Modeling excess deaths after a natural disaster with application to Hurricane Maria

Rivera, Roberto; Rolke, Wolfgang

doi:10.1002/sim.8314

Cited by 27 publications

(38 citation statements)

References 32 publications

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“…Therefore, based on the model results, we first predicted the SMR for each day of 2020 together with its 95% confidence interval. Thereafter, using the method reported by RIVERA et al [20], we estimated the cumulative number of excess deaths, as compared to 2019, and their correlated 95% credible sets (CS), which similarly to confidence interval synthesise the precision of the estimates but based on Bayesian methods. Finally, we expressed the excess in term of an n-fold increase in the cumulative number of deaths, using 2019 cumulative number of deaths as the reference.…”

Section: Discussionmentioning

confidence: 99%

Magnitude and time-course of excess mortality during COVID-19 outbreak: population-based empirical evidence from highly impacted provinces in northern Italy

et al. 2020

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BackgroundThe real impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on overall mortality remains uncertain as surveillance reports have attributed a limited number of deaths to novel coronavirus disease 2019 (COVID-19) during the outbreak. The aim of this study was to assess the excess mortality during the COVID-19 outbreak in highly impacted areas of northern Italy.MethodsWe analysed data on deaths that occurred in the first 4 months of 2020 provided by the health protection agencies (HPAs) of Bergamo and Brescia (Lombardy), building a time-series of daily number of deaths and predicting the daily standardised mortality ratio (SMR) and cumulative number of excess deaths through a Poisson generalised additive model of the observed counts in 2020, using 2019 data as a reference.ResultsWe estimated that there were 5740 (95% credible set (CS) 5552–5936) excess deaths in the HPA of Bergamo and 3703 (95% CS 3535–3877) in Brescia, corresponding to a 2.55-fold (95% CS 2.50–2.61) and 1.93 (95% CS 1.89–1.98) increase in the number of deaths. The excess death wave started a few days later in Brescia, but the daily estimated SMR peaked at the end of March in both HPAs, roughly 2 weeks after the introduction of lockdown measures, with significantly higher estimates in Bergamo (9.4, 95% CI 9.1–9.7).ConclusionExcess mortality was significantly higher than that officially attributed to COVID-19, disclosing its hidden burden likely due to indirect effects on the health system. Time-series analyses highlighted the impact of lockdown restrictions, with a lower excess mortality in the HPA where there was a smaller delay between the epidemic outbreak and their enforcement.

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Section: Discussionmentioning

confidence: 99%

Magnitude and time-course of excess mortality during COVID-19 outbreak: population-based empirical evidence from highly impacted provinces in northern Italy

et al. 2020

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“…The starting point was the date after the most recent inflection point in all-cause mortality, suggesting the onset of the COVID-19 pandemic, chosen to balance concerns of deaths prior to the official first cases and the sensitivity of the model to detect small excess mortality in the limited available data, exacerbated by the provisional counts being lower than actual deaths: February 29 for Washington State, March 28 for Florida, Indiana, and Massachusetts, and March 21 for the remaining 9 states and for the United States as a whole. This semiparametric model can incorporate population displacement [2], but we are not aware of significant displacement during the pandemic. Specifically, New York City was uniquely affected during this first wave of infections, with a public perception of widespread community infections acquired in a crowded metropolitan area that relies on public transportation.…”

Section: Semiparametric Modelmentioning

confidence: 99%

“…Let ̂ = E(D t |t,p t = 1,N t ,week t ,year t ), ̂ = E(D t |t,p t = 0,N t ,week t ,year t ), and ̂, ̂ estimate β o ,β 1 respectively. Then [2], (2) When p t = 0, then ̂ − ̂ = 0. Equation 2is the maximum likelihood estimator for expected excess deaths at t.…”

Section: Semiparametric Excess Mortality Modelmentioning

confidence: 99%

“…Hurricane Maria's mortality was officially only 64 deaths, but a 95% confidence interval for estimated excess mortality was between 1,069 and 1,568. [2] Deaths directly due to the COVID-19 pandemic may be underestimated due to under-diagnosis [3], insufficient postmortem Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests, not seeking healthcare [4], and ascertainment bias. Deaths indirectly due to an emergency are also common, due to an overloaded health system [5] or lack of healthcare access for routine care: during the 2014 West Africa Ebola epidemic, lack of routine care for malaria, HIV/AIDS, and tuberculosis led to an estimated 10,600 additional deaths in the area.…”

Section: Introductionmentioning

confidence: 99%

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Excess mortality in the United States during the first three months of the COVID-19 pandemic

2020

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Deaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for emergency response. This study estimates excess all-cause, pneumonia and influenza mortality during the coronavirus disease 2019 (COVID-19) pandemic using the 11 September 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance System (MSS) from 27 September 2015 to 9 May 2020, using semiparametric and conventional time-series models in 13 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Connecticut, Florida, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, Pennsylvania and Washington. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) 100 013–127 501 vs. 78 834 COVID-19 deaths) and 9 states: California (excess mortality 95% CI 3338–6344) vs. 2849 COVID-19 deaths); Connecticut (excess mortality 95% CI 3095–3952) vs. 2932 COVID-19 deaths); Illinois (95% CI 4646–6111) vs. 3525 COVID-19 deaths); Louisiana (excess mortality 95% CI 2341–3183 vs. 2267 COVID-19 deaths); Massachusetts (95% CI 5562–7201 vs. 5050 COVID-19 deaths); New Jersey (95% CI 13 170–16 058 vs. 10 465 COVID-19 deaths); New York (95% CI 32 538–39 960 vs. 26 584 COVID-19 deaths); and Pennsylvania (95% CI 5125–6560 vs. 3793 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise. Significant excess pneumonia deaths were also found for all locations and we estimated hundreds of excess influenza deaths in New York. We find that official COVID-19 mortality substantially understates actual mortality, excess deaths cannot be explained entirely by official COVID-19 death counts. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.

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“…Population displacement off the island and within Puerto Rico was widespread, although this was difficult to monitor directly. Direct and indirect mortality caused by the storm also increased in the months after the hurricane (8,(13)(14)(15)(16)(17), but estimating mortality was made more complicated by the migration of populations because the population at risk in different parts of the island was shifting over time. Due to Hurricane Maria, the US Census Bureau ceased operations of the Puerto Rico Community Survey (PRCS; a monthly survey of 36,000 housing units across the island) from October to December 2017.…”

mentioning

confidence: 99%

Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico

Acosta

Kishore

Irizarry

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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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.

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Modeling excess deaths after a natural disaster with application to Hurricane Maria

Cited by 27 publications

References 32 publications

Magnitude and time-course of excess mortality during COVID-19 outbreak: population-based empirical evidence from highly impacted provinces in northern Italy

Magnitude and time-course of excess mortality during COVID-19 outbreak: population-based empirical evidence from highly impacted provinces in northern Italy

Excess mortality in the United States during the first three months of the COVID-19 pandemic

Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico

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