Morbidity and Mortality of Unintentional Carbon Monoxide Poisoning: United States 2005 to 2018.

STUDY OBJECTIVE: Centers for Disease Control and Prevention conducts case surveillance through the National Notifiable Diseases Surveillance System (NNDSS). This study aimed to provide surveillance report of unintentional carbon monoxide poisoning across multiple data sources to provide baseline data for the new NNDSS carbon monoxide poisoning surveillance. METHODS: For the period 2005 to 2018, we used 4 data sources to describe unintentional carbon monoxide poisoning: exposures reported by poison centers, emergency department (ED) visits, hospitalizations, and deaths. We conducted descriptive analyses by the cause of exposure (fire, nonfire, or unknown), age, sex, season, and US census region. Additional analyses were conducted using poison center exposure case data focusing on the reported signs and symptoms, management site, and medical outcome. RESULTS: Annually, we observed 39.5 poison center exposure calls (per 1 million, nationally), 56.5 ED visits (per 1 million, across 17 states), 7.3 hospitalizations (per 1 million, in 26 states), and 3.3 deaths (per 1 million, nationally) due to unintentional carbon monoxide poisoning. For 2005 to 2018, there was a decrease in the crude rate for non-fire-related carbon monoxide poisonings from hospital, and death data. Non-fire-related cases comprised 74.0% of ED visits data, 60.1% of hospitalizations, and 40.9% of deaths compared with other unintentional causes. Across all data sources, unintentional carbon monoxide poisonings were most often reported during the winter season, notably in January and December. Children aged 0 to 9 years had the highest reported rates in poison center exposure case data and ED visits (54.1 and 70.5 per 1 million, respectively); adults older than 80 years had the highest rates of hospitalization and deaths (20.2 and 9.9 per 1 million, respectively); and deaths occurred more often among men and in the Midwest region. Poison center exposure call data revealed that 45.9% of persons were treated at a health care facility. Headaches, nausea, and dizziness/vertigo were the most reported symptoms. CONCLUSION: The crude rates in non-fire-related carbon monoxide poisonings from hospitalizations, and mortality significantly decreased over the study period (ie, 2005 to 2018). This surveillance report provides trends and characteristics of unintentional carbon monoxide poisoning and the baseline morbidities and mortality data for the Centers for Disease Control and Prevention national surveillance system of carbon monoxide poisoning.

Associations of Emergency Department Visits for Asthma with Precipitation and Temperature on Thunderstorm Days: A Time-Series Analysis of Data from Louisiana, USA, 2010-2012.

BACKGROUND: Studies of thunderstorm asthma to understand risk factors using high-resolution climate data and asthma outcomes on a large scale are scarce. Moreover, thunderstorm asthma is not well studied in the United States. OBJECTIVES: We examined whether climate parameters involved in thunderstorms are associated with emergency department (ED) visits for acute asthma attacks in the United States. METHODS: We analyzed 63,789 asthma-related, daily ED visits for all age groups, and thunderstorm-associated climate data in Louisiana during 2010 through 2012. We performed time-series analyses using quasi-Poisson regression models with natural cubic splines of date, parish, holiday, day of week, season, daily maximum concentrations of ozone (O3) and fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)], and daily mean pressure, precipitation, and temperature. Because of a significant interaction effect between temperature and lightning days on asthma-related visits, we performed stratified analyses by days with/without lightning or thunderstorm (defined by any lightning and precipitation). RESULTS: On thunderstorm days, higher asthma-related ED visits were associated with higher daily mean precipitation [relative risk (RR)=1.145 per 1 g/m2/s (95% CI: 1.009, 1.300)] and lower daily mean temperature [RR=1.011 per 1°C change (1.000-1.021)] without carry-over effect to the next non-thunderstorm day. These higher risks were found mainly among children and adults <65 years of age. We observed similar results on lightning days. However, we did not find similar associations for non-thunderstorm or non-lightning days. Daily maximum O3 and PM2.5 levels were not significantly associated with asthma ED visits on thunderstorm days. DISCUSSION: Higher precipitation and lower temperature on thunderstorm days appear to contribute to asthma attacks among people with asthma, suggesting they should consider taking precautions during thunderstorms. EDs should consider preparing for a potential increase of asthma-related visits and ensuring sufficient stock of emergency medication and supplies for forecasted severe thunderstorm days. https://doi.org/10.1289/EHP10440.

Developing indices to identify hotspots of skin cancer vulnerability among the Non-Hispanic White population in the United States.

PURPOSE: Skin cancer is the most common, yet oftentimes preventable, cancer type in the United States. Exposure to ultraviolet radiation from sunlight is the most prominent environmental risk factor for skin cancer. Besides environmental exposure, demographic characteristics such as race, age, and socioeconomic status may make some groups more vulnerable. An exploratory spatial clustering method is described for identifying clusters of vulnerability to skin cancer incidence and mortality based on composite indices, which combine data from environmental and demographic risk factors. METHODS: Based on county-level ultraviolet data and demographic risk factors, two vulnerability indices for skin cancer were generated using an additive percentile rank approach. With these indices, univariate local Moran's I spatial autocorrelation identified significant clusters, or hotspots, of neighboring counties with high overall vulnerability indices. Clusters were identified separately for skin cancer incidence and mortality. RESULTS: Counties with high vulnerabilities were spatially distributed across the United States in a pattern that generally increased to the South and West. Clusters of counties with high skin cancer incidence vulnerability were mostly observed in Utah and Colorado, even with highly conservative levels of significance. Meanwhile, clusters for skin cancer mortality vulnerability were observed in southern Alabama and west Florida as well as across north Alabama, north Georgia and up through the Tennessee-North Carolina area. CONCLUSIONS: Future skin cancer research and screening initiatives may use these innovative composite vulnerability indices and identified clusters to better target resources based on anticipated risk from underlying demographic and environmental factors.

Common and Unique Barriers to the Exchange of Administrative Healthcare Data in Environmental Public Health Tracking Program.

CDC's National Environmental Public Health Tracking Program (Tracking Program) receives administrative data annually from 25-30 states to track potential environmental exposures and to make data available for public access. In 2019, the CDC Tracking Program conducted a cross-sectional survey among principal investigators or program managers of the 26 funded programs to improve access to timely, accurate, and local data. All 26 funding recipients reported having access to hospital inpatient data, and most states (69.2%) regularly update data user agreements to receive the data. Among the respondents, 15 receive record-level data with protected health information (PHI) and seven receive record-level data without PHI. Regarding geospatial resolution, approximately 50.0% of recipients have access to the street address or census tract information, 34.6% have access to ZIP code, and 11.5% have other sub-county geographies (e.g., town). Only three states receive administrative data for their residents from all border states. The survey results will help the Tracking Program to identify knowledge gaps and perceived barriers to the use and accessibility of administrative data for the CDC Tracking Program. The information collected will inform the development of resources that can provide solutions for more efficient and timely data exchange.

Risk of Clinical Severity by Age and Race/Ethnicity Among Adults Hospitalized for COVID-19-United States, March-September 2020.

BACKGROUND: Older adults and people from certain racial and ethnic groups are disproportionately represented in coronavirus disease 2019 (COVID-19) hospitalizations and deaths. METHODS: Using data from the Premier Healthcare Database on 181 813 hospitalized adults diagnosed with COVID-19 during March-September 2020, we applied multivariable log-binomial regression to assess the associations between age and race/ethnicity and COVID-19 clinical severity (intensive care unit [ICU] admission, invasive mechanical ventilation [IMV], and death) and to determine whether the impact of age on clinical severity differs by race/ethnicity. RESULTS: Overall, 84 497 (47%) patients were admitted to the ICU, 29 078 (16%) received IMV, and 27 864 (15%) died in the hospital. Increased age was strongly associated with clinical severity when controlling for underlying medical conditions and other covariates; the strength of this association differed by race/ethnicity. Compared with non-Hispanic White patients, risk of death was lower among non-Hispanic Black patients (adjusted risk ratio, 0.96; 95% CI, 0.92-0.99) and higher among Hispanic/Latino patients (risk ratio [RR], 1.15; 95% CI, 1.09-1.20), non-Hispanic Asian patients (RR, 1.16; 95% CI, 1.09-1.23), and patients of other racial and ethnic groups (RR, 1.13; 95% CI, 1.06-1.21). Risk of ICU admission and risk of IMV were elevated among some racial and ethnic groups. CONCLUSIONS: These results indicate that age is a driver of poor outcomes among hospitalized persons with COVID-19. Additionally, clinical severity may be elevated among patients of some racial and ethnic minority groups. Public health strategies to reduce severe acute respiratory syndrome coronavirus 2 infection rates among older adults and racial and ethnic minorities are essential to reduce poor outcomes.

Hydroxychloroquine and Chloroquine Prescribing Patterns by Provider Specialty Following Initial Reports of Potential Benefit for COVID-19 Treatment - United States, January-June 2020.

Hydroxychloroquine and chloroquine, primarily used to treat autoimmune diseases and to prevent and treat malaria, received national attention in early March 2020, as potential treatment and prophylaxis for coronavirus disease 2019 (COVID-19) (1). On March 20, the Food and Drug Administration (FDA) issued an emergency use authorization (EUA) for chloroquine phosphate and hydroxychloroquine sulfate in the Strategic National Stockpile to be used by licensed health care providers to treat patients hospitalized with COVID-19 when the providers determine the potential benefit outweighs the potential risk to the patient.* Following reports of cardiac and other adverse events in patients receiving hydroxychloroquine for COVID-19 (2), on April 24, 2020, FDA issued a caution against its use† and on June 15, rescinded its EUA for hydroxychloroquine from the Strategic National Stockpile.§ Following the FDA's issuance of caution and EUA rescindment, on May 12 and June 16, the federal COVID-19 Treatment Guidelines Panel issued recommendations against the use of hydroxychloroquine or chloroquine to treat COVID-19; the panel also noted that at that time no medication could be recommended for COVID-19 pre- or postexposure prophylaxis outside the setting of a clinical trial (3). However, public discussion concerning the effectiveness of these drugs on outcomes of COVID-19 (4,5), and clinical trials of hydroxychloroquine for prophylaxis of COVID-19 continue.¶ In response to recent reports of notable increases in prescriptions for hydroxychloroquine or chloroquine (6), CDC analyzed outpatient retail pharmacy transaction data to identify potential differences in prescriptions dispensed by provider type during January-June 2020 compared with the same period in 2019. Before 2020, primary care providers and specialists who routinely prescribed hydroxychloroquine, such as rheumatologists and dermatologists, accounted for approximately 97% of new prescriptions. New prescriptions by specialists who did not typically prescribe these medications (defined as specialties accounting for ≤2% of new prescriptions before 2020) increased from 1,143 prescriptions in February 2020 to 75,569 in March 2020, an 80-fold increase from March 2019. Although dispensing trends are returning to prepandemic levels, continued adherence to current clinical guidelines for the indicated use of these medications will ensure their availability and benefit to patients for whom their use is indicated (3,4), because current data on treatment and pre- or postexposure prophylaxis for COVID-19 indicate that the potential benefits of these drugs do not appear to outweigh their risks.

Timing of State and Territorial COVID-19 Stay-at-Home Orders and Changes in Population Movement - United States, March 1-May 31, 2020.

SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is thought to spread from person to person primarily by the respiratory route and mainly through close contact (1). Community mitigation strategies can lower the risk for disease transmission by limiting or preventing person-to-person interactions (2). U.S. states and territories began implementing various community mitigation policies in March 2020. One widely implemented strategy was the issuance of orders requiring persons to stay home, resulting in decreased population movement in some jurisdictions (3). Each state or territory has authority to enact its own laws and policies to protect the public's health, and jurisdictions varied widely in the type and timing of orders issued related to stay-at-home requirements. To identify the broader impact of these stay-at-home orders, using publicly accessible, anonymized location data from mobile devices, CDC and the Georgia Tech Research Institute analyzed changes in population movement relative to stay-at-home orders issued during March 1-May 31, 2020, by all 50 states, the District of Columbia, and five U.S. territories.* During this period, 42 states and territories issued mandatory stay-at-home orders. When counties subject to mandatory state- and territory-issued stay-at-home orders were stratified along rural-urban categories, movement decreased significantly relative to the preorder baseline in all strata. Mandatory stay-at-home orders can help reduce activities associated with the spread of COVID-19, including population movement and close person-to-person contact outside the household.

Developing a surveillance system of sub-county data: Finding suitable population thresholds for geographic aggregations.

The Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program created standardized sub-county geographies that are comparable over time, place, and outcomes. Expected census tract-level counts were calculated for asthma emergency department visits and lung cancer. Census tracts were aggregated for various total population and sub-population thresholds, then suppression and stability were examined. A total of 5,000 persons was recommended for the more common outcome scheme and a total of 20,000 persons was recommended for the rare outcome scheme. Health outcomes with a median case count of 17.0 cases or higher should produce stable estimates at the census tract level. This project generated recommendations for three sub-county geographies that will be useful for surveillance purposes: census tract, a more common outcome aggregation scheme, and a rare outcome aggregation scheme. This methodology can be applied anywhere to aggregate geographic units and produce stable rates at a finer resolution.

Availability, access, analysis and dissemination of small-area data.

In this era of 'big data', there is growing recognition of the value of environmental, health, social and demographic data for research. Open government data initiatives are growing in number and in terms of content. Remote sensing data are finding widespread use in environmental research, including in low- and middle-income settings. While our ability to study environment and health associations across countries and continents grows, data protection rules and greater patient control over the use of their data present new challenges to using health data in research. Innovative tools that circumvent the need for the physical sharing of data by supporting non-disclosive sharing of information, or that permit spatial analysis without researchers needing access to underlying patient data can be used to support analyses while protecting data confidentiality. User-friendly visualizations, allowing small-area data to be seen and understood by non-expert audiences, are revolutionizing public and researcher interactions with data. The UK Small Area Health Statistics Unit's Environment and Health Atlas for England and Wales, and the US National Environmental Public Health Tracking Network offer good examples. Open data facilitates user-generated outputs, and 'mash-ups', and user-generated inputs from social media, mobile devices and wearable tech are new data streams that will find utility in future studies, and bring novel dimensions with respect to ethical use of small-area data.

Software application profile: the Rapid Inquiry Facility 4.0: an open access tool for environmental public health tracking.

The Rapid Inquiry Facility 4.0 (RIF) is a new user-friendly and open-access tool, developed by the UK Small Area Health Statistics Unit (SAHSU), to facilitate environment public health tracking (EPHT) or surveillance (EPHS). The RIF is designed to help public health professionals and academics to rapidly perform exploratory investigations of health and environmental data at the small-area level (e.g. postcode or detailed census areas) in order to identify unusual signals, such as disease clusters and potential environmental hazards, whether localized (e.g. industrial site) or widespread (e.g. air and noise pollution). The RIF allows the use of advanced disease mapping methods, including Bayesian small-area smoothing and complex risk analysis functionalities, while accounting for confounders. The RIF could be particularly useful to monitor spatio-temporal trends in mortality and morbidity associated with cardiovascular diseases, cancers, diabetes and chronic lung diseases, or to conduct local or national studies on air pollution, flooding, low-magnetic fields or nuclear power plants.

Public Perceptions of Environmental Public Health Risks in the United States.

Understanding public perceptions about environmental health hazards, exposures, and health impacts can help environmental public health practitioners to target and prioritize community activities, policy needs, and communication strategies. The online cross-sectional 2013 summer wave of the ConsumerStyles survey sampled U.S. adults and used questions from the Centers for Disease Control's Environmental Public Health Tracking Program to measure public awareness of governmental efforts to track environmental exposures and links to health impacts, as well as perceptions of environmental health issues. Unadjusted and adjusted logistic regressions examined the associations between demographic characteristics and level of awareness of government environmental public health efforts or level of concern about health risks associated with environmental pollutants. Responses were received from 4033 participants, yielding a response rate of 66.0%. More than half of respondents (57.8%) noted concerns about health risks from environmental pollutants. More than one-third (40.0%) of respondents reported awareness of government efforts. Nearly 40% of respondents felt that none of the health impacts listed in the survey were related to environmental issues. Multiple logistic regression models showed that non-Hispanic blacks, other races, females, people with a college or higher education, and people living in the Midwest or South regions were more likely than their counterparts to be concerned about how the environment affects their health. Future work should focus on improving risk communication, filling the information gap on environmental health issues, and understanding how perceptions change over time.

Age-Specific Associations of Ozone and Fine Particulate Matter with Respiratory Emergency Department Visits in the United States.

RATIONALE: Whereas associations between air pollution and respiratory morbidity for adults 65 years and older are well documented in the United States, the evidence for people under 65 is less extensive. To address this gap, the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program collected respiratory emergency department (ED) data from 17 states. OBJECTIVES: To estimate age-specific acute effects of ozone and fine particulate matter (particulate matter ≤2.5 mm in aerodynamic diameter [PM2.5]) on respiratory ED visits. METHODS: We conducted time-series analyses in 894 counties by linking daily respiratory ED visits with estimated ozone and PM2.5 concentrations during the week before the date of the visit. Overall effect estimates were obtained with a Bayesian hierarchical model to combine county estimates for each pollutant by age group (children, 0-18; adults, 19-64; adults ≥ 65, and all ages) and by outcome group (acute respiratory infection, asthma, chronic obstructive pulmonary disease, pneumonia, and all respiratory ED visits). MEASUREMENTS AND MAIN RESULTS: Rate ratios (95% credible interval) per 10-µg/m3 increase in PM2.5 and all respiratory ED visits were 1.024 (1.018-1.029) among children, 1.008 (1.004-1.012) among adults younger than 65 years, and 1.002 (0.996-1.007) among adults 65 and older. Per 20-ppb increase in ozone, rate ratios were 1.017 (1.011-1.023) among children, 1.051 (1.046-1.056) among adults younger than 65, and 1.033 (1.026-1.040) among adults 65 and older. Associations varied in magnitude by age group for each outcome group. CONCLUSIONS: These results address a gap in the evidence used to ensure adequate public health protection under national air pollution policies.

Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project.

OBJECTIVE: Small area data are key to better understanding the complex relationships between environmental health, health outcomes, and risk factors at a local level. In 2014, the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program (Tracking Program) conducted the Sub-County Data Pilot Project with grantees to consider integration of sub-county data into the National Environmental Public Health Tracking Network (Tracking Network). DESIGN: The Tracking Program and grantees developed sub-county-level data for several data sets during this pilot project, working to standardize processes for submitting data and creating required geographies. Grantees documented challenges they encountered during the pilot project and documented decisions. RESULTS: This article covers the challenges revealed during the project. It includes insights into geocoding, aggregation, population estimates, and data stability and provides recommendations for moving forward. CONCLUSION: National standards for generating, analyzing, and sharing sub-county data should be established to build a system of sub-county data that allow for comparison of outcomes, geographies, and time. Increasing the availability and accessibility of small area data will not only enhance the Tracking Network's capabilities but also contribute to an improved understanding of environmental health and informed decision making at a local level.

CDC's National Environmental Public Health Tracking Program in Action: Case Studies From State and Local Health Departments.

The Centers for Disease Control and Prevention's (CDC's) National Environmental Public Health Tracking Program (Tracking Program) is a multidisciplinary collaboration that involves the ongoing collection, integration, analysis, interpretation, and dissemination of data from environmental hazard monitoring, human exposure surveillance, and health effects surveillance. With a renewed focus on data-driven decision-making, the CDC's Tracking Program emphasizes dissemination of actionable data to public health practitioners, policy makers, and communities. The CDC's National Environmental Public Health Tracking Network (Tracking Network), a Web-based system with components at the national, state, and local levels, houses environmental public health data used to inform public health actions (PHAs) to improve community health. This article serves as a detailed landscape on the Tracking Program and Tracking Network and the Tracking Program's leading performance measure, "public health actions." Tracking PHAs are qualitative statements addressing a local problem or situation, the role of the state or local Tracking Program, how the problem or situation was addressed, and the action taken. More than 400 PHAs have been reported by funded state and local health departments since the Tracking Program began collecting PHAs in 2005. Three case studies are provided to illustrate the use of the Tracking Program resources and data on the Tracking Network, and the diversity of actions taken. Through a collaborative network of experts, data, and tools, the Tracking Program and its Tracking Network are actively informing state and local PHAs. In a time of competing priorities and limited funding, PHAs can serve as a powerful tool to advance environmental public health practice.

Rural and Urban Differences in Air Quality, 2008-2012, and Community Drinking Water Quality, 2010-2015 - United States.

PROBLEM/CONDITION: The places in which persons live, work, and play can contribute to the development of adverse health outcomes. Understanding the differences in risk factors in various environments can help to explain differences in the occurrence of these outcomes and can be used to develop public health programs, interventions, and policies. Efforts to characterize urban and rural differences have largely focused on social and demographic characteristics. A paucity of national standardized environmental data has hindered efforts to characterize differences in the physical aspects of urban and rural areas, such as air and water quality. REPORTING PERIOD: 2008-2012 for air quality and 2010-2015 for water quality. DESCRIPTION OF SYSTEM: Since 2002, CDC's National Environmental Public Health Tracking Program has collaborated with federal, state, and local partners to gather standardized environmental data by creating national data standards, collecting available data, and disseminating data to be used in developing public health actions. The National Environmental Public Health Tracking Network (i.e., the tracking network) collects data provided by national, state, and local partners and includes 21 health outcomes, exposures, and environmental hazards. To assess environmental factors that affect health, CDC analyzed three air-quality measures from the tracking network for all counties in the contiguous United States during 2008-2012 and one water-quality measure for 26 states during 2010-2015. The three air-quality measures include 1) total number of days with fine particulate matter (PM2.5) levels greater than the U.S. Environmental Protection Agency's (EPA's) National Ambient Air Quality Standards (NAAQS) for 24-hour average PM2.5 (PM2.5 days); 2) mean annual average ambient concentrations of PM2.5 in micrograms per cubic meter (mean PM2.5); and 3) total number of days with maximum 8-hour average ozone concentrations greater than the NAAQS (ozone days). The water-quality measure compared the annual mean concentration for a community water system (CWS) to the maximum contaminant level (MCL) defined by EPA for 10 contaminants: arsenic, atrazine, di(2-ethylhexyl) phthalate (DEHP), haloacetic acids (HAA5), nitrate, perchloroethene (PCE), radium, trichloroethene (TCE), total trihalomethanes (TTHM), and uranium. Findings are presented by urban-rural classification scheme: four metropolitan (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) and two nonmetropolitan (micropolitan and noncore) categories. Regression modeling was used to determine whether differences in the measures by urban-rural categories were statistically significant. RESULTS: Patterns for all three air-quality measures suggest that air quality improves as areas become more rural (or less urban). The mean total number of ozone days decreased from 47.54 days in large central metropolitan counties to 3.81 days in noncore counties, whereas the mean total number of PM2.5 days decreased from 11.21 in large central metropolitan counties to 0.95 in noncore counties. The mean average annual PM2.5 concentration decreased from 11.15 µg/m3 in large central metropolitan counties to 8.87 µg/m3 in noncore counties. Patterns for the water-quality measure suggest that water quality improves as areas become more urban (or less rural). Overall, 7% of CWSs reported at least one annual mean concentration greater than the MCL for all 10 contaminants combined. The percentage increased from 5.4% in large central metropolitan counties to 10% in noncore counties, a difference that was significant, adjusting for U.S. region, CWS size, water source, and potential spatial correlation. Similar results were found for two disinfection by-products, HAA5 and TTHM. Arsenic was the only other contaminant with a significant result. Medium metropolitan counties had 3.1% of CWSs reporting at least one annual mean greater than the MCL, compared with 2.4% in large central counties. INTERPRETATION: Noncore (rural) counties experienced fewer unhealthy air-quality days than large central metropolitan counties, likely because of fewer air pollution sources in the noncore counties. All categories of counties had a mean annual average PM2.5 concentration lower than the EPA standard. Among all CWSs analyzed, the number reporting one or more annual mean contaminant concentrations greater the MCL was small. The water-quality measure suggests that water quality worsens as counties become more rural, in regards to all contaminants combined and for the two disinfection by-products individually. Although significant differences were found for the water-quality measure, the odds ratios were very small, making it difficult to determine whether these differences have a meaningful effect on public health. These differences might be a result of variations in water treatment practices in rural versus urban counties. PUBLIC HEALTH ACTION: Understanding the differences between rural and urban areas in air and water quality can help public health departments to identify, monitor, and prioritize potential environmental public health concerns and opportunities for action. These findings suggest a continued need to develop more geographically targeted, evidence-based interventions to prevent morbidity and mortality associated with poor air and water quality.

Geographic Variation in the Association between Ambient Fine Particulate Matter (PM2.5) and Term Low Birth Weight in the United States.

BACKGROUND: Studies on the association between prenatal exposure to fine particulate matter ≤ 2.5 µm in aerodynamic diameter (PM2.5) and term low birth weight (LBW) have resulted in inconsistent findings. Most studies were conducted in snapshots of small geographic areas and no national study exists. OBJECTIVES: We investigated geographic variation in the associations between ambient PM2.5 during pregnancy and term LBW in the contiguous United States. METHODS: A total of 3,389,450 term singleton births in 2002 (37-44 weeks gestational age and birth weight of 1,000-5,500 g) were linked to daily PM2.5 via imputed birth days. We generated average daily PM2.5 during the entire pregnancy and each trimester. Multi-level logistic regression models with county-level random effects were used to evaluate the associations between term LBW and PM2.5 during pregnancy. RESULTS: Without adjusting for covariates, the odds of term LBW increased 2% [odds ratio (OR) = 1.02; 95% CI: 1.00, 1.03] for every 5-µg/m(3) increase in PM2.5 exposure during the second trimester only, which remained unchanged after adjusting for county-level poverty (OR = 1.02; 95% CI: 1.01, 1.04). The odds did change to null after adjusting for individual-level predictors (OR = 1.00; 95% CI: 0.99, 1.02). Multi-level analyses, stratified by census division, revealed significant positive associations of term LBW and PM2.5 exposure (during the entire pregnancy or a specific trimester) in three census divisions of the United States: Middle Atlantic, East North Central, and West North Central, and significant negative association in the Mountain division. CONCLUSIONS: Our study provided additional evidence on the associations between PM2.5 exposure during pregnancy and term LBW from a national perspective. The magnitude and direction of the estimated associations between PM2.5 exposure and term LBW varied by geographic locations in the United States.

Ozone, Fine Particulate Matter, and Chronic Lower Respiratory Disease Mortality in the United States.

RATIONALE: Short-term effects of air pollution exposure on respiratory disease mortality are well established. However, few studies have examined the effects of long-term exposure, and among those that have, results are inconsistent. OBJECTIVES: To evaluate long-term association between ambient ozone, fine particulate matter (PM2.5, particles with an aerodynamic diameter of 2.5 µm or less), and chronic lower respiratory disease (CLRD) mortality in the contiguous United States. METHODS: We fit Bayesian hierarchical spatial Poisson models, adjusting for five county-level covariates (percentage of adults aged ≥65 years, poverty, lifetime smoking, obesity, and temperature), with random effects at state and county levels to account for spatial heterogeneity and spatial dependence. MEASUREMENTS AND MAIN RESULTS: We derived county-level average daily concentration levels for ambient ozone and PM2.5 for 2001-2008 from the U.S. Environmental Protection Agency's down-scaled estimates and obtained 2007-2008 CLRD deaths from the National Center for Health Statistics. Exposure to ambient ozone was associated with an increased rate of CLRD deaths, with a rate ratio of 1.05 (95% credible interval, 1.01-1.09) per 5-ppb increase in ozone; the association between ambient PM2.5 and CLRD mortality was positive but statistically insignificant (rate ratio, 1.07; 95% credible interval, 0.99-1.14). CONCLUSIONS: This study links air pollution exposure data with CLRD mortality for all 3,109 contiguous U.S. counties. Ambient ozone may be associated with an increased rate of death from CLRD in the contiguous United States. Although we adjusted for selected county-level covariates and unobserved influences through Bayesian hierarchical spatial modeling, the possibility of ecologic bias remains.

Data to action: using environmental public health tracking to inform decision making.

CONTEXT: Public health surveillance includes dissemination of data and information to those who need it to take action to prevent or control disease. The concept of data to action is explicit in the mission of the Centers for Disease Control and Prevention's (CDC's) National Environmental Public Health Tracking Program (Tracking Program). The CDC has built a National Environmental Public Health Tracking Network (Tracking Network) to integrate health and environmental data to drive public health action (PHA) to improve communities' health. OBJECTIVE: To assess the utility of the Tracking Program and Tracking Network in environmental public health practice and policy making. DESIGN: We analyzed information on how Tracking (all program components hereafter referred to generally as "Tracking") has been used to drive PHAs within funded states and cities (grantees). Two case studies are presented to highlight Tracking's utility. SETTING: Analyses included all grantees funded between 2005 and 2013. PARTICIPANTS: Twenty-seven states, 3 cities, and the District of Columbia ever received funding. MAIN OUTCOME MEASURES: We categorized each PHA reported to determine how grantees became involved, their role, the problems addressed, and the overall action. RESULTS: Tracking grantees reported 178 PHAs from 2006 to 2013. The most common overall action was "provided information in response to concern" (n = 42), followed by "improved a public health program, intervention, or response plan" (n = 35). Tracking's role was most often either to enhance surveillance (24%) or to analyze data (23%). In 47% of PHAs, the underlying problem was a concern about possible elevated rates of a health outcome, a potential exposure, or a potential association between a hazard and a health outcome. PHAs were started by a request for assistance (48%), in response to an emergency (8%), and though routine work by Tracking programs (43%). CONCLUSION: Our review shows that the data, expertise, technical infrastructure, and other resources of the Tracking Program and Tracking Network are driving state and local PHAs.

Engaging academia to advance the science and practice of environmental public health tracking.

Public health agencies at the federal, state, and local level are responsible for implementing actions and policies that address health problems related to environmental hazards. These actions and policies can be informed by integrating or linking data on health, exposure, hazards, and population. The mission of the Centers for Disease Control and Prevention׳s National Environmental Public Health Tracking Program (Tracking Program) is to provide information from a nationwide network of integrated health, environmental hazard, and exposure data that drives actions to improve the health of communities. The Tracking Program and federal, state, and local partners collect, integrate, analyze, and disseminate data and information to inform environmental public health actions. However, many challenges exist regarding the availability and quality of data, the application of appropriate methods and tools to link data, and the state of the science needed to link and analyze health and environmental data. The Tracking Program has collaborated with academia to address key challenges in these areas. The collaboration has improved our understanding of the uses and limitations of available data and methods, expanded the use of existing data and methods, and increased our knowledge about the connections between health and environment. Valuable working relationships have been forged in this process, and together we have identified opportunities and improvements for future collaborations to further advance the science and practice of environmental public health tracking.