Exploring spatial heterogeneity in synergistic effects of compound climate hazards: Extreme heat and wildfire smoke on cardiorespiratory hospitalizations in California.

Extreme heat and wildfire smoke events are increasingly co-occurring in the context of climate change, especially in California. Extreme heat and wildfire smoke may have synergistic effects on population health that vary over space. We leveraged high-resolution satellite and monitoring data to quantify spatially varying compound exposures to extreme heat and wildfire smoke in California (2006-2019) at ZIP Code Tabulation Area (ZCTA) level. We found synergistic effects between extreme heat and wildfire smoke on daily cardiorespiratory hospitalizations at the state level. We also found spatial heterogeneity in such synergistic effects across ZCTAs. Communities with lower education attainment, lower health insurance coverage, lower income, lower proportion of automobile ownership, lower tree canopy coverage, higher population density, and higher proportions of racial/ethnic minorities experienced higher synergistic effects. This study highlights the need to incorporate compound hazards and environmental justice considerations into evidence-based policy development to protect populations from increasingly prevalent compound hazards.

Wildfire smoke knows no borders: Differential vulnerability to smoke effects on cardio-respiratory health in the San Diego-Tijuana region.

Exposure to fine particles in wildfire smoke is deleterious for human health and can increase cases of cardio-respiratory illnesses and related hospitalizations. Neighborhood-level risk factors can increase susceptibility to environmental hazards, such as air pollution from smoke, and the same exposure can lead to different health effects across populations. While the San Diego-Tijuana border can be exposed to the same wildfire smoke event, socio-demographic differences may drive differential effects on population health. We used the October 2007 wildfires, one the most devastating wildfire events in Southern California that brought smoke to the entire region, as a natural experiment to understand the differential effect of wildfire smoke on both sides of the border. We applied synthetic control methods to evaluate the effects of wildfire smoke on cardio-respiratory hospitalizations in the Municipality of Tijuana and San Diego County separately. During the study period (October 11th- October 26th, 2007), 2009 hospital admissions for cardio-respiratory diseases occurred in San Diego County while 37 hospital admissions were reported in the Municipality of Tijuana. The number of cases in Tijuana was much lower than San Diego, and a precise effect of wildfire smoke was detected in San Diego but not in Tijuana. However, social drivers can increase susceptibility to environmental hazards; the poverty rate in Tijuana is more than three times that of San Diego. Socio-demographics are important in modulating the effects of wildfire smoke and can be potentially useful in developing a concerted regional effort to protect populations on both sides of the border from the adverse health effects of wildfire smoke.

The role of the California tier system in controlling population mobility during the COVID-19 pandemic.

BACKGROUND: Policies to restrict population mobility are a commonly used strategy to limit the transmission of contagious diseases. Among measures implemented during the COVID-19 pandemic were dynamic stay-at-home orders informed by real-time, regional-level data. California was the first state in the U.S. to implement this novel approach; however, the effectiveness of California's four-tier system on population mobility has not been quantified. METHODS: Utilizing data from mobile devices and county-level demographic data, we evaluated the impact of policy changes on population mobility and explored whether demographic characteristics explained variability in responsiveness to policy changes. For each California county, we calculated the proportion of people staying home and the average number of daily trips taken per 100 persons, across different trip distances and compared this to pre-COVID-19 levels. RESULTS: We found that overall mobility decreased when counties moved to a more restrictive tier and increased when moving to a less restrictive tier, as the policy intended. When placed in a more restrictive tier, the greatest decrease in mobility was observed for shorter and medium-range trips, while there was an unexpected increase in the longer trips. The mobility response varied by geographic region, as well as county-level median income, gross domestic product, economic, social, and educational contexts, the prevalence of farms, and recent election results. CONCLUSIONS: This analysis provides evidence of the effectiveness of the tier-based system in decreasing overall population mobility to ultimately reduce COVID-19 transmission. Results demonstrate that socio-political demographic indicators drive important variability in such patterns across counties.

The Role of Ozone as a Mediator of the Relationship Between Heat Waves and Mortality in 15 French Urban Areas.

Heat and tropospheric ozone have acute impacts on rates of premature death. Warm temperatures affect the photochemical processes in ozone formation, suggesting ozone as a mediator of the acute health effect of heat on mortality. We assembled a summertime daily time-series data set of 15 French urban areas during 2000-2015 to decompose the acute total effect of heat waves on mortality into natural direct and indirect effects using a regression-based product method under the potential outcomes framework. For each area, we estimated the effect of heat waves on mortality using a quasi-Poisson model with adjustment for covariates such as lagged nitrogen dioxide concentration, and we modeled ozone with a linear regression of heat waves and the same set of covariates. We pooled estimates across areas using random-effects models. We also provide R software code (R Foundation for Statistical Computing, Vienna, Austria) with which to reproduce or replicate our analysis. Most areas demonstrated evidence of mediation by ozone, with the pooled relative risks for natural indirect effects being 1.03 (95% confidence interval (CI): 1.02, 1.05), 1.03 (95% CI: 1.01, 1.04), and 1.04 (95% CI: 1.00, 1.07) for nonaccidental, cardiovascular, and respiratory mortality, respectively. We found evidence of a mediation effect by ozone in the association between heat waves and mortality in France which varied by geographic location and cause of mortality.

Ecosyndemics: The potential synergistic health impacts of highways and dams in the Amazon.

Ecosyndemics refer to disease interactions that result from environmental changes commonly caused by humans. In this paper, we push scholarship on ecosyndemics into new territory by using the ecosyndemic framework to compare two case studies-the Southern Interoceanic highway in Peru and the Belo Monte hydroelectric dam in Brazil-to assess the likelihood of socio-environmental factors interacting and leading to ill health in a syndemic fashion. Assessing these two case studies using an ecosyndemic perspective, we find that the construction of dams and highways in tropical forests create the conditions for increases in vector-borne illnesses, surges in sex work and sexually-transmitted infections, and increased psychological stress resulting from violence, delinquency, and the erosion of social cohesion. We suggest that these processes could interact synergistically to increase an individual's immune burden and a population's overall morbidity. However, we find differences in the impacts of the Interoceanic highway and the Belo Monte dam on food, water, and cultural systems, and observed that community and corporate-level actions may bolster health in the face of rapid socio-ecological change. Looking at the case studies together, a complex picture of vulnerability and resilience, risk and opportunity, complicates straight-forward predictions of ecosyndemic interactions resulting from these development projects but highlights the role that the ecosyndemic concept can play in informing health impact assessments and future research. We conclude by proposing a conceptual model of the potential interactions between psychological stress, vector-borne illnesses, and sexaully-transmitted infections and suggest that future investigations of synergistic interactions among these factors draw from the biological, social, and ecological sciences.

Heat Waves and Emergency Department Visits Among the Homeless, San Diego, 2012-2019.

Objectives. To determine the effect of heat waves on emergency department (ED) visits for individuals experiencing homelessness and explore vulnerability factors. Methods. We used a unique highly detailed data set on sociodemographics of ED visits in San Diego, California, 2012 to 2019. We applied a time-stratified case-crossover design to study the association between various heat wave definitions and ED visits. We compared associations with a similar population not experiencing homelessness using coarsened exact matching. Results. Of the 24 688 individuals identified as experiencing homelessness who visited an ED, most were younger than 65 years (94%) and of non-Hispanic ethnicity (84%), and 14% indicated the need for a psychiatric consultation. Results indicated a positive association, with the strongest risk of ED visits during daytime (e.g., 99th percentile, 2 days) heat waves (odds ratio = 1.29; 95% confidence interval = 1.02, 1.64). Patients experiencing homelessness who were younger or elderly and who required a psychiatric consultation were particularly vulnerable to heat waves. Odds of ED visits were higher for individuals experiencing homelessness after matching to nonhomeless individuals based on age, gender, and race/ethnicity. Conclusions. It is important to prioritize individuals experiencing homelessness in heat action plans and consider vulnerability factors to reduce their burden. (Am J Public Health. 2022;112(1):98-106. https://doi.org/10.2105/AJPH.2021.306557).

Hot and cold flavors of southern California's Santa Ana winds: their causes, trends, and links with wildfire.

Santa Ana winds (SAWs) are associated with anomalous temperatures in coastal Southern California (SoCal). As dry air flows over SoCal's coastal ranges on its way from the elevated Great Basin down to sea level, all SAWs warm adiabatically. Many but not all SAWs produce coastal heat events. The strongest regionally averaged SAWs tend to be cold. In fact, some of the hottest and coldest observed temperatures in coastal SoCal are linked to SAWs. We show that hot and cold SAWs are produced by distinct synoptic dynamics. High-amplitude anticyclonic flow around a blocking high pressure aloft anchored at the California coast produces hot SAWs. Cold SAWs result from anticyclonic Rossby wave breaking over the northwestern U.S. Hot SAWs are preceded by warming in the Great Basin and dry conditions across the Southwestern U.S. Precipitation over the Southwest, including SoCal, and snow accumulation in the Great Basin usually precede cold SAWs. Both SAW flavors, but especially the hot SAWs, yield low relative humidity at the coast. Although cold SAWs tend to be associated with the strongest winds, hot SAWs tend to last longer and preferentially favor wildfire growth. Historically, out of large (> 100 acres) SAW-spread wildfires, 90% were associated with hot SAWs, accounting for 95% of burned area. As health impacts of SAW-driven coastal fall, winter and spring heat waves and impacts of smoke from wildfires have been recently identified, our results have implications for designing early warning systems. The long-term warming trend in coastal temperatures associated with SAWs is focused on January-March, when hot and cold SAW frequency and temperature intensity have been increasing and decreasing, respectively, over our 71-year record. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00382-021-05802-z.

Spatial variation in the joint effect of extreme heat events and ozone on respiratory hospitalizations in California.

Extreme heat and ozone are co-occurring exposures that independently and synergistically increase the risk of respiratory disease. To our knowledge, no joint warning systems consider both risks; understanding their interactive effect can warrant use of comprehensive warning systems to reduce their burden. We examined heterogeneity in joint effects (on the additive scale) between heat and ozone at small geographical scales. A within-community matched design with a Bayesian hierarchical model was applied to study this association at the zip code level. Spatially varying relative risks due to interaction (RERI) were quantified to consider joint effects. Determinants of the spatial variability of effects were assessed using a random effects metaregression to consider the role of demographic/neighborhood characteristics that are known effect modifiers. A total of 817,354 unscheduled respiratory hospitalizations occurred in California from 2004 to 2013 in the May to September period. RERIs revealed no additive interaction when considering overall joint effects. However, when considering the zip code level, certain areas observed strong joint effects. A lower median income, higher percentage of unemployed residents, and exposure to other air pollutants within a zip code drove stronger joint effects; a higher percentage of commuters who walk/bicycle, a marker for neighborhood wealth, showed decreased effects. Results indicate the importance of going beyond average measures to consider spatial variation in the health burden of these exposures and predictors of joint effects. This information can be used to inform early warning systems that consider both heat and ozone to protect populations from these deleterious effects in identified areas.

The effects of an air quality alert program on premature mortality: A difference-in-differences evaluation in the region of Paris.

BACKGROUND: Daily exposure to air pollution has been shown to increase cardiovascular and respiratory mortality. While increases in short-term exposure to air pollutants at any daily concentrations has been shown to be associated to adverse health outcomes, days with extreme levels, also known as air pollution peaks based on specific thresholds, have been used to implement air quality alerts in various cities across the globe. OBJECTIVES: We aimed at evaluating the potential effects of the Air Quality Alerts (AQA) system on different causes of premature mortality in Paris, France. METHODS: Air quality alerts (AQA) based on particulate matter (PM10) levels and related interventions were implemented in the region of Paris in 2008 and were revised to be more stringent in 2011. In this study, we applied a difference-in-differences (DID) approach coupled with propensity-score matching (PSM) to daily mortality data for the period 2000 to 2015 to evaluate the effects of the Paris AQA program on different causes of premature mortality for the entire population and for adults > 75 years old. RESULTS: Overall, results did not show evidence of a reduction in mortality of the PM10 AQA program when first implemented in 2008 with initial thresholds (80 µg/m3); DID estimates were slightly above 1 for cardiovascular and respiratory mortality. However, when evaluating the drastic reduction in revised thresholds in 2011 (50 µg/m3) to trigger interventions, we identified a reduction in cardiovascular (DID = 0.84, 95% CI: 0.755 to 0.930) mortality, but no change in respiratory mortality was detected (DID = 0.97, 95% CI: 0.796, 1.191). DISCUSSION: Our study suggests that AQA may not have health benefits for the population when thresholds are set at high daily PM10 levels. Given that such policies are implemented in many other metropolitan areas across the globe, evaluating the effectiveness of AQA is important to provide public authorities and researchers a rationale for defining specific thresholds and extending the scope of these policies to lower air pollution levels.

A Rapid Review of COVID-19 Vaccine Prioritization in the U.S.: Alignment between Federal Guidance and State Practice.

BACKGROUND: Population groups to be prioritized for COVID-19 vaccinations in the U.S. have been determined at the Federal level, but there is variation in how States have implemented guidance. This review examines how the position of population groups in vaccine priority lists varies between Federal guidance and State practice. METHODS: An online search of State vaccination prioritization plans was conducted. Data were extracted on each population group included and their relative position. A standardized ranking method was applied to provide a directional measure of variability in prioritization between State and Federal guidance, for each population group. RESULTS: Healthcare workers and those in long-term care facilities were largely prioritized in line with Federal guidance. Aside from early education staff, essential workers were often excluded at State level. Almost all States included the 65-74 year age group and most assigned them to a higher position than recommended in Federal guidance. Those with underlying medical conditions were similarly highly prioritized, although there was more variability across States. Some socially vulnerable groups (not included in Federal guidance) were highly prioritized by many States. CONCLUSIONS: The prioritization of groups for COVID-19 vaccination has been highly variable despite clear Federal guidance. Future guidance must be relevant to local needs, values, and constraints, to minimize any unwarranted heterogeneity in vaccine access across populations.

Racial/ethnic disparities in the association between fine particles and respiratory hospital admissions in San Diego county, CA.

Ambient air pollution exposure is associated with exacerbating respiratory illnesses. Race/ethnicity (R/E) have been shown to influence an individual's vulnerability to environmental health risks such as fine particles (PM 2.5). This study aims to assess the R/E disparities in vulnerability to air pollution with regards to respiratory hospital admissions in San Diego County, California where most days fall below National Ambient Air Quality Standards (NAAQS) for daily PM 2.5 concentrations. Daily PM 2.5 levels were estimated at the zip code level using a spatial interpolation using inverse-distance weighting from monitor networks. The association between daily PM 2.5 levels and respiratory hospital admissions in San Diego County over a 15-year period from 1999 to 2013 was assessed with a time-series analysis using a multi-level Poisson regression model. Cochran Q tests were used to assess the effect modification of race/ethnicity on this association. Daily fine particle levels varied greatly from 1 µg/m3 to 75.86 µg/m3 (SD = 6.08 µg/m3) with the majority of days falling below 24-hour NAAQS for PM 2.5 of 35 µg/m3. For every 10 µg/m3 increase in PM 2.5 levels, Black and White individuals had higher rates (8.6% and 6.2%, respectively) of hospitalization for respiratory admissions than observed in the county as a whole (4.1%). Increases in PM 2.5 levels drive an overall increase in respiratory hospital admissions with a disparate burden of health effects by R/E group. These findings suggest an opportunity to design interventions that address the unequal burden of air pollution among vulnerable communities in San Diego County that exist even below NAAQS for daily PM 2.5 concentrations.

Modeling the Impact of Population Intervention Strategies on Reducing Health Disparities: Water, Sanitation, and Hygiene Interventions and Childhood Diarrheal Disease in Peru.

Access to safe water and basic sanitation and hygiene facilities (WASH) are important for childhood health globally. However, inequalities in WASH access persist, and local governments need to better understand the potential impact of scaling up WASH services on childhood health. Using 2011 Peru Demographic and Health Survey data as a case study, we applied a modified substitution estimator approach to assess the impact of scaling up access (20-100%) to WASH on diarrhea prevalence among children < 5 years. The modified substitution estimator approach can help identify population subgroups or areas where WASH interventions and sustained implementation could be most beneficial and reduce existing disparities. Using findings from a recent meta-analysis and computing bootstrapped estimates and 95% CIs, we examined inequalities in the effect of WASH on self-reported diarrhea by urbanicity, maternal education level, household wealth, and district of residence. Increasing access (100% change) to improved water sources, sanitation, and hygiene facilities reduced population-level prevalence of childhood diarrhea by 8.2% (95% CI: 4.1, 12.3), 5.5% (95% CI: 0.7, 9.8), and 5.2% (95% CI: 2.2, 8.1), respectively. In stratified analyses, increased access to improved water sources and hygiene facilities was associated with decreased prevalence of diarrhea, with the largest reduction in rural areas and households with lower maternal education and lower wealth. Our findings suggest targeted WASH implementation in Peru is needed in rural areas and among lower socioeconomic-status households. In addition, even low levels of change in overall WASH access may decrease diarrhea prevalence.

Author Correction: Revealing the air pollution burden associated with internal Migration in Peru.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Revealing the air pollution burden associated with internal Migration in Peru.

This study aims to quantify changes in outdoor (ambient) air pollution exposure from different migration patterns within Peru and quantify its effect on premature mortality. Data on ambient fine particulate matter (PM2.5) was obtained from the National Aeronautics and Space Administration (NASA). Census data was used to calculate rates of within-country migration at the district level. We calculated differences in PM2.5 exposure between "current" (2016-2017) and "origin" (2012) districts for each migration patterns. Using an exposure-response relationship for PM2.5 extracted from a meta-analysis, and mortality rates from the Peruvian Ministry of Health, we quantified premature mortality attributable to each migration pattern. Changes in outdoor PM2.5 exposure were observed between 2012 and 2016 with highest levels of PM2.5 in the Department of Lima. A strong spatial autocorrelation of outdoor PM2.5 values (Moran's I = 0.847, p-value=0.001) was observed. In Greater Lima, rural-to-urban and urban-to-urban migrants experienced 10-fold increases in outdoor PM2.5 exposure in comparison with non-migrants. Changes in outdoor PM2.5 exposure due to migration drove 137.1 (95%CI: 93.2, 179.4) premature deaths related to air pollution, with rural-urban producing the highest risk of mortality from exposure to higher levels of ambient air pollution. Our results demonstrate that the rural-urban and urban-urban migrant groups have higher rates of air pollution-related deaths.

Defining heat waves and extreme heat events using sub-regional meteorological data to maximize benefits of early warning systems to population health.

BACKGROUND: Extreme heat events have been consistently associated with an increased risk of hospitalization for various hospital diagnoses. Classifying heat events is particularly relevant for identifying the criteria to activate early warning systems. Heat event classifications may also differ due to heterogeneity in climates among different geographic regions, which may occur at a small scale. Using local meteorological data, we identified heat waves and extreme heat events that were associated with the highest burden of excess hospitalizations within the County of San Diego and quantified discrepancies using county-level meteorological criteria. METHODS: Eighteen event classifications were created using various combinations of temperature metric, percentile, and duration for both county-level and climate zone level meteorological data within San Diego County. Propensity score matching and Poisson regressions were utilized to ascertain the association between heat wave exposure and risk of hospitalization for heat-related illness and dehydration for the 1999-2013 period. We estimated both relative and absolute risks for each heat event classification in order to identify optimal definitions of heat waves and extreme heat events for the whole city and in each climate zone to target health impacts. RESULTS: Heat-related illness differs vastly by level (county or zone-specific), definition, and risk measure. We found the county-level definitions to be systematically biased when compared to climate zone definitions with the largest discrepancy of 56 attributable hospitalizations. The relative and attributable risks were often minimally correlated, which exemplified that relative risks alone are not adequate to optimize heat waves definitions. CONCLUSIONS: Definitions based on county-level defined thresholds do not provide an accurate picture of the observed health effects and will fail to maximize the potential effectiveness of heat warning systems. Absolute rather than relative risks are a more appropriate measure to define the set of criteria to activate early warnings systems and thus maximize public health benefits.

Extreme heat episodes and risk of preterm birth in California, 2005-2013.

BACKGROUND: Preterm birth is a leading cause of infant morbidity and mortality. Identifying potentially modifiable triggers toward the end of gestation, such as extreme heat, can improve understanding of the role of acute stress on early deliveries and inform warning systems. In this study we examined the association between extreme heat, variously defined during the last week of gestation, and risk of preterm birth among mothers in California. METHODS: We created a population-based cohort comprised of 1,967,300 mothers who had live, singleton births in California, from May through September 2005-2013. Daily temperature data estimated at the maternal zip code of residence was used to create 12 definitions of extreme heat with varying relative temperatures (75th, 90th, 95th, and 98th percentiles) and durations (at least 2, 3, or 4 consecutive days). We estimated risk of preterm birth (<37 gestational weeks) in relation to exposure to extreme heat during the last week of gestation with multi-level Cox proportional hazard regression models, adjusting for maternal characteristics, sex of neonate, and seasonality. We also included randomly generated data, SAS code, and estimates for reproducibility purposes. RESULTS: Approximately 7% of the cohort had a preterm birth. For all definitions of extreme heat, the risk of preterm birth was consistently higher among mothers who experienced an extreme heat episode during their last week of gestation. Hazard ratios ranged from 1.008 (95% CI: 0.997, 1.021) to 1.128 (95% CI: 1.052, 1.210), with increasing associations as the relative temperature and duration of extreme heat episode increased. CONCLUSION: This study adds to the previous literature by considering multiple definitions of extreme heat and applying a time-to-event framework. Findings suggest that acute exposure to extreme heat during the last week of gestation may trigger an earlier delivery. Implementing heat warning systems targeted toward pregnant women may improve birth outcomes.

Examining the joint effects of heatwaves, air pollution, and green space on the risk of preterm birth in California.

BACKGROUND: Exposure to high air temperature in late pregnancy is increasingly recognized as a risk factor for preterm birth (PTB). However, the combined effects of heatwaves with air pollution and green space are still unexplored. In the context of climate change, investigating the interaction between environmental factors and identifying communities at higher risk is important to better understand the etiological mechanisms and design targeted interventions towards certain women during pregnancy. OBJECTIVES: To examine the combined effects of heatwaves, air pollution and green space exposure on the risk of PTB. METHODS: California birth certificate records for singleton births (2005-2013) were obtained. Residential zip code-specific daily temperature during the last week of gestation was used to create 12 definitions of heatwave with varying temperature thresholds and durations. We fit multi-level Cox proportional hazard models with time to PTB as the outcome and gestational week as the temporal unit. Relative risk due to interaction (RERI) was applied to estimate the additive interactive effect of air pollution and green space on the effect of heatwaves on PTB. RESULTS: In total, 1,967,300 births were included in this study. For PM2.5, PM10 and O3, we found positive additive interactions (RERIs >0) between heatwaves and higher air pollution levels. Combined effects of heatwaves and green space indicated negative interactions (RERIs <0) for less intense heatwaves (i.e., shorter duration or relatively low temperature), whereas there were potential positive interactions (RERIs >0) for more intense heatwaves. CONCLUSION: This study found synergistic harmful effects for heatwaves with air pollution, and potential positive interactions with lack of green space on PTB. Implementing interventions, such as heat warning systems and behavioral changes, targeted toward pregnant women at risk for high air pollution and low green space exposures may optimize the benefits of reducing acute exposure to extreme heat before delivery.

Impact of heat on mortality and morbidity in low and middle income countries: A review of the epidemiological evidence and considerations for future research.

Heat waves and high air temperature are associated with increased morbidity and mortality. However, the majority of research conducted on this topic is focused on high income areas of the world. Although heat waves have the most severe impacts on vulnerable populations, relatively few studies have studied their impacts in low and middle income countries (LMICs). The aim of this paper is to review the existing evidence in the literature on the impact of heat on human health in LMICs. We identified peer-reviewed epidemiologic studies published in English between January 1980 and August 2018 investigating potential associations between high ambient temperature or heat waves and mortality or morbidity. We selected studies according to the following criteria: quantitative studies that used primary and/or secondary data and report effect estimates where ambient temperature or heat waves are the main exposure of interest in relation to human morbidity or mortality within LMICs. Of the total 146 studies selected, eighty-two were conducted in China, nine in other countries of East Asia and the Pacific, twelve in South Asia, ten in Sub-Saharan Africa, eight in the Middle East and North Africa, and seven in each of Latin America and Europe. The majority of studies (92.9%) found positive associations between heat and human morbidity/mortality. Additionally, while outcome variables and study design differed greatly, most utilized a time-series study design and examined overall heath related morbidity/mortality impacts in an entire population, although it is notable that the selected studies generally found that the elderly, women, and individuals within the low socioeconomic brackets were the most vulnerable to the effects of high temperature. By highlighting the existing evidence on the impact of extreme heat on health in LMICs, we hope to determine data needs and help direct future studies in addressing this knowledge gap. The focus on LMICs is justified by the lack of studies and data studying the health burden of higher temperatures in these regions even though LMICs have a lower capacity to adapt to high temperatures and thus an increased risk.

A quantile regression approach to examine fine particles, term low birth weight, and racial/ethnic disparities.

BACKGROUND: Exposure to fine particulate matter (PM2.5) during pregnancy has been shown to be associated with reduced birth weight and racial/ethnic minorities have been found to be more vulnerable. Previous studies have focused on the mean value of birth weight associated with PM2.5, which may mask meaningful differences. We applied a quantile regression approach to investigate the variation by percentile of birth weight and compared non-Hispanic (NH) Black, NH White, and Hispanic mothers. METHODS: Data for singleton births in California from October 24, 2005 to February 27, 2010 were collected from the birth records accessed from the California Department of Public Health. Air pollution monitoring data collected by the California Air Resources Board and interpolated for each zip code using an inverse-distance weighting approach, and linked to maternal zip code of residence reported on the birth certificate. Multilevel linear regression models were conducted with mother's residential zip code tabulation area as a random effect. Multilevel quantile regression models were used to analyze the association at different percentiles of birth weight (5th, 10th, 25th, 50th, 75th, 90th, 95th), as well as examine the heterogeneity in this association between racial/ethnic groups. RESULTS: Linear regression revealed that a 10 µg/m3 increase in PM2.5 exposure during pregnancy is associated with a mean birth weight decrease of 7.31 g [95% confidence interval (CI): 8.10, 6.51] and NH Black mothers are the most vulnerable. Results of the quantile regression are not constant across quantiles. For NH Black mothers whose infants had the lowest birthweight of less than 2673 g (5th percentile), a 10 µg/m3 increase in PM2.5 exposure is associated with a decrease of 18.57 g [95% CI: 22.23, 14.91], while it is associated with a decrease of 7.77 g [95% CI: 8.73, 6.79] for NH White mothers and 7.76 [8.52, 7.00] decrease for Hispanic mothers at the same quantile. CONCLUSION: Results of the quantile regression revealed greater disparities, particularly for infants with the lowest birth weight. By identifying vulnerable populations, we can promote and implement policies to confront these health disparities.