Integrated causal-predictive machine learning models for tropical cyclone epidemiology.

Strategic preparedness reduces the adverse health impacts of hurricanes and tropical storms, referred to collectively as tropical cyclones (TCs), but its protective impact could be enhanced by a more comprehensive and rigorous characterization of TC epidemiology. To generate the insights and tools necessary for high-precision TC preparedness, we introduce a machine learning approach that standardizes estimation of historic TC health impacts, discovers common patterns and sources of heterogeneity in those health impacts, and enables identification of communities at highest health risk for future TCs. The model integrates (i) a causal inference component to quantify the immediate health impacts of recent historic TCs at high spatial resolution and (ii) a predictive component that captures how TC meteorological features and socioeconomic/demographic characteristics of impacted communities are associated with health impacts. We apply it to a rich data platform containing detailed historic TC exposure information and records of all-cause mortality and cardiovascular- and respiratory-related hospitalization among Medicare recipients. We report a high degree of heterogeneity in the acute health impacts of historic TCs, both within and across TCs, and, on average, substantial TC-attributable increases in respiratory hospitalizations. TC-sustained windspeeds are found to be the primary driver of mortality and respiratory risks.

Health implications of urban tree canopy policy scenarios in Denver and Phoenix: A quantitative health impact assessment.

BACKGROUND: Urban tree canopy (UTC) goals are a popular policy to increase urban vegetation, support climate strategies, and encourage a healthy environment. Health studies related to UTC are needed across cities to support evidence-based decision-making. METHODS: We used a quantitative Health Impact Assessment (HIA) to model the annual number of premature deaths prevented, and the number of stroke and dementia cases, under UTC goals in Denver, Colorado, and Phoenix, Arizona, USA, using standing policy goals (20% and 25% UTC, respectively) and 50% ("half-way") attainment scenarios from current levels (16.5% and 13% UTC, respectively), using publicly accessible national datasets, and a proportional representation of UTC change to standardize across methodologies. We estimated UTC health impacts by relating UTC with scenario-based changes in the Normalized Difference Vegetation Index (NDVI) and considered health equity in UTC distributions and benefits. RESULTS: We projected that at 2020 populations, uniform 20% UTC attainment across Denver block groups would avert 200 (95% uncertainty interval: (UI) 100, 306) annual premature deaths among adults 18 and older, along with 4.1 (95% UI: 2.2, 6.7) annual cases of stroke (adults ≥35), and 2.6 (95% UI: 1.5, 4.1) cases of dementia (adults ≥65), with "halfway" attainment from current levels (16.5% UTC) capturing ∼64% of these benefits. In Phoenix, uniform 25% UTC would annually prevent 368 (95% UI: 181, 558) premature deaths, 8.7 (95% UI: 4.7, 13.9) cases of stroke, and 5,1 (95% UI: 2.9, 8.0) of dementia, with the "halfway" scenario (17% UTC) achieving ∼44% of these results. Both cities saw significantly different greenspace exposures and health outcomes by socioeconomic vulnerability. Denver had more spatially and socioeconomically heterogeneous projected health benefits than Phoenix. CONCLUSIONS: Implementing UTC goals can prevent excess mortality and chronic diseases among urban residents. UTC goals can be used as a health promotion and prevention tool.

Ten simple rules for finding and selecting R packages.

R is an increasingly preferred software environment for data analytics and statistical computing among scientists and practitioners. Packages markedly extend R's utility and ameliorate inefficient solutions to data science problems. We outline 10 simple rules for finding relevant packages and determining which package is best for your desired use. We begin in Rule 1 with tips on how to consider your purpose, which will guide your search to follow, where, in Rule 2, you'll learn best practices for finding and collecting options. Rules 3 and 4 will help you navigate packages' profiles and explore the extent of their online resources, so that you can be confident in the quality of the package you choose and assured that you'll be able to access support. In Rules 5 and 6, you'll become familiar with how the R Community evaluates packages and learn how to assess the popularity and utility of packages for yourself. Rules 7 and 8 will teach you how to investigate and track package development processes, so you can further evaluate their merit. We end in Rules 9 and 10 with more hands-on approaches, which involve digging into package code.

Hurricanes and Mortality among Patients Receiving Dialysis.

BACKGROUND: Hurricanes are severe weather events that can disrupt power, water, and transportation systems. These disruptions may be deadly for patients requiring maintenance dialysis. We hypothesized that the mortality risk among patients requiring maintenance dialysis would be increased in the 30 days after a hurricane. METHODS: Patients registered as requiring maintenance dialysis in the United States Renal Data System who initiated treatment between January 1, 1997 and December 31, 2017 in one of 108 hurricane-afflicted counties were followed from dialysis initiation until transplantation, dialysis discontinuation, a move to a nonafflicted county, or death. Hurricane exposure was determined as a tropical cyclone event with peak local wind speeds ≥64 knots in the county of a patient's residence. The risk of death after the hurricane was estimated using time-varying Cox proportional hazards models. RESULTS: The median age of the 187,388 patients was 65 years (IQR, 53-75) and 43.7% were female. There were 27 hurricanes and 105,398 deaths in 529,339 person-years of follow-up on dialysis. In total, 29,849 patients were exposed to at least one hurricane. Hurricane exposure was associated with a significantly higher mortality after adjusting for demographic and socioeconomic covariates (hazard ratio, 1.13; 95% confidence interval, 1.05 to 1.22). The association persisted when adjusting for seasonality. CONCLUSIONS: Patients requiring maintenance dialysis have a higher mortality risk in the 30 days after a hurricane.

Tracking the transmission dynamics of COVID-19 with a time-varying coefficient state-space model.

The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space model for infectious disease transmission. The foundation of this model is a time-varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID-19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID-19.

Association of Tropical Cyclones With County-Level Mortality in the US.

Importance: Tropical cyclones have a devastating effect on society, but a comprehensive assessment of their association with cause-specific mortality over multiple years of study is lacking. Objective: To comprehensively evaluate the association of county-level tropical cyclone exposure and death rates from various causes in the US. Design, Setting, and Participants: A retrospective observational study using a Bayesian conditional quasi-Poisson model to examine how tropical cyclones were associated with monthly death rates. Data from 33.6 million deaths in the US were collected from the National Center for Health Statistics over 31 years (1988-2018), including residents of the 1206 counties in the US that experienced at least 1 tropical cyclone during the study period. Exposures: Tropical cyclone days per county-month, defined as number of days in a month with a sustained maximal wind speed 34 knots or greater. Main Outcomes and Measures: Monthly cause-specific county-level death rates by 6 underlying causes of death: cancers, cardiovascular diseases, infectious and parasitic diseases, injuries, neuropsychiatric conditions, and respiratory diseases. The model yielded information about the association between each additional cyclone day per month and monthly county-level mortality compared with the same county-month in different years, up to 6 months after tropical cyclones, and how these estimated associations varied by age, sex, and social vulnerability. The unit of analysis was county-month. Results: There were 33 619 393 deaths in total (16 691 681 females and 16 927 712 males; 8 587 033 aged 0-64 years and 25 032 360 aged 65 years or older) from the 6 causes recorded in 1206 US counties. There was a median of 2 tropical cyclone days experienced in total in included US counties. Each additional cyclone day was associated with increased death rates in the month following the cyclone for injuries (3.7% [95% credible interval {CrI}, 2.5%-4.9%]; 2.0 [95% CrI, 1.3-2.7] additional deaths per 1 000 000 for 2018 monthly age-standardized median rate [DPM]; 54.3 to 56.3 DPM), infectious and parasitic diseases (1.8% [95% CrI, 0.1%-3.6%]; 0.2 [95% CrI, 0.0-0.4] additional DPM; 11.7 to 11.9 DPM), respiratory diseases (1.3% [95% CrI, 0.2%-2.4%]; 0.6 [95% CrI, 0.1-1.1] additional DPM; 44.9 to 45.5 DPM), cardiovascular diseases (1.2% [95% CrI, 0.6%-1.7%]; 1.5 [95% CrI, 0.8-2.2] additional DPM; 129.6 to 131.1 DPM), neuropsychiatric conditions (1.2% [95% CrI, 0.1%-2.4%]; 0.6 [95% CrI, 0.1-1.2] additional DPM; 52.1 to 52.7 DPM), with no change for cancers (-0.3% [95% CrI, -0.9% to 0.3%]; -0.3 [95% CrI, -0.9 to 0.3] additional DPM; 100.4 to 100.1 DPM). Conclusions and Relevance: Among US counties that experienced at least 1 tropical cyclone from 1988-2018, each additional cyclone day per month was associated with modestly higher death rates in the months following the cyclone for several causes of death, including injuries, infectious and parasitic diseases, cardiovascular diseases, neuropsychiatric conditions, and respiratory diseases.

Tropical Cyclone Exposures and Risks of Emergency Medicare Hospital Admission for Cardiorespiratory Diseases in 175 Urban United States Counties, 1999-2010.

BACKGROUND: Although injuries experienced during hurricanes and other tropical cyclones have been relatively well-characterized through traditional surveillance, less is known about tropical cyclones' impacts on noninjury morbidity, which can be triggered through pathways that include psychosocial stress or interruption in medical treatment. METHODS: We investigated daily emergency Medicare hospitalizations (1999-2010) in 180 US counties, drawing on an existing cohort of high-population counties. We classified counties as exposed to tropical cyclones when storm-associated peak sustained winds were ≥21 m/s at the county center; secondary analyses considered other wind thresholds and hazards. We matched storm-exposed days to unexposed days by county and seasonality. We estimated change in tropical cyclone-associated hospitalizations over a storm period from 2 days before to 7 days after the storm's closest approach, compared to unexposed days, using generalized linear mixed-effect models. RESULTS: For 1999-2010, 175 study counties had at least one tropical cyclone exposure. Cardiovascular hospitalizations decreased on the storm day, then increased following the storm, while respiratory hospitalizations were elevated throughout the storm period. Over the 10-day storm period, cardiovascular hospitalizations increased 3% (95% confidence interval = 2%, 5%) and respiratory hospitalizations increased 16% (95% confidence interval = 13%, 20%) compared to matched unexposed periods. Relative risks varied across tropical cyclone exposures, with strongest association for the most restrictive wind-based exposure metric. CONCLUSIONS: In this study, tropical cyclone exposures were associated with a short-term increase in cardiorespiratory hospitalization risk among the elderly, based on a multi-year/multi-site investigation of US Medicare beneficiaries ≥65 years.

Community-wide Mortality Rates in Beijing, China, During the July 2012 Flood Compared with Unexposed Periods.

BACKGROUND: On 21-22 July 2012, Beijing, China, suffered its heaviest rainfall in 60 years. Two studies have estimated the fatality toll of this disaster using a traditional surveillance approach. However, traditional surveillance can miss disaster-related deaths, including a substantial number of deaths from natural causes triggered by disaster exposure. Here, we investigated community-wide mortality risk during this flood compared with rates in unexposed reference periods. METHODS: We compared community-wide mortality rates on the peak flood day and the four following days to seasonally matched nonflood days in previous years (2008-2011), controlling for potential confounders, to estimate the relative risks (RRs) of daily mortality among Beijing residents associated with this flood. RESULTS: On 21 July 2012, the flood-associated RRs were 1.34 (95% confidence interval = 1.11, 1.61) for all-cause, 1.37 (1.01, 1.85) for circulatory, and 4.40 (2.98, 6.51) for accidental mortality, compared with unexposed periods. We observed no evidence of increased risk of respiratory mortality. For the flood period of 21-22 July 2012, we estimated a total of 79 excess deaths among Beijing residents; by contrast, only 34 deaths were reported among Beijing residents in a study using a traditional surveillance approach. CONCLUSIONS: To our knowledge, this is the first study analyzing community-wide changes in mortality rates during the 2012 flood in Beijing and one of the first to do so for any major flood worldwide. This study offers critical evidence on flood-related health impacts, as urban flooding is expected to become more frequent and severe in China.

The Future of Climate Epidemiology: Opportunities for Advancing Health Research in the Context of Climate Change.

In the coming decades, climate change is expected to dramatically affect communities worldwide, altering the patterns of many ambient exposures and disasters, including extreme temperatures, heat waves, wildfires, droughts, and floods. These exposures, in turn, can affect risks for a variety of human diseases and health outcomes. Climate epidemiology plays an important role in informing policy related to climate change and its threats to public health. Climate epidemiology leverages deep, integrated collaborations between epidemiologists and climate scientists to understand the current and potential future impacts of climate-related exposures on human health. A variety of recent and ongoing developments in climate science are creating new avenues for epidemiologic contributions. Here, we discuss the contributions of climate epidemiology and describe some key current research directions, including research to better characterize uncertainty in climate health projections. We end by outlining 3 developing areas of climate science that are creating opportunities for high-impact epidemiologic advances in the near future: 1) climate attribution studies, 2) subseasonal to seasonal forecasts, and 3) decadal predictions.

The impact of heat waves on mortality in Northwest India.

Heat waves are anticipated to worsen with climate change. India, an understudied area with >15% of the world's population, commonly experiences temperature extremes and already resembles potential future climates of more temperate regions. Registry data from local municipal corporations and government offices were collected and translated, yielding daily all-cause mortality for 4 communities in Northwest India for all or part of the period 2000-2012. Heat waves were defined as ≥2 days with local temperature ≥97th percentile for that community. An alternate definition matching that used by the Indian Meteorological Department was also developed, to enhance policy relevance. Community-specific average daily maximum temperature over the entire record ranged from 32.5 to 34.2 °C (90.5-93.6 °F). Across communities, total mortality increased 18.1% during heat wave days compared with non-heat-wave days [95% confidence interval (CI): -5.3%, 47.3%], with the highest risk in Jaipur (29.9% [95% CI: 24.6%, 34.9%]). Evidence of effect modification by heat wave characteristics (intensity, duration, and timing in season) was limited. Findings indicate health risks associated with heat waves in communities with high baseline temperatures. Results can inform heat wave-health assessments in temperate regions in future, and improve our understanding of temperature-health associations under climate change. Further investigation of potential effect modification by heat wave characteristics is needed.

Wildfire-specific Fine Particulate Matter and Risk of Hospital Admissions in Urban and Rural Counties.

BACKGROUND: The health impacts of wildfire smoke, including fine particles (PM2.5), are not well understood and may differ from those of PM2.5 from other sources due to differences in concentrations and chemical composition. METHODS: First, for the entire Western United States (561 counties) for 2004-2009, we estimated daily PM2.5 concentrations directly attributable to wildfires (wildfires-specific PM2.5), using a global chemical transport model. Second, we defined smoke wave as ≥2 consecutive days with daily wildfire-specific PM2.5 > 20 µg/m, with sensitivity analysis considering 23, 28, and 37 µg/m. Third, we estimated the risk of cardiovascular and respiratory hospital admissions associated with smoke waves for Medicare enrollees. We used a generalized linear mixed model to estimate the relative risk of hospital admissions on smoke wave days compared with matched comparison days without wildfire smoke. RESULTS: We estimated that about 46 million people of all ages were exposed to at least one smoke wave during 2004 to 2009 in the Western United States. Of these, 5 million are Medicare enrollees (≥65 years). We found a 7.2% (95% confidence interval: 0.25%, 15%) increase in risk of respiratory admissions during smoke wave days with high wildfire-specific PM2.5 (>37 µg/m) compared with matched non smoke wave days. We did not observe an association between smoke wave days with wildfire-specific PM2.5 ≤ 37 µg/mand respiratory or cardiovascular admissions. Respiratory effects of wildfire-specific PM2.5 may be stronger than that of PM2.5 from other sources. CONCLUSION: Short-term exposure to wildfire-specific PM2.5was associated with risk of respiratory diseases in the elderly population in the Western United States during severe smoke days. See video abstract at, http://links.lww.com/EDE/B137.

Data for Community Health Assessment in Rural Colorado: A Comparison of Electronic Health Records to Public Health Surveys to Describe Childhood Obesity.

CONTEXT: Community-level data are necessary to inform community health assessments and to plan for appropriate interventions. However, data derived from public health surveys may be limited or unavailable in rural locations. OBJECTIVE: We compared 2 sources of data for community health assessment in rural Colorado, electronic health records (EHRs) and routine public health surveys. DESIGN: Comparison of cross-sectional measures of childhood/youth obesity prevalence and data quality. SETTING: Two rural Colorado counties, La Plata and Prowers. PARTICIPANTS: The EHR cohort comprised patients 2 to 19 years of age who underwent a visit with the largest health care provider in each county. These data included sex, age, weight, height, race, ethnicity, and insurance status. Public health survey data were obtained from 2 surveys, the Colorado Child Health Survey (2-14 years of age) and the Healthy Kids Colorado Survey (15-19 years of age) and included caregiver and self-reported height and weight estimates. MAIN OUTCOME MEASURES: We calculated body mass index percentile for each patient and survey respondent and determined overweight/obesity prevalence by county. We evaluated data source quality indicators according to a rubric developed for this analysis. RESULTS: The EHR sample captured approximately 35% (n = 3965) and 70% (n = 2219) of all children living in La Plata and Prowers Counties, respectively. The EHR prevalence estimates of overweight/obesity were greater in precision than survey data in both counties among children 2 to 14 years of age. In addition, the EHR data were more timely and geographically representative than survey data and provided directly measured height and weight. Conversely, survey data were easier to access and more demographically representative of the overall population. CONCLUSIONS: Electronic health records describing the prevalence of obesity among children/youth living in rural Colorado may complement public health survey data for community health assessment and health improvement planning.

Temperature, ozone, and mortality in urban and non-urban counties in the northeastern United States.

BACKGROUND: Most health effects studies of ozone and temperature have been performed in urban areas, due to the available monitoring data. We used observed and interpolated data to examine temperature, ozone, and mortality in 91 urban and non-urban counties. METHODS: Ozone measurements were extracted from the Environmental Protection Agency's Air Quality System. Meteorological data were supplied by the National Center for Atmospheric Research. Observed data were spatially interpolated to county centroids. Daily internal-cause mortality counts were obtained from the National Center for Health Statistics (1988-1999). A two-stage Bayesian hierarchical model was used to estimate each county's increase in mortality risk from temperature and ozone. We examined county-level associations according to population density and compared urban (≥1,000 persons/mile(2)) to non-urban (<1,000 persons/mile(2)) counties. Finally, we examined county-level characteristics that could explain variation in associations by county. RESULTS: A 10 ppb increase in ozone was associated with a 0.45% increase in mortality (95% PI: 0.08, 0.83) in urban counties, while this same increase in ozone was associated with a 0.73% increase (95% PI: 0.19, 1.26) in non-urban counties. An increase in temperature from 70°F to 90°F (21.2°C 32.2°C) was associated with a 8.88% increase in mortality (95% PI: 7.38, 10.41) in urban counties and a 8.08% increase (95% PI: 6.16, 10.05) in non-urban counties. County characteristics, such as population density, percentage of families living in poverty, and percentage of elderly residents, partially explained the variation in county-level associations. CONCLUSIONS: While most prior studies of ozone and temperature have been performed in urban areas, the impacts in non-urban areas are significant, and, for ozone, potentially greater. The health risks of increasing temperature and air pollution brought on by climate change are not limited to urban areas.

Characterizing occupational heat-related mortality in the United States, 2000-2010: an analysis using the Census of Fatal Occupational Injuries database.

BACKGROUND: Occupational heat-related mortality is not well studied and risk factors remain largely unknown. This paper describes the epidemiological characteristics of heat-related deaths among workers in the US 2000-2010. METHODS: Fatality data were obtained at the Bureau of Labor Statistics from the confidential on-site Census of Fatal Occupational Injuries database. Fatality rates and risk ratios with 95% confidence intervals were calculated by year, sex, age group, ethnicity, race, state, and industry. RESULTS: Between 2000 and 2010, 359 occupational heat-related deaths were identified in the U.S., for a yearly average fatality rate of 0.22 per 1 million workers. Highest rates were found among Hispanics, men, the agriculture and construction industries, the state of Mississippi, and very small establishments. CONCLUSIONS: This study provides the first comprehensive national profile of heat-related deaths in the U.S. workplace. Prevention efforts should be directed at small businesses and at industries and individuals with the highest risk.

Heat-related emergency hospitalizations for respiratory diseases in the Medicare population.

RATIONALE: The heat-related risk of hospitalization for respiratory diseases among the elderly has not been quantified in the United States on a national scale. With climate change predictions of more frequent and more intense heat waves, it is of paramount importance to quantify the health risks related to heat, especially for the most vulnerable. OBJECTIVES: To estimate the risk of hospitalization for respiratory diseases associated with outdoor heat in the U.S. elderly. METHODS: An observational study of approximately 12.5 million Medicare beneficiaries in 213 United States counties, January 1, 1999 to December 31, 2008. We estimate a national average relative risk of hospitalization for each 10°F (5.6°C) increase in daily outdoor temperature using Bayesian hierarchical models. MEASUREMENTS AND MAIN RESULTS: We obtained daily county-level rates of Medicare emergency respiratory hospitalizations (International Classification of Diseases, Ninth Revision, 464-466, 480-487, 490-492) in 213 U.S. counties from 1999 through 2008. Overall, each 10°F increase in daily temperature was associated with a 4.3% increase in same-day emergency hospitalizations for respiratory diseases (95% posterior interval, 3.8, 4.8%). Counties' relative risks were significantly higher in counties with cooler average summer temperatures. CONCLUSIONS: We found strong evidence of an association between outdoor heat and respiratory hospitalizations in the largest population of elderly studied to date. Given projections of increasing temperatures from climate change and the increasing global prevalence of chronic pulmonary disease, the relationship between heat and respiratory morbidity is a growing concern.

The epidemiology of occupational heat exposure in the United States: a review of the literature and assessment of research needs in a changing climate.

In recent years, the United States has experienced record-breaking summer heat. Climate change models forecast increasing US temperatures and more frequent heat wave events in the coming years. Exposure to environmental heat is a significant, but overlooked, workplace hazard that has not been well-characterized or studied. The working population is diverse; job function, age, fitness level, and risk factors to heat-related illnesses vary. Yet few studies have examined or characterized the incidence of occupational heat-related morbidity and mortality. There are no federal regulatory standards to protect workers from environmental heat exposure. With climate change as a driver for adaptation and prevention of heat disorders, crafting policy to characterize and prevent occupational heat stress for both indoor and outdoor workers is increasingly sensible, practical, and imperative.

Increased Mortality Risks from a Spectrum of Causes of Tropical Cyclone Exposure - China, 2013-2018.

What is already known about this topic?: Tropical cyclone (TC) has a substantial and adverse impact on non-accidental mortality. However, whether heterogeneity exists when examining deaths from sub-causes and how TC impacts non-accidental mortality in the short term remain unclear. What is added by this report?: This study found substantial associations at lag 0 between TC exposure and circulatory and respiratory mortality. TC exposures were associated with increased risks for several mortality sub-causes at lag 0 day, including ischemic heart disease, myocardial infarction, cardiac arrest, cerebrovascular disease, stroke, chronic obstructive pulmonary disease, and Parkinson's disease. What are the implications for public health practice?: This finding suggests an urgent need to expand the public health focus of natural disaster management to include non-accidental mortality and sub-causes.

Short-term excess mortality following tropical cyclones in the United States.

Knowledge of excess deaths after tropical cyclones is critical to understanding their impacts, directly relevant to policies on preparedness and mitigation. We applied an ensemble of 16 Bayesian models to 40.7 million U.S. deaths and a comprehensive record of 179 tropical cyclones over 32 years (1988-2019) to estimate short-term all-cause excess deaths. The deadliest tropical cyclone was Hurricane Katrina in 2005, with 1491 [95% credible interval (CrI): 563, 3206] excess deaths (>99% posterior probability of excess deaths), including 719 [95% CrI: 685, 752] in Orleans Parish, LA (>99% probability). Where posterior probabilities of excess deaths were >95%, there were 3112 [95% CrI: 2451, 3699] total post-hurricane force excess deaths and 15,590 [95% CrI: 12,084, 18,835] post-gale to violent storm force deaths; 83.1% of post-hurricane force and 70.0% of post-gale to violent storm force excess deaths occurred more recently (2004-2019); and 6.2% were in least socially vulnerable counties.

Is the relation between ozone and mortality confounded by chemical components of particulate matter? Analysis of 7 components in 57 US communities.

Epidemiologic studies have linked tropospheric ozone pollution and human mortality. Although research has shown that this relation is not confounded by particulate matter when measured by mass, little scientific evidence exists on whether confounding exists by chemical components of the particle mixture. Using mortality and particulate matter with aerodynamic diameter ≤2.5 µm (PM(2.5)) component data from 57 US communities (2000-2005), the authors investigate whether the ozone-mortality relation is confounded by 7 components of PM(2.5): sulfate, nitrate, silicon, elemental carbon, organic carbon matter, sodium ion, and ammonium. Together, these components constitute most PM(2.5) mass in the United States. Estimates of the effect of ozone on mortality were almost identical before and after controlling for the 7 components of PM(2.5) considered (mortality increase/10-ppb ozone increase, before and after controlling: ammonium, 0.34% vs. 0.35%; elemental carbon, 0.36% vs. 0.37%; nitrate, 0.27% vs. 0.26%; organic carbon matter, 0.34% vs. 0.31%; silicon, 0.36% vs. 0.37%; sodium ion, 0.21% vs. 0.18%; and sulfate, 0.35% vs. 0.38%). Additionally, correlations were weak between ozone and each particulate component across all communities. Previous research found that the ozone-mortality relation is not confounded by particulate matter measured by mass; this national study indicates that the relation is also robust to control for specific components of PM(2.5).

Lights out: impact of the August 2003 power outage on mortality in New York, NY.

BACKGROUND: Little is known about how power outages affect health. We investigated mortality effects of the largest US blackout to date, 14-15 August 2003 in New York, NY. METHODS: We estimated mortality risk in New York, NY, using a generalized linear model with data from 1987-2005. We incorporated possible confounders, including weather and long-term and seasonal mortality trends. RESULTS: During the blackout, mortality increased for accidental deaths (122% [95% confidence interval = 28%-287%]) and nonaccidental (ie, disease-related) deaths (25% [12%-41%]), resulting in approximately 90 excess deaths. Increased mortality was not from deaths being advanced by a few days; rather, mortality risk remained slightly elevated through August 2003. CONCLUSIONS: To our knowledge, this is the first analysis of power outages and nonaccidental mortality. Understanding the impact of power outages on human health is relevant, given that increased energy demand and climate change are likely to put added strain on power grids.