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Official COVID-19 mortality statistics are strongly influenced by local diagnostic capacity, strength of the healthcare and vital registration systems, and death certification criteria and capacity, often resulting in significant undercounting of COVID-19 attributable deaths. Excess mortality, which is defined as the increase in observed death counts compared to a baseline expectation, provides an alternate measure of the mortality shock - both direct and indirect - of the COVID-19 pandemic. Here, we use data from civil death registers from a convenience sample of 90 municipalities across the state of Gujarat, India, to estimate the impact of the COVID-19 pandemic on all-cause mortality. Using a model fit to weekly data from January 2019 to February 2020, we estimated excess mortality over the course of the pandemic from March 2020 to April 2021. We estimated 21,300 [95% CI: 20,700, 22,000] excess deaths across these municipalities in this period, representing a 44% [95% CI: 43%, 45%] increase over the expected baseline. The sharpest increase in deaths in our sample was observed in late April 2021, with an estimated 678% [95% CI: 649%, 707%] increase in mortality from expected counts. The 40 to 65 age group experienced the highest increase in mortality relative to the other age groups. We found substantial, yet similar, increases in mortality for males and females. Our excess mortality estimate for these 90 municipalities, representing approximately 5% of the states population, exceeds the official COVID-19 death count for the entire state of Gujarat, even before the delta wave of the pandemic in India peaked in May 2021. Prior studies have concluded that true pandemic-related mortality in India greatly exceeds official counts. This study, using data directly from the first point of official death registration data recording, provides incontrovertible evidence of the high excess mortality in Gujarat from March 2020 to April 2021.
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BACKGROUNDThe criteria used to allocate scarce COVID-19 vaccines are hotly contested. While some are pushing just to get vaccines into arms as quickly as possible, others advocate prioritization in terms of risk. OBJECTIVEOur aim is to use demographic models to show the enormous potential of vaccine risk-prioritization in saving lives. METHODSWe develop a simple mathematical model that accounts for the age distribution of the population and of COVID-19 mortality. This model considers only the direct live-savings for those who receive the vaccine, and does not account for possible indirect effects of vaccination. We apply this model to the United States, Japan, and Bangladesh. RESULTSIn the United States, we find age-prioritization would reduce deaths during a vaccine campaign by about 93 percent relative to no vaccine and 85 percent relative to age-neutral vaccine distribution. In countries with younger age structures, such as Bangladesh, the benefits of age-prioritization are even greater. CONTRIBUTIONFor policy makers, our findings give additional support to risk-prioritized allocation of COVID-19 vaccines. For demographers, our results show how the age-structures of the population and of disease mortality combine into an expression of risk concentration that shows the benefits of prioritized allocation. This measure can also be used to study the effects of prioritizing other dimensions of risk such as underlying health conditions.
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The current coronavirus disease 2019 (COVID-19) pandemic has impacted dense urban populations particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality patterns, and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. We find that among all age groups, there is a strong association between socioeconomic status and both mortality -measured either by direct COVID-19 attributed deaths or excess deaths- and public health capacity. Specifically, we show that behavioral factors like human mobility, as well as health system factors such as testing volumes, testing delays, and test positivity rates are associated with disease outcomes. These robust patterns suggest multiple possibly interacting pathways that can explain the observed disease burden and mortality differentials: (i) in lower socioeconomic status municipalities, human mobility was not reduced as much as in more affluent municipalities; (ii) testing volumes in these locations were insufficient early in the pandemic and public health interventions were applied too late to be effective; (iii) test positivity and testing delays were much higher in less affluent municipalities, indicating an impaired capacity of the health-care system to contain the spread of the epidemic; and (iv) infection fatality rates appear much higher in the lower end of the socioeconomic spectrum. Together, these findings highlight the exacerbated consequences of health-care inequalities in a large city of the developing world, and provide practical methodological approaches useful for characterizing COVID-19 burden and mortality in other segregated urban centers.
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SARS-CoV-2 is transmitted primarily through close, person-to-person interactions. In the absence of a vaccine, interventions focused on physical distancing have been widely used to reduce community transmission. These physical distancing policies can only control the spread of SARS-CoV-2 if they are able to reduce the amount of close interpersonal contact in a population. To quantify the impact of these policies over the first months of the COVID-19 pandemic in the United States, we conducted three waves of contact surveys between March 22 and June 23, 2020. We find that rates of interpersonal contact have been dramatically reduced at all ages in the US, with an 82% (95% CI:80% - 83%) reduction in the average number of daily contacts observed during the first wave compared to pre-pandemic levels. We find that this decline reduced the reproduction number, R0, to below one in March and early April (0.66, 95% CI:0.35 - 0.88). However, with easing of physical distancing measures, we find increases in interpersonal contact rates over the subsequent two waves, pushing R0 above 1. We also find significant differences in numbers of reported contacts by age, gender, race and ethnicity. Certain demographic groups, including people under 45, males, and Black and Hispanic respondents, have significantly higher contact rates than the rest of the population. Tracking changes in interpersonal contact patterns can provide rapid assessments of the impact of physical distancing policies over the course of the pandemic and help identify at-risk populations.
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BackgroundThe United States is now the country reporting the highest number of 2019 coronavirus disease (COVID-19) cases and deaths. However, little is known about the epidemiology and burden of severe COVID-19 to inform planning within healthcare systems and modeling of intervention impact. MethodsWe assessed incidence, duration of hospitalization, and clinical outcomes of acute COVID-19 inpatient admissions in a prospectively-followed cohort of 9,596,321 individuals enrolled in comprehensive, integrated healthcare delivery plans from Kaiser Permanente in California and Washington state. We also estimated the effective reproductive number (RE) describing transmission in the study populations. ResultsData covered 1277 hospitalized patients with laboratory- or clinically-confirmed COVID-19 diagnosis by April 9, 2020. Cumulative incidence of first COVID-19 acute inpatient admission was 10.6-12.4 per 100,000 cohort members across the study regions. Mean censoring-adjusted duration of hospitalization was 10.7 days (2.5-97.5%iles: 0.8-30.1) among survivors and 13.7 days (2.5-97.5%iles: 1.7-34.6) among non-survivors. Among all hospitalized confirmed cases, censoring-adjusted probabilities of ICU admission and mortality were 41.9% (95% confidence interval: 34.1-51.4%) and 17.8% (14.3-22.2%), respectively, and higher among men than women. We estimated RE was 1.43 (1.17-1.73), 2.09 (1.63-2.69), and 1.47 (0.07-2.59) in Northern California, Southern California, and Washington, respectively, for infections acquired March 1, 2020. RE declined to 0.98 (0.76-1.27), 0.89 (0.74-1.06), and 0.92 (0.05-1.55) respectively, for infections acquired March 20, 2020. ConclusionsWe identify high probability of ICU admission, long durations of stay, and considerable mortality risk among hospitalized COVID-19 cases in the western United States. Reductions in RE have occurred in conjunction with implementation of non-pharmaceutical interventions.