Statistical Methods for Mortality and Mobility Estimation after Natural Disasters

Abstract

Population displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal post-disaster resource allocation and calculation of measures of public health interest. Furthermore, quantifying the impact of natural disasters or epidemics is critical for guiding policy decisions and interventions. When the effects of an event are long-lasting and difficult to detect in the short term, the accumulated effects can be devastating. Mortality is one of the most reliably measured health outcomes, partly due to its unambiguous definition. As a result, excess mortality estimates are an increasingly effective approach for quantifying the effect of an event. In Chapter 1 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. On average, municipalities with a smaller population size lost a bigger proportion of their population. Finally, our analysis measures a general shift of population from rural to urban centers within the island. In Chapter 2 we present a model that accounts for sources of variation associated with mortality and characterizes concerning increases in mortality rates with smooth functions of time that provide statistical power. The model allows for discontinuities in the smooth functions to model sudden increases due to direct effects. Finally, we implement a flexible estimation approach that permits both surveillance of such increases in mortality rates and careful characterization of the effect of a past event. We demonstrate our method’s utility by estimating excess mortality after hurricanes and epidemics in the United States and Puerto Rico, and use Hurricane Maria as a case study to show appealing properties that are unique to our method compared to current approaches. In Chapter 3 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. Our excess mortality estimate for these 90 municipalities, representing approximately 5% of the state’s 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.