Daily temperature variability and mental health-related hospital visits in New York State.

BACKGROUND: Despite plausible behavioral and physiological pathways, limited evidence exists on how daily temperature variability is associated with acute mental health-related episodes. OBJECTIVES: We aimed to explore associations between daily temperature range (DTR) and mental health-related hospital visits using data of all hospital records in New York State during 1995-2014. We further examined factors that may modify these associations, including age, sex, hospital visit type and season. METHODS: Using a case-crossover design with distributed lag non-linear DTR terms (0-6 days), we estimated associations between ZIP Code-level DTR and hospital visits for mood (4.6 million hospital visits), anxiety (2.4 million), adjustment (∼368,000), and schizophrenia disorders (∼211,000), controlling for daily mean temperature, via conditional logistic regression models. We assessed potential heterogeneity by age, sex, hospital visit type (in-patient vs. out-patient), and season (summer, winter, and transition seasons). RESULTS: For all included outcomes, we observed positive associations from period minimum DTR (0.1 °C) until 25th percentile (5.2 °C) and between mean DTR (7.7 °C) and 90th percentile (12.2 °C), beyond which we observed negative associations. For mood disorders, an increase in DTR from 0.1 °C to 12.2 °C was associated with a cumulative 16.0% increase (95% confidence interval [CI]: 12.8, 19.2%) in hospital visit rates. This increase was highest during transition seasons (32.5%; 95%CI: 26.4, 39.0%) compared with summer (10.7%; 95%CI: 4.8, 16.8%) and winter (-1.6%; 95%CI: -7.6, 4.7%). For adjustment and schizophrenia disorders, the strongest associations were seen among the youngest group (0-24 years) with almost no association in the oldest group (65+ years). We observed no evidence for modification by sex and hospital visit type. DISCUSSION: Daily temperature variability was positively associated with mental health-related hospital visits within specific DTR ranges in New York State, after controlling for daily mean temperature. Given uncertainty on how climate change modifies temperature variability, additional research is crucial to comprehend the implications of these findings, particularly under different scenarios of future temperature variability.

Comparing Approximated Heat Stress Measures Across the United States.

Climate change is escalating the threat of heat stress to global public health, with the majority of humans today facing increasingly severe and prolonged heat waves. Accurate weather data reflecting the complexity of measuring heat stress is crucial for reducing the impact of extreme heat on health worldwide. Previous studies have employed Heat Index (HI) and Wet Bulb Globe Temperature (WBGT) metrics to understand extreme heat exposure, forming the basis for heat stress guidelines. However, systematic comparisons of meteorological and climate data sets used for these metrics and the related parameters, like air temperature, humidity, wind speed, and solar radiation crucial for human thermoregulation, are lacking. We compared three heat measures (HImax, WBGTBernard, and WBGTLiljegren) approximated from gridded weather data sets (ERA5-Land, PRISM, Daymet) with ground-based data, revealing strong agreement from HI and WBGTBernard (R 2 0.76-0.95, RMSE 1.69-6.64°C). Discrepancies varied by Köppen-Geiger climates (e.g., Adjusted R 2 HImax 0.88-0.95, WBGTBernard 0.79-0.97, and WBGTLiljegren 0.80-0.96), and metrological input variables (Adjusted R 2 T max 0.86-0.94, T min 0.91-0.94, Wind 0.33, Solarmax 0.38, Solaravg 0.38, relative humidity 0.51-0.74). Gridded data sets can offer reliable heat exposure assessment, but further research and local networks are vital to reduce measurement errors to fully enhance our understanding of how heat stress measures link to health outcomes.

The association between temperature and alcohol- and substance-related disorder hospital visits in New York State.

BACKGROUND: Limited evidence exists on how temperature increases are associated with hospital visits from alcohol- and substance-related disorders, despite plausible behavioral and physiological pathways. METHODS: In the present study, we implemented a case-crossover design, which controls for seasonal patterns, long-term trends, and non- or slowly-varying confounders, with distributed lag non-linear temperature terms (0-6 days) to estimate associations between daily ZIP Code-level temperature and alcohol- and substance-related disorder hospital visit rates in New York State during 1995-2014. We also examined four substance-related disorder sub-causes (cannabis, cocaine, opioid, sedatives). RESULTS: Here we show that, for alcohol-related disorders, a daily increase in temperature from the daily minimum (-30.1 °C (-22.2 °F)) to the 75th percentile (18.8 °C (65.8 °F)) across 0-6 lag days is associated with a cumulative 24.6% (95%CI,14.6%-34.6%) increase in hospital visit rates, largely driven by increases on the day of and day before hospital visit, with an association larger outside New York City. For substance-related disorders, we find evidence of a positive association at temperatures from the daily minimum (-30.1 °C (-22.2 °F)) to the 50th percentile (10.4 °C (50.7 °F)) (37.7% (95%CI,27.2%-48.2%), but not at higher temperatures. Findings are consistent across age group, sex, and social vulnerability. CONCLUSIONS: Our work highlights how hospital visits from alcohol- and substance-related disorders are currently impacted by elevated temperatures and could be further affected by rising temperatures resulting from climate change. Enhanced social infrastructure and health system interventions could mitigate these impacts.

We investigated the relationship between temperature and hospital visits related to alcohol and other drugs including cannabis, cocaine, opioids, and sedatives in New York State. We found that higher temperatures resulted in more hospital visits for alcohol. For other drugs, higher temperatures also resulted in more hospital visits but only up to a certain temperature level. Our findings suggest that rising temperatures, including those caused by climate change, may influence hospital visits for alcohol and other drugs, emphasizing the need for appropriate and proportionate social and health interventions, as well as highlighting potential hidden burdens of climate change.

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.

Exploring Relevant Time Windows in the Association Between PM2.5 Exposure and Amyotrophic Lateral Sclerosis: A Case-Control Study in Denmark.

Studies suggest a link between particulate matter less than or equal to 2.5 µm in diameter (PM2.5) and amyotrophic lateral sclerosis (ALS), but to our knowledge critical exposure windows have not been examined. We performed a case-control study in the Danish population spanning the years 1989-2013. Cases were selected from the Danish National Patient Registry based on International Classification of Diseases codes. Five controls were randomly selected from the Danish Civil Registry and matched to a case on vital status, age, and sex. PM2.5 concentration at residential addresses was assigned using monthly predictions from a dispersion model. We used conditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for confounding. We evaluated exposure to averaged PM2.5 concentrations 12-24 months, 2-6 years, and 2-11 years pre-ALS diagnosis; annual lagged exposures up to 11 years prediagnosis; and cumulative associations for exposure in lags 1-5 years and 1-10 years prediagnosis, allowing for varying association estimates by year. We identified 3,983 cases and 19,915 controls. Cumulative exposure to PM2.5 in the period 2-6 years prediagnosis was associated with ALS (OR = 1.06, 95% CI: 0.99, 1.13). Exposures in the second, third, and fourth years prediagnosis were individually associated with higher odds of ALS (e.g., for lag 1, OR = 1.04, 95% CI: 1.00, 1.08). Exposure to PM2.5 within 6 years before diagnosis may represent a critical exposure window for ALS.

Excess Injury Mortality in Washington State During the 2021 Heat Wave.

PUBLIC HEALTH IMPLICATIONS: Under global warming scenarios, heat waves of this magnitude will become much more common. Adaptation and planning efforts are needed to protect residents of the historically temperate Pacific Northwest for a range of health outcomes. (Am J Public Health. 2023;113(6):657-660. https://doi.org/10.2105/AJPH.2023.307269).

Assessment of COVID-19 as the Underlying Cause of Death Among Children and Young People Aged 0 to 19 Years in the US.

Importance: COVID-19 was the underlying cause of death for more than 940 000 individuals in the US, including at least 1289 children and young people (CYP) aged 0 to 19 years, with at least 821 CYP deaths occurring in the 1-year period from August 1, 2021, to July 31, 2022. Because deaths among US CYP are rare, the mortality burden of COVID-19 in CYP is best understood in the context of all other causes of CYP death. Objective: To determine whether COVID-19 is a leading (top 10) cause of death in CYP in the US. Design, Setting, and Participants: This national population-level cross-sectional epidemiological analysis for the years 2019 to 2022 used data from the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (WONDER) database on underlying cause of death in the US to identify the ranking of COVID-19 relative to other causes of death among individuals aged 0 to 19 years. COVID-19 deaths were considered in 12-month periods between April 1, 2020, and August 31, 2022, compared with deaths from leading non-COVID-19 causes in 2019, 2020, and 2021. Main Outcomes and Measures: Cause of death rankings by total number of deaths, crude rates per 100 000 population, and percentage of all causes of death, using the National Center for Health Statistics 113 Selected Causes of Death, for ages 0 to 19 and by age groupings (<1 year, 1-4 years, 5-9 years, 10-14 years, 15-19 years). Results: There were 821 COVID-19 deaths among individuals aged 0 to 19 years during the study period, resulting in a crude death rate of 1.0 per 100 000 population overall; 4.3 per 100 000 for those younger than 1 year; 0.6 per 100 000 for those aged 1 to 4 years; 0.4 per 100 000 for those aged 5 to 9 years; 0.5 per 100 000 for those aged 10 to 14 years; and 1.8 per 100 000 for those aged 15 to 19 years. COVID-19 mortality in the time period of August 1, 2021, to July 31, 2022, was among the 10 leading causes of death in CYP aged 0 to 19 years in the US, ranking eighth among all causes of deaths, fifth in disease-related causes of deaths (excluding unintentional injuries, assault, and suicide), and first in deaths caused by infectious or respiratory diseases when compared with 2019. COVID-19 deaths constituted 2% of all causes of death in this age group. Conclusions and Relevance: The findings of this study suggest that COVID-19 was a leading cause of death in CYP. It caused substantially more deaths in CYP annually than any vaccine-preventable disease historically in the recent period before vaccines became available. Various factors, including underreporting and not accounting for COVID-19's role as a contributing cause of death from other diseases, mean that these estimates may understate the true mortality burden of COVID-19. The findings of this study underscore the public health relevance of COVID-19 to CYP. In the likely future context of sustained SARS-CoV-2 circulation, appropriate pharmaceutical and nonpharmaceutical interventions (eg, vaccines, ventilation, air cleaning) will continue to play an important role in limiting transmission of the virus and mitigating severe disease in CYP.

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.

Estimates of excess mortality for the five Nordic countries during the COVID-19 pandemic 2020-2021.

BACKGROUND: Excess mortality during the COVID-19 pandemic is of major scientific and political interest. METHODS: We critically reviewed different estimates of all-cause excess mortality for the five Nordic countries (Denmark, Finland, Iceland, Norway and Sweden), which have been much studied during the COVID-19 pandemic, using the latest register data to discuss uncertainties and implications. RESULTS: We show using back-calculation of expected deaths from Nordic all-cause deaths that the Institute for Health Metrics and Evaluation model is a clear outlier in the compared estimates and likely substantially overestimates excess mortality of Finland and Denmark, and probably Sweden. Our review suggests a range of total Nordic excess deaths of perhaps 15 000-20 000, but results are sensitive to assumptions in the models as shown. CONCLUSIONS: We document substantial heterogeneity and uncertainty in estimates of excess mortality. All estimates should be taken with caution in their interpretation as they miss detailed account of demographics, such as changes in the age group populations over the study period.

Long-term Traffic-related Air Pollutant Exposure and Amyotrophic Lateral Sclerosis Diagnosis in Denmark: A Bayesian Hierarchical Analysis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Limited evidence suggests ALS diagnosis may be associated with air pollution exposure and specifically traffic-related pollutants. METHODS: In this population-based case-control study, we used 3,937 ALS cases from the Danish National Patient Register diagnosed during 1989-2013 and matched on age, sex, year of birth, and vital status to 19,333 population-based controls free of ALS at index date. We used validated predictions of elemental carbon (EC), nitrogen oxides (NO x ), carbon monoxide (CO), and fine particles (PM 2.5 ) to assign 1-, 5-, and 10-year average exposures pre-ALS diagnosis at study participants' present and historical residential addresses. We used an adjusted Bayesian hierarchical conditional logistic model to estimate individual pollutant associations and joint and average associations for traffic-related pollutants (EC, NO x , CO). RESULTS: For a standard deviation (SD) increase in 5-year average concentrations, EC (SD = 0.42 µg/m 3 ) had a high probability of individual association with increased odds of ALS (11.5%; 95% credible interval [CrI] = -1.0%, 25.6%; 96.3% posterior probability of positive association), with negative associations for NO x (SD = 20 µg/m 3 ) (-4.6%; 95% CrI = 18.1%, 8.9%; 27.8% posterior probability of positive association), CO (SD = 106 µg/m 3 ) (-3.2%; 95% CrI = 14.4%, 10.0%; 26.7% posterior probability of positive association), and a null association for nonelemental carbon fine particles (non-EC PM 2.5 ) (SD = 2.37 µg/m 3 ) (0.7%; 95% CrI = 9.2%, 12.4%). We found no association between ALS and joint or average traffic pollution concentrations. CONCLUSIONS: This study found high probability of a positive association between ALS diagnosis and EC concentration. Further work is needed to understand the role of traffic-related air pollution in ALS pathogenesis.

Ambient temperature and risk of urinary tract infection in California: A time-stratified case-crossover study using electronic health records.

BACKGROUND: In the United States (US), urinary tract infections (UTI) lead to more than 10 million office visits each year. Temperature and season are potentially important risk factors for UTI, particularly in the context of climate change. METHODS: We examined the relationship between ambient temperature and outpatient UTI diagnoses among patients followed from 2015 to 2017 in two California healthcare systems: Kaiser Permanente Southern California (KPSC) and Sutter Health in Northern California. We identified UTI diagnoses in adult patients using diagnostic codes and laboratory records from electronic health records. We abstracted patient age, sex, season of diagnosis, and linked community-level Index of Concentration at the Extremes (ICE-I, a measure of wealth and poverty concentration) based on residential address. Daily county-level average ambient temperature was assembled from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). We implemented distributed lag nonlinear models (DLNM) to assess the association between UTI and lagged daily temperatures. Main analyses were confined to women. In secondary analyses, we stratified by season, healthcare system, and community-level ICE-I. RESULTS: We observed 787,186 UTI cases (89% among women). We observed a threshold association between ambient temperature and UTI among women: an increase in daily temperature from the 5th percentile (6.0 ˚C) to the mean (16.2 ˚C) was associated with a 3.2% (95% CI: 2.4, 3.9%) increase in same-day UTI diagnosis rate, whereas an increase from the mean to 95th percentile was associated with no change in UTI risk (0.0%, 95% CI: -0.7, 0.6%). In secondary analyses, we observed the clearest monotonic increase in the rate of UTI diagnosis with higher temperatures in the fall. Associations did not differ meaningfully by healthcare system or community-level ICE-I. Results were robust to alternate model specifications. DISCUSSION: Increasing temperature was related to higher rate of outpatient UTI, particularly in the shoulder seasons (spring, autumn).

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.

The association between ambient temperature variability and myocardial infarction in a New York-State-based case-crossover study: An examination of different variability metrics.

BACKGROUND: Short-term temperature variability has been consistently associated with mortality, with limited evidence for cardiovascular outcomes. Previous studies have used multiple metrics to measure temperature variability; however, those metrics do not capture hour-to-hour changes in temperature. OBJECTIVES: We assessed the correlation between sub-daily temperature-change-over-time metrics and previously-used metrics, and estimated associations with myocardial infarction (MI) hospitalizations. METHODS: Hour-to-hour change-over-time was measured via three metrics: 24-hr mean absolute hourly first difference, 24-hr maximum absolute hourly first difference, and 24-hr mean hourly first difference. We first assessed the Spearman correlations between these metrics and four previously-used metrics (24-hr standard deviation of hourly temperature, 24-hr diurnal temperature range, 48-hr standard deviation of daily minimal and maximal temperatures, and 48-hr difference of daily mean temperature), using hourly data from the North America Land Data Assimilation System-2 Model. Subsequently, we estimated the association between these metrics and primary MI hospitalization in adult residents of New York State for 2000-2015 using a time-stratified case-crossover design. RESULTS: The hour-to-hour change-over-time metrics were correlated, but not synonymous, with previously-used metrics. We observed 809,259 MI, 45% of which were among females and the mean (standard deviation) age was 70 (15). An increase from mean to 90th percentile in mean absolute first difference of temperature was associated with a 2.04% (95% Confidence Interval [CI]: 1.30-2.78%) increase in MI rate. An increase from mean to 90th percentile in mean first difference also yielded a positive association (1.86%; 95%CI: 1.09-2.64%). We observed smaller- or similar-in-magnitude positive associations for previously-used metrics. DISCUSSION: First, short-term hour-to-hour temperature change was positively associated with MI risk. Second, all other variability metrics yielded positive associations with MI, with varying magnitude. In future research on temperature variability, researchers should define their research question, including which aspects of variability they intend to measure, and apply the appropriate metric. ALTERNATIVE: All metrics of temperature variability, including short-term hour-to-hour temperature changes, were positively associated with MI risk, though the magnitude of effect estimates varied by metric.

Anomalously warm weather and acute care visits in patients with multiple sclerosis: A retrospective study of privately insured individuals in the US.

BACKGROUND: As the global climate changes in response to anthropogenic greenhouse gas emissions, weather and temperature are expected to become increasingly variable. Although heat sensitivity is a recognized clinical feature of multiple sclerosis (MS), a chronic demyelinating disorder of the central nervous system, few studies have examined the implications of climate change for patients with this disease. METHODS AND FINDINGS: We conducted a retrospective cohort study of individuals with MS ages 18-64 years in a nationwide United States patient-level commercial and Medicare Advantage claims database from 2003 to 2017. We defined anomalously warm weather as any month in which local average temperatures exceeded the long-term average by ≥1.5°C. We estimated the association between anomalously warm weather and MS-related inpatient, outpatient, and emergency department visits using generalized log-linear models. From 75,395,334 individuals, we identified 106,225 with MS. The majority were women (76.6%) aged 36-55 years (59.0%). Anomalously warm weather was associated with increased risk for emergency department visits (risk ratio [RR] = 1.043, 95% CI: 1.025-1.063) and inpatient visits (RR = 1.032, 95% CI: 1.010-1.054). There was limited evidence of an association between anomalously warm weather and MS-related outpatient visits (RR = 1.010, 95% CI: 1.005-1.015). Estimates were similar for men and women, strongest among older individuals, and exhibited substantial variation by season, region, and climate zone. Limitations of the present study include the absence of key individual-level measures of socioeconomic position (i.e., race/ethnicity, occupational status, and housing quality) that may determine where individuals live-and therefore the extent of their exposure to anomalously warm weather-as well as their propensity to seek treatment for neurologic symptoms. CONCLUSIONS: Our findings suggest that as global temperatures rise, individuals with MS may represent a particularly susceptible subpopulation, a finding with implications for both healthcare providers and systems.

Tropical cyclone exposure is associated with increased hospitalization rates in older adults.

Hurricanes and other tropical cyclones have devastating effects on society. Previous case studies have quantified their impact on some health outcomes for particular tropical cyclones, but a comprehensive assessment over longer periods is currently missing. Here, we used data on 70 million Medicare hospitalizations and tropical cyclone exposures over 16 years (1999-2014). We formulated a conditional quasi-Poisson model to examine how tropical cyclone exposure (days greater than Beaufort scale gale-force wind speed; ≥34 knots) affect hospitalizations for 13 mutually-exclusive, clinically-meaningful causes. We found that tropical cyclone exposure was associated with average increases in hospitalizations from several causes over the week following exposure, including respiratory diseases (14.2%; 95% confidence interval [CI]: 10.9-17.9%); infectious and parasitic diseases (4.3%; 95%CI: 1.2-8.1%); and injuries (8.7%; 95%CI: 6.0-11.8%). Average decadal tropical cyclone exposure in all impacted counties would be associated with an estimated 16,772 (95%CI: 8,265-25,278) additional hospitalizations. Our findings demonstrate the need for targeted preparedness strategies for hospital personnel before, during, and after tropical cyclones.