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Significance Understanding the population composition and distribution of a region affected by a major natural disaster is vital for the allocation of resources to communities in need and critical to inform mortality estimates. Currently, the US Census Bureau is the only institution that publishes reliable population estimates for the United States and its territories. Since these are published once per year, it is impossible to use census-based population estimates to assess short-term postdisaster out-of-jurisdiction migration and within-jurisdiction migration. The utilization of social media traces, coupled with mobile phone data, could provide live estimates of postdisaster population changes in disaster-affected areas., 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.

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 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. 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.

Objective To estimate the direct and indirect effects of the covid-19 pandemic on mortality in 2020 in 29 high income countries with reliable and complete age and sex disaggregated mortality data. Design Time series study of high income countries. Setting Austria, Belgium, Czech Republic, Denmark, England and Wales, Estonia, Finland, France, Germany, Greece, Hungary, Israel, Italy, Latvia, Lithuania, the Netherlands, New Zealand, Northern Ireland, Norway, Poland, Portugal, Scotland, Slovakia, Slovenia, South Korea, Spain, Sweden, Switzerland, and United States. Participants Mortality data from the Short-term Mortality Fluctuations data series of the Human Mortality Database for 2016-20, harmonised and disaggregated by age and sex. Interventions Covid-19 pandemic and associated policy measures. Main outcome measures Weekly excess deaths (observed deaths versus expected deaths predicted by model) in 2020, by sex and age (0-14, 15-64, 65-74, 75-84, and ≥85 years), estimated using an over-dispersed Poisson regression model that accounts for temporal trends and seasonal variability in mortality. Results An estimated 979 000 (95% confidence interval 954 000 to 1 001 000) excess deaths occurred in 2020 in the 29 high income countries analysed. All countries had excess deaths in 2020, except New Zealand, Norway, and Denmark. The five countries with the highest absolute number of excess deaths were the US (458 000, 454 000 to 461 000), Italy (89 100, 87 500 to 90 700), England and Wales (85 400, 83 900 to 86 800), Spain (84 100, 82 800 to 85 300), and Poland (60 100, 58 800 to 61 300). New Zealand had lower overall mortality than expected (−2500, −2900 to −2100). In many countries, the estimated number of excess deaths substantially exceeded the number of reported deaths from covid-19. The highest excess death rates (per 100 000) in men were in Lithuania (285, 259 to 311), Poland (191, 184 to 197), Spain (179, 174 to 184), Hungary (174, 161 to 188), and Italy (168, 163 to 173); the highest rates in women were in Lithuania (210, 185 to 234), Spain (180, 175 to 185), Hungary (169, 156 to 182), Slovenia (158, 132 to 184), and Belgium (151, 141 to 162). Little evidence was found of subsequent compensatory reductions following excess mortality. Conclusion Approximately one million excess deaths occurred in 2020 in these 29 high income countries. Age standardised excess death rates were higher in men than women in almost all countries. Excess deaths substantially exceeded reported deaths from covid-19 in many countries, indicating that determining the full impact of the pandemic on mortality requires assessment of excess deaths. Many countries had lower deaths than expected in children

Importance: Monitoring health systems during and after natural disasters, epidemics, or outbreaks 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. Objective: We aim to leverage the improved access to mortality data to develop data-driven approaches that can help monitor health systems and quantify the effects on mortality of natural disasters, epidemics, or outbreaks. Design, Setting, and Participants: To demonstrate the utility of our approach we conducted several retrospective time-series analyses of mortality assessment after natural disasters. We obtained individual-level mortality records from the Department of Health of Puerto Rico from January 1985 to May 2020 to study the effects of hurricanes Hugo, Georges, and Maria in September 1989, September 1998, and September 2017, respectively. Further, we obtained daily mortality counts from Florida, New Jersey, and Louisiana’s Vital Statistic systems from January 2015 to December 2018, January 2007 to December 2015, and January 2003 to December 2006, respectively, to study the effects of hurricanes Irma in 2017, Sandy in 2013, and Katrina in 2005. Finally, we obtained individual-level mortality data from the Cook county, IL, medical examiners office, and state-specific weekly mortality counts from the Center for Disease Control and Prevention to assess the effect of the COVID-19 pandemic on the US health system. Exposures: Hurricanes Maria, Georges, and Hugo in Puerto Rico, Irma in Florida, Sandy in New Jersey, and Katrina in Louisiana, the Chikungunya outbreak in Puerto Rico, and the COVID-19 pandemic in the United States. Main Outcomes: We estimate and provide uncertainty assessments for percent increase from expected mortality, estimated excess deaths, and difference across groups. Results: We found that the death rate increase in Puerto Rico after Maria and Georges was substantially higher than the other hurricanes we examined. Further, we find that excess mortality in the US was already above 100,000 on May 9, 2020, with over 58% of these occurred in New York, New Jersey, Massachusetts, and Pennsylvania, and that effects of this pandemic were worse for elderly black individuals compared to whites of the same age. Conclusions and Relevance: Our approach can be used to monitor or assess health systems by estimating increased mortality rates and excess deaths from mortality records. Key Points Question: Can we estimate excess mortality and provide accurate uncertainty assessments from vital statistics data? Findings: We developed statistical methodology that accounts for key sources of variability and provides accurate estimates and their standard errors for excess mortality. We applied the approach to datasets from several US states including periods affected by hurricanes and epidemics. We found an elevated and persistent increase in mortality after hurricanes in Puerto Rico that was substantially higher than in other US states. We also found that excess mortality in the US during the COVID-19 pandemic reached 100,000 by May 9, 2020. Finally, we found significant differences in the effects of this pandemic across racial groups in the US. Meaning: Data-driven approaches can help monitor health systems and quantify the effects on mortality of natural disasters, epidemics, or outbreaks.

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 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 to 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 data source. Census data predict a population loss of just over 129,000 from July 17 to July 2018, a 4% decrease; air travel data predicts a population loss of 168,295 for the same period of time, a 5% decrease; mobile phone based estimates predicts a loss of 235,375 form 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 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.

ImportanceHurricane Maria made landfall in Puerto Rico on September 20, 2017. As recently as May of this year (2018), the official death count was 64. After a study describing a household survey reported a much higher death count estimate, as well as evidence of population displacement, extensive loss of services, and a prolonged death rate the government released death registry data. These newly released data will permit a better understanding of the effects of this hurricane.ObjectiveProvide a detailed description of the effects on mortality of Hurricane Maria and compare to other hurricanes.DesignWe fit a statistical model to mortality data that accounts for seasonal and non-hurricane related yearly effects. We then estimated the deviation from the expected death rate as a function of time.SettingWe fit this model to 1985-2018 Puerto Rico daily data, which includes the dates of hurricanes Hugo, Georges, and Maria, 2015-2018 Florida daily data, which includes the dates of Hurricane Irma, 2002-2004 Louisiana monthly data, which includes the date of Hurricane Katrina, and 2000-2016 New Jersey monthly data, which includes the date of Hurricane Sandy.ResultsWe find a prolonged increase in death rate after Maria and Katrina, lasting at least 207 and 125 days, resulting in excess deaths estimates of 3,400 (95% CI, 3,100-3,700), and 1,800 (95% CI, 1,600-2100) respectively, showing that Maria had a more long term damaging impact. Surprisingly, we also find that in 1998, Georges had a comparable impact to Katrina’s with a prolonged increase of 106 days resulting in 1,400 (95% CI, 1,200-1,700) excess deaths. For Hurricane Maria, we find sharp increases in a small number of causes of deaths, including diseases of the circulatory, endocrine and respiratory system, as well as bacterial infections and suicides.Conclusion and RelevanceOur analysis suggests that since at least 1998, Puerto Rico’s health system has been in a precarious state. Without a substantial intervention, it appears that if hit with another strong hurricane, Puerto Ricans will suffer the unnecessary death of hundreds of its citizens.Key PointsQuestion: How does the effect of Hurricane Maria on mortality in Puerto Rico compare to the effect of other hurricanes in Puerto Rico and other United States jurisdictions?Findings: We estimate about 3,000 excess deaths after Maria, a higher toll than Katrina. Only other comparable effect was after Georges, also in Puerto Rico. For Georges and Maria, we observe a prolonged death rate increase of more than 10% lasting several months. The causes of death that increased after Maria are consistent with a collapsed health systemMeaning: Puerto Rico’s health system does not appear to be ready to withstand another strong hurricane.