Estimation of all-cause excess mortality by age-specific mortality patterns for countries with incomplete vital statistics: a population-based study of the case of Peru during the first wave of the COVID-19 pandemic

Abstract

Summary Background All-cause excess mortality is a comprehensive measure of the combined direct and indirect effects of COVID-19 on mortality. Estimates are usually derived from Civil Registration and Vital Statistics (CRVS) systems, but these do not include non-registered deaths, which may be affected by changes in vital registration coverage over time. Methods Our analytical framework and empirical strategy account for registered mortality and under-registration. This provides a better estimate of the actual mortality impact of the first wave of the COVID-19 pandemic in Peru. We use population and crude mortality rate projections from Peru's National Institute of Statistics and Information (INEI, in Spanish), individual-level registered COVID-19 deaths from the Ministry of Health (MoH), and individual-level registered deaths by region and age since 2017 from the National Electronic Deaths Register (SINADEF, in Spanish). We develop a novel framework combining different estimates and using quasi-Poisson models to estimate total excess mortality across regions and age groups. Also, we use logistic mixed-effects models to estimate the coverage of the new SINADEF system. Findings We estimate that registered mortality underestimates national mortality by 37•1% (95% CI 23% - 48•5%) across 26 regions and nine age groups. We estimate total all-cause excess mortality during the period of analysis at 173,099 (95% CI 153,669 - 187,488) of which 108,943 (95% CI 96,507 - 118,261) were captured by the vital registration system. Deaths at age 60 and over accounted for 74•1% (95% CI 73•9% - 74•7%) of total excess deaths, and there were fewer deaths than expected in younger age groups. Lima region, on the Pacific coast and including the national capital, accounts for the highest share of excess deaths, 87,781 (95% CI 82,294 - 92,504), while in the opposite side regions of Apurimac and Huancavelica account for less than 300 excess deaths. Interpretation Estimating excess mortality in low- and middle-income countries (LMICs) such as Peru must take under-registration of mortality into account. Combining demographic trends with data from administrative registries reduces uncertainty and measurement errors. In countries like Peru, this is likely to produce significantly higher estimates of excess mortality than studies that do not take these effects into account. Funding None.

Analysis of Excess All-Cause Mortality and COVID-19 Mortality in Peru: Observational Study

“…Worldwide, Peru is the country with the highest number of COVID-19 deaths per 100,000 population [ 9 ]. This has caused an excess of all-cause mortality [ 4 ] and compared with other countries, in 2020 Peru experienced the largest excess mortality among 103 countries studied [ 1 ]. This excess all-cause mortality recorded in 2020 is clearly related to the health crisis caused by the COVID-19 pandemic.…”

“…During the COVID-19 pandemic, an all-cause mortality excess has been recorded in several countries, including Peru [ 1 , 2 , 3 , 4 ]. This all-cause mortality excess varied substantially across countries [ 1 , 2 , 3 ] because of measures taken to handle the COVID-19 pandemic, demographic and socio-economic characteristics, and capacity of health care systems [ 5 , 6 , 7 , 8 ].…”

Excess all-cause deaths stratified by sex and age in Peru: a time series analysis during the COVID-19 pandemic

“…Third, comparison with other studies may be limited, where excess mortality uses complex models that apply periodic splines or Fourier harmonics to smooth and homogenise possible fluctuations in expected deaths and standardise their measurements. 39 We used the proportion of mortality count excess and a standard measure (P-score), which is simple and easy to replicate in any context and similar to that carried out in other studies. 8 29 Moreover, while other studies estimated the expected deaths as the average number of deaths between 2015 and 2019, we only used the 3 years preceding the pandemic (2017-2019).…”

Estimating national excess mortality from subnational data: application to Argentina

“…Prominent examples include the United States of America, where both reported COVID-19 and excess deaths during the first wave of the pandemic (March–April 2020) were limited to the eastern states such as New York and New Jersey. In Peru, excess deaths in the region of Lima started well before the region of Tacna ( 10 ). In Ecuador, spread began at the regional coastlines before it reached inland ( 7 ).…”