November 2019 1 has since spread to 238 countries and territories globally. As of January 2025, 777.3 million people have been infected, resulting in 7.08 million deaths. 2 The pandemic's impact has varied across age groups and demographics, with older adults facing higher mortality rates 3 and men experiencing a greater risk of severe illness and death 4 . India has recorded the highest number of confirmed cases and deaths in Asia, although the spread has been uneven across the country, with certain states enduring multiple waves of infections. In India, in the first wave approximately 60% of COVID-19 cases occurred among individuals under 45, with a fatality rate of 12%. 5 The virus has also significantly impacted younger populations and individuals without pre-existing health conditions, contributing to increased hospitalizations and fatalities. Accurate data collection is essential for policymakers to effectively allocate resources and mitigate the health and socio-economic consequences of the pandemic. The burden of disease analysis is a valuable tool for measuring the health impact of diseases and associated risk factors. Disability-adjusted life years (DALYs), which combine mortality and morbidity data, serve as a key metric in evaluating the health impact. Estimating COVID-19’s impact using DALYs and Years of Potential Productive Life Lost (YPPLL) can help generate insights across different regions. However, acquiring the necessary data for these estimates poses challenges, as it requires the integration of various data sources. The COVID-19 pandemic was one of the most critical challenges, with significant consequences for individuals, society, and the national economy. While individuals suffered from health issues and loss of productivity, the national economy experienced reduced output and higher unemployment rates. Numerous studies have explored the health and socioeconomic impact of COVID-19 in India, though most rely on small or localized datasets. However, no studies have provided a comprehensive socioeconomic analysis of COVID-19's health effects using nationally representative datasets. This study seeks to address that research gap by analyzing nationally representative datasets along with morbidity and mortality data from public sources, using a burden of disease approach to assess the health and economic impacts of COVID-19 in India, disaggregated at the state level. A substantial socio-demographic and economic variation across states exist in India, which rationales the current study to contextualize the state-level variations in these impacts. The findings of the current study will be helpful to shape the need based resource allocation and preparedness strategies, ultimately contributing to the development of a more resilient healthcare system. Methods Data sources and triangualation The analysis utilized multiple data sources. Districtwise age-sex distributions of COVID-19 cases were derived from the national COVID-19 testing database 6 maintained by the Indian Council of Medical Research (ICMR). National and state-level projected population data 7 along with COVID mortality figures, were sourced from official counts provided by PRS Legislative Research 8 , a trusted digital repository endorsed by the Government of India. Additionally, the study incorporated state-specific data obtained either directly or through designated health portals like the Kerala COVID-19 Death Information System (CDIS) 9 , West Bengal Health Portal 10 , Tamil Nadu’s Stopcorona portal 11 and Karnataka’s COVID Information Portal 12 . For states like Maharashtra, Rajasthan, Gujarat, Andhra Pradesh, Odisha Punjab, Bihar, Jharkhand, Himachal Pradesh, Haryana, Delhi and Chhattisgarh-where age-sex distribution of COVID-19 deaths was not publicly available- the distribution from a published study 13 was used, as these states accounted for approximately 60% of all COVID deaths in India. For the five North East (NE) states, Assam’s age sex distribution was applied. In cases where states underreported COVID-19 deaths, adjustments were made to align with the Ministry of Health and Family Welfare’s counts, ensuring reliable estimates. Data on the working-age population (15–60 years) was drawn from the 2019-20 Periodic Labour Force survey (PLFS) by Ministry of Statistics and Planning Implementation (MoSPI) 14 . State-wise age and sex-specific life expectancies for 2020 were obtained from Registrar General of India (RGI) website 15 . The study also utilized data on the proportion of cases at various disease severity levels from the National Clinical Registry for COVID-19 (NCRC), providing crucial insights into the progression of COVID-19 and its implications for healthcare resource management. The estimation method The clinical progression of COVID-19 from infection to recovery or death has been adapted from the model developed by McArthur et al 16 . Due to the absence of data on post-recovery or hospital discharge, this analysis does not account for the burden associated with post-COVID-19 complications or long-term functional impairments. Health burden indices The Burden of Disease was calculated following the protocol for country studies developed by the European Burden of Disease Network 17 . The health burden indices were estimated using the standard formula for Disability Adjusted Life Years(DALYs); DALYs = YLD + YLL where YLD represent the years lived with disability, reflecting the number of years an individual spends in ill health and YLL refers to the years of life lost due to premature death, defined as dying before the ideal life expectancy. While the Global Burden of Disease study 18 has removed the assumptions of time and age preferences in its latest estimates, these factors are still crucial for evaluating the future economic impact. As a result, they have been included in study’s analysis. The formula for calculating Years of Life Lost (YLL) with uniform age weighting and a 3% discount rate is 19 is YLL discounted = \(\:\frac{1-{e}^{-rLa}}{r}\:\:\) where r = 0.03 is the discount rate and L a is the standard life expectancy at age a. The formula for calculating Years Lived with Disability (YLD) using the incidence-based approach with uniform age weighting and r = 0.03 as the discount rate is YLD discounted,i,s = I i,s x D i,s \(\:\text{x}\:\frac{1-{e}^{-r{D}_{i,s}}}{r}\:\) x D Wi,s Where: r = 0.03 is the discount rate, I i,s is the incidence of COVID-19 in the i th age group at the s th stage/sequelae, D i,s is the duration of disability due to COVID-19 in the i th age group at the s th stage/sequelae D Wi,s is the disability weight for s th sequelae of COVID-19 in the i th age group, with disability weight ranging from 0 (complete health) to 1 (death). In this study, the stages of COVID-19, their descriptions, and the corresponding disability weights are presented in Table 1 , adapted from Wyper et al 20 . The duration of disability for patients in home isolation was assumed to be 7 days, even though the Government of India guidelines recommended a 14-day home quarantine. For hospitalized patients, the duration of disability was determined for each decennial age group using data from the National Clinical Registry for COVID-19 (NCRC). Table 1 Description of COVID-19 stages and the disability weights for each stage Stage of the disease Description Data input as proxy Disability weight Asymptomatic Has infection but experiences no symptoms Data not available Nil Mild (Home isolated) Has infection and symptoms but not hospitalized 91% of cases observed during the study period 0.051 Mild (Hospitalized) Has symptoms, hospitalized but not requiring supplemental oxygen support Percentage of hospitalized cases who are mild from the National COVID Clinical Registry 0.051 Moderate Has symptoms, hospitalized and requiring supplemental oxygen support in the form of Nasal canula or non-breather mask or face mask) Percentage of hospitalized cases who are moderate from the National COVID Clinical Registry 0.133 Severe hospitalized and requiring supplemental oxygen support in the form of BiPAP, CPAP, High flow nasal oxygen or on invasive ventilation support or on ECMO Percentage of hospitalized cases who are severe from the National COVID Clinical Registry 0.655 Economic burden indices The cost of lost productivity (CPL) was calculated using the human capital 21 , which takes into account three key parameters: the length of time absent from work due to illness, the market wage, and the labor force participation rate. Years of Potential Productive Life Lost (YPPLL) 22 due to COVID-19 was estimated separately for males and females. The YPPLL was valued using the per capita income for each state as an approximation of the foregone productivity during the period of absence and the years of working life lost due to premature mortality. The formula for YPPLL is: $$\:YPPLL=\:{\sum\:}_{i=1\:}^{\:n}\:{D}_{i}\times\:\:{w}_{i}\times\:\:\frac{1}{{\left(1+d\right)}^{wi-1}}\:\:\:\:\:\:\:\:|\:\:\:\:i=1,\:2,\:\dots\:,\:n$$ where: i represents the ith working age group, n is the age group that ends at 60 years, Di is the number of deaths at age i; wi is the productive years remaining at age of death (calculated as 60 minus the midpoint of the ith age group), d is the discount rate, which was set to the bank rate proposed by the Reserve Bank of India (RBI) = 0.0425 23 . For each state, cost of Productivity loss (CPL) was estimated for both absenteeism( temporary) and premature mortality(permanent losses), using the following formulas: $$\:{CPL}_{Premature\:mortality}=\:{\sum\:}_{i=1\:}^{n}\:{YPPLL}_{i}*\:per\:capita\:GDP*{P}_{i}\:$$ $$\:{CPL}_{absenteeism}=\:{\sum\:}_{i=1\:}^{n}\:S*{L}_{i}*{N}_{i}*{P}_{i}$$ Where P i is the proportion of working population, in the i th age group, in that state S is the average daily salary, calculated as the per capita GDP of that state by 313 (number of paid working days in a year accounting for 52 Sundays) L i is the average recovery time for the ith age group N i is the number of incident cases in the i th age group The costs were adjusted to reflect 2021 values 24 . Recovery times were based on data from the National Clinical Registry for COVID-19 (NCRC), assuming 14 days for asymptomatic and mild cases, 21 days for severe cases, and 30 days for critical cases. We assumed that 91% of those infected were home isolated, in accordance with Government of India guidelines 25 and 9% were hospitalized. Among the hospitalized patients, the proportions of those in mild, moderate and severe categories were obtained from the National COVID-19 Clinical Registry and these proportions were assumed to be consistent across all states. The estimations were done on an excel sheet after extracting the relevant mobidity and mortality data from various data sources and organizing them by age and gender in the required format. The step-by-step estimation process is outlined in Fig. 2 . Findings The proportion of hospitalized patients across the three severity stages and the duration of disability experiences by COVID-19 patients are presented in Table 2 . The duration of disability is calculated as the third quartile, measured from the onset of COVID-19 symptoms to the date of hospital discharge. This suggests that older adults, particularly those over 40 experience more severe illness and longer periods of disability. Table 2 Severity percentage among hospitalized cases and average days of recovery Age-Group Percentage (%) among hospitalized cases Average days to recovery/discharge Mild/ Moderate Severe Critical Mild/ Moderate Severe Critical < 1 70 20 10 11 14 14 1 to 5 81 14 4 10 13 22 5 to 10 88 9 3 11 16 14 10 to 15 87 10 3 11 16 20 15 to 20 87 12 1 11 13 23 20 to 25 87 12 1 12 15 21 25 to 30 81 17 2 12 15 23 30 to 35 72 25 3 12 16 19 35 to 40 64 32 4 13 16 21 40 to 45 58 36 6 13 16 22 45 to 50 56 40 5 12 17 22 50 to 55 52 42 6 13 17 22 55 to 60 52 41 7 13 17 22 60 to 65 49 44 7 13 17 22 65 to 70 48 46 6 13 17 24 70 to 75 47 49 5 13 18 23 75 to 80 44 51 6 12 18 25 80+ 43 52 5 13 18 22 As of September 30, 2021, India recorded approximately 33.6 million confirmed cases of COVID-19, with males accounting for 61% (20.05 million) of the total cases. The state-wise distribution showed Maharashtra as having the highest incidence,with about 6.58 million cases, followed by Kerala (3.41 million), Karnataka (3.24 million), Tamil Nadu (2.8 million), and Andhra Pradesh (2.29 million). Indias cumulative death toll reached 450,000, with Maharashtra, with 139,000 fatalities, emerged as the state with the highest death toll, followed by Karnataka (37,777), Tamil Nadu (35,579), Delhi (25,530), Kerala (24,963), and Uttar Pradesh (22,891). Notably, Himachal Pradesh reported the lowest death count among the larger states(3,653), while the collective total for the seven northeastern states was the lowest regional death toll. (Table 3 ) Table 3 Number of COVID positives and reported number of COVID deaths in all the states, India. States COVID Cases COVID deaths Males Females Both Males Females Both India 2,00,48,750 1,35,46,993 3,35,95,743 2,89,899 1,51,709 4,50,037 Andhra Pradesh 13,20,377 9,70,651 22,91,028 9,349 4,814 14,163 Assam 3,75,938 2,41,172 6,17,110 4,111 1,762 5,873 Bihar 5,25,099 2,33,206 7,58,305 6,376 3,284 9,660 Chhattisgarh 6,13,149 4,16,625 10,29,774 3,392 1,746 13,563 Delhi 8,99,892 6,17,580 15,17,472 16,852 8,678 25,530 Goa 1,06,651 76,617 1,83,268 2,409 1,244 3,653 Gujarat 5,83,505 3,48,491 9,31,996 6,663 3,431 10,094 Haryana 5,65,723 3,54,676 9,20,399 6,518 3,356 9,874 Himachal Pradesh 1,22,812 85,667 2,08,479 2,418 1,245 3,663 Jammu And Kashmir 2,00,514 1,08,665 3,09,179 2,725 1,701 4,426 Jharkhand 2,59,034 1,34,857 3,93,891 3,392 1,746 5,138 Karnataka 19,17,446 13,25,387 32,42,833 24,188 13,589 37,777 Kerala 17,88,680 16,17,560 34,06,240 14,480 10,483 24,963 Madhya Pradesh 5,98,912 3,60,081 9,58,993 7,048 3,472 10,520 Maharashtra 39,23,872 26,57,355 65,81,227 91,759 47,252 1,39,011 Odisha 5,65,723 3,54,676 9,20,399 5,411 2,787 8,198 Punjab 4,32,200 2,58,132 6,90,332 10,902 5,614 16,516 Rajasthan 7,60,030 3,94,589 11,54,619 5,910 3,044 8,954 Tamil Nadu 16,32,873 11,66,368 27,99,241 23,751 11,828 35,579 Telangana 83,901 50,126 1,34,027 3,499 1,545 5,044 Uttar Pradesh 12,42,043 6,63,597 19,05,640 15,163 7,726 22,891 Uttarakhand 2,17,941 1,27,947 3,45,888 4,982 2,409 7,393 West Bengal 9,21,649 6,83,147 16,04,796 12,601 6,207 18,808 Other Northeast 2,48,833 1,98,924 4,47,757 3,987 1,709 5,696 Other Union territories 1,41,953 1,00,897 2,42,850 2,013 1,037 3,050 In India, COVID-19 cases resulted in a total of 1,182 years with disability, with 62% of this burden borne by males.The pandemic’s initial waves led to an estimated 6.7 million YLLs, with males accounting for 64.3%. Maharashtra had the highest YLL at 2.17 million, constituting 32.3% of the national total (Supplementary Table 4). The age adjusted YLL, YLD, DALYs, deaths and infections rates per 100,000 was estimated to compare the rates across the different states. (Table 4 ). Goa had the highest age adjusted rate (3071.1) for YLLs because of the high death rates per 100000. Compared to other states, Goa had a very high infection rate per 100000 and hence the morbidity index (YLD) was comparatively highest 11194 (197.36,11,194.00). Table 4 Adjusted YLL, YLD, DALY, Death and Infection rates per 100,000 for different states States Adj YLL 95% CI Adj YLD 95% CI Adj DALYs 95% CI Adj deaths 95% CI Adj infections 95% CI ANDHRA PRADESH 303.4 (301.96-304.84) 0.14 (0.10–0.17) 303.53 (302.10-304.97) 25.31 (24.89–25.72) 4113.2 (4,107.87-5,207.24) ASSAM 323.69 (321.74-325.63) 0.06 (0.03–0.08) 323.74 (321.80-325.69) 19.04 (18.56–19.53) 1740.03 (1,735.69-1,768.76) BIHAR 174.79 (173.86-175.71) 0.02 (0.01–0.03) 174.81 (173.88-175.74) 13.99 (13.71–14.27) 720.12 (718.49-952.04) CHATTISGARH 715.64 (712.34-718.94) 0.11 (0.07–0.15) 715.75 (712.45-719.06) 58.56 (57.57–59.55) 3522.95 (3,516.14-3,529.75) DELHI 2263.12 (2,256.28-2,269.97) 0.24 (0.18–0.31) 2263.37 (2,256.52-2,270.21) 146.17 (144.38-147.97) 7139.28 (7,127.92-8,553.65) GOA 3071.01 (3,043.91-3,098.10) 0.44 (0.11–0.77) 3071.45 (3,044.36-3,098.54) 236.48 (228.81-244.15) 11194 (197.36-11,194.00) GUJARAT 331.74 (330.07-333.41) 0.07 (0.04–0.09) 331.8 (330.14-333.47) 22.51 (22.07–22.94) 1980.85 (1,976.83-1,984.87) HARYANA 573.56 (570.67-576.45) 0.1 (0.07–0.14) 573.66 (570.77-576.55) 38.52 (37.76–39.28) 3093.84 (3,087.52-3,100.16) HIMACHAL PRADESH 724.67 (718.80-730.54) 0.09 (0.02–0.15) 724.76 (718.88-730.63) 45.27 (43.80-46.74) 2667.48 (2,656.02-2,678.93) JAMMU AND KASHMIR 588.21 (583.83-592.59) 0.08 (0.03–0.13) 588.29 (583.91-592.67) 38.95 (37.80-40.09) 2233.88 (2,226.00–2,241.75) JHARKHAND 252.14 (250.36-253.93) 0.03 (0.01–0.05) 252.18 (250.39-253.96) 18.24 (17.74–18.74) 1077.88 (1,074.51-1,081.24) KARNATAKA 765.06 (762.99-767.13) 0.15 (0.12–0.18) 765.21 (763.14-767.28) 56.69 (56.12–57.26) 4624.62 (4,619.59-4,629.66) KERALA 687.94 (685.53-690.36) 0.31 (0.00-0.31) 688.26 (66.94-688.26) 53.91 (10.55–53.91) 9415.89 (137.87-9,415.89) MADHYA PRADESH 221.11 (220.02–222.20) 0.07 (0.05–0.08) 221.17 (220.08-222.26) 15.53 (15.23–15.82) 1200.06 (1,197.66-1,202.46) MAHARASHTRA 1704.33 (1,702.06-1,706.60) 0.17 (0.15–0.20) 1704.51 (1,702.24-1,706.78) 110.66 (110.07-111.24) 5247.57 (5,243.56-5,251.58) ODISHA 286.17 (284.62-287.72) 0.06 (0.04–0.09) 286.23 (284.68-287.78) 18.28 (17.89–18.68) 1956.65 (1,952.66-1,960.65) PUNJAB 799.77 (796.69-802.86) 0.07 (0.04–0.10) 799.84 (796.76-802.93) 51.14 (50.36–51.92) 2137.69 (2,113.81-2,161.58) RAJASTHAN 211.01 (209.89-212.14) 0.05 (0.03–0.06) 211.06 (209.93-212.19) 14.69 (14.38–14.99) 1480.72 (1,478.02-1,483.42) TAMIL NADU 530.03 (528.55-531.51) 0.21 (0.18–0.25) 530.25 (528.76-531.73) 38.38 (37.98–38.77) 3364.92 (3,360.98-3,368.86) TELENGANA 205.95 (204.51-207.39) 0.01 (0.00-0.03) 205.96 (200.66-207.41) 13.59 (13.21–13.96) 341.13 (339.30-342.96) UTTAR-PRADESH 194.29 (193.65-194.93) 0.03 (0.02–0.04) 194.32 (193.68-194.95) 13.04 (12.87–13.21) 900.58 (899.30-901.86) UTTARAKHAND 1056.32 (1,050.13-1,062.51) 0.1 (0.04–0.16) 1056.43 (1,050.24-1,062.62) 70.82 (69.20-72.43) 2979.93 (2,969.99-2,989.86) WEST BENGAL 248.01 (247.07-248.96) 0.03 (0.02–0.04) 248.04 (247.09-248.99) 18.45 (18.19–18.71) 1517.6 (1,515.25-1,519.94) Other NE 680.81 (676.65-684.96) 0.09 (0.04–0.14) 680.89 (676.74-685.05) 40.05 (39.01–41.09) 2736.99 (2,677.83-2,796.15) OTHER UTs 2010.29 (1,991.91-2,028.66) 0.23 (0.07–0.39) 2010.51 (1,992.14-2,028.89) 139.53 (134.58-144.48) 6341 (6,315.78-6,366.22) The total DALYs associated with COVID-19 in India amount to 6.70 million, with males comprising 65% of these lost years. Males generally accounted for 62–68% of the national disease burden, but exceptions were noted in Jammu and Kashmir and Kerala, where the DALYs attributed to females were higher, suggesting a greater female mortality in these regions.(Supplementary File Regional disparities in COVID-19 infection rates and adjusted death rates highlight variations in transmission intensity and mortality burden. Southern and western states experienced higher transmission rates than other regions, indicating geographical clustering of COVID-19 cases influenced by population density, mobility, and healthcare infrastructure. Age-adjusted death rates varied across states, with Goa reporting the highest death rate at 236 per 100,000, followed by Delhi (146), Maharashtra (111), and Uttarakhand (71). Uttar Pradesh had the lowest age-adjusted death rate at 13 per 100,000, reflecting variations in the pandemic's severity across regions (Fig. 3 ). Similarly, DALY rates exhibited regional disparities. Goa had the highest DALY rate ( 3,071 per 100,000), followed by Delhi (2,263), Maharashtra (1,705), and Uttarakhand (1,056). The central region showed lower DALY rates, with Bihar recording the lowest DALY rate at 174 per 100,000, suggesting relatively lower disease burden in this region compared to southern and western states. Age-specific COVID-19 mortality distribution across states offers insights into the demographics of mortality. Maharashtra had the highest mortality across all age categories, peaking in the 60–69 age group with around 40,000 deaths. This pattern was broadly replicated across the other states, though the numbers were much lesser. Delhi saw about 5,000 deaths in this age group, with lower numbers in other states. Mortality data from seven states intervals allowed comparisons, revealing that Karnataka had the highest mortality (5000 deaths) in the 65–70 year age group, while Uttar Pradesh had the younger mortality profile, peaking in the 60–65 year age group. Kerala’s mortality was concentrated among individuals aged over 60, highlighting the vulnerability of its older population(Figs. 4 A and 4 B) During the first and second waves of COVID-19, India lost 1.309 million years of potential productive life (YPPLL). Maharshtra recorded the highest (390,000 years)followed by Karnataka (110,000), Tamil Nadu (98000) and Uttar Pradesh (90,000). Goa had the lowest YPPLL( 9,147) years, indicating a relatively smaller impact. India’s cumulative cost of productivity loss (CPL) due to COVID-19 amounted to ₹195.05 billion from premature mortality among the working-age population and ₹268.13 billion owing to absenteeism among infected individuals. Maharashtra incurred the highest economic burden due to premature mortality (₹588.9 billion) followed by Karnataka (₹202.7 billion), Tamil Nadu (₹173.7 billion), and Delhi (₹164 billion). Maharashtra also led in the absenteeism -related CPL (₹481.7 billion), followed by Karnataka (₹335.1 billion), Kerala (₹292.9 billion), and Tamil Nadu (₹269.5 billion). Jammu and Kashmir reported the lowest CPL from mortality (₹0.79 billion) while Bihar had the least absenteeism-related CPL (₹1.25 billion) (Table 5 ) Table 5 Comparison of YPPLL and Cost of productivity loss among different states State YPPLL (Unit?) CPL mortality (In ₹) CPL morbidity (In ₹) All India 13,09,883 1,95,05,07,27,000 2,68,13,10,26,964 Andhra Pradesh 39,806 5,72,57,87,492 18,25,74,26,936 Assam 28,766 1,64,78,35,008 1,78,82,79,164 Bihar 27,150 81,17,97,202 1,25,50,25,841 Chhattisgarh 38,120 3,48,71,47,700 5,36,50,07,597 Delhi 71,754 16,40,33,22,191 18,48,29,86,048 Goa 9,147 2,83,45,27,201 2,70,85,74,539 Gujarat 28,370 4,61,95,43,484 8,81,97,41,855 Haryana 27,752 4,62,54,20,214 8,39,63,90,235 Himachal Pradesh 10,295 1,65,63,72,810 1,87,83,23,048 Jammu And Kashmir 11,446 79,23,87,004 1,32,75,94,444 Jharkhand 14,441 82,62,46,616 1,36,29,98,641 Karnataka 1,10,963 20,27,96,20,490 33,51,90,02,852 Kerala 42,518 6,07,35,02,935 29,29,24,45,360 Madhya Pradesh 35,685 3,01,67,04,978 4,64,64,74,074 Maharashtra 3,90,701 58,89,39,80,792 48,16,73,71,692 Odisha 23,041 1,79,89,29,159 4,01,41,74,433 Punjab 46,420 4,95,33,57,189 4,12,11,47,954 Rajasthan 25,166 2,39,23,18,060 5,12,24,25,994 Tamil Nadu 98,401 17,37,20,50,192 26,95,37,24,740 Telangana 17,904 3,40,48,15,294 1,40,19,77,813 Uttar-Pradesh 89,985 3,30,85,96,815 4,25,16,38,967 Uttarakhand 24,425 3,29,34,52,201 3,03,35,35,907 West Bengal 61,156 5,24,53,03,740 6,54,09,57,188 Other NE 27,899 3,57,77,53,460 2,80,64,04,905 Other UTs 8572 5,94,12,45,011 9,80,56,93,340 Across all measures of disease and economic burden, Maharashtra ranked highest followed by Karnataka and Kerala. Males disproportionately bore the impact, contributing 55–65% of deaths, infections, and DALYs while accounting for over 80% of the productivity losses due to mortality, particularly in Bihar, Delhi, and West Bengal(Tables 6 and 7 ). Table 6 Top ten states with COVID disease burden Deaths Infections YLLS YLD DALYS Maharashtra 139011 Maharashtra 6581227 Maharashtra 2167720 Maharashtra 219 Maharashtra 2167938 Karnataka 37777 Kerala 3406240 Karnataka 523976 Kerala 117 Karnataka 524081 Tamil Nadu 35579 Karnataka 3242833 Tamil Nadu 490310 Karnataka 105 Tamil Nadu 490487 Delhi 25530 Tamil Nadu 2799241 Delhi 419951 Andhra Pradesh 76 Delhi 420001 Kerala 24963 Andhra Pradesh 2291028 Uttar-Pradesh 355752 Uttar- Pradesh 59 Uttar- Pradesh 355811 Uttar- Pradesh 22891 Uttar- Pradesh 1905640 Kerala 312365 Madhya Pradesh 53 Kerala 312482 West Bengal 18808 West Bengal 1604796 West Bengal 264932 Delhi 51 West Bengal 259883 Punjab 16516 Delhi 1517472 Punjab 257873 Rajasthan 33 Chhattisgarh 180297 Andhra Pradesh 14163 Rajasthan 1154619 Chhattisgarh 180265 Chhattisgarh 33 Andhra Pradesh 171509 Chhattisgarh 13563 Chhattisgarh 1029774 Andhra Pradesh 171433 West Bengal 31 Madhya Pradesh 158357 Table 7 Top ten states with higher cost of productivity loss Rank State CPL mortality (in million) State CPL morbidity (in millions) 1 Maharashtra 58,894 Maharashtra 48,167 2 Karnataka 20,280 Karnataka 33,519 3 Tamil Nadu 17,372 Kerala 29,292 4 Delhi 16,403 Tamil Nadu 26,954 5 Kerala 6,074 Delhi 18,483 6 Other UTs 5,941 Andhra Pradesh 18,257 7 Andhra Pradesh 5,726 Other UTs 9,806 8 West Bengal 5,245 Gujarat 8,820 9 Punjab 4,953 Haryana 8,396 10 Haryana 4,625 West Bengal 6,541 The supplementary appendix provides further detailed statistics on age and gender-wise impacts,on YLLs, YLDs, DALYs, YPPLL, and CPL providing a comprehensive demographic analysis of the pandemic's impact. Discussion The COVID-19 pandemic has revealed itself as one of the most significant global health crises, affecting various regions at multiple levels. Comparative studies from India, focusing on states like Kerala and Maharashtra, provide insights into the diverse impacts across different regions, as well as discrepancies in reported data. Various sources, including health ministries, independent researchers, and media platforms, provide data on COVID-19 cases, hospitalizations, deaths, and recoveries. However, estimating metrics like infection fatality rates and excess deaths poses challenges due to underdetection of cases and variations in reporting practices 26 – 29 . Researchers and modelers utilize this data to predict infection rates and inform policy decisions 30 – 36 Calculating premature mortality in terms of Years of Life Lost (YLL) is essential, particularly since COVID-19 affects individuals below the age of 60. Studies from different countries, including Korea 37 , Italy 38 , and India 39 , 40 , have provided insights into YLLs and DALYs associated with COVID-19. However, comprehensive DALY estimates for COVID-19 in India are still lacking, with most studies focusing on specific health states or using country-specific disability weights. For example, Kerala's study estimated around 10,400 deaths by June 2021, while the current analysis shows over 24,963 deaths, highlighting a significant difference likely due to different timeframes and data sources. Kerala’s COVID-19 cases were also higher in this analysis at 3.41 million compared to 2.68 million reported by John et al 39 . These variations can be attributed to differences in the methodology, use of population projections for 2021 versus census data from 2011. Additionally, the disability weights in our analysis were based on broadly observable stages aligning with WHO severity definitions, unlike the granular stages employed in the Kerala paper. This difference in severity definitions, used for hospitalized patients in the COVID clinical registry, influenced the input data for disease duration and severity percentages, leading to higher estimates of economic and disease burden for Kerala. Our findings indicate a more extensive effect on working-age populations, exacerbated by heightened mortality and morbidity rates. The economic ramifications are evident in the Cost of Productivity Loss (CPL) due to mortality, amounting to ₹6,983,392 for males and ₹2,483,980 for females. Our findings for Maharashtra align and diverge from those of Vasistha et al. 41 , presenting a comprehensive perspective on COVID-19's impact across different age groups. Both studies report a substantial increase in Years of Life Lost (YLL) and a higher incidence of COVID-19 cases among younger age groups (20–49 years), with confirmed cases peaking in the 30–39 age group. However, our study documents a significantly higher total number of confirmed COVID-19 cases (6,581,277) and deaths (139,011) compared to Vasistha et al. (1,901,654 cases and 48,746 deaths). This disparity is likely due to the broader time frame and utilization of official data sources in our analysis. In our study, the discounted YLL for the 50–59 age group stands at 539,159, indicating severe impacts on this demographic. In contrast, Vasistha et al. report an increased percentage share of Years of Potential Life Lost (YPLL) among working adults aged 45–65, rising from 33–50% due to COVID-19. The DALYs per 1,000 in our study was 17.05 compared to 11.57 reported by Vasistha et al. When comparing our findings with those of John D et al. for West Bengal, both studies indicate a higher incidence of COVID-19 cases among the working-age population, particularly in the 31–60 age group (1,604,796 cases and 18,808 deaths vs. 1,711,957 cases and 19,864 deaths). The similarity in case distribution suggests consistent disease spread patterns across age cohorts in West Bengal. Both studies highlight that the elderly population contributed significantly to productivity losses, particularly in the 46–60 age group. However, there are notable differences in the economic estimates due to methodological variations. Both studies, nonetheless, consistently demonstrate the greater losses incurred by male cohorts across all age groups, underscoring the gender disparity in COVID-19's impact. The COVID-19 pandemic has disproportionately affected older individuals, with adults over 65 years representing 80% of hospitalizations and having a 23-fold greater risk of death compared to those under 65 42 . This indicates that older adults, especially those above 40, may experience more severe illness and prolonged disability due to physical deterioration and comorbidities such as cardiovascular disease, diabetes, and obesity. The extended lockdowns and restrictions on movements limited in-person counseling and exacerbated health issues among the elderly 43 . Although our study did not directly assess the risk of hospitalization, studies have shown that unvaccinated adults had a higher risk of moderate to severe, critical, or fatal COVID-19 (OR 1.54; 95% CI 1.09–2.16) and an increased risk of COVID-19-associated mortality (OR 1.80; 95% CI 1.10–2.87) compared to vaccinated individuals 44 . This study, while comprehensive, acknowledges limitations. A significant constraint pertains to the assumptions made to infer death distributions for certain states lacking detailed, age-specific mortality data. Despite rigorous efforts to obtain reliable data from governmental sources, data gaps necessitated reliance on estimations that may influence the overall precision of the disease burden assessment. Another observation could be that in a country of 1350 million, the analysis shows only 34 million cases suggesting probably under reporting. In this regards, it may be mentioned that the data on confirmed COVID-19 cases were obtained from the ICMR National COVID testing database, which was the official and only national repository for all registered testing laboratories across India. This database served as a centralized platform that ensured uniformity and standardization of testing data throughout the country. The data extraction covered the period up to 30th September 2021. The database included information from all COVID-19 testing laboratories that had been approved and registered by the relevant national authorities. Importantly, only the COVID-19 test reports issued by these registered laboratories were recognized as legitimate and valid for any medical or administrative claims, including diagnostic confirmation and reporting purposes. This ensured the authenticity and reliability of the testing data used for the study. By leveraging this comprehensive national database, the analysis minimized discrepancies and inaccuracies that could arise from unregistered or unverified testing sources, thereby maintaining the credibility and accuracy of the findings. The disparity in the number of reported COVID-19 cases between states can be attributed to several demographic, health system, epidemiological, and policy-related factors. Some states consistently conducted a higher number of tests per capita employing systematic and widespread testing that included symptomatic, asymptomatic, and high-risk populations while others concentrated primarily on symptomatic individuals or close contacts. So these states’ health surveillance system, captured cases more comprehensively. Population density and urbanization also played a significant role. States with predominantly rural population, experienced relatively lower rates of virus transmission due to lower population density in rural areas. Whereas states with a higher degree of urbanization and densely packed cities, witnessed faster spread of the virus, particularly in crowded urban areas. Well-established healthcare infrastructure and proactive disease surveillance enabled early detection and reporting of COVID-19 cases in some states whereas in those states with limited healthcare access and infrastructure, especially in rural areas, led to many cases going undetected. Differences in health-seeking behavior further contributed to this difference. Policy implementation and pandemic response also influenced case numbers. States like Kerala implemented strong public health measures early in the pandemic, including rigorous contact tracing, isolation, and quarantine protocols, ensuring higher detection rates. Migration and mobility patterns were another critical factor. Demographics and socioeconomic factors also played a role. States with a higher proportion of elderly people, who are more vulnerable to symptomatic infection and complications, leading to higher testing and case detection rates. Additionally, the dynamics of the disease and the emergence of variants influenced the positivity rates. Vaccination campaigns also played a role. The findings of this study provide a strong foundation for shaping future policy and research efforst. The calculation of Disability-Adjusted Life Years (DALYs) offers a crucial metric to assess the potential impact of various government-led health interventions across different diseases. Such evaluations can guide resource allocation and help prioritize health programs, leading to more effective use of public funds. Additionally, the study highlights the critical need for ongoing investment in comprehensive health data systems. Future research should address existing data gaps, improving disease burden estimation methodologies. Long term studies that track the effectiveness of health policies by measuring DALYs averted could yield valuable insights into the cost-effectiveness and overall outcomes of national and state health initiatives. Achieving this will require consistent funding and the development of a systematic framework for continuous data collection and analysis. Conclusion This comprehensive analysis, represents a pioneering effort in quantifying the Years of Life Lost (YLL), Years Lived with Disability (YLD), Disability-Adjusted Life Years (DALY), and Years of Potential Productive Life Lost (YPPLL) across all Indian states in the context of COVID-19. By comparing these findings with other studies, it becomes evident that policymakers can strategically leverage these estimates to address log-term economic and health ramifications of the pandemic, especially in states like Kerala. The methodological framework developed in this study offers a scalable model that can be adapted not only to other Indian states but also for developing nations with accessible government disease surveillance data. Quantifying the economic burden through metrics like DALYs, YPPLL, and CPL provides a crucial foundation for inter-state comparison and helps inform the allocation of healthcare resources in regions where such resources are limited. A more detailed analysis at the subgroup level can further identify vulnerable populations that may require targeted interventions. This study also offers a critical assessment of the survival landscape during the ongoing pandemic and highlights the urgent need for strengthened preventive measures. It calls for immediate action across multiple sectors, including workplace safety, public transportation, residential sanitation and comprehensive health measures, to reduce the spread of the virus, particularly in heavily affected areas like Maharashtra. These measures are essential to easing burden on impacted households and improving the efficacy of vaccination efforts. A tailored response to the specific vulnerabilities identified in this analysis is crucial for a coordinated effort to manage and ultimately overcome the challenges posed by pandemics like COVID-19. Limitation The manuscript provides COVID 19 burden estimates for most of the larger states for which complete data for the analysis was available. For the northeastern states (excluding Assam) the population projections of RGI is available in a combined format. Neither are the age specific life expectancy values available for these states.Hence, we had to provide estimates for seven north eastern states in a combined manner. It is important to note that COVID-19 cases and deaths from the seven remaining northeastern states viz. (Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura) collectively account for 447,757 cases and 5,696 deaths, which represents only 1.3% of the total reported cases and deaths in India. Similarly, the RGI provides population projections for union territories in a combined format, and age- and gender-specific case and death data were not available for each union territory individually. The combined reported cases and deaths from Andaman and Nicobar Islands, Dadra and Nagar Haveli, Daman and Diu, Ladakh, Lakshadweep, Puducherry, and Chandigarh amounted to 242,850 cases and 3,050 deaths, accounting for approximately 0.68% and 0.72% of the total cases and deaths, respectively. However, we have provided estimates for Goa and Jammu and Kashmir, where the necessary data was available. Declarations Funding: Indian Council of Medical Research. Clinical Trial Number: Not applicable Authors’ Contributions Conceptualization: GRM and JY (lead) Data curation: GRM and FK Formal analysis: GRM and JY (lead), UV, KS, TA, AG,SS,SS,FK (supporting) Funding acquisition: GRM and JY Investigation: GRM, TA,AG,SS,SSMethodology: GRM, UV,JY,KS Project administration: GRM,JY,TA (lead), AG,SS,SS(supporting) Resources: All authors Supervision: GRM,UV Validation: GRM,JY, TA Visualization: GRM,UV,JY,TA (lead), KS,AG,SS,SS,FK (supporting) Writing – original draft: GRM,UV,JY (lead), KS,TA,AG,SS,SS,FK (supporting) Writing – review & editing: All authors Human Ethics and Consent to Participate : Institutional Ethics Clearance obtained . Since this is a secondary analysis based on published data consent to participate certification is not applicable. Consent to publish : Yes References Hao YJ, et al. The origins of COVID-19 pandemic: A brief overview. Transbound Emerg Dis. 2022;69. 10.1111/tbed.14732 . WHO Coronavirus (COVID-19.) 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COVID-19.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6568384/v1/bd0286a5a3d4fd0755155490.png"},{"id":85817677,"identity":"b7d41d6e-3b5a-474d-872e-93da939b10ce","added_by":"auto","created_at":"2025-07-02 06:02:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":381523,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3: Geographical variations in adjusted rates per 100000 for YLL, YLD, DALYs, Infection and deaths\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6568384/v1/7620181d2ca2139ecbbfe168.png"},{"id":85817673,"identity":"82087f7b-8be7-445f-8666-551cc357ada0","added_by":"auto","created_at":"2025-07-02 06:02:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4a : Distribution of COVID deaths across different age groups ()\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4b: Distribution of COVID deaths across different age groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6568384/v1/fa558b19e3c5ebd6d891daaf.png"},{"id":90966710,"identity":"0da6554b-ff86-45d0-a047-69d3a3456e67","added_by":"auto","created_at":"2025-09-10 06:38:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2118471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6568384/v1/74a75365-822e-403e-a234-b2e7b7e222b6.pdf"},{"id":85818953,"identity":"b565d6b3-9677-4945-b715-92629c3799a1","added_by":"auto","created_at":"2025-07-02 06:10:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18647,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6568384/v1/232e5c30d0b3b89ae6a99db5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Statewise Analysis of the Socioeconomic and Health Impacts of the COVID-19 Pandemic in India: Lessons for Future Health System Preparedness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe coronavirus disease (COVID-19) discovered in Wuhan, China in November 2019\u003csup\u003e1\u003c/sup\u003e has since spread to 238 countries and territories globally. As of January 2025, 777.3\u0026nbsp;million people have been infected, resulting in 7.08\u0026nbsp;million deaths.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The pandemic's impact has varied across age groups and demographics, with older adults facing higher mortality rates\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and men experiencing a greater risk of severe illness and death\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. India has recorded the highest number of confirmed cases and deaths in Asia, although the spread has been uneven across the country, with certain states enduring multiple waves of infections. In India, in the first wave approximately 60% of COVID-19 cases occurred among individuals under 45, with a fatality rate of 12%.\u003csup\u003e5\u003c/sup\u003e The virus has also significantly impacted younger populations and individuals without pre-existing health conditions, contributing to increased hospitalizations and fatalities.\u003c/p\u003e \u003cp\u003eAccurate data collection is essential for policymakers to effectively allocate resources and mitigate the health and socio-economic consequences of the pandemic. The burden of disease analysis is a valuable tool for measuring the health impact of diseases and associated risk factors. Disability-adjusted life years (DALYs), which combine mortality and morbidity data, serve as a key metric in evaluating the health impact. Estimating COVID-19\u0026rsquo;s impact using DALYs and Years of Potential Productive Life Lost (YPPLL) can help generate insights across different regions. However, acquiring the necessary data for these estimates poses challenges, as it requires the integration of various data sources.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic was one of the most critical challenges, with significant consequences for individuals, society, and the national economy. While individuals suffered from health issues and loss of productivity, the national economy experienced reduced output and higher unemployment rates. Numerous studies have explored the health and socioeconomic impact of COVID-19 in India, though most rely on small or localized datasets. However, no studies have provided a comprehensive socioeconomic analysis of COVID-19's health effects using nationally representative datasets.\u003c/p\u003e \u003cp\u003eThis study seeks to address that research gap by analyzing nationally representative datasets along with morbidity and mortality data from public sources, using a burden of disease approach to assess the health and economic impacts of COVID-19 in India, disaggregated at the state level. A substantial socio-demographic and economic variation across states exist in India, which rationales the current study to contextualize the state-level variations in these impacts. The findings of the current study will be helpful to shape the need based resource allocation and preparedness strategies, ultimately contributing to the development of a more resilient healthcare system.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources and triangualation\u003c/h2\u003e \u003cp\u003eThe analysis utilized multiple data sources. Districtwise age-sex distributions of COVID-19 cases were derived from the national COVID-19 testing database\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e maintained by the Indian Council of Medical Research (ICMR). National and state-level projected population data\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e along with COVID mortality figures, were sourced from official counts provided by PRS Legislative Research\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, a trusted digital repository endorsed by the Government of India.\u003c/p\u003e \u003cp\u003eAdditionally, the study incorporated state-specific data obtained either directly or through designated health portals like the Kerala COVID-19 Death Information System (CDIS)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, West Bengal Health Portal\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, Tamil Nadu\u0026rsquo;s Stopcorona portal\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and Karnataka\u0026rsquo;s COVID Information Portal\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. For states like Maharashtra, Rajasthan, Gujarat, Andhra Pradesh, Odisha Punjab, Bihar, Jharkhand, Himachal Pradesh, Haryana, Delhi and Chhattisgarh-where age-sex distribution of COVID-19 deaths was not publicly available- the distribution from a published study\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003ewas used, as these states accounted for approximately 60% of all COVID deaths in India. For the five North East (NE) states, Assam\u0026rsquo;s age sex distribution was applied. In cases where states underreported COVID-19 deaths, adjustments were made to align with the Ministry of Health and Family Welfare\u0026rsquo;s counts, ensuring reliable estimates.\u003c/p\u003e \u003cp\u003eData on the working-age population (15\u0026ndash;60 years) was drawn from the 2019-20 Periodic Labour Force survey (PLFS) by Ministry of Statistics and Planning Implementation (MoSPI)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. State-wise age and sex-specific life expectancies for 2020 were obtained from Registrar General of India (RGI) website \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The study also utilized data on the proportion of cases at various disease severity levels from the National Clinical Registry for COVID-19 (NCRC), providing crucial insights into the progression of COVID-19 and its implications for healthcare resource management.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe estimation method\u003c/h3\u003e\n\u003cp\u003eThe clinical progression of COVID-19 from infection to recovery or death has been adapted from the model developed by McArthur et al\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Due to the absence of data on post-recovery or hospital discharge, this analysis does not account for the burden associated with post-COVID-19 complications or long-term functional impairments.\u003c/p\u003e\n\u003ch3\u003eHealth burden indices\u003c/h3\u003e\n\u003cp\u003eThe Burden of Disease was calculated following the protocol for country studies developed by the European Burden of Disease Network\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The health burden indices were estimated using the standard\u003c/p\u003e \u003cp\u003eformula for Disability Adjusted Life Years(DALYs); DALYs\u0026thinsp;=\u0026thinsp;YLD\u0026thinsp;+\u0026thinsp;YLL where YLD represent the years lived with disability, reflecting the number of years an individual spends in ill health and YLL refers to the years of life lost due to premature death, defined as dying before the ideal life expectancy.\u003c/p\u003e \u003cp\u003eWhile the Global Burden of Disease study\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e has removed the assumptions of time and age preferences in its latest estimates, these factors are still crucial for evaluating the future economic impact. As a result, they have been included in study\u0026rsquo;s analysis.\u003c/p\u003e \u003cp\u003eThe formula for calculating Years of Life Lost (YLL) with uniform age weighting and a 3% discount rate is\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e is\u003c/p\u003e \u003cp\u003e \u003cem\u003eYLL\u003c/em\u003e \u003csub\u003e\u003cem\u003ediscounted\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e=\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1-{e}^{-rLa}}{r}\\:\\:\\)\u003c/span\u003e\u003c/span\u003ewhere r\u0026thinsp;=\u0026thinsp;0.03 is the discount rate and L\u003csub\u003ea\u003c/sub\u003e is the standard life expectancy at age a.\u003c/p\u003e \u003cp\u003eThe formula for calculating Years Lived with Disability (YLD) using the incidence-based approach with uniform age weighting and r\u0026thinsp;=\u0026thinsp;0.03 as the discount rate is\u003c/p\u003e \u003cp\u003e \u003cem\u003eYLD\u003c/em\u003e \u003csub\u003e\u003cem\u003ediscounted,i,s\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e= I\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,s\u003c/em\u003e\u003c/sub\u003e x \u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,s\u003c/em\u003e\u003c/sub\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{x}\\:\\frac{1-{e}^{-r{D}_{i,s}}}{r}\\:\\)\u003c/span\u003e\u003c/span\u003ex \u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003eWi,s\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.03 is the discount rate,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eI\u003csub\u003ei,s\u003c/sub\u003e is the incidence of COVID-19 in the i\u003csup\u003eth\u003c/sup\u003e age group at the s\u003csup\u003eth\u003c/sup\u003e stage/sequelae,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eD\u003csub\u003ei,s\u003c/sub\u003e is the duration of disability due to COVID-19 in the i\u003csup\u003eth\u003c/sup\u003e age group at the s\u003csup\u003eth\u003c/sup\u003e stage/sequelae D\u003csub\u003eWi,s\u003c/sub\u003e is the disability weight for s\u003csup\u003eth\u003c/sup\u003e sequelae of COVID-19 in the i\u003csup\u003eth\u003c/sup\u003e age group, with disability weight ranging from 0 (complete health) to 1 (death).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn this study, the stages of COVID-19, their descriptions, and the corresponding disability weights are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, adapted from Wyper et al\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The duration of disability for patients in home isolation was assumed to be 7 days, even though the Government of India guidelines recommended a 14-day home quarantine. For hospitalized patients, the duration of disability was determined for each decennial age group using data from the National Clinical Registry for COVID-19 (NCRC).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of COVID-19 stages and the disability weights for each stage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage of the disease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData input as proxy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisability weight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsymptomatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHas infection but experiences no symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData not available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNil\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild (Home isolated)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHas infection and symptoms but not hospitalized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91% of cases observed during the study period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild (Hospitalized)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHas symptoms, hospitalized but not requiring supplemental oxygen support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of hospitalized cases who are mild from the National COVID Clinical Registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHas symptoms, hospitalized and requiring supplemental oxygen support in the form of Nasal canula or non-breather mask or face mask)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of hospitalized cases who are moderate from the National COVID Clinical Registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehospitalized and requiring supplemental oxygen support in the form of BiPAP, CPAP, High flow nasal oxygen or on invasive ventilation support or on ECMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of hospitalized cases who are severe from the National COVID Clinical Registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eEconomic burden indices\u003c/h3\u003e\n\u003cp\u003eThe cost of lost productivity (CPL) was calculated using the human capital\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, which takes into account three key parameters: the length of time absent from work due to illness, the market wage, and the labor force participation rate.\u003c/p\u003e \u003cp\u003eYears of Potential Productive Life Lost (YPPLL)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e due to COVID-19 was estimated separately for males and females. The YPPLL was valued using the per capita income for each state as an approximation of the foregone productivity during the period of absence and the years of working life lost due to premature mortality.\u003c/p\u003e \u003cp\u003eThe formula for YPPLL is:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:YPPLL=\\:{\\sum\\:}_{i=1\\:}^{\\:n}\\:{D}_{i}\\times\\:\\:{w}_{i}\\times\\:\\:\\frac{1}{{\\left(1+d\\right)}^{wi-1}}\\:\\:\\:\\:\\:\\:\\:\\:|\\:\\:\\:\\:i=1,\\:2,\\:\\dots\\:,\\:n$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ei represents the ith working age group,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003en is the age group that ends at 60 years,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDi is the number of deaths at age i;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ewi is the productive years remaining at age of death (calculated as 60 minus the midpoint of the ith age group),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ed is the discount rate, which was set to the bank rate proposed by the Reserve Bank of India (RBI)\u0026thinsp;=\u0026thinsp;0.0425\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFor each state, cost of Productivity loss (CPL) was estimated for both absenteeism( temporary) and premature mortality(permanent losses), using the following formulas:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{CPL}_{Premature\\:mortality}=\\:{\\sum\\:}_{i=1\\:}^{n}\\:{YPPLL}_{i}*\\:per\\:capita\\:GDP*{P}_{i}\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{CPL}_{absenteeism}=\\:{\\sum\\:}_{i=1\\:}^{n}\\:S*{L}_{i}*{N}_{i}*{P}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eP\u003csub\u003ei\u003c/sub\u003e is the proportion of working population, in the i\u003csup\u003eth\u003c/sup\u003e age group, in that state\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eS is the average daily salary, calculated as the per capita GDP of that state by 313 (number of paid working days in a year accounting for 52 Sundays)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eL\u003csub\u003ei\u003c/sub\u003e is the average recovery time for the ith age group\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eN\u003csub\u003ei\u003c/sub\u003e is the number of incident cases in the i\u003csup\u003eth\u003c/sup\u003e age group\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe costs were adjusted to reflect 2021 values\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Recovery times were based on data from the National Clinical Registry for COVID-19 (NCRC), assuming 14 days for asymptomatic and mild cases, 21 days for severe cases, and 30 days for critical cases.\u003c/p\u003e \u003cp\u003eWe assumed that 91% of those infected were home isolated, in accordance with Government of India guidelines\u003csup\u003e25\u003c/sup\u003e and 9% were hospitalized. Among the hospitalized patients, the proportions of those in mild, moderate and severe categories were obtained from the National COVID-19 Clinical Registry and these proportions were assumed to be consistent across all states.\u003c/p\u003e \u003cp\u003eThe estimations were done on an excel sheet after extracting the relevant mobidity and mortality data from various data sources and organizing them by age and gender in the required format. The step-by-step estimation process is outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Findings","content":"\u003cp\u003eThe proportion of hospitalized patients across the three severity stages and the duration of disability experiences by COVID-19 patients are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The duration of disability is calculated as the third quartile, measured from the onset of COVID-19 symptoms to the date of hospital discharge. This suggests that older adults, particularly those over 40 experience more severe illness and longer periods of disability.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSeverity percentage among hospitalized cases and average days of recovery\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAge-Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePercentage (%) among hospitalized cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eAverage days to recovery/discharge\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMild/ Moderate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCritical\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMild/ Moderate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCritical\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 to 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 to 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 to 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 to 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 to 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 to 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 to 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 to 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 to 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 to 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 to 55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 to 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 to 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 to 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 to 75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 to 80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs of September 30, 2021, India recorded approximately 33.6\u0026nbsp;million confirmed cases of COVID-19, with males accounting for 61% (20.05\u0026nbsp;million) of the total cases. The state-wise distribution showed Maharashtra as having the highest incidence,with about 6.58\u0026nbsp;million cases, followed by Kerala (3.41\u0026nbsp;million), Karnataka (3.24\u0026nbsp;million), Tamil Nadu (2.8\u0026nbsp;million), and Andhra Pradesh (2.29\u0026nbsp;million). Indias cumulative death toll reached 450,000, with Maharashtra, with 139,000 fatalities, emerged as the state with the highest death toll, followed by Karnataka (37,777), Tamil Nadu (35,579), Delhi (25,530), Kerala (24,963), and Uttar Pradesh (22,891). Notably, Himachal Pradesh reported the lowest death count among the larger states(3,653), while the collective total for the seven northeastern states was the lowest regional death toll. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of COVID positives and reported number of COVID deaths in all the states, India.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eStates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eCOVID Cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eCOVID deaths\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMales\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMales\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2,00,48,750\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1,35,46,993\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3,35,95,743\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2,89,899\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1,51,709\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4,50,037\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,20,377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,70,651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22,91,028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14,163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,75,938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,41,172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,17,110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBihar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,25,099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,33,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,58,305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChhattisgarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,13,149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,16,625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,29,774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,99,892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,17,580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15,17,472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25,530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,06,651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76,617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,83,268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGujarat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,83,505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,48,491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,31,996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaryana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,65,723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,54,676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,20,399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHimachal Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,22,812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85,667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,08,479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJammu And Kashmir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,00,514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,08,665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,09,179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJharkhand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,59,034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,34,857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,93,891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKarnataka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,17,446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,25,387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32,42,833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKerala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,88,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,17,560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34,06,240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14,480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMadhya Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,98,912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,60,081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,58,993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaharashtra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,23,872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26,57,355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65,81,227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91,759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47,252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,39,011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOdisha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,65,723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,54,676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,20,399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePunjab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,32,200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,58,132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,90,332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRajasthan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,60,030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,94,589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,54,619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTamil Nadu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,32,873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,66,368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,99,241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35,579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTelangana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83,901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50,126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,34,027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUttar Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12,42,043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,63,597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,05,640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15,163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22,891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUttarakhand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,17,941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,27,947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,45,888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Bengal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,21,649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,83,147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,04,796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12,601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18,808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Northeast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,48,833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,98,924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,47,757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Union territories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,41,953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,00,897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,42,850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn India, COVID-19 cases resulted in a total of 1,182 years with disability, with 62% of this burden borne by males.The pandemic\u0026rsquo;s initial waves led to an estimated 6.7\u0026nbsp;million YLLs, with males accounting for 64.3%. Maharashtra had the highest YLL at 2.17\u0026nbsp;million, constituting 32.3% of the national total (Supplementary Table\u0026nbsp;4). The age adjusted YLL, YLD, DALYs, deaths and infections rates per 100,000 was estimated to compare the rates across the different states. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Goa had the highest age adjusted rate (3071.1) for YLLs because of the high death rates per 100000. Compared to other states, Goa had a very high infection rate per 100000 and hence the morbidity index (YLD) was comparatively highest 11194 (197.36,11,194.00).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdjusted YLL, YLD, DALY, Death and Infection rates per 100,000 for different states\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdj YLL\u003c/p\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdj YLD\u003c/p\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdj DALYs\u003c/p\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdj deaths\u003c/p\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdj infections\u003c/p\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eANDHRA PRADESH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e303.4\u003c/p\u003e\n \u003cp\u003e(301.96-304.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003cp\u003e(0.10\u0026ndash;0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e303.53\u003c/p\u003e\n \u003cp\u003e(302.10-304.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.31\u003c/p\u003e\n \u003cp\u003e(24.89\u0026ndash;25.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4113.2\u003c/p\u003e\n \u003cp\u003e(4,107.87-5,207.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eASSAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323.69\u003c/p\u003e\n \u003cp\u003e(321.74-325.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003e(0.03\u0026ndash;0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323.74\u003c/p\u003e\n \u003cp\u003e(321.80-325.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.04\u003c/p\u003e\n \u003cp\u003e(18.56\u0026ndash;19.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1740.03\u003c/p\u003e\n \u003cp\u003e(1,735.69-1,768.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIHAR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.79\u003c/p\u003e\n \u003cp\u003e(173.86-175.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003cp\u003e(0.01\u0026ndash;0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.81\u003c/p\u003e\n \u003cp\u003e(173.88-175.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.99\u003c/p\u003e\n \u003cp\u003e(13.71\u0026ndash;14.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e720.12\u003c/p\u003e\n \u003cp\u003e(718.49-952.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHATTISGARH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e715.64\u003c/p\u003e\n \u003cp\u003e(712.34-718.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e(0.07\u0026ndash;0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e715.75\u003c/p\u003e\n \u003cp\u003e(712.45-719.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.56\u003c/p\u003e\n \u003cp\u003e(57.57\u0026ndash;59.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3522.95\u003c/p\u003e\n \u003cp\u003e(3,516.14-3,529.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDELHI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2263.12\u003c/p\u003e\n \u003cp\u003e(2,256.28-2,269.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003e(0.18\u0026ndash;0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2263.37\u003c/p\u003e\n \u003cp\u003e(2,256.52-2,270.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146.17\u003c/p\u003e\n \u003cp\u003e(144.38-147.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7139.28\u003c/p\u003e\n \u003cp\u003e(7,127.92-8,553.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3071.01\u003c/p\u003e\n \u003cp\u003e(3,043.91-3,098.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003cp\u003e(0.11\u0026ndash;0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3071.45\u003c/p\u003e\n \u003cp\u003e(3,044.36-3,098.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236.48\u003c/p\u003e\n \u003cp\u003e(228.81-244.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11194\u003c/p\u003e\n \u003cp\u003e(197.36-11,194.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGUJARAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e331.74\u003c/p\u003e\n \u003cp\u003e(330.07-333.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003cp\u003e(0.04\u0026ndash;0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e331.8\u003c/p\u003e\n \u003cp\u003e(330.14-333.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.51\u003c/p\u003e\n \u003cp\u003e(22.07\u0026ndash;22.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1980.85\u003c/p\u003e\n \u003cp\u003e(1,976.83-1,984.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHARYANA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e573.56\u003c/p\u003e\n \u003cp\u003e(570.67-576.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e(0.07\u0026ndash;0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e573.66\u003c/p\u003e\n \u003cp\u003e(570.77-576.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.52\u003c/p\u003e\n \u003cp\u003e(37.76\u0026ndash;39.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3093.84\u003c/p\u003e\n \u003cp\u003e(3,087.52-3,100.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIMACHAL PRADESH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e724.67\u003c/p\u003e\n \u003cp\u003e(718.80-730.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e(0.02\u0026ndash;0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e724.76\u003c/p\u003e\n \u003cp\u003e(718.88-730.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.27\u003c/p\u003e\n \u003cp\u003e(43.80-46.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2667.48\u003c/p\u003e\n \u003cp\u003e(2,656.02-2,678.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eJAMMU AND KASHMIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e588.21\u003c/p\u003e\n \u003cp\u003e(583.83-592.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e(0.03\u0026ndash;0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e588.29\u003c/p\u003e\n \u003cp\u003e(583.91-592.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.95\u003c/p\u003e\n \u003cp\u003e(37.80-40.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2233.88\u003c/p\u003e\n \u003cp\u003e(2,226.00\u0026ndash;2,241.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eJHARKHAND\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252.14\u003c/p\u003e\n \u003cp\u003e(250.36-253.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e(0.01\u0026ndash;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252.18\u003c/p\u003e\n \u003cp\u003e(250.39-253.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.24\u003c/p\u003e\n \u003cp\u003e(17.74\u0026ndash;18.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1077.88\u003c/p\u003e\n \u003cp\u003e(1,074.51-1,081.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eKARNATAKA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e765.06\u003c/p\u003e\n \u003cp\u003e(762.99-767.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e(0.12\u0026ndash;0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e765.21\u003c/p\u003e\n \u003cp\u003e(763.14-767.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.69\u003c/p\u003e\n \u003cp\u003e(56.12\u0026ndash;57.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4624.62\u003c/p\u003e\n \u003cp\u003e(4,619.59-4,629.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eKERALA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e687.94\u003c/p\u003e\n \u003cp\u003e(685.53-690.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003cp\u003e(0.00-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e688.26\u003c/p\u003e\n \u003cp\u003e(66.94-688.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.91\u003c/p\u003e\n \u003cp\u003e(10.55\u0026ndash;53.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9415.89\u003c/p\u003e\n \u003cp\u003e(137.87-9,415.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMADHYA PRADESH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221.11\u003c/p\u003e\n \u003cp\u003e(220.02\u0026ndash;222.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003cp\u003e(0.05\u0026ndash;0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221.17\u003c/p\u003e\n \u003cp\u003e(220.08-222.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.53\u003c/p\u003e\n \u003cp\u003e(15.23\u0026ndash;15.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1200.06\u003c/p\u003e\n \u003cp\u003e(1,197.66-1,202.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAHARASHTRA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1704.33\u003c/p\u003e\n \u003cp\u003e(1,702.06-1,706.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(0.15\u0026ndash;0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1704.51\u003c/p\u003e\n \u003cp\u003e(1,702.24-1,706.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.66\u003c/p\u003e\n \u003cp\u003e(110.07-111.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5247.57\u003c/p\u003e\n \u003cp\u003e(5,243.56-5,251.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eODISHA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286.17\u003c/p\u003e\n \u003cp\u003e(284.62-287.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003e(0.04\u0026ndash;0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286.23\u003c/p\u003e\n \u003cp\u003e(284.68-287.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.28\u003c/p\u003e\n \u003cp\u003e(17.89\u0026ndash;18.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1956.65\u003c/p\u003e\n \u003cp\u003e(1,952.66-1,960.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePUNJAB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e799.77\u003c/p\u003e\n \u003cp\u003e(796.69-802.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003cp\u003e(0.04\u0026ndash;0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e799.84\u003c/p\u003e\n \u003cp\u003e(796.76-802.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.14\u003c/p\u003e\n \u003cp\u003e(50.36\u0026ndash;51.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2137.69\u003c/p\u003e\n \u003cp\u003e(2,113.81-2,161.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAJASTHAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211.01\u003c/p\u003e\n \u003cp\u003e(209.89-212.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e(0.03\u0026ndash;0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211.06\u003c/p\u003e\n \u003cp\u003e(209.93-212.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.69\u003c/p\u003e\n \u003cp\u003e(14.38\u0026ndash;14.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1480.72\u003c/p\u003e\n \u003cp\u003e(1,478.02-1,483.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAMIL NADU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e530.03\u003c/p\u003e\n \u003cp\u003e(528.55-531.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e(0.18\u0026ndash;0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e530.25\u003c/p\u003e\n \u003cp\u003e(528.76-531.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.38\u003c/p\u003e\n \u003cp\u003e(37.98\u0026ndash;38.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3364.92\u003c/p\u003e\n \u003cp\u003e(3,360.98-3,368.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTELENGANA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205.95\u003c/p\u003e\n \u003cp\u003e(204.51-207.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e(0.00-0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205.96\u003c/p\u003e\n \u003cp\u003e(200.66-207.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.59\u003c/p\u003e\n \u003cp\u003e(13.21\u0026ndash;13.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e341.13\u003c/p\u003e\n \u003cp\u003e(339.30-342.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUTTAR-PRADESH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194.29\u003c/p\u003e\n \u003cp\u003e(193.65-194.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e(0.02\u0026ndash;0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194.32\u003c/p\u003e\n \u003cp\u003e(193.68-194.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.04\u003c/p\u003e\n \u003cp\u003e(12.87\u0026ndash;13.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e900.58\u003c/p\u003e\n \u003cp\u003e(899.30-901.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUTTARAKHAND\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1056.32\u003c/p\u003e\n \u003cp\u003e(1,050.13-1,062.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e(0.04\u0026ndash;0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1056.43\u003c/p\u003e\n \u003cp\u003e(1,050.24-1,062.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.82\u003c/p\u003e\n \u003cp\u003e(69.20-72.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2979.93\u003c/p\u003e\n \u003cp\u003e(2,969.99-2,989.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWEST BENGAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248.01\u003c/p\u003e\n \u003cp\u003e(247.07-248.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e(0.02\u0026ndash;0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248.04\u003c/p\u003e\n \u003cp\u003e(247.09-248.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.45\u003c/p\u003e\n \u003cp\u003e(18.19\u0026ndash;18.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1517.6\u003c/p\u003e\n \u003cp\u003e(1,515.25-1,519.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther NE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e680.81\u003c/p\u003e\n \u003cp\u003e(676.65-684.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e(0.04\u0026ndash;0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e680.89\u003c/p\u003e\n \u003cp\u003e(676.74-685.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.05\u003c/p\u003e\n \u003cp\u003e(39.01\u0026ndash;41.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2736.99\u003c/p\u003e\n \u003cp\u003e(2,677.83-2,796.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOTHER UTs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010.29\u003c/p\u003e\n \u003cp\u003e(1,991.91-2,028.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003cp\u003e(0.07\u0026ndash;0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010.51\u003c/p\u003e\n \u003cp\u003e(1,992.14-2,028.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.53\u003c/p\u003e\n \u003cp\u003e(134.58-144.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6341\u003c/p\u003e\n \u003cp\u003e(6,315.78-6,366.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe total DALYs associated with COVID-19 in India amount to 6.70\u0026nbsp;million, with males comprising 65% of these lost years. Males generally accounted for 62\u0026ndash;68% of the national disease burden, but exceptions were noted in Jammu and Kashmir and Kerala, where the DALYs attributed to females were higher, suggesting a greater female mortality in these regions.(Supplementary File\u003c/p\u003e\n\u003cp\u003eRegional disparities in COVID-19 infection rates and adjusted death rates highlight variations in transmission intensity and mortality burden. Southern and western states experienced higher transmission rates than other regions, indicating geographical clustering of COVID-19 cases influenced by population density, mobility, and healthcare infrastructure. Age-adjusted death rates varied across states, with Goa reporting the highest death rate at 236 per 100,000, followed by Delhi (146), Maharashtra (111), and Uttarakhand (71). Uttar Pradesh had the lowest age-adjusted death rate at 13 per 100,000, reflecting variations in the pandemic\u0026apos;s severity across regions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSimilarly, DALY rates exhibited regional disparities. Goa had the highest DALY rate ( 3,071 per 100,000), followed by Delhi (2,263), Maharashtra (1,705), and Uttarakhand (1,056). The central region showed lower DALY rates, with Bihar recording the lowest DALY rate at 174 per 100,000, suggesting relatively lower disease burden in this region compared to southern and western states.\u003c/p\u003e\n\u003cp\u003eAge-specific COVID-19 mortality distribution across states offers insights into the demographics of mortality. Maharashtra had the highest mortality across all age categories, peaking in the 60\u0026ndash;69 age group with around 40,000 deaths. This pattern was broadly replicated across the other states, though the numbers were much lesser. Delhi saw about 5,000 deaths in this age group, with lower numbers in other states. Mortality data from seven states intervals allowed comparisons, revealing that Karnataka had the highest mortality (5000 deaths) in the 65\u0026ndash;70 year age group, while Uttar Pradesh had the younger mortality profile, peaking in the 60\u0026ndash;65 year age group. Kerala\u0026rsquo;s mortality was concentrated among individuals aged over 60, highlighting the vulnerability of its older population(Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB)\u003c/p\u003e\n\u003cp\u003eDuring the first and second waves of COVID-19, India lost 1.309\u0026nbsp;million years of potential productive life (YPPLL). Maharshtra recorded the highest (390,000 years)followed by Karnataka (110,000), Tamil Nadu (98000) and Uttar Pradesh (90,000). Goa had the lowest YPPLL( 9,147) years, indicating a relatively smaller impact.\u003c/p\u003e\n\u003cp\u003eIndia\u0026rsquo;s cumulative cost of productivity loss (CPL) due to COVID-19 amounted to ₹195.05\u0026nbsp;billion from premature mortality among the working-age population and ₹268.13\u0026nbsp;billion owing to absenteeism among infected individuals. Maharashtra incurred the highest economic burden due to premature mortality (₹588.9\u0026nbsp;billion) followed by Karnataka (₹202.7\u0026nbsp;billion), Tamil Nadu (₹173.7\u0026nbsp;billion), and Delhi (₹164\u0026nbsp;billion). Maharashtra also led in the absenteeism -related CPL (₹481.7\u0026nbsp;billion), followed by Karnataka (₹335.1\u0026nbsp;billion), Kerala (₹292.9\u0026nbsp;billion), and Tamil Nadu (₹269.5\u0026nbsp;billion). Jammu and Kashmir reported the lowest CPL from mortality (₹0.79\u0026nbsp;billion) while Bihar had the least absenteeism-related CPL (₹1.25\u0026nbsp;billion) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of YPPLL and Cost of productivity loss among different states\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eState\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYPPLL (Unit?)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCPL mortality\u003c/p\u003e\n \u003cp\u003e(In ₹)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCPL morbidity\u003c/p\u003e\n \u003cp\u003e(In ₹)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll India\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e13,09,883\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1,95,05,07,27,000\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2,68,13,10,26,964\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,72,57,87,492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18,25,74,26,936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,64,78,35,008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,78,82,79,164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBihar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81,17,97,202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,25,50,25,841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChhattisgarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38,120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,48,71,47,700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,36,50,07,597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71,754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,40,33,22,191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18,48,29,86,048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,83,45,27,201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,70,85,74,539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGujarat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,61,95,43,484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,81,97,41,855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaryana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,62,54,20,214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,39,63,90,235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHimachal Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,65,63,72,810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,87,83,23,048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJammu And Kashmir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79,23,87,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,32,75,94,444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJharkhand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14,441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82,62,46,616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,36,29,98,641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKarnataka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,10,963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,27,96,20,490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33,51,90,02,852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKerala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42,518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,07,35,02,935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29,29,24,45,360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMadhya Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35,685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,01,67,04,978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,64,64,74,074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaharashtra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,90,701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58,89,39,80,792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,16,73,71,692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOdisha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,79,89,29,159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,01,41,74,433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePunjab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46,420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,95,33,57,189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,12,11,47,954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRajasthan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25,166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,39,23,18,060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,12,24,25,994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTamil Nadu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98,401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,37,20,50,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26,95,37,24,740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTelangana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,40,48,15,294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,40,19,77,813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUttar-Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89,985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,30,85,96,815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,25,16,38,967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUttarakhand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,29,34,52,201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,03,35,35,907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Bengal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61,156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,24,53,03,740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,54,09,57,188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther NE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,57,77,53,460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,80,64,04,905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther UTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,94,12,45,011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,80,56,93,340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAcross all measures of disease and economic burden, Maharashtra ranked highest followed by Karnataka and Kerala. Males disproportionately bore the impact, contributing 55\u0026ndash;65% of deaths, infections, and DALYs while accounting for over 80% of the productivity losses due to mortality, particularly in Bihar, Delhi, and West Bengal(Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e ).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop ten states with COVID disease burden\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDeaths\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eInfections\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eYLLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eYLD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDALYS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaharashtra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaharashtra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6581227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaharashtra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2167720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaharashtra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaharashtra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2167938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKarnataka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKerala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3406240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKarnataka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e523976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKerala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKarnataka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e524081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTamil Nadu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKarnataka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3242833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTamil Nadu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e490310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKarnataka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTamil Nadu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e490487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTamil Nadu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2799241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e419951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e420001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKerala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2291028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUttar-Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e355752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUttar-\u003c/p\u003e\n \u003cp\u003ePradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUttar-\u003c/p\u003e\n \u003cp\u003ePradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e355811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUttar-\u003c/p\u003e\n \u003cp\u003ePradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUttar-\u003c/p\u003e\n \u003cp\u003ePradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1905640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKerala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e312365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMadhya Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKerala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e312482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Bengal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Bengal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1604796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Bengal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Bengal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e259883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePunjab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1517472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePunjab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e257873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRajasthan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChhattisgarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRajasthan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1154619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChhattisgarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChhattisgarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChhattisgarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChhattisgarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1029774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Bengal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMadhya Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop ten states with higher cost of productivity loss\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eState\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCPL mortality (in million)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eState\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCPL morbidity (in millions)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaharashtra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58,894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaharashtra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKarnataka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKarnataka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33,519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTamil Nadu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKerala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29,292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTamil Nadu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26,954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKerala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18,483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther UTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18,257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther UTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Bengal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGujarat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePunjab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaryana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaryana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Bengal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe supplementary appendix provides further detailed statistics on age and gender-wise impacts,on YLLs, YLDs, DALYs, YPPLL, and CPL providing a comprehensive demographic analysis of the pandemic\u0026apos;s impact.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe COVID-19 pandemic has revealed itself as one of the most significant global health crises, affecting various regions at multiple levels. Comparative studies from India, focusing on states like Kerala and Maharashtra, provide insights into the diverse impacts across different regions, as well as discrepancies in reported data. Various sources, including health ministries, independent researchers, and media platforms, provide data on COVID-19 cases, hospitalizations, deaths, and recoveries. However, estimating metrics like infection fatality rates and excess deaths poses challenges due to underdetection of cases and variations in reporting practices\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Researchers and modelers utilize this data to predict infection rates and inform policy decisions\u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32 CR33 CR34 CR35\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Calculating premature mortality in terms of Years of Life Lost (YLL) is essential, particularly since COVID-19 affects individuals below the age of 60. Studies from different countries, including Korea\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, Italy\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and India\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, have provided insights into YLLs and DALYs associated with COVID-19. However, comprehensive DALY estimates for COVID-19 in India are still lacking, with most studies focusing on specific health states or using country-specific disability weights.\u003c/p\u003e \u003cp\u003eFor example, Kerala's study estimated around 10,400 deaths by June 2021, while the current analysis shows over 24,963 deaths, highlighting a significant difference likely due to different timeframes and data sources. Kerala\u0026rsquo;s COVID-19 cases were also higher in this analysis at 3.41\u0026nbsp;million compared to 2.68\u0026nbsp;million reported by John et al\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These variations can be attributed to differences in the methodology, use of population projections for 2021 versus census data from 2011. Additionally, the disability weights in our analysis were based on broadly observable stages aligning with WHO severity definitions, unlike the granular stages employed in the Kerala paper. This difference in severity definitions, used for hospitalized patients in the COVID clinical registry, influenced the input data for disease duration and severity percentages, leading to higher estimates of economic and disease burden for Kerala. Our findings indicate a more extensive effect on working-age populations, exacerbated by heightened mortality and morbidity rates. The economic ramifications are evident in the Cost of Productivity Loss (CPL) due to mortality, amounting to ₹6,983,392 for males and ₹2,483,980 for females.\u003c/p\u003e \u003cp\u003eOur findings for Maharashtra align and diverge from those of Vasistha et al.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, presenting a comprehensive perspective on COVID-19's impact across different age groups. Both studies report a substantial increase in Years of Life Lost (YLL) and a higher incidence of COVID-19 cases among younger age groups (20\u0026ndash;49 years), with confirmed cases peaking in the 30\u0026ndash;39 age group. However, our study documents a significantly higher total number of confirmed COVID-19 cases (6,581,277) and deaths (139,011) compared to Vasistha et al. (1,901,654 cases and 48,746 deaths). This disparity is likely due to the broader time frame and utilization of official data sources in our analysis.\u003c/p\u003e \u003cp\u003eIn our study, the discounted YLL for the 50\u0026ndash;59 age group stands at 539,159, indicating severe impacts on this demographic. In contrast, Vasistha et al. report an increased percentage share of Years of Potential Life Lost (YPLL) among working adults aged 45\u0026ndash;65, rising from 33\u0026ndash;50% due to COVID-19. The DALYs per 1,000 in our study was 17.05 compared to 11.57 reported by Vasistha et al.\u003c/p\u003e \u003cp\u003eWhen comparing our findings with those of John D et al. for West Bengal, both studies indicate a higher incidence of COVID-19 cases among the working-age population, particularly in the 31\u0026ndash;60 age group (1,604,796 cases and 18,808 deaths vs. 1,711,957 cases and 19,864 deaths). The similarity in case distribution suggests consistent disease spread patterns across age cohorts in West Bengal. Both studies highlight that the elderly population contributed significantly to productivity losses, particularly in the 46\u0026ndash;60 age group. However, there are notable differences in the economic estimates due to methodological variations. Both studies, nonetheless, consistently demonstrate the greater losses incurred by male cohorts across all age groups, underscoring the gender disparity in COVID-19's impact.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic has disproportionately affected older individuals, with adults over 65 years representing 80% of hospitalizations and having a 23-fold greater risk of death compared to those under 65\u003csup\u003e42\u003c/sup\u003e. This indicates that older adults, especially those above 40, may experience more severe illness and prolonged disability due to physical deterioration and comorbidities such as cardiovascular disease, diabetes, and obesity. The extended lockdowns and restrictions on movements limited in-person counseling and exacerbated health issues among the elderly\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough our study did not directly assess the risk of hospitalization, studies have shown that unvaccinated adults had a higher risk of moderate to severe, critical, or fatal COVID-19 (OR 1.54; 95% CI 1.09\u0026ndash;2.16) and an increased risk of COVID-19-associated mortality (OR 1.80; 95% CI 1.10\u0026ndash;2.87) compared to vaccinated individuals\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study, while comprehensive, acknowledges limitations. A significant constraint pertains to the assumptions made to infer death distributions for certain states lacking detailed, age-specific mortality data. Despite rigorous efforts to obtain reliable data from governmental sources, data gaps necessitated reliance on estimations that may influence the overall precision of the disease burden assessment.\u003c/p\u003e \u003cp\u003eAnother observation could be that in a country of 1350\u0026nbsp;million, the analysis shows only 34\u0026nbsp;million cases suggesting probably under reporting. In this regards, it may be mentioned that the data on confirmed COVID-19 cases were obtained from the ICMR National COVID testing database, which was the official and only national repository for all registered testing laboratories across India. This database served as a centralized platform that ensured uniformity and standardization of testing data throughout the country. The data extraction covered the period up to 30th September 2021. The database included information from all COVID-19 testing laboratories that had been approved and registered by the relevant national authorities.\u003c/p\u003e \u003cp\u003eImportantly, only the COVID-19 test reports issued by these registered laboratories were recognized as legitimate and valid for any medical or administrative claims, including diagnostic confirmation and reporting purposes. This ensured the authenticity and reliability of the testing data used for the study. By leveraging this comprehensive national database, the analysis minimized discrepancies and inaccuracies that could arise from unregistered or unverified testing sources, thereby maintaining the credibility and accuracy of the findings.\u003c/p\u003e \u003cp\u003eThe disparity in the number of reported COVID-19 cases between states can be attributed to several demographic, health system, epidemiological, and policy-related factors. Some states consistently conducted a higher number of tests per capita employing systematic and widespread testing that included symptomatic, asymptomatic, and high-risk populations while others concentrated primarily on symptomatic individuals or close contacts. So these states\u0026rsquo; health surveillance system, captured cases more comprehensively.\u003c/p\u003e \u003cp\u003ePopulation density and urbanization also played a significant role. States with predominantly rural population, experienced relatively lower rates of virus transmission due to lower population density in rural areas. Whereas states with a higher degree of urbanization and densely packed cities, witnessed faster spread of the virus, particularly in crowded urban areas. Well-established healthcare infrastructure and proactive disease surveillance enabled early detection and reporting of COVID-19 cases in some states whereas in those states with limited healthcare access and infrastructure, especially in rural areas, led to many cases going undetected. Differences in health-seeking behavior further contributed to this difference.\u003c/p\u003e \u003cp\u003ePolicy implementation and pandemic response also influenced case numbers. States like Kerala implemented strong public health measures early in the pandemic, including rigorous contact tracing, isolation, and quarantine protocols, ensuring higher detection rates.\u003c/p\u003e \u003cp\u003eMigration and mobility patterns were another critical factor. Demographics and socioeconomic factors also played a role. States with a higher proportion of elderly people, who are more vulnerable to symptomatic infection and complications, leading to higher testing and case detection rates. Additionally, the dynamics of the disease and the emergence of variants influenced the positivity rates. Vaccination campaigns also played a role.\u003c/p\u003e \u003cp\u003eThe findings of this study provide a strong foundation for shaping future policy and research efforst. The calculation of Disability-Adjusted Life Years (DALYs) offers a crucial metric to assess the potential impact of various government-led health interventions across different diseases. Such evaluations can guide resource allocation and help prioritize health programs, leading to more effective use of public funds. Additionally, the study highlights the critical need for ongoing investment in comprehensive health data systems. Future research should address existing data gaps, improving disease burden estimation methodologies. Long term studies that track the effectiveness of health policies by measuring DALYs averted could yield valuable insights into the cost-effectiveness and overall outcomes of national and state health initiatives. Achieving this will require consistent funding and the development of a systematic framework for continuous data collection and analysis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis comprehensive analysis, represents a pioneering effort in quantifying the Years of Life Lost (YLL), Years Lived with Disability (YLD), Disability-Adjusted Life Years (DALY), and Years of Potential Productive Life Lost (YPPLL) across all Indian states in the context of COVID-19.\u003c/p\u003e \u003cp\u003eBy comparing these findings with other studies, it becomes evident that policymakers can strategically leverage these estimates to address log-term economic and health ramifications of the pandemic, especially in states like Kerala. The methodological framework developed in this study offers a scalable model that can be adapted not only to other Indian states but also for developing nations with accessible government disease surveillance data. Quantifying the economic burden through metrics like DALYs, YPPLL, and CPL provides a crucial foundation for inter-state comparison and helps inform the allocation of healthcare resources in regions where such resources are limited. A more detailed analysis at the subgroup level can further identify vulnerable populations that may require targeted interventions.\u003c/p\u003e \u003cp\u003eThis study also offers a critical assessment of the survival landscape during the ongoing pandemic and highlights the urgent need for strengthened preventive measures. It calls for immediate action across multiple sectors, including workplace safety, public transportation, residential sanitation and comprehensive health measures, to reduce the spread of the virus, particularly in heavily affected areas like Maharashtra. These measures are essential to easing burden on impacted households and improving the efficacy of vaccination efforts. A tailored response to the specific vulnerabilities identified in this analysis is crucial for a coordinated effort to manage and ultimately overcome the challenges posed by pandemics like COVID-19.\u003c/p\u003e"},{"header":"Limitation","content":"\u003cp\u003eThe manuscript provides COVID 19 burden estimates for most of the larger states for which complete data for the analysis was available. For the northeastern states (excluding Assam) the population projections of RGI is available in a combined format. Neither are the age specific life expectancy values available for these states.Hence, we had to provide estimates for seven north eastern states in a combined manner. It is important to note that COVID-19 cases and deaths from the seven remaining northeastern states viz. (Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura) collectively account for 447,757 cases and 5,696 deaths, which represents only 1.3% of the total reported cases and deaths in India.\u003c/p\u003e \u003cp\u003eSimilarly, the RGI provides population projections for union territories in a combined format, and age- and gender-specific case and death data were not available for each union territory individually. The combined reported cases and deaths from Andaman and Nicobar Islands, Dadra and Nagar Haveli, Daman and Diu, Ladakh, Lakshadweep, Puducherry, and Chandigarh amounted to 242,850 cases and 3,050 deaths, accounting for approximately 0.68% and 0.72% of the total cases and deaths, respectively. However, we have provided estimates for Goa and Jammu and Kashmir, where the necessary data was available.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eIndian Council of Medical Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number: Not applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: GRM and JY \u0026nbsp;(lead)\u003c/p\u003e\n\u003cp\u003eData curation: GRM and FK\u003c/p\u003e\n\u003cp\u003eFormal analysis: GRM and JY (lead), UV, KS, TA, AG,SS,SS,FK (supporting)\u003c/p\u003e\n\u003cp\u003eFunding acquisition: GRM and JY \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInvestigation: GRM, TA,AG,SS,SSMethodology: GRM, UV,JY,KS\u003c/p\u003e\n\u003cp\u003eProject administration: GRM,JY,TA (lead), AG,SS,SS(supporting)\u003c/p\u003e\n\u003cp\u003eResources: All authors\u003c/p\u003e\n\u003cp\u003eSupervision: GRM,UV\u003c/p\u003e\n\u003cp\u003eValidation: GRM,JY, TA\u003c/p\u003e\n\u003cp\u003eVisualization: GRM,UV,JY,TA (lead), KS,AG,SS,SS,FK (supporting)\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: GRM,UV,JY (lead), KS,TA,AG,SS,SS,FK (supporting)\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: All authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e: Institutional Ethics Clearance obtained . Since this is a secondary analysis based on published data consent to participate certification is not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e: Yes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHao YJ, et al. The origins of COVID-19 pandemic: A brief overview. Transbound Emerg Dis. 2022;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/tbed.14732\u003c/span\u003e\u003cspan address=\"10.1111/tbed.14732\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO Coronavirus (COVID-19.) Dashboard | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://covid19.who.int/\u003c/span\u003e\u003cspan address=\"https://covid19.who.int/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchultz MJ, van Oosten PJ, Hol L. Mortality among elderly patients with COVID-19 ARDS\u0026mdash;age still does matter. Pulmonology. 2023;29:353\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFabi\u0026atilde;o J et al. Why do men have worse COVID-19-related outcomes? A systematic review and meta-analysis with sex adjusted for age. Brazilian J Med Biol Res 55, (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e88% of Covid-19 fatalities. 40% of cases in 45\u0026thinsp;+\u0026thinsp;age group: Govt data | Latest News India - Hindustan Times. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hindustantimes.com/india-news/88-of-covid-fatalities-40-of-cases-in-45-age-group-govt-data/story-0RvZ2kT1CXMRonZjl6pGlL.html\u003c/span\u003e\u003cspan address=\"https://www.hindustantimes.com/india-news/88-of-covid-fatalities-40-of-cases-in-45-age-group-govt-data/story-0RvZ2kT1CXMRonZjl6pGlL.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatalog | Open Government Data (OGD). 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Official Kerala COVID-19 Statistics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dashboard.kerala.gov.in/covid/index.php\u003c/span\u003e\u003cspan address=\"https://dashboard.kerala.gov.in/covid/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWelcome to WB HEALTH Portal. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wbhealth.gov.in/pages/corona/bulletin\u003c/span\u003e\u003cspan address=\"https://www.wbhealth.gov.in/pages/corona/bulletin\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ehttps://\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003estopcorona.tn.gov.in/\u003c/span\u003e\u003cspan address=\"http://stopcorona.tn.gov.in/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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(2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIndia -. SAMPLE REGISTRATION SYSTEM (SRS)-ABRIDGED LIFE TABLES 2016\u0026ndash;2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://censusindia.gov.in/nada/index.php/catalog/44377\u003c/span\u003e\u003cspan address=\"https://censusindia.gov.in/nada/index.php/catalog/44377\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcArthur L, et al. Review of Burden, Clinical Definitions, and Management of COVID-19 Cases. 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Uncertainty in the Spread of COVID-19: An Analysis in the Context of India. Indian J Sci Technol. 2021;14:3157\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhajanchi S, Sarkar K, Mondal J, Perc M. \u003cem\u003eDynamics of the COVID-19 pandemic in India\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeyanthi V. COVID-19 outbreak: An overview and India\u0026rsquo;s perspectives on the management of infection. Indian J Sci Technol. 2020;13:3716\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasayat AK, Pati SN, Maharana A. Predicting the COVID-19 positive cases in India with concern to Lockdown by using Mathematical and Machine Learning based Models. \u003cem\u003emedRxiv\u003c/em\u003e 2020.05.16.20104133 (2020) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2020.05.16.20104133\u003c/span\u003e\u003cspan address=\"10.1101/2020.05.16.20104133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRafiq D, Suhail SA, Bazaz MA. Evaluation and prediction of COVID-19 in India: A case study of worst hit states. Chaos Solitons Fractals. 2020;139:110014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhajanchi S, Sarkar K. Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India. \u003cem\u003eChaos\u003c/em\u003e 30, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiwari V, Deyal N, Bisht NS. 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Impact of intervention on the spread of COVID-19 in India: A model based study. arXiv (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJo M-W et al. The Burden of Disease due to COVID-19 in Korea Using Disability-Adjusted Life Years. J Korean Med Sci 35, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNurchis MC, et al. Impact of the burden of COVID-19 in italy: Results of disability-adjusted life years (dalys) and productivity loss. Int J Environ Res Public Health. 2020;17:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohn D, Narassima MS, Menon J, Rajesh JG, Banerjee A. Estimation of the economic burden of COVID-19 using disability-adjusted life years (DALYs) and productivity losses in Kerala, India: a model-based analysis. BMJ Open. 2021;11:e049619.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVasishtha G, Mohanty SK, Mishra US, Dubey M, Sahoo U. Impact of COVID-19 infection on life expectancy, premature mortality, and DALY in Maharashtra, India. BMC Infect Dis 21, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVasishtha G, Mohanty SK, Mishra US, Dubey M, Sahoo U. Impact of COVID-19 infection on life expectancy, premature mortality, and DALY in Maharashtra, India. BMC Infect Dis. 2021;21:2\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmber L, Mueller, Maeve SMN, David A. Sinclair. Why does COVID-19 disproportionately affect older people? Aging (Albany NY). 2020;12:9959\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorgan T, et al. Older people\u0026rsquo;s views on loneliness during COVID-19 lockdowns. Aging Ment Heal. 2024;28:142\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNevejan L, et al. Severity of COVID-19 among Hospitalized Patients: Omicron Remains a Severe Threat for Immunocompromised Hosts. Viruses. 2022;14:2736.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Years of Potential Productive Life Lost (YPPLL), Disability-Adjusted Life Years (DALYs)","lastPublishedDoi":"10.21203/rs.3.rs-6568384/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6568384/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe 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 were 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's per capita income, to compare the economic impact of COVID-19 and productivity loss disparities across states.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings: \u003c/strong\u003eFrom 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 during this period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation: \u003c/strong\u003eThe pandemic has 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.\u003c/p\u003e","manuscriptTitle":"A Statewise Analysis of the Socioeconomic and Health Impacts of the COVID-19 Pandemic in India: Lessons for Future Health System Preparedness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 06:02:51","doi":"10.21203/rs.3.rs-6568384/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b273368-9d68-40a8-b064-a992c236f207","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T06:38:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 06:02:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6568384","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6568384","identity":"rs-6568384","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}