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Smoke from wildfires poses a substantial threat to health in communities near and far. To mitigate the extent and potential damage of wildfires, prescribed burning techniques are commonly employed as land management tools; however, they introduce their own smoke-related risks. This study investigates the impact of prescribed fires on daily average PM2.5 and maximum daily 8-h averaged O3 (MDA8-O3) concentrations and estimates premature deaths associated with short-term exposure to prescribed fire PM2.5 and MDA8-O3 in Georgia and surrounding areas of the Southeastern US from 2015 to 2020. Our findings indicate that over the study domain, prescribed fire contributes to average daily PM2.5 by 0.94 ± 1.45 µg/m3 (mean ± standard deviation), accounting for 14.0% of year-round ambient PM2.5. Higher average daily contributions were predicted during the extensive burning season (January-April): 1.43 ± 1.97 µg/m3 (20.0% of ambient PM2.5). Additionally, prescribed burning is also responsible for an annual average increase of 0.36 ± 0.61 ppb in MDA8-O3 (approximately 0.8% of ambient MDA8-O3) and 1.3% (0.62 ± 0.88 ppb) during the extensive burning season. We estimate that short-term exposure to prescribed fire PM2.5 and MDA8-O3 could have caused 2665 (95% confidence interval (CI): 2249-3080) and 233 (95% CI: 148-317) excess deaths, respectively. These results suggest that smoke from prescribed burns increases the mortality. However, refraining from such burns may escalate the risk of wildfires; therefore, the trade-offs between the health impacts of wildfires and prescribed fires, including morbidity, need to be taken into consideration in future studies.

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In 2020 the U.S. Federal Committee on Statistical Methodology (FCSM) released "A Framework for Data Quality", organized by 11 dimensions of data quality grouped among three domains of quality (utility, objectivity, integrity). This paper addresses the use of the FCSM Framework for data quality assessments of blended data. The FCSM Framework applies to all types of data, however best practices for implementation have not been documented. We applied the FCSM Framework for three health-research related case studies. For each case study, assessments of data quality dimensions were performed to identify threats to quality, possible mitigations of those threats, and trade-offs among them. From these assessments the authors concluded: 1) data quality assessments are more complex in practice than anticipated and expert guidance and documentation are important; 2) each dimension may not be equally important for different data uses; 3) data quality assessments can be subjective and having a quantitative tool could help explain the results, however, quantitative assessments may be closely tied to the intended use of the dataset; 4) there are common trade-offs and mitigations for some threats to quality among dimensions. This paper is one of the first to apply the FCSM Framework to specific use-cases and illustrates a process for similar data uses.

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Unprecedented heat waves can affect all persons, but some are more sensitive to the effects of heat, including children and adults with underlying health conditions, pregnant women, and outdoor workers. Many regions of the United States experienced record-breaking high temperatures in 2023, with populations exposed to extremely high temperatures for prolonged periods. CDC examined emergency department (ED) visits associated with heat-related illness (HRI) from the National Syndromic Surveillance Program and compared daily HRI ED visit rates during the warm-season months (May-September) of 2023 with those during 2018-2022. In the 2023 warm-season months, daily HRI ED visit rates peaked in several regions and remained elevated for a prolonged duration. More males than females sought care in EDs for HRI, especially males aged 18-64 years. CDC issued multiple public health alerts using the Epidemic Information Exchange system to bring attention to increases in ED utilization for HRI. Deaths and illnesses associated with heat exposure are a continuing public health concern as climate change results in longer, hotter, and more frequent episodes of extreme heat. Near real-time monitoring of weather conditions and adverse health outcomes can guide public health practitioners' timing of risk communication and implementation of prevention measures associated with extreme heat.

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Importance: Wildfires, intensified by climate change, have known effects on physical health but their effects on mental health are less well characterized. It has been hypothesized that the residential proximity to a large wildfire can exacerbate underlying mental health conditions as evidenced by increased prescriptions of psychotropic medications. Objective: To evaluate the association between the occurrence of large wildfires and the prescription rates of psychotropic medications immediately following the start of the fire. Design, Setting, and Participants: This cohortstudy used an interrupted time-series analysis to compare psychotropic medication prescriptions in the 6 weeks before and after each of 25 wildfires. The setting was California counties within metropolitan statistical areas (MSAs) experiencing large wildfires from 2011 through 2018. Participants included individuals residing in California MSAs with prescriptions of psychotropic medications recorded in the Merative MarketScan Research Database (MarketScan) during the study period. Statistical analysis was performed for these 25 large wildfires occurring between September 2011 and November 2018. Exposure: Residential proximity to large wildfires that burned more than 25 000 acres occurring in a California county within an MSA. Main Outcomes and Measures: Prescriptions of psychotropic medications, including antidepressants, antipsychotics, anxiolytics, hypnotics, and mood-stabilizers, with statins as a negative control outcome. Results: For the study period, prescription data and patient-level attributes were extracted for 7 115 690 unique individuals (annual mean [range]: 889 461 [455 705-1 426 928] individuals) enrolled in MarketScan and residing in fire-affected MSAs. This study found a statistically significant increase in prescriptions of antidepressants (rate ratio [RR], 1.04 [95% CI, 1.01-1.07]), anxiolytics (RR, 1.05 [95% CI, 1.02-1.09]), and mood-stabilizing medications (RR, 1.06 [95% CI, 1.01-1.13]) in the fire period compared with the prefire baseline. However, the prescriptions of antipsychotics, hypnotics, and the negative control outcome, statins, showed no significant association. Conclusions and Relevance: In this cohort study of large California wildfires, the occurrence of wildfire was associated with increased mental health burden as reflected in increased prescription rates of certain psychotropic medications. The findings underscore the need for further scientific examination into the mental health effects of wildfires and the allocation of mental health resources in disaster responses. California experienced a substantial burden of wildfires from 2011 to 2018, and as wildfires become more intense and frequent in the context of anthropogenic climate change, it is increasingly important to understand and address their mental health effects.

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BACKGROUND: Little is known about the role of air quality in fatal asthma exacerbations among children. METHODS: We collected information about 80 deaths that occurred in North Carolina from 2001 through 2016, among children aged 5-17 years, with asthma identified as the primary cause of death. We linked information about each death with county-level estimates of particulate matter ≤2.5 µm (PM2.5) and ozone (O3). Using the linked data, we conducted a case-crossover analysis of associations between PM2.5 and O3 lagged by 3-5 days with the odds of fatal asthma exacerbations. RESULTS: In the highest tertile of PM2.5 lag(3-5), the odds of a fatal exacerbation of asthma were more than twice the odds in the lowest tertile (odds ratio = 2.2; 95% confidence interval = 1.1, 4.6). CONCLUSION: These findings from North Carolina provide evidence to support the hypothesis that ambient air pollution increases the risk of fatal exacerbations of asthma among children.

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BACKGROUND: Prescribed agricultural burning is a common land management practice, but little is known about the health effects from the resulting smoke exposure. OBJECTIVE: To examine the association between smoke from prescribed burning and cardiorespiratory outcomes in the U.S. state of Kansas. METHODS: We analyzed a zip code-level, daily time series of primary cardiorespiratory emergency department (ED) visits for February-May (months when prescribed burning is common in Kansas) in the years 2009-2011 (n = 109,220). Given limited monitoring data, we formulated a measure of smoke exposure using non-traditional datasets, including fire radiative power and locational attributes from remote sensing data sources. We then assigned a population-weighted potential smoke impact factor (PSIF) to each zip code, based on fire intensity, smoke transport, and fire proximity. We used Poisson generalized linear models to estimate the association between PSIF on the same day and in the past 3 days and asthma, respiratory including asthma, and cardiovascular ED visits. RESULTS: During the study period, prescribed burning took place on approximately 8 million acres in Kansas. Same-day PSIF was associated with a 7% increase in the rate of asthma ED visits when adjusting for month, year, zip code, meteorology, day of week, holidays, and correlation within zip codes (rate ratio [RR]: 1.07; 95% confidence interval [CI]: 1.01, 1.13). Same-day PSIF was not associated with a combined outcome of respiratory ED visits (RR [95% CI]: 0.99 [0.97, 1.02]), or cardiovascular ED visits (RR [95% CI]: 1.01 [0.98, 1.04]). There was no consistent association between PSIF during the past 3 days and any of the outcomes. SIGNIFICANCE: These results suggest an association between smoke exposure and asthma ED visits on the same day. Elucidating these associations will help guide public health programs that address population-level exposure to smoke from prescribed burning.

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Prescribed burning is a major source of a fine particular matter, especially in the southeastern United States, and quantifying emissions from burning operations accurately is an integral part of ascertaining air quality impacts. For instance, a critical factor in calculating fire emissions is identifying fire activity information (e.g., location, date/time, fire type, and area burned) and prior estimations of prescribed fire activity used for calculating emissions have either used burn permit records or satellite-based remote sensing products. While burn permit records kept by state agencies are a reliable source, they are not always available or readily accessible. Satellite-based remote sensing products are currently used to fill the data gaps, especially in regional studies; however, they cannot differentiate prescribed burns from the other types of fires. In this study, we developed novel algorithms to distinguish prescribed burns from wildfires and agricultural burns in a satellite-derived product, Fire INventory from NCAR (FINN). We matched and compared the burned areas from permit records and FINN at various spatial scales: individual fire level, 4 km grid level, and state level. The methods developed in this study are readily usable for differentiating burn type, matching and comparing the burned area between two datasets at various resolutions, and estimating prescribed burn emissions. The results showed that burned areas from permits and FINN have a weak correlation at the individual fire level, while the correlation is much higher for the 4 km grid and state levels. Since matching at the 4 km grid level showed a relatively higher correlation and chemical transport models typically use grid-based emissions, we used the linear regression relationship between FINN and permit burned areas at the grid level to adjust FINN burned areas. This adjustment resulted in a reduction in FINN-burned areas by 34%. The adjusted burned area was then used as input to the BlueSky Smoke Modeling Framework to provide long-term, three-dimensional prescribed burning emissions for the southeastern United States. In this study, we also compared emissions from different methods (FINN or BlueSky) and different data sources (adjusted FINN or permits) to evaluate uncertainties of our emission estimation. The comparison results showed the impacts of the burned area, method, and data source on prescribed burning emission estimations.

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Background: Wildfires are increasing in magnitude, frequency, and severity. Populations in the wildland-urban interface and in downwind communities are at increased risk of exposure to elevated concentrations of fine particulate matter (PM2.5) and other harmful components of wildfire smoke. We conducted this analysis to evaluate the use of modeled predictions of wildfire smoke to create county-level measures of smoke exposure for public health research and surveillance. Methods: We evaluated four years (2015-2018) of grid-based North American Mesoscale (NAM)-derived PM2.5 forecasts from the U.S. Forest Service BlueSky modeling framework with monitoring data from the Environmental Protection Agency Air Quality System (AQS), the Interagency Monitoring of Protected Visual Environments (IMPROVE), the Western Regional Climate Center (WRCC), and the Interagency Real Time Smoke Monitoring (AIRSIS) programs. To assess relationships between model-derived estimates and monitor-based observations, we assessed Spearman's correlations by spatial (i.e., county, level of urbanization, states in the western United States impacted by major wildfires, and climate regions) and temporal (i.e., month and wildfire activity periods) characteristics. We then generated county-level smoke estimates and examined spatial and temporal patterns in total and person-days of smoke exposure. Results: Across all counties in the coterminous United States and for all days, the correlation between county-level model- and monitor-derived PM2.5 estimates was 0.14 (p < 0.001). Correlations were stronger using data from temporary monitors and for areas and days impacted by high wildfire smoke, especially in the western United States. Correlations between county-level model- and monitor-derived estimates in non-metropolitan counties, and at higher concentrations ranged from 0.25 to 0.54 (p < 0.001). Conclusions: In general, public health practitioners and health researchers need to consider the pros and cons associated with modeled data products for conducting health analyses. Our results support the use of model-derived smoke estimates to identify communities impacted by heavy smoke events, especially during emergency response and for communities located near wildfire episodes.

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The physical and mental health impacts of wildfires are wide-ranging. We assessed associations between exposure to wildfire smoke and self-reported symptoms affecting mental health among adults living in Oregon. We linked by interview date and county of residence survey responses from 5807 adults who responded to the 2018 Behavioral Risk Factor Surveillance System's depression and anxiety module with smoke plume density, a proxy for wildfires and wildfire smoke exposure. Associations between weeks in the past year with medium and heavy smoke plume densities and symptoms affecting mental health during the two weeks before the interview date were estimated using predicted marginal probabilities from logistic regression models. In the year before completing the interview, 100% of respondents experienced ≥2 weeks of medium or heavy smoke, with an average exposure duration of 32 days. Nearly 10% reported being unable to stop or control their worrying more than half the time over the past two weeks. Medium or heavy smoke for 6 or more weeks in the past year, compared to ≤4 weeks in the past year, was associated with a 30% higher prevalence of being unable to stop or control worrying more than half the time during the past two weeks (prevalence ratio: 1.30, 95% confidence interval: 1.03, 1.65). Among adults in Oregon, selected symptoms affecting mental health were associated with extended durations of medium and heavy smoke. These findings highlight the burden of such symptoms among adults living in communities affected by wildfires and wildfire smoke.

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Extreme heat exposure increases the risk for heat-related illnesses (HRIs) and deaths, and comprehensive strategies to prevent HRIs are increasingly important in a warming climate (1). An estimated 702 HRI-associated deaths and 67,512 HRI-associated emergency department visits occur in the United States each year (2,3). In 2020, Phoenix and Yuma, Arizona, experienced a record 145 and 148 days, respectively, of temperatures >100°F (37.8°C), and a record 522 heat-related deaths occurred in the state. HRIs are preventable through individual and community-based strategies*,†; cooling centers,§ typically air-conditioned or cooled buildings designated as sites to provide respite and safety during extreme heat, have been established in Maricopa and Yuma counties to reduce HRIs among at-risk populations, such as older adults. This analysis examined trends in HRIs by age during 2010-2020 for Maricopa and Yuma counties and data from a survey of older adults related to cooling center availability and use in Yuma County during 2018-2019. Data from CDC's Social Vulnerability Index (SVI) were also used to overlay cooling center locations with SVI scores. During 2010-2020, heat days, defined as days with an excessive heat warning issued by the National Weather Service Phoenix Office,¶ for any part of Maricopa and Yuma counties (4), increased in both Maricopa County (1.18 days per year) and Yuma County (1.71 days per year) on average. Adults aged ≥65 years had higher rates of HRI hospitalization compared with those aged <65 years. In a survey of 39 adults aged ≥65 years in Yuma County, 44% reported recent HRI symptoms, and 18% reported electricity cost always or sometimes constrained their use of air conditioning. Barriers to cooling center access among older adults include awareness of location and transportation. Collaboration among diverse community sectors and health profession education programs is important to better prepare for rising heat exposure and HRIs. States and communities can implement adaptation and evaluation strategies to mitigate and assess heat risk, such as the use of cooling centers to protect communities disproportionately affected by HRI during periods of high temperatures.

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Traffic-related emissions continue to be a significant source of air pollution in the United States (US) and around the globe. Evidence has shown that previous policies implemented to restrict-traffic flows have affected air pollution levels. Thus, mitigation strategies associated with the COVID-19 pandemic that modified population-level mobility patterns provide a unique opportunity to study air pollution change across the US. For instance, to slow the spread of the pandemic, state and local governments started implementing various mitigation actions, including stay-at-home directives, social distancing measures, school closures, and travel restrictions. This scoping review aimed to summarize the existing evidence about how air quality changed through mitigation practices throughout the pandemic in the US. We found 66 articles that fit our inclusion criteria. Generally, the consolidated results revealed that nitrogen dioxide (NO2) and carbon monoxide (CO) decreased across the country. Studies observed mixed directions and magnitudes of change for fine and coarse particulate matter (PM2.5, PM10), ozone (O3), and sulfur dioxide (SO2). Few articles tried to explain this notable heterogeneity in air quality changes by associating contextual factors, such as mobility, traffic flow, and demographic factors. However, all studies agreed that the change in air pollution was nonuniform across the US and even varied within a city.

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Record high temperatures are occurring more frequently in the United States, and climate change is causing heat waves to become more intense (1), directly impacting human health, including heat-related illnesses and deaths. On average, approximately 700 heat-related deaths occur in the United States each year (2). In the northwestern United States, increasing temperatures are projected to cause significant adverse health effects in the coming years (3). During June 25-30, 2021, most of Oregon and Washington were under a National Weather Service excessive heat warning.* Hot conditions persisted in parts of Oregon, Washington, or Idaho through at least July 14, 2021. The record-breaking heat had the largest impact in Oregon and Washington, especially the Portland metropolitan area, with temperatures reaching 116°F (46.7°C), which is 42°F (5.6°C) hotter than the average daily maximum June temperature.

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BACKGROUND: While year-round exposure to pollen is linked to a large burden of allergic diseases, location-specific risk information on pollen types and allergy outcomes are limited. We characterize the relationship between acute exposure to tree, grass and weed pollen taxa and two allergy outcomes (allergic rhinitis physician visit and prescription allergy medication fill) across 28 metropolitan statistical areas (MSA) in the United States. METHODS: We obtained daily pollen data from National Allergy Bureau (NAB) monitors at these 28 MSAs for 2008-2015. We revised the NAB guidelines to classify taxa-specific pollen severity each day. Daily information on allergic rhinitis and prescribed allergy medications for individuals with employer-based health insurance from the IBM MarketScan Research database for these MSAs. We combined the daily pollen and health data for each MSA into a longitudinal dataset. We conducted a MSA-specific conditional quasi-Poisson regression analysis to assess how different levels of pollen concentration impact the health outcomes, controlling for local air pollution, meteorology and Influenza-like illness (ILI). We used a random effects meta-analysis to produce an overall risk estimate for each pollen type and health outcome. RESULTS: The seasonal distribution of pollen taxa and associated health impacts varied across the MSAs. Relative risk of allergic rhinitis visits increased as concentrations increased for all pollen types; relative risk of medication fills increased for tree and weed pollen only. We observed an increase in health risk even on days with moderate levels of pollen concentration. 7-day average concentration of pollen had stronger association with the health outcomes compared to the same-day measure. Controlling for air pollution and ILI had little impact on effect estimates. CONCLUSION: This analysis expands the catalogue of associations between different pollen taxa and allergy-related outcomes across multiple MSAs. The effect estimates we present can be used to project the burden of allergic disease in specific locations in the future as well inform patients with allergies on impending pollen exposure.

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BACKGROUND: Diabetes is especially prevalent among African Americans. Prior studies suggest that long-term exposure to ambient air pollution may be associated with greater incidence of diabetes, but results remain heterogeneous. Few studies have included large numbers of African Americans. METHODS: We assessed diabetes status and concentrations of 1- and 3-year fine particulate matter (PM2.5) and ozone (O3) among African American participants of the Jackson Heart Study at visits 1 (2000-2004, N = 5128) and 2 (2005-2008, N = 2839). We used mixed-effect modified Poisson regression to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of incidence of diabetes by visit 2 and prevalence ratios (PRs) of the association between air pollution exposure and prevalent diabetes at visits 1 and 2. We adjusted for potential confounding by patient characteristics, as well as inverse probability weights of diabetes at visit 2, accounting for clustering by census tract. RESULTS: We observed associations between incident diabetes and interquartile range increase in 1-year O3 (RR 1.34, 95% CI = 1.11, 1.61) and 3-year O3 (RR 0.88, 95% CI = 0.76, 1.02). We observed associations between prevalent diabetes and 1-year PM2.5 (PR 1.08, 95% CI = 1.00, 1.17), 1-year O3 (PR 1.18, 95% CI = 1.10, 1.27), and 3-year O3 (PR 0.95, 95% CI = 0.90, 1.01) at visit 2. CONCLUSIONS: Our results provide some evidence of positive associations between indicators of long-term PM2.5 and O3 exposure and diabetes. This study is particularly relevant to African Americans, who have higher prevalence of diabetes but relatively few studies of environmental pollution risk factors.

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INTRODUCTION: African Americans are disproportionately affected by high blood pressure, which may be associated with exposure to air pollutants, such as fine particulate matter and ozone. METHODS: Among African American Jackson Heart Study participants, this study examined associations between 1-year and 3-year mean fine particulate matter and ozone concentrations with prevalent and incident hypertension at Visits 1 (2000-2004, n=5,191) and 2 (2005-2008, n=4,105) using log binomial regression. Investigators examined associations with systolic blood pressure, diastolic blood pressure, pulse pressure, and mean arterial pressure using linear regression and hierarchical linear models, adjusting for sociodemographic, behavioral, and clinical characteristics. Analyses were conducted in 2017-2019. RESULTS: No associations were observed between fine particulate matter or ozone concentration and prevalent or incident hypertension. In linear models, an IQR increase in 1-year ozone concentration was associated with 0.67 mmHg higher systolic blood pressure (95% CI=0.27, 1.06), 0.42 mmHg higher diastolic blood pressure (95% CI=0.20, 0.63), and 0.50 mmHg higher mean arterial pressure (95% CI=0.26, 0.74). In hierarchical models, fine particulate matter was inversely associated with systolic blood pressure (-0.72, 95% CI= -1.31, -0.13), diastolic blood pressure (-0.69, 95% CI= -1.02, -0.36), and mean arterial pressure (-0.71, 95% CI= -1.08, -0.33). Attenuated associations were observed with 1-year concentrations and at Visit 1. CONCLUSIONS: Positive associations were observed between ozone and systolic blood pressure, diastolic blood pressure, and mean arterial pressure, and inverse associations between fine particulate matter and systolic blood pressure, diastolic blood pressure, and mean arterial pressure in an African American population with high (56%) prevalence of hypertension. Effect sizes were small and may not be clinically relevant.

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The objective of this study is to assess ambient temperatures' and extreme heat events' contribution to work-related emergency department (ED) visits for hyperthermia in the southeastern United States to inform prevention. Through a collaborative network and established data framework, work-related ED hyperthermia visits in five participating southeastern U.S. states were analyzed using a time stratified case-crossover design. For exposure metrics, day- and location-specific measures of ambient temperatures and county-specific identification of extreme heat events were used. From 2010 to 2012, 5,017 work-related hyperthermia ED visits were seen; 2,298 (~46%) of these visits occurred on days when the daily maximum heat index was at temperatures the Occupational Safety and Health Administration designates as having "lower" or "moderate" heat risk. A 14% increase in risk of ED visit was seen for a 1°F increase in average daily mean temperature, modeled as linear predictor across all temperatures. A 54% increase in risk was seen for work-related hyperthermia ED visits during extreme heat events (two or more consecutive days of unusually high temperatures) when controlling for average daily mean temperature. Despite ambient heat being a well-known risk to workers' health, this study's findings indicate ambient heat contributed to work-related ED hyperthermia visits in these five states. Used alone, existing OSHA heat-risk levels for ambient temperatures did not appear to successfully communicate workers' risk for hyperthermia in this study. Findings should inform future heat-alert communications and policies, heat prevention efforts, and heat-illness prevention research for workers in the southeastern United States.

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Tracking concentrations of regional airborne pollen is valuable for a variety of fields including plant and animal ecology as well as human health. However, current methods for directly measuring regional pollen concentrations are labor-intensive, requiring special equipment and manual counting by professionals leading to sparse data availability in select locations. Here, we use publicly available Google Trends data to evaluate whether searches for the term "pollen" can be used to approximate local observed early-season pollen concentrations as reported by the National Allergy Bureau across 25 U.S. regions from 2012-2017, in the context of site-specific characteristics. Our findings reveal that two major factors impact the ability of internet search data to approximate observed pollen: (1) volume/availability of internet search data, which is tied to local population size and media use; and (2) signal intensity of the seasonal peak in searches. Notably, in regions and years where internet search data was abundant, we found strong correlations between local search patterns and observed pollen, thus revealing a potential source of daily pollen data across the U.S. where observational pollen data are not reliably available.

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Deaths attributable to natural heat exposure, although generally considered preventable (1), represent a continuing public health concern in the United States. During 2004-2018, an average of 702 heat-related deaths occurred in the United States annually. To study patterns in heat-related deaths by age group, sex, race/ethnicity, and level of urbanization, and to explore comorbid conditions associated with deaths resulting from heat exposure, CDC analyzed nationally comprehensive mortality data from the National Vital Statistics System (NVSS).* The rate of heat-related mortality tended to be higher among males, persons aged ≥65 years, non-Hispanic American Indian/Alaska Natives, and persons living in noncore nonmetropolitan and large central metropolitan counties. Natural heat exposure was a contributing cause of deaths attributed to certain chronic medical conditions and other external causes. Preparedness and response initiatives directed toward extreme heat events, currently underway at local, state, and national levels, can contribute to reducing morbidity and mortality associated with natural heat exposure. Successful public health interventions† to mitigate heat-related deaths include conducting outreach to vulnerable communities to increase awareness of heat-related symptoms and provide guidance for staying cool and hydrated, particularly for susceptible groups at risk such as young athletes and persons who are older or socially isolated (2). Improved coordination across various health care sectors could inform local activities to protect health during periods of high heat. For instance, jurisdictions can monitor weather conditions and syndromic surveillance data to guide timing of risk communication and other measures (e.g., developing and implementing heat response plans, facilitating communication and education activities) to prevent heat-related mortality in the United States. CDC also recommends that federal, state, local, and tribal jurisdictions open cooling centers or provide access to public locations with air conditioning for persons in need of a safe, cool, environment during hot weather conditions. In light of the coronavirus disease 2019 (COVID-19) pandemic, CDC updated its guidance on the use of cooling centers to provide best practices (e.g., potential changes to staffing procedures, separate areas for persons with symptoms of COVID-19, and physical distancing) to reduce the risk for introducing and transmitting SARS COV-2, the virus that causes COVID-19, into cooling centers.§.

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