Objectives To examine the relationships of intra-day hourly temperature variability (HTV) with mental health outcomes in subtropical climates and effect modification by demographic characteristics and season. Methods We applied a time-series meta-analytic framework to analyze daily temperature conditions and hospital visits across the 53 largest Texas counties between 2015 and 2019. A generalized additive model combined with a distributed lag non-linear model was used to examine exposure-lag-response relationships in each region, and a random effects meta-analysis was applied to estimate the pooled effects across counties, followed by stratified analyses to assess effect modification. Results The highest cumulative mental health risks were observed over lag 0–1 days, with increased intra-day HTV associated with higher risks of mental health-related outpatient hospital visits. Compared to young adults, children/youths were at greater risk of outpatient hospital visits for overall mental and behavioral disorders (F00-F99) associated with HTV, whereas older adults showed greater increases in outpatient visits for substance use disorders (F10-F19) in relation to HTV. Additionally, effects peaked at lag 0–1 in most seasons, but winter showed delayed and prolonged cumulative effects. Conclusions The seasonal differences underscore the need to pay particular attention to the heightened risks in spring and prolonged risks in winter. Additionally, greater susceptibility of children/youth and older adults to HTV-related mental disorders suggests that healthcare systems and school-based mental health services should incorporate seasonal monitoring protocols and enhance mental health screening and outreach during high-risk periods of elevated temperature variability in subtropical regions.

Emerging research has examined the mental health impact of built environment features in the context of climate disasters; however, evidence remains fragmented and heterogeneous. While existing reviews have synthesized particular built environment characteristics such as greenspace and housing under extreme weather events, a comprehensive synthesis examining diverse built environment features in relation to disaster-related mental health outcomes is lacking. This systematic review synthesized empirical evidence to assess these associations and identify research gaps and priorities for future study. Drawing upon seven electronic databases: Web of Science, Medline, ProQuest, EMBASE, PsycINFO, CINAHL, and Environmental Complete and following PRISMA guidelines, this review identified a final set of 22 articles. Almost half of the included studies were published between 2020 and 2024; most were cross-sectional studies conducted in the United States. Overall, the association of built environment features with mental health has been examined in diverse disasters, such as hurricanes, drought, heatwaves, and floods. Mental health assessments have focused on a range of outcomes, notably PTSD and psychological distress. We identified 12 studies that examined the role of built environment features in predicting climate disaster-related mental health outcomes, considering urbanicity, land cover, housing type, and characteristics of neighborhood vulnerability. Ten studies examined the role of four features in modifying the direction and strength of the relationship between climate disasters and mental health, namely urbanicity, land cover, neighborhood deprivation, and sky view factor. The synthesized evidence provides new insights into the psychological effect of built environments and policy implications for disaster preparedness and recovery.

Millions of lives are lost due to air pollution, underscoring the urgent need to understand the factors that influence air quality. Here, we employ a multidisciplinary approach, integrating media data analysis, behavioral experiments, and agent-based modeling to explore the impact of inaccurate air quality reports on travel behavior and actual air quality. Media analysis reveals a negative correlation between the accuracy of air pollution reports and the air quality index. Behavioral experiments demonstrate that media reports inaccurately showing good air quality reduce individuals' perceived pollution levels, which in turn leads to increased travel behavior. Furthermore, agent-based modeling simulations show that individual changes in travel behavior can collectively lead to increased air pollutant emissions and deteriorated overall air quality. This study highlights the critical role of accurate media reporting for air pollution management.

This study presents a population-specific adaptation of the COMFA model, customized to estimate thermal stress levels of older adults. We refer to this index as the COMFA thermal comfort-older adults (COMFA-OA) model. This model incorporates physiological adjustments to enhance accuracy in estimating thermal stress for the older demographic. Key modifications include empirical updates to sweat heat loss, core temperature, and metabolic rate equations to reflect age-related changes in heat regulation and metabolic efficiency. The model was validated using the ASHRAE Global Thermal Comfort Database II and a field experiment assessing thermal sensation of older adults during warm and hot seasons in Texas. Results show COMFA-OA (MAE = 4.03, RMSE = 4.14) outperforms traditional models like PET (MAE = 4.72, RMSE = 4.83), UTCI (MAE = 4.82, RMSE = 4.95), and COMFA (MAE = 4.17, RMSE = 4.29) in accuracy of predicting thermal sensation vote, demonstrating greater multinomial logit model fit (AIC: 4435.3) and achieving competitive computational efficiency (TOPS = 97.54) second only to original COMFA (TOPS = 331.21) and COMFAcourtyard (TOPS = 481.87). Sensitivity analysis identified air temperature, radiant temperature, age, weight, and height as primary contributors to thermal comfort variance in older adults. The COMFA-OA model offers a practical tool for environmental management and public health applications aimed at promoting climate resilience and thermal security for older populations that are vulnerable to heat conditions.

With the widespread prevalence of mobile devices, ecological momentary assessment (EMA) can be combined with geospatial data acquired through geographic techniques like global positioning system (GPS) and geographic information system. This technique enables the consideration of individuals' health and behavior outcomes of momentary exposures in spatial contexts, mostly referred to as "geographic ecological momentary assessment" or "geographically explicit EMA" (GEMA). However, the definition, scope, methods, and applications of GEMA remain unclear and unconsolidated. To fill this research gap, we conducted a systematic review to synthesize the methodological insights, identify common research interests and applications, and furnish recommendations for future GEMA studies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines to systematically search peer-reviewed studies from six electronic databases in 2022. Screening and eligibility were conducted following inclusion criteria. The risk of bias assessment was performed, and narrative synthesis was presented for all studies. From the initial search of 957 publications, we identified 47 articles included in the review. In public health, GEMA was utilized to measure various outcomes, such as psychological health, physical and physiological health, substance use, social behavior, and physical activity. GEMA serves multiple research purposes: 1) enabling location-based EMA sampling, 2) quantifying participants' mobility patterns, 3) deriving exposure variables, 4) describing spatial patterns of outcome variables, and 5) performing data linkage or triangulation. GEMA has advanced traditional EMA sampling strategies and enabled location-based sampling by detecting location changes and specified geofences. Furthermore, advances in mobile technology have prompted considerations of additional sensor-based data in GEMA. Our results highlight the efficacy and feasibility of GEMA in public health research. Finally, we discuss sampling strategy, data privacy and confidentiality, measurement validity, mobile applications and technologies, and GPS accuracy and missing data in the context of current and future public health research that uses GEMA.

Perinatal exposure to heat and air pollution has been shown to affect the risk of preterm birth (PTB). However, limited evidence exists regarding their joint effects, particularly in heavily polluted regions like China. This study utilized data from the ongoing China Birth Cohort Study, including 103 040 birth records up to December 2020, and hourly measurements of air pollution (PM 2.5 , NO 2 , and O 3 ) and temperature. We assessed the nonlinear associations between air pollution and temperature extereme exposures and PTB by employing generalized additive models with restricted cubic slines. Air pollution and temperature thresholds (corresponding to minimum PTB risks) were determined by the lowest Akaike Information Criterion. We found that maternal exposures to PM 2.5 , NO 2 , O 3 , and both low and high temperature during the third trimester of pregnancy were independently associated with increased risk of PTB. The adjusted risk ratios for PTB of PM 2.5 , O 3 , NO 2 , and temperature at the 95th percentile against thresholds were 1.32 (95% CI: 1.23, 1.42), 1.33 (95% CI: 1.18, 1.50), 1.44 (95% CI: 1.33, 1.56) and 1.70 (95% CI: 1.56, 1.85), respectively. Positive additive interactions [relative excess risk due to interaction (RERI) > 0] of PM 2.5 –high temperature (HT), O 3 –HT, O 3 –low temperature (LT) are identified, but the interactive effects of PM 2.5 and LT were negative (RERI < 0). These observed independent effects of air pollution and temperature, along with their potential joint effects, have important implications for future studies and the development of public health policies aimed at improving perinatal health outcomes.

As climate change exerts wide ranging health impacts, there is a surge of interest in the associations between climatic factors and mental and behavioral disorders (MBDs). Existing quantitative syntheses focus mainly on heat and high temperature exposure, neglecting the effects of other climatic factors and their synergies. The objective of this study is to conduct a systematic review and meta-analysis of the evidence of associations between climatic exposure and combined mental and behavioral health conditions and specific mental disorders (e.g., schizophrenia, dementia). A systematic search was conducted April 11-16, 2022 using Web of Science, Medline, ProQuest, EMBASE, PsycINFO, CINAHL, and Environment Complete. Screening and eligibility screening followed inclusion criteria based on population, exposure, comparator, and outcome guidelines. Risk of bias assessment was performed, a narrative synthesis was first presented for all studies, and random-effect meta-analyses were performed when at least three studies were available for a specific exposure-outcome pair. Certainty of evidence was evaluated following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool. The search process yielded 7696 initial results, from which we identified 88 studies to include in the review set. Climatic factors reported included air temperature, solar radiation/sunshine, barometric pressure, precipitation, relative humidity, wind direction/speed, and thermal index. Outcomes including MBD incidences (e.g., schizophrenia, mood disorders, neurotic disorders), mental health-related mortality, and self-reported psychological states. Meta-analysis showed that heatwaves (pooled RR = 1.05, 95 % CI = 1.02-1.08) and extreme high temperatures (99th percentile: pooled RR = 1.18, 95 % CI = 1.08-1.29) were associated with higher risk of MBD. Cold extremes, however, were not associated with MBD risk. The findings further identified an association between increases in a thermal index (i.e., apparent temperature) and elevated risk of MBD (pooled RR = 1.06, 95 % CI = 1.03-1.12); specifically, a 99th percentile high temperature was associated with increased schizophrenia risk (pooled RR = 1.07, 95 % CI = 1.01-1.12). Risk of bias assessment showed most studies to have low or moderately low risks, while a few studies were rated probably high in confounding, selection bias, outcome measurement, and reporting bias. GRADE evaluation revealed moderate certainty of evidence on thermal comfort index and MBD, but low certainty related to air temperature or sunshine duration. These findings call attention to the heterogeneity of exposure measures and the utility of thermal indices that consider the synergistic effects of meteorological factors. Methodological concerns such as the linearity assumption and cumulative effects are discussed.

Heatwaves and urban heat islands disproportionately affect residents of urban areas. Past studies on the heat vulnerability indexes (HVI) to evaluate the heat-related risk have two major limitations: the inability to capture street-level human heat stress and reliance on single meteorological proxies to measure heat exposure. To address these gaps, this study examines the impact of streetlevel outdoor thermal comfort (OTC) on heat vulnerability in the city of Houston, Texas. OTC refers to an individual's thermal perception of their surroundings. The study estimates the impacts of HVI scores and energy budget (EB) values of OTC on heat-related disease while investigating their spatial distributions and clusters. The results show that the explanatory power of the suggested models on the number of emergency department (ED) visits improved when the streetlevel OTC had higher HVI scores and more comfortable conditions. A positive bivariate relationship was found between the HVI scores and EB values, showing the highest explanatory power (adj-r2) of around 36%. Chronic disease and heat exposure significantly affected the HVI, whereas tree and sky view factors were crucial determinants of the EB values. These findings provide a new approach to heat vulnerability evaluation at the human scale to effectively address heat-related risk.

Residents of public or subsidized housing experience social and environmental disadvantages and are therefore particularly vulnerable to disasters. Although research has highlighted risks associated with common disasters, such as floods and heatwaves, to date little is known about the impacts of infrequent disasters, such as a winter storm in a subtropical climate with mild winter weather. Even less is known about factors related to disaster preparation by and coping strategies of these disadvantaged residents. The present study aimed to examine the impacts of the 2021 Winter Storm Uri on subsided housing residents and the factors that enabled or hindered their effective adaptation. We conducted semi-structured interviews two to four months post-disaster with residents from eleven subsidized housing sites in College Station, Texas. A total of 33 participants aged between 24 and 90 completed the interviews. Guided by relevant theories such as the Protective Motivation Theory (PMT), a combination of inductive and deductive coding was carried out using MaxQDA qualitative data analysis software, and codes, themes, and categories were identified reiteratively. The results demonstrated the process involving cognitive appraisal, preparation intention, and behavioral adaptation and the roles played by environmental stressors and infrastructural modifiers in subsidized housing. Specifically, we found: 1) insufficient risk communication and the digital divide hindered formation of accurate hazard appraisal, 2) high perceived costs of preparation contributed to low coping appraisal, 3) Differences in physical infrastructure conditions were linked to varying adaptation capacities and behaviors, and 4) overall social isolation was partly remedied by clustered social ties formed within neighborhoods that were homophily. The implications for future disaster policy and planning are discussed.

Background: Independent and joint effects of perinatal exposure to air pollution and heat stress on preterm birth (PTB) remain less explored. Methods: We obtained 103,040 birth records from September 2018 to December 2020. Gestational age was recorded in days, and PTB was defined as gestational age < 37 completed weeks. Hourly air pollution (PM2·5, NO2, and O3) and temperature data were obtained from the national monitoring stations and ERA5 land reanalysis dataset, respectively. Generalized additive models with restricted cubic splines were performed to evaluate the independent and joint associations, as well as the shape of exposure-response curve between trimester-specific exposure and gestational age and PTB, adjusting for covariates. Findings: Nonlinear associations with threshold effects between air pollutants, temperature and preterm birth were observed. The adjusted risk ratios for PTB of PM2·5, O3, NO2, and temperature at the 95 th percentile against thresholds were 1·32 (95% CI: 1·23, 1·42), 1·28 (95% CI: 1·19, 1·39), 1·44 (95% CI: 1·33, 1·56) and 1·70 (95% CI: 1·56, 1·85), respectively. Positive additive interactions [relative excess risk due to interaction, RERI > 0] of PM2.5 –High Temperature (HT), O3 –HT, O3 –Low Temperature (LT) are identified, but the interactive effects of PM 2·5 and LT were negative (RERI < 0). Interpretation: Maternal exposures to PM2.5, O3, and both low and high temperature were independently associated with increased risk of preterm birth. Air pollutants and high temperature extremes showed additive interaction effects. Given the potential interactive effects, health measures for pregnant women should consider both air pollution and temperature.Funding Information: This study was supported by the National Key Research and Development Program of China (2016YFC1000101, 2018YFE0106900), National Natural Science Foundation of China (81872582, 81872583, M-0420), Guangdong Provincial Natural Science Foundation Team Project (2018B030312005), Natural Science Foundation of Guangdong Province (2021A1515011754, 2021B1515020015, 2020A1515011131), and Research Grants Council of the Hong Kong Special Administrative Region, China (HKBU22201820).Declaration of Interests: None declared.Ethics Approval Statement: The study protocol was approved by the ethics committee of the Sun Yat-sen University.

Place-based structural inequalities can have critical implications for the health of vulnerable populations. Historical urban policies, such as redlining, have contributed to current inequalities in exposure to intra-urban heat. However, it is unknown whether these spatial inequalities are associated with disparities in heat-related health outcomes. The aim of this study is to determine the relationships between historical redlining, intra-urban heat conditions, and heat-related emergency department visits using data from eleven Texas cities. At the zip code level, the proportion of historical redlining was determined, and heat exposure was measured using daytime and nighttime land surface temperature (LST). Heat-related inpatient and outpatient rates were calculated based on emergency department visit data that included ten categories of heat-related diseases between 2016 and 2019. Regression or spatial error/lag models revealed significant associations between higher proportions of redlined areas in the neighborhood and higher LST (Coef. = 0.0122, 95% CI = 0.0039 - 0.0205). After adjusting for indicators of social vulnerability, neighborhoods with higher proportions of redlining showed significantly elevated heat-related outpatient visit rate (Coef. = 0.0036, 95% CI = 0.0007-0.0066) and inpatient admission rate (Coef. = 0.0018, 95% CI = 0.0001-0.0035). These results highlight the role of historical discriminatory policies on the disparities of heat-related illness and suggest a need for equity-based urban heat planning and management strategies.

Global climate change has increased the risks of extreme weather-related disasters, leading to severe public health burdens. In February 2021, Winter Storm Uri brought severe cold to southern United States and caused unprecedented health and safety concerns. Residents in subsidized rental housing were among the most vulnerable to cold stress during such a cold storm. However, existing research on the assessment and mitigation of cold stress in underserved neighborhoods in warmer climate zones is limited, which results in the negligence of cold event preparedness and mitigation policies. Therefore, this study aims to assess the micrometeorological conditions and human cold stress in subsidized housing neighborhoods during the 2021 Winter Storm and determine the extent to which cold mitigation windbreak designs are effective in reducing cold stress. Field measurements, ENVI-met simulations, and biometeorological calculations were conducted to reconstruct the microclimate conditions and cold stress during the storm, and three cold-mitigation windbreak designs with varying foliage densities were evaluated. Results showed that the conditions were categorized as “extreme cold stress” for the majority of the day, but especially during nighttime. Areas close to the buildings were generally warmer, and the wind-blocking effects of a building decreased as the distance to the building increased. A moderately dense-foliage windbreak was the most effective in reducing wind speed and improving thermal comfort. Intentional environmental modifications to alter wind velocity and disaster relief programs that provide emergency clothing supplies during power outage may be beneficial to these underserved communities.

Summary: During the period of COVID-19 epidemic, there was no report on the clinical strategies for the delivery of pregnant women This study aimed to develop