Uncovering social and environmental factors that increase the burden of climate-sensitive diarrheal infections on children.

Climate-sensitive infectious diseases are an issue of growing concern due to global warming and the related increase in the incidence of extreme weather and climate events. Diarrhea, which is strongly associated with climatic factors, remains among the leading causes of child death globally, disproportionately affecting populations in low- and middle-income countries (LMICs). We use survey data for 51 LMICs between 2000 and 2019 in combination with gridded climate data to estimate the association between precipitation shocks and reported symptoms of diarrheal illness in young children. We account for differences in exposure risk by climate type and explore the modifying role of various social factors. We find that droughts are positively associated with diarrhea in the tropical savanna regions, particularly during the dry season and dry-to-wet and wet-to-dry transition seasons. In the humid subtropical regions, we find that heavy precipitation events are associated with increased risk of diarrhea during the dry season and the transition from dry-to-wet season. Our analysis of effect modifiers highlights certain social vulnerabilities that exacerbate these associations in the two climate zones and present opportunities for public health intervention. For example, we show that stool disposal practices, child feeding practices, and immunizing against the rotavirus modify the association between drought and diarrhea in the tropical savanna regions. In the humid subtropical regions, household's source of water and water disinfection practices modify the association between heavy precipitation and diarrhea. The evidence of effect modification varies depending on the type and duration of the precipitation shock.

A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2.5 in California (2006-2020).

Though fine particulate matter (PM2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM2.5 presents a challenge since measurements of the smoke contribution to PM2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies.

Mediating Role of Fine Particles Abatement on Pediatric Respiratory Health During COVID-19 Stay-at-Home Order in San Diego County, California.

Lower respiratory tract infections disproportionately affect children and are one of the main causes of hospital referral and admission. COVID-19 stay-at-home orders in early 2020 led to substantial reductions in hospital admissions, but the specific contribution of changes in air quality through this natural experiment has not been examined. Capitalizing on the timing of the stay-at-home order, we quantified the specific contribution of fine-scale changes in PM2.5 concentrations to reduced respiratory emergency department (ED) visits in the pediatric population of San Diego County, California. We analyzed data on pediatric ED visits (n = 72,333) at the ZIP-code level for respiratory complaints obtained from the ED at Rady Children's Hospital in San Diego County (2015-2020) and ZIP-code level PM2.5 from an ensemble model integrating multiple machine learning algorithms. We examined the decrease in respiratory visits in the pediatric population attributable to the stay-at-home order and quantified the contribution of changes in PM2.5 exposure using mediation analysis (inverse of odds ratio weighting). Pediatric respiratory ED visits dropped during the stay-at-home order (starting on 19 March 2020). Immediately after this period, PM2.5 concentrations, relative to the counterfactual values based in the 4-year baseline period, also decreased with important spatial variability across ZIP codes in San Diego County. Overall, we found that decreases in PM2.5 attributed to the stay-at-home order contributed to explain 4% of the decrease in pediatric respiratory ED visits. We identified important spatial inequalities in the decreased incidence of pediatric respiratory illness and found that brief decline in air pollution levels contributed to a decrease in respiratory ED visits.

Climate change contributions to future atmospheric river flood damages in the western United States.

Atmospheric rivers (ARs) generate most of the economic losses associated with flooding in the western United States and are projected to increase in intensity with climate change. This is of concern as flood damages have been shown to increase exponentially with AR intensity. To assess how AR-related flood damages are likely to respond to climate change, we constructed county-level damage models for the western 11 conterminous states using 40 years of flood insurance data linked to characteristics of ARs at landfall. Damage functions were applied to 14 CMIP5 global climate models under the RCP4.5 "intermediate emissions" and RCP8.5 "high emissions" scenarios, under the assumption that spatial patterns of exposure, vulnerability, and flood protection remain constant at present day levels. The models predict that annual expected AR-related flood damages in the western United States could increase from $1 billion in the historical period to $2.3 billion in the 2090s under the RCP4.5 scenario or to $3.2 billion under the RCP8.5 scenario. County-level projections were developed to identify counties at greatest risk, allowing policymakers to target efforts to increase resilience to climate change.

A Perspective on Pediatric Respiratory Outcomes During California Wildfires Due to Smoke and PM2.5 Exposure.

As wildfires increase in prevalence and intensity across California and globally, it is anticipated that more children will be exposed to wildfire smoke, and thus face associated adverse health outcomes. Here, we provide a concise summary of the respiratory effects of California's wildfires on pediatric healthcare utilization, examine global examples of wildfire smoke exposure within the pediatric population and associated physiological effects, and assess the efficacy of metrics used to measure and communicate air quality during wildfires within the United States and elsewhere.

Precipitation variability and risk of infectious disease in children under 5 years for 32 countries: a global analysis using Demographic and Health Survey data.

BACKGROUND: Precipitation variability is a potentially important driver of infectious diseases that are leading causes of child morbidity and mortality worldwide. Disentangling the links between precipitation variability and disease risk is crucial in a changing climate. We aimed to investigate the links between precipitation variability and reported symptoms of infectious disease (cough, fever, and diarrhoea) in children younger than 5 years. METHODS: We used nationally representative survey data collected between 2014 and 2019 from Demographic and Health Survey (DHS) surveys for 32 low-income to middle-income countries in combination with high-resolution precipitation data (via the Climate Hazards Group InfraRed Precipitation with Station dataset). We only included DHS data for which interview dates and GPS coordinates (latitude and longitude) of household clusters were available. We used a regression modelling approach to assess the relationship between different precipitation variability measures and infectious disease symptoms (cough, fever, and diarrhoea), and explored the effect modification of different climate zones and disease susceptibility factors. FINDINGS: Our global analysis showed that anomalously wet conditions increase the risk of cough, fever, and diarrhoea symptoms in humid, subtropical regions. These health risks also increased in tropical savanna regions as a result of anomalously dry conditions. Our analysis of susceptibility factors suggests that unimproved sanitation and unsafe drinking water sources are exacerbating these effects, particularly for rural populations and in drought-prone areas in tropical savanna. INTERPRETATION: Weather shifts can affect the survival and transmission of pathogens that are particularly harmful to young children. As our findings show, the health burden of climate-sensitive infectious diseases can be substantial and is likely to fall on populations that are already among the most disadvantaged, including households living in remote rural areas and those lacking access to safe water and sanitation infrastructure. FUNDING: University of California, San Diego FY19 Center Launch programme.

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).

Extreme heat, preterm birth, and stillbirth: A global analysis across 14 lower-middle income countries.

Stillbirths and complications from preterm birth are two of the leading causes of neonatal deaths across the globe. Lower- to middle-income countries (LMICs) are experiencing some of the highest rates of these adverse birth outcomes. Research has suggested that environmental determinants, such as extreme heat, can increase the risk of preterm birth and stillbirth. Under climate change, extreme heat events have become more severe and frequent and are occurring in differential seasonal patterns. Little is known about how extreme heat affects the risk of preterm birth and stillbirth in LMICs. Thus, it is imperative to examine how exposure to extreme heat affects adverse birth outcomes in regions with some of the highest rates of preterm and stillbirths. Most of the evidence linking extreme heat and adverse birth outcomes has been generated from high-income countries (HICs) notably because measuring temperature in LMICs has proven challenging due to the scarcity of ground monitors. The paucity of health data has been an additional obstacle to study this relationship in LMICs. In this study, globally gridded meteorological data was linked with spatially and temporally resolved Demographic and Health Surveys (DHS) data on adverse birth outcomes. A global analysis of 14 LMICs was conducted per a pooled time-stratified case-crossover design with distributed-lag nonlinear models to ascertain the relationship between acute exposure to extreme heat and PTB and stillbirths. We notably found that experiencing higher maximum temperatures and smaller diurnal temperature range during the last week before birth increased the risk of preterm birth and stillbirth. This study is the first global assessment of extreme heat events and adverse birth outcomes and builds the evidence base for LMICs.

Ignitions explain more than temperature or precipitation in driving Santa Ana wind fires.

Autumn and winter Santa Ana wind (SAW)-driven wildfires play a substantial role in area burned and societal losses in southern California. Temperature during the event and antecedent precipitation in the week or month prior play a minor role in determining area burned. Burning is dependent on wind intensity and number of human-ignited fires. Over 75% of all SAW events generate no fires; rather, fires during a SAW event are dependent on a fire being ignited. Models explained 40 to 50% of area burned, with number of ignitions being the strongest variable. One hundred percent of SAW fires were human caused, and in the past decade, powerline failures have been the dominant cause. Future fire losses can be reduced by greater emphasis on maintenance of utility lines and attention to planning urban growth in ways that reduce the potential for powerline ignitions.

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.

Fine Particles in Wildfire Smoke and Pediatric Respiratory Health in California.

BACKGROUND AND OBJECTIVES: Exposure to airborne fine particles with diameters ≤2.5 µm (PM2.5) pollution is a well-established cause of respiratory diseases in children; whether wildfire-specific PM2.5 causes more damage, however, remains uncertain. We examine the associations between wildfire-specific PM2.5 and pediatric respiratory health during the period 2011-2017 in San Diego County, California, and compare these results with other sources of PM2.5. METHODS: Visits to emergency and urgent care facilities of Rady's Children Hospital network in San Diego County, California, by individuals (aged ≤19 years) with ≥1 of the following respiratory conditions: difficulty breathing, respiratory distress, wheezing, asthma, or cough were regressed on daily, community-level exposure to wildfire-specific PM2.5 and PM2.5 from ambient sources (eg, traffic emissions). RESULTS: A 10-unit increase in PM2.5 (from nonsmoke sources) was estimated to increase the number of admissions by 3.7% (95% confidence interval: 1.2% to 6.1%). In contrast, the effect of PM2.5 attributable to wildfire was estimated to be a 30.0% (95% confidence interval: 26.6% to 33.4%) increase in visits. CONCLUSIONS: Wildfire-specific PM2.5 was found to be ∼10 times more harmful on children's respiratory health than PM2.5 from other sources, particularly for children aged 0 to 5 years. Even relatively modest wildfires and associated PM2.5 resolved on our record produced major health impacts, particularly for younger children, in comparison with ambient PM2.5.

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.

Wildfire smoke impacts respiratory health more than fine particles from other sources: observational evidence from Southern California.

Wildfires are becoming more frequent and destructive in a changing climate. Fine particulate matter, PM2.5, in wildfire smoke adversely impacts human health. Recent toxicological studies suggest that wildfire particulate matter may be more toxic than equal doses of ambient PM2.5. Air quality regulations however assume that the toxicity of PM2.5 does not vary across different sources of emission. Assessing whether PM2.5 from wildfires is more or less harmful than PM2.5 from other sources is a pressing public health concern. Here, we isolate the wildfire-specific PM2.5 using a series of statistical approaches and exposure definitions. We found increases in respiratory hospitalizations ranging from 1.3 to up to 10% with a 10 µg m-3 increase in wildfire-specific PM2.5, compared to 0.67 to 1.3% associated with non-wildfire PM2.5. Our conclusions point to the need for air quality policies to consider the variability in PM2.5 impacts on human health according to the sources of emission.

Thermal thresholds heighten sensitivity of West Nile virus transmission to changing temperatures in coastal California.

Temperature is widely known to influence the spatio-temporal dynamics of vector-borne disease transmission, particularly as temperatures vary across critical thermal thresholds. When temperature conditions exhibit such 'transcritical variation', abrupt spatial or temporal discontinuities may result, generating sharp geographical or seasonal boundaries in transmission. Here, we develop a spatio-temporal machine learning algorithm to examine the implications of transcritical variation for West Nile virus (WNV) transmission in the Los Angeles metropolitan area (LA). Analysing a large vector and WNV surveillance dataset spanning 2006-2016, we found that mean temperatures in the previous month strongly predicted the probability of WNV presence in pools of Culex quinquefasciatus mosquitoes, forming distinctive inhibitory (10.0-21.0°C) and favourable (22.7-30.2°C) mean temperature ranges that bound a narrow 1.7°C transitional zone (21-22.7°C). Temperatures during the most intense months of WNV transmission (August/September) were more strongly associated with infection probability in Cx. quinquefasciatus pools in coastal LA, where temperature variation more frequently traversed the narrow transitional temperature range compared to warmer inland locations. This contributed to a pronounced expansion in the geographical distribution of human cases near the coast during warmer-than-average periods. Our findings suggest that transcritical variation may influence the sensitivity of transmission to climate warming, and that especially vulnerable locations may occur where present climatic fluctuations traverse critical temperature thresholds.

Santa Ana Winds of Southern California Impact PM2.5 With and Without Smoke From Wildfires.

Fine particulate matter (PM2.5) raises human health concerns since it can deeply penetrate the respiratory system and enter the bloodstream, thus potentially impacting vital organs. Strong winds transport and disperse PM2.5, which can travel over long distances. Smoke from wildfires is a major episodic and seasonal hazard in Southern California (SoCal), where the onset of Santa Ana winds (SAWs) in early fall before the first rains of winter is associated with the region's most damaging wildfires. However, SAWs also tend to improve visibility as they sweep haze particles from highly polluted areas far out to sea. Previous studies characterizing PM2.5 in the region are limited in time span and spatial extent, and have either addressed only a single event in time or short time series at a limited set of sites. Here we study the space-time relationship between daily levels of PM2.5 in SoCal and SAWs spanning 1999-2012 and also further identify the impact of wildfire smoke on this relationship. We used a rolling correlation approach to characterize the spatial-temporal variability of daily SAW and PM2.5. SAWs tend to lower PM2.5 levels, particularly along the coast and in urban areas, in the absence of wildfires upwind. On the other hand, SAWs markedly increase PM2.5 in zip codes downwind of wildfires. These empirical relationships can be used to identify windows of vulnerability for public health and orient preventive measures.

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.

Respiratory hospitalizations and wildfire smoke: a spatiotemporal analysis of an extreme firestorm in San Diego County, California.

Wildfire smoke adversely impacts respiratory health as fine particles can penetrate deeply into the lungs. Epidemiological studies of differential impacts typically target population subgroups in terms of vulnerability to wildfire smoke. Such information is useful to customize smoke warnings and mitigation actions for specific groups of individuals. In addition to individual vulnerability, it is also important to assess spatial patterns of health impacts to identify vulnerable communities and tailor public health actions during wildfire smoke events. METHODS: We assess the spatiotemporal variation in respiratory hospitalizations in San Diego County during a set of major wildfires in 2007, which led to a substantial public health burden. We propose a spatial within-community matched design analysis, adapted to the study of wildfire impacts, coupled with a Bayesian Hierarchical Model, that explicitly considers the spatial variation of respiratory health associated with smoke exposure, compared to reference periods before and after wildfires. We estimate the signal-to-noise ratio to ultimately gauge the precision of the Bayesian model output. RESULTS: We find the highest excess hospitalizations in areas covered by smoke, mainly ZIP codes contained by and immediately downwind of wildfire perimeters, and that excess hospitalizations tend to follow the distribution of smoke plumes across space (ZIP codes) and time (days). CONCLUSIONS: Analyzing the spatiotemporal evolution of exposure to wildfire smoke is necessary due to variations in smoke plume extent, particularly in this region where the most damaging wildfires are associated with strong wind conditions.