Total Excess Mortality Surveillance for Real-Time Decision-Making in Disasters and Crises.

Crises such as Hurricane Maria and the coronavirus disease 2019 (COVID-19) pandemic have revealed that untimely reporting of the death toll results in inadequate interventions, impacts communication, and fuels distrust on response agencies. Delays in establishing mortality are due to the contested definition of deaths attributable to a disaster and lack of rapid collection of vital statistics data from inadequate health system infrastructure. Readily available death counts, combined with geographic, demographic, and socioeconomic data, can serve as a baseline to build a continuous mortality surveillance system. In an emergency setting, real-time Total, All-cause, Excess Mortality (TEM) can be a critical tool, granting authorities timely information ensuring a targeted response and reduce disaster impact. TEM measurement can identify spikes in mortality, including geographic disparities and disproportionate deaths in vulnerable populations. This study recommends that measuring total, all-cause, excess mortality as a first line of response should become the global standard for measuring disaster impact.

Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: a time-series analysis.

BACKGROUND: Hurricane Maria struck Puerto Rico on Sept 20, 2017, devastating the island. Controversy surrounded the official death toll, fuelled by estimates of excess mortality from academics and investigative journalists. We analysed all-cause excess mortality following the storm. METHODS: We did a time-series analysis in Puerto Rico from September, 2017, to February, 2018. Mortality data were from the Puerto Rico Vital Statistics System. We developed two counterfactual scenarios to establish the population at risk. In the first scenario, the island's population was assumed to track the most recent census estimates. In the second scenario, we accounted for the large-scale population displacement. Expected mortality was projected for each scenario through over-dispersed log-linear regression from July, 2010, to August, 2017, taking into account changing distributions of age, sex, and municipal socioeconomic development, as well as both long-term and seasonal trends in mortality. Excess mortality was calculated as the difference between observed and expected deaths. FINDINGS: Between September, 2017, and February, 2018, we estimated that 1191 excess deaths (95% CI 836-1544) occurred under the census scenario. Under the preferred displacement scenario, we estimated that 2975 excess deaths (95% CI 2658-3290) occurred during the same observation period. The ratio of observed to expected mortality was highest for individuals living in municipalities with the lowest socioeconomic development (1·43, 95% CI 1·39-1·46), and for men aged 65 years or older (1·33, 95% CI 1·30-1·37). Excess risk persisted in these groups throughout the observation period. INTERPRETATION: Analysis of all-cause mortality with vital registration data allows for unbiased estimation of the impact of disasters associated with natural hazards and is useful for public health surveillance. It does not depend on certified cause of death, the basis for the official death toll in Puerto Rico. Although all sectors of Puerto Rican society were affected, recovery varied by municipal socioeconomic development and age groups. This finding calls for equitable disaster preparedness and response to protect vulnerable populations in disasters. FUNDING: Forensic Science Bureau, Department of Public Safety, and Milken Institute School of Public Health of The George Washington University (Washington, DC, USA).