Generalizability of Heat-related Health Risk Associations Observed in a Large Healthcare Claims Database of Patients with Commercial Health Insurance.

Background: Extreme ambient heat is unambiguously associated with a higher risk of illness and death. The Optum Labs Data Warehouse (OLDW), a database of medical claims from US-based patients with commercial or Medicare Advantage health insurance, has been used to quantify heat-related health impacts. Whether results for the insured subpopulation are generalizable to the broader population has, to our knowledge, not been documented.

Association between Combined Sewer Overflow Events and Gastrointestinal Illness in Massachusetts Municipalities with and without River-Sourced Drinking Water, 2014-2019.

Background: Combined sewer overflow (CSO) events release untreated wastewater into surface waterbodies during heavy precipitation and snowmelt. Combined sewer systems serve people in the United States, primarily in urban and suburban municipalities in the Midwest and Northeast. Predicted increases in heavy precipitation events driven by climate change underscore the importance of quantifying potential health risks associated with CSO events.

Warm season ambient ozone and children's health in the USA.

Background: Over 120 million people in the USA live in areas with unsafe ozone (O3) levels. Studies among adults have linked exposure to worse lung function and higher risk of asthma and chronic obstructive pulmonary disease (COPD). However, few studies have examined the effects of O3 in children, and existing studies are limited in terms of their geographic scope or outcomes considered.

Short term exposure to low level ambient fine particulate matter and natural cause, cardiovascular, and respiratory morbidity among US adults with health insurance: case time series study.

Objective: To estimate the excess relative and absolute risks of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease associated with daily exposure to fine particulate matter (PM) at concentrations below the new World Health Organization air quality guideline limit among adults with health insurance in the contiguous US.

Design: Case time series study.

Setting: US national administrative healthcare claims database.

Survey of extreme heat public health preparedness plans and response activities in the most populous jurisdictions in the United States.

Background: Increasingly frequent and intense extreme heat events (EHEs) are indicative of climate change impacts, and urban areas' social and built environments increase their risk for health consequences. Heat action plans (HAPs) are a strategy to bolster municipal EHE preparedness. The objective of this research is to characterize municipal interventions to EHEs and compare U.

Planning to Reduce the Health Impacts of Extreme Heat: A Content Analysis of Heat Action Plans in Local United States Jurisdictions.

To examine commonalities and gaps in the content of local US heat action plans (HAPs) designed to decrease the adverse health effects of extreme heat. We used content analysis to identify common strategies and gaps in extreme heat preparedness among written HAPs in the United States from jurisdictions that serve municipalities with more than 200 000 residents. We reviewed, coded, and analyzed plans to assess the prevalence of key components and strategies.

Examining the Optimal Placement of Cooling Centers to Serve Populations at High Risk of Extreme Heat Exposure in 81 US Cities.

Objective: Although extreme heat can impact the health of anyone, certain groups are disproportionately affected. In urban settings, cooling centers are intended to reduce heat exposure by providing air-conditioned spaces to the public. We examined the characteristics of populations living near cooling centers and how well they serve areas with high social vulnerability.

Internet searches and heat-related emergency department visits in the United States.

Emerging research suggests that internet search patterns may provide timely, actionable insights into adverse health impacts from, and behavioral responses to, days of extreme heat, but few studies have evaluated this hypothesis, and none have done so across the United States. We used two-stage distributed lag nonlinear models to quantify the interrelationships between daily maximum ambient temperature, internet search activity as measured by Google Trends, and heat-related emergency department (ED) visits among adults with commercial health insurance in 30 US metropolitan areas during the warm seasons (May to September) from 2016 to 2019. Maximum daily temperature was positively associated with internet searches relevant to heat, and searches were in turn positively associated with heat-related ED visits.

Association Between Ambient Heat and Risk of Emergency Department Visits for Mental Health Among US Adults, 2010 to 2019.

Importance: The implications of extreme heat for physical health outcomes have been well documented. However, the association between elevated ambient temperature and specific mental health conditions remains poorly understood.

Objective: To investigate the association between ambient heat and mental health-related emergency department (ED) visits in the contiguous US among adults overall and among potentially sensitive subgroups.

Ambient heat and risks of emergency department visits among adults in the United States: time stratified case crossover study.

Objective: To quantify the association between ambient heat and visits to the emergency department (ED) for any cause and for cause specific conditions in the conterminous United States among adults with health insurance.

Design: Time stratified case crossover analyses with distributed lag non-linear models.

Setting: US nationwide administrative healthcare claims database.

Quantifying the Impacts of Pre- and Post-Conception TSH Levels on Birth Outcomes: An Examination of Different Machine Learning Models.

Background: While previous studies identified risk factors for diverse pregnancy outcomes, traditional statistical methods had limited ability to quantify their impacts on birth outcomes precisely. We aimed to use a novel approach that applied different machine learning models to not only predict birth outcomes but systematically quantify the impacts of pre- and post-conception serum thyroid-stimulating hormone (TSH) levels and other predictive characteristics on birth outcomes.

Methods: We used data from women who gave birth in Shanghai First Maternal and Infant Hospital from 2014 to 2015.