Excess Deaths After Hurricane Maria in Puerto Rico

COVID-19-related excess mortality - an overview of the current evidence

Analysis of excess deaths, defined as the difference in the total number of deaths in an emergency compared to the number of deaths expected under normal conditions, allows a more reliable assessment of the impact on health systems caused by the global threat of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2). So far, data for the two years of the pandemic (2020-2021) indicates the occurrence of 14.9 million excess deaths according to WHO (World Health Organization) estimates. The purpose of the analysis conducted was to define the concept and identify the causes of excess mortality during the COVID-19 pandemic. Inconsistent and unreliable death registration systems; overburdened health systems in low- and middle-income countries; reduced access to medical services for patients with health problems other than COVID-19; the introduction of social distancing and lockdown rules, which translated into increased deaths from psychiatric illnesses and addictions; political considerations and media messages that interfered with vaccination acceptance and adherence; and the additional impact of other natural disasters (hurricanes, floods, drought) were identified as the most important reasons for excess deaths occurrence. The correct identification of country-specific factors and the correct response and countermeasures taken appear crucial in terms of limiting the negative impact of the current pandemic, but also of future threats of a similar nature, in order to reduce excess deaths.

Using the Generalized Synthetic Control Method to Estimate the Impact of Extreme Weather Events on Population Health

BACKGROUND

Traditional epidemiologic approaches such as time-series or case-crossover designs are often used to estimate the effects of extreme weather events but can be limited by unmeasured confounding. Quasi-experimental methods are a family of methods that leverage natural experiments to adjust for unmeasured confounding indirectly. The recently developed generalized synthetic control method that exploits the timing of an exposure is well suited to estimate the impact of acute environmental events on health outcomes. To demonstrate how this method can be used to study extreme weather events, we examined the impact of the 20-26 October 2007 Southern California wildfire storm on respiratory hospitalizations.

METHODS

We used generalized synthetic control to compare the average number of ZIP code-level respiratory hospitalizations during the wildfire storm between ZIP codes that were classified as exposed versus unexposed to wildfire smoke. We considered wildfire exposure eligibility for each ZIP code using fire perimeters and satellite-based smoke plume data. We retrieved respiratory hospitalization discharge data from the Office of Statewide Health Planning and Development. R code to implement the generalized synthetic control method is included for reproducibility.

RESULTS

The analysis included 172 exposed and 578 unexposed ZIP codes. We estimated that the average effect of the wildfire storm among the exposed ZIP codes was an 18% (95% confidence interval: 10% to 29%) increase in respiratory hospitalizations.

CONCLUSIONS

We illustrate the use of generalized synthetic control to leverage natural experiments to quantify the health impacts of extreme weather events when traditional approaches are unavailable or limited by assumptions.

Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico

Population displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal postdisaster resource allocation and calculation of measures of public health interest such as mortality estimates. Here, we analyzed data generated by mobile phones and social media to estimate the weekly island-wide population at risk and within-island geographic heterogeneity of migration in Puerto Rico after Hurricane Maria. We compared these two data sources with population estimates derived from air travel records and census data. We observed a loss of population across all data sources throughout the study period; however, the magnitude and dynamics differ by the data source. Census data predict a population loss of just over 129,000 from July 2017 to July 2018, a 4% decrease; air travel data predict a population loss of 168,295 for the same period, a 5% decrease; mobile phone-based estimates predict a loss of 235,375 from July 2017 to May 2018, an 8% decrease; and social media-based estimates predict a loss of 476,779 from August 2017 to August 2018, a 17% decrease. On average, municipalities with a smaller population size lost a bigger proportion of their population. Moreover, we infer that these municipalities experienced greater infrastructure damage as measured by the proportion of unknown locations stemming from these regions. Finally, our analysis measures a general shift of population from rural to urban centers within the island. Passively collected data provide a promising supplement to current at-risk population estimation procedures; however, each data source has its own biases and limitations.