Although most cases of coronavirus disease 2019 (COVID-19) have occurred in low-resource countries, little is known about the epidemiology of the disease in such contexts. Data from the Indian states of Tamil Nadu and Andhra Pradesh provide a detailed view into severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission pathways and mortality in a high-incidence setting. Reported cases and deaths have been concentrated in younger cohorts than would be expected from observations in higher-income countries, even after accounting for demographic differences across settings. Among 575,071 individuals exposed to 84,965 confirmed cases, infection probabilities ranged from 4.7 to 10.7% for low-risk and high-risk contact types, respectively. Same-age contacts were associated with the greatest infection risk. Case fatality ratios spanned 0.05% at ages of 5 to 17 years to 16.6% at ages of 85 years or more. Primary data from low-resource countries are urgently needed to guide control measures.

Background SARS-CoV-2 has spread substantially within India over multiple waves of the ongoing COVID-19 pandemic. However, the risk factors and disease burden associated with COVID-19 in India remain poorly understood. We aimed to assess predictors of infection and mortality within an active surveillance study, and to probe the completeness of case and mortality surveillance. Methods In this prospective, active surveillance study, we used data collected under expanded programmatic surveillance testing for SARS-CoV-2 in the district of Madurai, Tamil Nadu, India (population of 3 266 000 individuals). Prospective testing via RT-PCR was done in individuals with fever or acute respiratory symptoms as well as returning travellers, frontline workers, contacts of laboratory-confirmed COVID-19 cases, residents of containment zones, patients undergoing medical procedures, and other risk groups. Standardised data collection on symptoms and chronic comorbid conditions was done as part of routine intake. Additionally, seroprevalence of anti-SARS-CoV-2 immunoglobulin G was assessed via a cross-sectional survey recruiting adults across 38 clusters within Madurai District from Oct 19, 2020, to Nov 5, 2020. We estimated adjusted odds ratios (aORs) for positive RT-PCR results comparing individuals by age, sex, comorbid conditions, and aspects of clinical presentation. We estimated case-fatality ratios (CFRs) over the 30-day period following RT-PCR testing stratified by the same variables, and adjusted hazard ratios (aHRs) for death associated with age, sex, and comorbidity. We estimated infection-fatality ratios (IFRs) on the basis of age-specific seroprevalence. Results Between May 20, 2020, and Oct 31, 2020, 13·5 diagnostic tests were done per 100 inhabitants within Madurai, as compared to 7·9 tests per 100 inhabitants throughout India. From a total of 440 253 RT-PCR tests, 15 781 (3·6%) SARS-CoV-2 infections were identified, with 8720 (5·4%) of 160 273 being positive among individuals with symptoms, and 7061 (2·5%) of 279 980 being positive among individuals without symptoms, at the time of presentation. Estimated aORs for symptomatic RT-PCR-confirmed infection increased continuously by a factor of 4·3 from ages 0–4 years to 80 years or older. By contrast, risk of asymptomatic RT-PCR-confirmed infection did not differ across ages 0–44 years, and thereafter increased by a factor of 1·6 between ages 45–49 years and 80 years or older. Seroprevalence was 40·1% (95% CI 35·8–44·6) at age 15 years or older by the end of the study period, indicating that RT-PCR clinical testing and surveillance testing identified only 1·4% (1·3–1·6%) of all infections in this age group. Among RT-PCR-confirmed cases, older age, male sex, and history of cancer, diabetes, other endocrine disorders, hypertension, other chronic circulatory disorders, respiratory disorders, and chronic kidney disease were each associated with elevated risk of mortality. The CFR among RT-PCR-confirm...

Background India has been severely affected by the ongoing COVID-19 pandemic. However, due to shortcomings in disease surveillance, the burden of mortality associated with COVID-19 remains poorly understood. We aimed to assess changes in mortality during the pandemic in Chennai, Tamil Nadu, using data on all-cause mortality within the district. Methods For this observational study, we analysed comprehensive death registrations in Chennai, from Jan 1, 2016, to June 30, 2021. We estimated expected mortality without the effects of the COVID-19 pandemic by fitting models to observed mortality time series during the pre-pandemic period, with stratification by age and sex. Additionally, we considered three periods of interest: the first 4 weeks of India's first lockdown (March 24 to April 20, 2020), the 4-month period including the first wave of the pandemic in Chennai (May 1 to Aug 31, 2020), and the 4-month period including the second wave of the pandemic in Chennai (March 1 to June 30, 2021). We computed the difference between observed and expected mortality from March 1, 2020, to June 30, 2021, and compared pandemic-associated mortality across socioeconomically distinct communities (measured with use of 2011 census of India data) with regression analyses. Findings Between March 1, 2020, and June 30, 2021, 87 870 deaths were registered in areas of Chennai district represented by the 2011 census, exceeding expected deaths by 25 990 (95% uncertainty interval 25 640–26 360) or 5·18 (5·11–5·25) excess deaths per 1000 people. Stratified by age, excess deaths numbered 21·02 (20·54–21·49) excess deaths per 1000 people for individuals aged 60–69 years, 39·74 (38·73–40·69) for those aged 70–79 years, and 96·90 (93·35–100·16) for those aged 80 years or older. Neighbourhoods with lower socioeconomic status had 0·7% to 2·8% increases in pandemic-associated mortality per 1 SD increase in each measure of community disadvantage, due largely to a disproportionate increase in mortality within these neighbourhoods during the second wave. Conversely, differences in excess mortality across communities were not clearly associated with socioeconomic status measures during the first wave. For each increase by 1 SD in measures of community disadvantage, neighbourhoods had 3·6% to 8·6% lower pandemic-associated mortality during the first 4 weeks of India's country-wide lockdown, before widespread SARS-CoV-2 circulation was underway in Chennai. The greatest reductions in mortality during this early lockdown period were observed among men aged 20–29 years, with 58% (54–62) fewer deaths than expected from pre-pandemic trends. Interpretation Mortality in Chennai increased substantially but heterogeneously during the COVID-19 pandemic, with the greatest burden concentrated in disadvantaged communities. Reported COVID-19 deaths greatly underestimated pandemic-associated mortality. Funding National Institute of Gener...

Globally, excess deaths during 2020–21 outnumbered documented COVID-19 deaths by 9.5 million, primarily driven by deaths in low- and middle-income countries (LMICs) with limited vital surveillance. Here we unravel the contributions of probable COVID-19 deaths from other changes in mortality related to pandemic control measures using medically-certified death registrations from Madurai, India—an urban center with well-functioning vital surveillance. Between March, 2020 and July, 2021, all-cause deaths in Madurai exceeded expected levels by 30% (95% confidence interval: 27–33%). Although driven by deaths attributed to cardiovascular or cerebrovascular conditions, diabetes, senility, and other uncategorized causes, increases in these attributions were restricted to medically-unsupervised deaths, and aligned with surges in confirmed or attributed COVID-19 mortality, likely reflecting mortality among unconfirmed COVID-19 cases. Implementation of lockdown measures was associated with a 7% (0–13%) reduction in all-cause mortality, driven by reductions in deaths attributed to injuries, infectious diseases and maternal conditions, and cirrhosis and other liver conditions, respectively, but offset by a doubling in cancer deaths. Our findings help to account for gaps between documented COVID-19 mortality and excess all-cause mortality during the pandemic in an LMIC setting.