scholarly journals Post-Hurricane Vital Statistics Expose Fragility of Puerto Rico’s Health System

2018 ◽  
Author(s):  
Rolando J. Acosta ◽  
Rafael A. Irizarry
Keyword(s):  
Puerto Rico ◽  
Health System ◽  
Death Rate ◽  
Hurricane Sandy ◽  
Mortality Data ◽  
Monthly Data ◽  
Daily Data ◽  
Unnecessary Death ◽  
Death Count ◽  
Excess Deaths

AbstractImportanceHurricane 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.

scholarly journals In Puerto Rico, excess deaths following Hurricane Georges persisted for three months

2018 ◽  
Author(s):  
Alexis R Santos
Keyword(s):  
Puerto Rico ◽  
Upper Limit ◽  
The World ◽  
Basic Services ◽  
Drinkable Water ◽  
Population Shift ◽  
Climate Disasters ◽  
Death Count ◽  
Climate Disaster ◽  
Excess Deaths

The interruption in basic services such as electricity, drinkable water, and exposure to atypical circumstances following climate disasters increases mortality risk within the settings affected by these events. Recently, some members of academia have argued that no methodology exists to study excess deaths attributable to climate disasters. This study uses death records for Puerto Rico between 1990 and 1998 to assess excess deaths following Hurricane Georges by comparing death counts for 1998 with patterns of variation from the previous eight years. Because no population shift occurred in that decade, other than expected ones based on historical information, the average number of deaths is indicative of expected deaths and the confidence intervals are the ranges of accepted variation. If a count following a climate disaster exceeds the upper limit of the confidence interval these deaths could be considered above the historical ranges of variation and this excess could be associated with the climate disaster of interest. Death counts for September-November 1998 indicate that 819 deaths were in excess of historical ranges of variation. When the year in which Hurricane Hortense is excluded from the construction of the ranges of variation, the excess is 945 deaths. A total of 811 or 937 are missing in comparison to the official death count for this Hurricane. Considering that death counts data structures are comparable across the countries of the world, this method can be used to analyze the effect of other climate disasters.

scholarly journals Evaluation of death count estimates for Puerto Rico following Hurricane Maria

2018 ◽  
Author(s):  
Alexis R Santos ◽  
Jeffrey T. Howard
Keyword(s):  
Puerto Rico ◽  
Incomplete Information ◽  
Confidence Intervals ◽  
Vital Statistics ◽  
Initial Estimate ◽  
Excess Death ◽  
Estimation Process ◽  
Climate Disasters ◽  
Death Count ◽  
Excess Deaths

This descriptive finding examines estimates of death counts following Hurricane Maria in Puerto Rico for September and October 2017. We evaluate the monthly death count estimates and estimates of excess deaths in Puerto Rico based on historical patterns of variability by month for the 2010-2016 and published official death counts for 2017. Official death records from the Puerto Rico Vital Statistics Systems by month and year (2010-2016) were used to produce means and 95% confidence intervals (95% C.I.) for each month. Death count and excess death estimates for September and November 2017 are employed to: (1) illustrate the estimation process and (2) assess the accuracy of these estimates when compared to official death counts for the same period. Estimates produced with incomplete information were 2,987 (95% C.I. 2,900-3,074) and 3,043 (95% C.I. 2,995-3,091) for September and October 2017, respectively. Corresponding official death counts for the same months for 2017 were 2,928 and 3,040. Using estimated death counts, 1,085 excess deaths (95% C.I. 950-1,220) were estimated in November 2017. Using official counts yielded 1,023 excess deaths (95% C.I. 956-1,090). Despite initially overestimating the number of deaths in September and October by 1.04%, subsequent estimate of excess deaths using official death counts was within the 95% C.I. of the initial estimate. Our findings demonstrate the timely production of death count estimates following climate disasters using historical death records and a thorough study of previous experiences.

scholarly journals Robustness and bias of European excess death estimates in 2020 under varying model specifications

2021 ◽  
Author(s):  
Jonas Schöley
Keyword(s):  
Death Rate ◽  
Cross Validation ◽  
European Countries ◽  
Rate Model ◽  
Causal Interpretation ◽  
Excess Death ◽  
Country Level ◽  
Death Count ◽  
Excess Deaths

Various procedures are in use to calculate excess deaths during the ongoing COVID-19 pandemic. Using weekly death counts from 20 European countries, we evaluate the robustness of excess death estimates to the choice of model for expected deaths and perform a cross-validation analysis to assess the error and bias in each model's predicted death counts. We find that the different models produce very similar patterns of weekly excess deaths but disagree substantially on the level of excess. While the exact country ranking along percent excess death in 2020 is sensitive to the choice of model the top and bottom ranks are robustly identified. On the country level, the 5-year average death rate model tends to produce the lowest excess death estimates, whereas high excess deaths are produced by the popular 5-year average death count and Euromomo-style Serfling models. Cross-validation revealed these estimates to be biased under a causal interpretation of "expected deaths had COVID-19 not happened."

scholarly journals Using excess deaths and testing statistics to determine COVID-19 mortalities

2021 ◽  
Author(s):  
Lucas Böttcher ◽  
Maria R. D’Orsogna ◽  
Tom Chou
Keyword(s):  
New York ◽  
New York City ◽  
Disease Prevalence ◽  
Statistical Testing ◽  
Biased Sampling ◽  
Mortality Data ◽  
Sir Epidemic Model ◽  
Excess Death ◽  
Sir Epidemic ◽  
Excess Deaths

AbstractFactors such as varied definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US from January 2020 until February 2021 is 9$$\%$$ % higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find statistically insignificant or even negative excess deaths for at least most of 2020 in places such as Germany, Denmark, and Norway.

Excess Mortality During the COVID-19 Pandemic in Philadelphia

2021 ◽  
pp. e1-e6
Author(s):  
Megan Todd ◽  
Meagan Pharis ◽  
Sam P. Gulino ◽  
Jessica M. Robbins ◽  
Cheryl Bettigole
Keyword(s):  
Public Health ◽  
Excess Mortality ◽  
Poisson Model ◽  
People Of Color ◽  
Mortality Data ◽  
Registration System ◽  
Vital Registration System ◽  
All Cause Mortality ◽  
Race Ethnicity ◽  
Excess Deaths

Objectives. To estimate excess all-cause mortality in Philadelphia, Pennsylvania, during the COVID-19 pandemic and understand the distribution of excess mortality in the population. Methods. With a Poisson model trained on recent historical data from the Pennsylvania vital registration system, we estimated expected weekly mortality in 2020. We compared these estimates with observed mortality to estimate excess mortality. We further examined the distribution of excess mortality by age, sex, and race/ethnicity. Results. There were an estimated 3550 excess deaths between March 22, 2020, and January 2, 2021, a 32% increase above expectations. Only 77% of excess deaths (n=2725) were attributed to COVID-19 on the death certificate. Excess mortality was disproportionately high among older adults and people of color. Sex differences varied by race/ethnicity. Conclusions. Excess deaths during the pandemic were not fully explained by COVID-19 mortality; official counts significantly undercount the true death toll. Far from being a great equalizer, the COVID-19 pandemic has exacerbated preexisting disparities in mortality by race/ethnicity. Public Health Implications. Mortality data must be disaggregated by age, sex, and race/ethnicity to accurately understand disparities among groups. (Am J Public Health. Published online ahead of print June 10, 2021: e1–e6. https://doi.org/10.2105/AJPH.2021.306285 )

scholarly journals Excess mortality in Guadeloupe and Martinique, islands of the French West Indies, during the chikungunya epidemic of 2014

2018 ◽  
Vol 146 (16) ◽  
pp. 2059-2065 ◽  
Author(s):  
A. R. R. Freitas ◽  
P. M. Alarcón-Elbal ◽  
M. R. Donalisio
Keyword(s):  
Strong Correlation ◽  
Excess Mortality ◽  
Pearson Correlation ◽  
Age Groups ◽  
Epidemiological Data ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Mortality And Morbidity ◽  
The Impact ◽  
Excess Deaths

AbstractIn some chikungunya epidemics, deaths are not completely captured by traditional surveillance systems, which record case and death reports. We evaluated excess deaths associated with the 2014 chikungunya virus (CHIKV) epidemic in Guadeloupe and Martinique, Antilles. Population (784 097 inhabitants) and mortality data, estimated by sex and age, were accessed from the Institut National de la Statistique et des Études Économiques in France. Epidemiological data, cases, hospitalisations and deaths on CHIKV were obtained from the official epidemiological reports of the Cellule de Institut de Veille Sanitaire in France. Excess deaths were calculated as the difference between the expected and observed deaths for all age groups for each month in 2014 and 2015, considering the upper limit of 99% confidence interval. The Pearson correlation coefficient showed a strong correlation between monthly excess deaths and reported cases of chikungunya (R= 0.81,p< 0.005) and with a 1-month lag (R= 0.87,p< 0.001); and a strong correlation was also observed between monthly rates of hospitalisation for CHIKV and excess deaths with a delay of 1 month (R= 0.87,p< 0.0005). The peak of the epidemic occurred in the month with the highest mortality, returning to normal soon after the end of the CHIKV epidemic. There were excess deaths in almost all age groups, and excess mortality rate was higher among the elderly but was similar between male and female individuals. The overall mortality estimated in the current study (639 deaths) was about four times greater than that obtained through death declarations (160 deaths). Although the aetiological diagnosis of all deaths associated with CHIKV infection is not always possible, already well-known statistical tools can contribute to the evaluation of the impact of CHIKV on mortality and morbidity in the different age groups.

scholarly journals Use of Death Counts From Vital Statistics to Calculate Excess Deaths in Puerto Rico Following Hurricane Maria

JAMA ◽  
2018 ◽  
Vol 320 (14) ◽  
pp. 1491 ◽  
Author(s):  
Alexis R. Santos-Lozada ◽  
Jeffrey T. Howard
Keyword(s):  
Puerto Rico ◽  
Vital Statistics ◽  
Excess Deaths

Projected All-Cause Deaths Attributable to COVID-19–Related Unemployment in the United States

2021 ◽  
Vol 111 (4) ◽  
pp. 696-699
Author(s):  
Ellicott C. Matthay ◽  
Kate A. Duchowny ◽  
Alicia R. Riley ◽  
Sandro Galea
Keyword(s):  
United States ◽  
The United States ◽  
Mortality Data ◽  
Economic Policies ◽  
Gender And Education ◽  
Hazard Ratios ◽  
Low Education ◽  
Unemployment Data ◽  
Bureau Of Labor Statistics ◽  
Excess Deaths

Objectives. To project the range of excess deaths potentially associated with COVID-19–related unemployment in the United States and quantify inequities in these estimates by age, race/ethnicity, gender, and education. Methods. We used previously published meta-analyzed hazard ratios (HRs) for the unemployment–mortality association, unemployment data from the Bureau of Labor Statistics, and mortality data from the National Center for Health Statistics to estimate 1-year age-standardized deaths attributable to COVID-19–related unemployment for US workers aged 25 to 64 years. To accommodate uncertainty, we tested ranges of unemployment and HR scenarios. Results. Our best estimate is that there will be 30 231 excess deaths attributable to COVID-19–related unemployment between April 2020 and March 2021. Across scenarios, attributable deaths ranged from 8315 to 201 968. Attributable deaths were disproportionately high among Blacks, men, and those with low education. Conclusions. Deaths attributable to COVID-19–related unemployment will add to those directly associated with the virus and will disproportionately burden groups already experiencing incommensurate COVID-19 mortality. Public Health Implications. Supportive economic policies and interventions addressing long-standing harmful social structures are essential to mitigate the unequal health harms of COVID-19.

scholarly journals Interpreting, analysing and modelling COVID-19 mortality data

2020 ◽  
Vol 101 (3) ◽  
pp. 1751-1776
Author(s):  
Didier Sornette ◽  
Euan Mearns ◽  
Michael Schatz ◽  
Ke Wu ◽  
Didier Darcet
Keyword(s):  
Elderly People ◽  
Time Window ◽  
Governance Structure ◽  
Logistic Equation ◽  
Mortality Data ◽  
Asian Countries ◽  
Western Countries ◽  
The Difference ◽  
The Relationship ◽  
Death Count

Abstract We present results on the mortality statistics of the COVID-19 epidemic in a number of countries. Our data analysis suggests classifying countries in five groups, (1) Western countries, (2) East Block, (3) developed Southeast Asian countries, (4) Northern Hemisphere developing countries and (5) Southern Hemisphere countries. Comparing the number of deaths per million inhabitants, a pattern emerges in which the Western countries exhibit the largest mortality rate. Furthermore, comparing the running cumulative death tolls as the same level of outbreak progress in different countries reveals several subgroups within the Western countries and further emphasises the difference between the five groups. Analysing the relationship between deaths per million and life expectancy in different countries, taken as a proxy of the preponderance of elderly people in the population, a main reason behind the relatively more severe COVID-19 epidemic in the Western countries is found to be their larger population of elderly people, with exceptions such as Norway and Japan, for which other factors seem to dominate. Our comparison between countries at the same level of outbreak progress allows us to identify and quantify a measure of efficiency of the level of stringency of confinement measures. We find that increasing the stringency from 20 to 60 decreases the death count by about 50 lives per million in a time window of 20  days. Finally, we perform logistic equation analyses of deaths as a means of tracking the dynamics of outbreaks in the “first wave” and estimating the associated ultimate mortality, using four different models to identify model error and robustness of results. This quantitative analysis allows us to assess the outbreak progress in different countries, differentiating between those that are at a quite advanced stage and close to the end of the epidemic from those that are still in the middle of it. This raises many questions in terms of organisation, preparedness, governance structure and so on.

scholarly journals Influenza Vaccine Match with Circulating Strains and Indicators of Influenza Strain Impact - Canada 1980 to 1992

1998 ◽  
Vol 9 (3) ◽  
pp. 143-148 ◽  
Author(s):  
Edward Ellis ◽  
John M Weber ◽  
Wilf Cuff ◽  
Susan G Mackenzie
Keyword(s):  
Influenza Vaccine ◽  
Ecological Study ◽  
Influenza Season ◽  
National Laboratory ◽  
Mortality Rates ◽  
Mortality Data ◽  
Influenza Strain ◽  
Previous Season ◽  
Vaccine Antigens ◽  
Excess Deaths

OBJECTIVES: To determine the similarity between influenza vaccine antigens and viruses associated with laboratory-confirmed infections by virus type/subtype, strain and influenza season; to correlate pneumonia and influenza hospitalization and mortality rates with the number of laboratory-confirmed influenza infections in an influenza season; and to develop predictive indicators of the likely incidence of current strains in the following season.DESIGN: Ecological study using national laboratory, pneumonia and influenza hospitalization and mortality data.SETTING: Canada, influenza seasons from 1980 to 1992.POPULATION STUDIED: Individuals with laboratory-confirmed influenza infections, pneumonia and influenza hospitalizations or deaths.INTERVENTION: Influenza immunization.MAIN RESULTS: Similarity of circulating strains and vaccine antigens was 99% for A(H1N1), 65% for A(H3N2) and 65% for B strains. During outbreaks, pneumonia and influenza hospitalization, and mortality rates increased 19% or less and 21% or less for A(H1N1), respectively; 28% or less and 51% or less for A(H3N2), and 19% or less and 16% or less for B strains. There were usually fewer than 25 laboratory-confirmed A(H1N1) infections with a particular strain in a season if there had been more than 25 infections with similar strains the previous season. For A(H3N2), the figure was 100, and for B it was 150.CONCLUSIONS: Matches were excellent for A(H1N1) and good for A(H3N2) plus B strains. Hospitalization and mortality rates increased substantially during outbreaks, eg, estimated 1609 excess deaths during a widespread A(H3N2) outbreak. This study identifies relationships that provide some ability to predict the incidence of a particular influenza strain in a coming season based on the incidence of strains similar to it in the previous season.

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