INTRODUCTION: Local data are increasingly needed for public health practice. County-level data on disabilities can be a valuable complement to existing estimates of disabilities. The objective of this study was to describe the county-level prevalence of disabilities among US adults and identify geographic clusters of counties with a higher or lower prevalence of disabilities. METHODS: We applied a multilevel logistic regression and poststratification approach to geocoded 2018 Behavioral Risk Factor Surveillance System data, Census 2018 county-level population estimates, and American Community Survey 2014-2018 poverty estimates to generate county-level estimates for 6 functional disabilities and any disability type. We used cluster-outlier spatial statistical methods to identify clustered counties. RESULTS: Among 3,142 counties, median estimated prevalence was 29.5% for any disability and differed by type: hearing (8.0%), vision (4.9%), cognition (11.5%), mobility (14.9%), self-care (3.7%), and independent living (7.2%). The spatial autocorrelation statistic, Moran's I, was 0.70 for any disability and 0.60 or greater for all 6 types of disability, indicating that disabilities were highly clustered at the county level. We observed similar spatial cluster patterns in all disability types except hearing disability. CONCLUSION: The results suggest substantial differences in disability prevalence across US counties. These data, heretofore unavailable from a health survey, may help with planning programs at the county level to improve the quality of life for people with disabilities.

BACKGROUND: In this study, the authors report on the geospatial distributions of periodontists and adults with severe periodontitis in the United States. METHODS: The authors used geospatial analysis to describe the distribution of periodontists and adults, periodontists vis-à-vis estimated density of adults with severe periodontitis, and their ratios to adults with severe periodontitis. The authors identified locations of 5,415 practicing periodontists through the 2014 National Provider Identifier Registry, linked them with the weighted census number of adults, and estimated the number of adults within a series of circular distance zones. RESULTS: Approximately 60% of adults 30 through 79 years lived within 5 miles of a periodontist, 73% within 10 miles, 85% within 20 miles, and 97% within 50 miles. Proximity to a periodontist varied widely. In urban areas, 95% of adults resided within 10 miles of a periodontist and 100% within 20 miles. Only 24% of adults in rural areas lived within 10 miles of a periodontist. Most periodontists (96.1%) practiced in urban areas, clustering along the eastern and western coasts and in the Midwest, 3.1% in urban clusters elsewhere, and 0.8% in rural areas. Ratios of fewer than 8,000 adults with periodontitis to 1 or more periodontists within 10 miles were clustered mostly in the Northeast, central East Coast, Florida, West Coast, Arizona, and Midwest. CONCLUSIONS: In this study, the authors identified wide variations in geographic proximity to a practicing periodontist for adults with severe periodontitis. PRACTICAL IMPLICATIONS: Dental practitioners may provide preventive care and counseling for periodontitis and referrals for specialty care. Geographic proximity to specialized periodontal care may vary widely by locality.

The potential for a population at a given location to utilize a health service can be estimated using a newly developed measure called the supply-concentric demand accumulation (SCDA) spatial availability index. Spatial availability is the amount of demand at the given location that can be satisfied by the supply of services at a facility, after discounting the intervening demand among other populations that are located nearer to a facility location than the given population location. This differs from spatial accessibility measures which treat absolute distance or travel time as the factor that impedes utilization. The SCDA is illustrated using pulmonary rehabilitation (PR), which is a treatment for people with chronic obstructive pulmonary disease (COPD). The spatial availability of PR was estimated for each Census block group in Georgia using the 1105 residents who utilized one of 45 PR facilities located in or around Georgia. Data was provided by the Centers for Medicare & Medicaid Services. The geographic patterns of the SCDA spatial availability index and the two-step floating catchment area (2SFCA) spatial accessibility index were compared with the observed PR utilization rate using bivariate local indicators of spatial association. The SCDA index was more associated with PR utilization (Morans I = 0.607, P < 0.001) than was the 2SFCA (Morans I = 0.321, P < 0.001). These results suggest that the measures of spatial availability may be a better way to estimate the health care utilization potential than measures of spatial accessibility.

Multiple chronic conditions (MCC) reduce quality of life and are associated with high per capita health care spending. One potential way to reduce Medicare spending for MCC is to identify counties whose populations have high levels of spending compared to level of disease burden. Using a nationally representative sample of Medicare Fee-for-Service beneficiaries, this paper presents a method to measure the collective burden of several chronic conditions in a population, which the authors have termed the concentration of chronic conditions (CCC). The authors observed a significantly positive linear relationship between the CCC measure and county-level per capita Medicare spending. This area-level measure can be operationalized to identify counties that might benefit from targeted efforts designed to optimally manage and prevent chronic illness.

Objectives. To demonstrate a flexible and practical method to obtain near real-time estimates of the number of at-risk community-dwelling adults with a chronic condition in a defined area potentially affected by a public health emergency.Methods. We used small area estimation with survey responses from the 2016 Behavioral Risk Factor Surveillance System together with a geographic information system to predict the number of adults with chronic obstructive pulmonary disease who lived in the forecasted path of Hurricane Florence in North and South Carolina in 2018.Results. We estimated that a range of 32 002 to 676 536 adults with chronic obstructive pulmonary disease resided between 50 and 200 miles of 3 consecutive daily forecasted landfalls. The number of affected counties ranged from 8 to 10 (at 50 miles) to as many as 119 to 127 (at 200 miles).Conclusions. Community preparedness is critical to anticipating, responding to, and ameliorating these health threats. We demonstrated the feasibility of quickly producing detailed estimates of the number of residents with chronic conditions who may face life-threatening situations because of a natural disaster. These methods are applicable to a range of planning and response scenarios.

Introduction: Alzheimer's disease and related dementias (ADRD) cause a high burden of morbidity and mortality in the United States. Age, race, and ethnicity are important risk factors for ADRD. Method(s): We estimated the future US burden of ADRD by age, sex, and race and ethnicity by applying subgroup-specific prevalence among Medicare Fee-for-Service beneficiaries aged >=65 years in 2014 to subgroup-specific population estimates for 2014 and population projection data from the United States Census Bureau for 2015 to 2060. Result(s): The burden of ADRD in 2014 was an estimated 5.0 million adults aged >=65 years or 1.6% of the population, and there are significant disparities in ADRD prevalence among population subgroups defined by race and ethnicity. ADRD burden will double to 3.3% by 2060 when 13.9 million Americans are projected to have the disease. Discussion(s): These estimates can be used to guide planning and interventions related to caring for the ADRD population and supporting caregivers.

CONTEXT: Excessive alcohol use is responsible for 88 000 deaths in the United States annually and cost the United States $249 billion in 2010. There is strong scientific evidence that regulating alcohol outlet density is an effective intervention for reducing excessive alcohol consumption and related harms, but there is no standard method for measuring this exposure. PROGRAM: We overview the strategies available for measuring outlet density, discuss their advantages and disadvantages, and provide examples of how they can be applied in practice. IMPLEMENTATION: The 3 main approaches for measuring density are container-based (eg, number of outlets in a county), distance-based (eg, average distance between a college and outlets), and spatial access-based (eg, weighted distance between town center and outlets). EVALUATION: While container-based measures are the simplest to calculate and most intuitive, distance-based or spatial access-based measures are unconstrained by geopolitical boundaries and allow for assessment of clustering (an amplifier of certain alcohol-related harms). Spatial access-based measures can also be adjusted for population size/demographics but are the most resource-intensive to produce. DISCUSSION: Alcohol outlet density varies widely across and between locations and over time, which is why it is important to measure it. Routine public health surveillance of alcohol outlet density is important to identify problem areas and detect emerging ones. Distance- or spatial access-based measures of alcohol outlet density are more resource-intensive than container-based measures but provide a much more accurate assessment of exposure to alcohol outlets and can be used to assess clustering, which is particularly important when assessing the relationship between density and alcohol-related harms, such as violent crime.

CONTEXT: Excessive alcohol use is responsible for 88 000 deaths in the United States annually and cost the United States $249 billion in 2010. There is strong scientific evidence that regulating alcohol outlet density is an effective intervention for reducing excessive alcohol consumption and related harms, but there is no standard method for measuring this exposure. PROGRAM: We overview the strategies available for measuring outlet density, discuss their advantages and disadvantages, and provide examples of how they can be applied in practice. IMPLEMENTATION: The 3 main approaches for measuring density are container-based (eg, number of outlets in a county), distance-based (eg, average distance between a college and outlets), and spatial access-based (eg, weighted distance between town center and outlets). EVALUATION: While container-based measures are the simplest to calculate and most intuitive, distance-based or spatial access-based measures are unconstrained by geopolitical boundaries and allow for assessment of clustering (an amplifier of certain alcohol-related harms). Spatial access-based measures can also be adjusted for population size/demographics but are the most resource-intensive to produce. DISCUSSION: Alcohol outlet density varies widely across and between locations and over time, which is why it is important to measure it. Routine public health surveillance of alcohol outlet density is important to identify problem areas and detect emerging ones. Distance- or spatial access-based measures of alcohol outlet density are more resource-intensive than container-based measures but provide a much more accurate assessment of exposure to alcohol outlets and can be used to assess clustering, which is particularly important when assessing the relationship between density and alcohol-related harms, such as violent crime.

A spatially adaptive floating catchment is a circular area that expands outward from a provider location until the estimated demand for services in the nearest population locations exceeds the observed number of health care services performed at the provider location. This new way of creating floating catchments was developed to address the change of spatial support problem (COSP) by upscaling the availability of the service observed at a provider location to the county-level so that its geographic association with utilization could be measured using the same spatial support. Medicare Fee-for-Service claims data were used to identify beneficiaries aged >/=65 years who received outpatient pulmonary rehabilitation (PR) in the Southeastern United States in 2014 (n=8798), the number of PR treatments these beneficiaries received (n=132,508), and the PR providers they chose (n=426). The positive correlation between PR availability and utilization was relatively low, but statistically significant (r=0.619, p<0.001) indicating that most people use the nearest available PR services, but some travel long distances. SAFCs can be created using data from health care systems that collect claim-level utilization data that identifies the locations of providers chosen by beneficiaries of a specific health care procedure.

BACKGROUND: We used a multilevel regression and poststratification approach to generate estimates of health-related outcomes using Behavioral Risk Factor Surveillance System 2013 (BRFSS) data for the 500 US cities. We conducted an empirical study to investigate whether the approach is robust using different health surveys. METHODS: We constructed a multilevel logistic model with individual-level age, sex, and race/ethnicity as predictors (Model I), and sequentially added educational attainment (Model II) and area-level poverty (Model III) for 5 health-related outcomes using the nationwide BRFSS, the Massachusetts BRFSS 2013 (a state subset of nationwide BRFSS), and the Boston BRFSS 2010/2013 (an independent survey), respectively. We applied each model to the Boston population (2010 Census) to predict each outcome in Boston and compared each with corresponding Boston BRFSS direct estimates. RESULTS: Using Model I for the nationwide BRFSS, estimates of diabetes, high blood pressure, physical inactivity, and binge drinking fell within the 95% confidence interval of corresponding Boston BRFSS direct estimates. Adding educational attainment and county-level poverty (Models II and III) further improved their accuracy, particularly for current smoking (the model-based estimate was 15.2% by Model I and 18.1% by Model II). The estimates based on state BRFSS and Boston BRFSS models were similar to those based on the nationwide BRFSS, but area-level poverty did not improve the estimates significantly. CONCLUSION: The estimates of health-related outcomes were similar using different health surveys. Model specification could vary by surveys with different geographic coverage.

OBJECTIVE: To assess spatial accessibility measures to on-premise alcohol outlets at census block, census tract, county, and state levels for the United States. METHODS: Using network analysis in a geographic information system, we computed distance-based measures (Euclidean distance, driving distance, and driving time) to on-premise alcohol outlets for the entire U.S. at the census block level. We then calculated spatial access-based measures, specifically a population-weighted spatial accessibility index and population-weighted distances (Euclidean distance, driving distance, and driving time) to alcohol outlets at the census tract, county, and state levels. A multilevel model-based sensitivity analysis was conducted to evaluate the associations between different on-premise alcohol outlet accessibility measures and excessive drinking outcomes. RESULTS: The national average population-weighted driving time to the nearest 7 on-premise alcohol outlets was 5.89 min, and the average population-weighted driving distance was 2.63 miles. At the state level, population-weighted driving times ranged from 1.67 min (DC) to 15.29 min (Arizona). Population-weighted driving distances ranged from 0.67 miles (DC) to 7.91 miles (Arkansas). At the county level, population-weighted driving times and distances exhibited significant geographic variations, and averages for both measures increased by the degree of county rurality. The population-weighted spatial accessibility indexes were highly correlated to respective population-weighted distance measures. Sensitivity analysis demonstrated that population weighted accessibility measures were more sensitive to excessive drinking outcomes than were population weighted distance measures. CONCLUSIONS: These results can be used to assess the relationship between geographic access to on-premise alcohol outlets and health outcomes. This study demonstrates a flexible and robust method that can be applied or modified to quantify spatial accessibility to public resources such as healthy food stores, medical care providers, and parks and greenspaces, as well as, quantify spatial exposure to local adverse environments such as tobacco stores and fast food restaurants.

Chronic obstructive pulmonary disease (COPD) accounts for the majority of deaths from chronic lower respiratory diseases, the third leading cause of death in the United States in 2015 and the fourth leading cause in 2016. Major risk factors include tobacco exposure, occupational and environmental exposures, respiratory infections, and genetics.(dagger) State variations in COPD outcomes (1) suggest that it might be more common in states with large rural areas. To assess urban-rural variations in COPD prevalence, hospitalizations, and mortality; obtain county-level estimates; and update state-level variations in COPD measures, CDC analyzed 2015 data from the Behavioral Risk Factor Surveillance System (BRFSS), Medicare hospital records, and death certificate data from the National Vital Statistics System (NVSS). Overall, 15.5 million adults aged >/=18 years (5.9% age-adjusted prevalence) reported ever receiving a diagnosis of COPD; there were approximately 335,000 Medicare hospitalizations (11.5 per 1,000 Medicare enrollees aged >/=65 years) and 150,350 deaths in which COPD was listed as the underlying cause for persons of all ages (40.3 per 100,000 population). COPD prevalence, Medicare hospitalizations, and deaths were significantly higher among persons living in rural areas than among those living in micropolitan or metropolitan areas. Among seven states in the highest quartile for all three measures, Arkansas, Kentucky, Mississippi, and West Virginia were also in the upper quartile (>/=18%) for rural residents. Overcoming barriers to prevention, early diagnosis, treatment, and management of COPD with primary care provider education, Internet access, physical activity and self-management programs, and improved access to pulmonary rehabilitation and oxygen therapy are needed to improve quality of life and reduce COPD mortality.

Because conducting population-based oral health screening is resource intensive, oral health data at small-area levels (e.g., county-level) are not commonly available. We applied the multilevel logistic regression and poststratification method to estimate county-level prevalence of untreated dental caries among children aged 6-9years in the United States using data from the National Health and Nutrition Examination Survey (NHANES) 2005-2010 linked with various area-level data at census tract, county and state levels. We validated model-based national estimates against direct estimates from NHANES. We also compared model-based estimates with direct estimates from select State Oral Health Surveys (SOHS) at state and county levels. The model with individual-level covariates only and the model with individual-, census tract- and county-level covariates explained 7.2% and 96.3% respectively of overall county-level variation in untreated caries. Model-based county-level prevalence estimates ranged from 4.9% to 65.2% with median of 22.1%. The model-based national estimate (19.9%) matched the NHANES direct estimate (19.8%). We found significantly positive correlations between model-based estimates for 8-year-olds and direct estimates from the third-grade State Oral Health Surveys (SOHS) at state level for 34 states (Pearson coefficient: 0.54, P=0.001) and SOHS estimates at county level for 53 New York counties (Pearson coefficient: 0.38, P=0.006). This methodology could be a useful tool to characterize county-level disparities in untreated dental caries among children aged 6-9years and complement oral health surveillance to inform public health programs especially when local-level data are not available although the lack of external validation due to data unavailability should be acknowledged.

INTRODUCTION: Local health authorities need small-area estimates for prevalence of chronic diseases and health behaviors for multiple purposes. We generated city-level and census-tract-level prevalence estimates of 27 measures for the 500 largest US cities. METHODS: To validate the methodology, we constructed multilevel logistic regressions to predict 10 selected health indicators among adults aged 18 years or older by using 2013 Behavioral Risk Factor Surveillance System (BRFSS) data; we applied their predicted probabilities to census population data to generate city-level, neighborhood-level, and zip-code-level estimates for the city of Boston, Massachusetts. RESULTS: By comparing the predicted estimates with their corresponding direct estimates from a locally administered survey (Boston BRFSS 2010 and 2013), we found that our model-based estimates for most of the selected health indicators at the city level were close to the direct estimates from the local survey. We also found strong correlation between the model-based estimates and direct survey estimates at neighborhood and zip code levels for most indicators. CONCLUSION: Findings suggest that our model-based estimates are reliable and valid at the city level for certain health outcomes. Local health authorities can use the neighborhood-level estimates if high quality local health survey data are not otherwise available.

PROBLEM/CONDITION: Persons living in rural areas are recognized as a health disparity population because the prevalence of disease and rate of premature death are higher than for the overall population of the United States. Surveillance data about health-related behaviors are rarely reported by urban-rural status, which makes comparisons difficult among persons living in metropolitan and nonmetropolitan counties. REPORTING PERIOD: 2013. DESCRIPTION OF SYSTEM: The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit-dialed landline- and cellular-telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health-risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services related to the leading causes of death and disability. BRFSS data were analyzed for 398,208 adults aged ≥18 years to estimate the prevalence of five self-reported health-related behaviors (sufficient sleep, current nonsmoking, nondrinking or moderate drinking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations) by urban-rural status. For this report, rural is defined as the noncore counties described in the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. RESULTS: Approximately one third of U.S. adults practice at least four of these five behaviors. Compared with adults living in the four types of metropolitan counties (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan), adults living in the two types of nonmetropolitan counties (micropolitan and noncore) did not differ in the prevalence of sufficient sleep; had higher prevalence of nondrinking or moderate drinking; and had lower prevalence of current nonsmoking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations. The overall age-adjusted prevalence of reporting at least four of the five health-related behaviors was 30.4%. The prevalence among the estimated 13.3 million adults living in noncore counties was lower (27.0%) than among those in micropolitan counties (28.8%), small metropolitan counties (29.5%), medium metropolitan counties (30.5%), large fringe metropolitan counties (30.2%), and large metropolitan centers (31.7%). INTERPRETATION: This is the first report of the prevalence of these five health-related behaviors for the six urban-rural categories. Nonmetropolitan counties have lower prevalence of three and clustering of at least four health-related behaviors that are associated with the leading chronic disease causes of death. Prevalence of sufficient sleep was consistently low and did not differ by urban-rural status. PUBLIC HEALTH ACTION: Chronic disease prevention efforts focus on improving the communities, schools, worksites, and health systems in which persons live, learn, work, and play. Evidence-based strategies to improve health-related behaviors in the population of the United States can be used to reach the Healthy People 2020 objectives for these five self-reported health-related behaviors (sufficient sleep, current nonsmoking, nondrinking or moderate drinking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations). These findings suggest an ongoing need to increase public awareness and public education, particularly in rural counties where prevalence of these health-related behaviors is lowest.

BACKGROUND: Geographic clusters in prevalence and hospitalizations for chronic obstructive pulmonary disease (COPD) have been identified at national, state, and county levels. The study objective is to identify county-level geographic accessibility to pulmonologists for adults with COPD. METHODS: Service locations of 12,392 practicing pulmonologists and 248,160 primary care physicians were identified from the 2013 National Provider Identifier Registry and weighted by census block-level populations within a series of circular distance buffer zones. Model-based county-level population counts of US adults aged >18 years with COPD were estimated from the 2013 Behavioral Risk Factor Surveillance System. The percentages of all estimated adults with potential access to at least one provider type and the county-level ratio of adults with COPD per pulmonologist were estimated for selected distances. RESULTS: The majority of US adults (100% in urbanized areas, 99.5% in urban clusters, and 91.7% in rural areas) had geographic access to a primary care physician within a 10-mile buffer distance; almost all (>99.9%) had access to a primary care physician within 50 miles. At least one pulmonologist within 10 miles was available for 97.5% of US adults living in urbanized areas, but only for 38.3% in urban clusters and 34.5% in rural areas. When distance increased to 50 miles, at least one pulmonologist was available for 100% in urbanized areas, 93.2% in urban clusters, and 95.2% in rural areas. County-level ratios of adults with COPD per pulmonologist varied greatly across the US with residents in many counties in the Midwest having no pulmonologist within 50 miles. CONCLUSIONS: County-level geographic variations in pulmonologist access for adults with COPD suggest that those adults with limited access will have to depend upon care from primary care physicians.

Doctor-diagnosed arthritis is a common chronic condition that affects approximately 52.5 million (22.7%) adults in the United States and is a leading cause of disability (1,2). The prevalence of doctor-diagnosed arthritis has been well documented at the national level (1), but little has been published at the state level and the county level, where interventions are carried out and can have their greatest effect. To estimate the prevalence of doctor-diagnosed arthritis among adults at the state and county levels, CDC analyzed data from the 2014 Behavioral Risk Factor Surveillance System (BRFSS). This report summarizes the results of that analysis, which found that, for all 50 states and the District of Columbia (DC) overall, the age-standardized median prevalence of doctor-diagnosed arthritis was 24% (range = 18.8%-35.5%). The age-standardized model-predicted prevalence of doctor-diagnosed arthritis varied substantially by county, with estimates ranging from 15.8% to 38.6%. The high prevalence of arthritis in all counties, and the high frequency of arthritis-attributable limitations (1) among adults with arthritis, suggests that states and counties might benefit from expanding underused, evidence-based interventions for arthritis that can reduce arthritis symptoms and improve self-management.

The objective of the study was to estimate the prevalence of periodontitis at state and local levels across the United States by using a novel, small area estimation (SAE) method. Extended multilevel regression and poststratification analyses were used to estimate the prevalence of periodontitis among adults aged 30 to 79 y at state, county, congressional district, and census tract levels by using periodontal data from the National Health and Nutrition Examination Survey (NHANES) 2009-2012, population counts from the 2010 US census, and smoking status estimates from the Behavioral Risk Factor Surveillance System in 2012. The SAE method used age, race, gender, smoking, and poverty variables to estimate the prevalence of periodontitis as defined by the Centers for Disease Control and Prevention/American Academy of Periodontology case definitions at the census block levels and aggregated to larger administrative and geographic areas of interest. Model-based SAEs were validated against national estimates directly from NHANES 2009-2012. Estimated prevalence of periodontitis ranged from 37.7% in Utah to 52.8% in New Mexico among the states (mean, 45.1%; median, 44.9%) and from 33.7% to 68% among counties (mean, 46.6%; median, 45.9%). Severe periodontitis ranged from 7.27% in New Hampshire to 10.26% in Louisiana among the states (mean, 8.9%; median, 8.8%) and from 5.2% to 17.9% among counties (mean, 9.2%; median, 8.8%). Overall, the predicted prevalence of periodontitis was highest for southeastern and southwestern states and for geographic areas in the Southeast along the Mississippi Delta, as well as along the US and Mexico border. Aggregated model-based SAEs were consistent with national prevalence estimates from NHANES 2009-2012. This study is the first-ever estimation of periodontitis prevalence at state and local levels in the United States, and this modeling approach complements public health surveillance efforts to identify areas with a high burden of periodontitis.

BACKGROUND: Increases in population and life expectancy of Americans may result in shortages of endocrinologists by 2020. This study aims to assess variations in geographic accessibility to endocrinologists in the US, by age group at state and county levels, and by urban/rural status, and distance. METHODS: We used the 2012 National Provider Identifier Registry to obtain office locations of all adult and pediatric endocrinologists in the US. The population with geographic access to an endocrinologist within a series of 6 distance radii, centered on endocrinologist practice locations, was estimated using the US Census 2010 block-level population. We assumed that persons living within the same circular buffer zone of an endocrinologist location have the same geographic accessibility to that endocrinologist. The geographic accessibility (the percentage of the population with geographic access to at least one endocrinologist) and the population-to-endocrinologist ratio for each geographic area were estimated. RESULTS: By using 20 miles as the distance radius, geographic accessibility to at least one pediatric/adult endocrinologist for age groups 0-17, 18-64, and ≥65 years was 64.1 %, 85.4 %, and 82.1 %. The overall population-to-endocrinologist ratio within 20 miles was 39,492:1 for children, 29,887:1 for adults aged 18-64 years, and 6,194:1 for adults aged ≥65 years. These ratios varied considerably by state, county, urban/rural status, and distance. CONCLUSIONS: This study demonstrates that there are geographic variations of accessibility to endocrinologists in the US. The areas with poorer geographic accessibility warrant further study of the effect of these variations on disease prevention, detection, and management of endocrine diseases in the US population. Our findings of geographic access to endocrinologists also may provide valuable information for medical education and health resources allocation.

In this issue of Preventing Chronic Disease, Moodley et al (1) present the results of a spatial analysis of the locations of advertisements for sugar-sweetened beverages (SSBs) and vendors who sell SSBs in relation to the location of schools in 5 neighborhoods in South Africa. In their article, “Obesogenic Environments: Access to and Advertising of Sugar-Sweetened Beverages in Soweto, South Africa,” the authors used a global positioning system (GPS) and a digital camera to gather data on the locations of SSB advertisements and vendors. Their innovative and low-cost approach could be replicated in any setting, including the United States, where time-sensitive point-location data on environmental exposure are needed but are unavailable through more traditional data-collection sources. In this sense, their approach to gathering data is situated within the broader technological developments of volunteered geographic information, crowdsourced data, and GPS-enabled mobile technology for public health (2–6). | Although the main objective of Moodley et al was to provide a descriptive analysis of the intensity of SSB advertising, their approach to using technology deserves to be highlighted because it may be of great value to public health practitioners. To this end, Preventing Chronic Disease readers may find valuable some additional examples of the use of handheld GPS devices or smartphones for data collection for chronic disease epidemiology. Smartphones are GPS-enabled, and photographs taken with smartphone cameras are encoded with a GPS location. Software applications for smartphones that allow photographs to be exported and their location information to be stored on a convenient database include commercial applications such as Collector for ArcGIS (Esri, http://doc.arcgis.com/en/collector/) and open-source free applications such as Ushahidi (www.ushahidi.com/product/ushahidi/).

Small area estimation is a statistical technique used to produce reliable estimates for smaller geographic areas than those for which the original surveys were designed. Such small area estimates (SAEs) often lack rigorous external validation. In this study, we validated our multilevel regression and poststratification SAEs from 2011 Behavioral Risk Factor Surveillance System data using direct estimates from 2011 Missouri County-Level Study and American Community Survey data at both the state and county levels. Coefficients for correlation between model-based SAEs and Missouri County-Level Study direct estimates for 115 counties in Missouri were all significantly positive (0.28 for obesity and no health-care coverage, 0.40 for current smoking, 0.51 for diabetes, and 0.69 for chronic obstructive pulmonary disease). Coefficients for correlation between model-based SAEs and American Community Survey direct estimates of no health-care coverage were 0.85 at the county level (811 counties) and 0.95 at the state level. Unweighted and weighted model-based SAEs were compared with direct estimates; unweighted models performed better. External validation results suggest that multilevel regression and poststratification model-based SAEs using single-year Behavioral Risk Factor Surveillance System data are valid and could be used to characterize geographic variations in health indicators at local levels (such as counties) when high-quality local survey data are not available.

INTRODUCTION: Sleep insufficiency is a major health risk factor. Exposure to environmental noise may affect sleep duration and quality. The objective of this study was to assess the relationship between airport noise exposure and insufficient sleep in the United States by using data from the Behavioral Risk Factor Surveillance System (BRFSS). METHODS: Data on the number of days without enough rest or sleep for approximately 750,000 respondents to the 2008 and 2009 BRFSS were linked with data on noise exposure modeled using the US Federal Aviation Administration's (FAA's) Integrated Noise Model for 95 major US airports for corresponding years. Noise exposure data were stratified into 3 groups depending on noise levels. People living outside airport noise exposure zones were included as a reference category. RESULTS: We found 8.6 mean days of insufficient sleep in the previous 30 days among 745,868 adults; 10.8% reported insufficient sleep for all 30 days; and 30.1% reported no days of insufficient sleep. After controlling for individual sociodemographics and ZIP Code-level socioeconomic status, we found no significant differences in sleep insufficiency between the 3 noise exposure zones and the zone outside. CONCLUSION: This research demonstrates the feasibility of conducting a national study of airport noise and sleep using an existing public health surveillance dataset and recommends methods for improving the accuracy of such studies; some of these recommendations were implemented in recent FAA-sponsored studies. Validation of BRFSS sleep measures and refined ways of collecting data are needed to determine the optimal measures of sleep for such a large-scale survey and to establish the relationship between airport noise and sleep.

Chronic diseases are an important public health problem, which can result in morbidity, mortality, disability, and decreased quality of life. Chronic diseases represented seven of the top 10 causes of death in the United States in 2010 (Murphy SL, Xu J, Kochanek KD. Deaths: final data for 2010. Natl Vital Stat Rep 2013;6. Available at http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_04.pdf Adobe PDF file). Chronic diseases and risk factors vary by geographic area such as state and county, where essential public health interventions are implemented. The chronic disease indicators (CDIs) were established in the late 1990s through collaboration among CDC, the Council of State and Territorial Epidemiologists, and the Association of State and Territorial Chronic Disease Program Directors (now the National Association of Chronic Disease Directors) to enable public health professionals and policymakers to retrieve data for chronic diseases and risk factors that have a substantial impact on public health. This report describes the latest revisions to the CDIs, which were developed on the basis of a comprehensive review during 2011-2013. The number of indicators is increasing from 97 to 124, with major additions in systems and environmental indicators and additional emphasis on high-impact diseases and conditions as well as emerging topics.

OBJECTIVE: We assessed the relationship between active duty status and daily insufficient sleep in a telephone survey. METHODS: U.S. military service status (recent defined as past 12 months and past defined as >12 months ago) and daily insufficient sleep in the past 30 days were assessed among 566,861 adults aged 18 to 64 years and 271,202 adults aged ≥65 years in the 2009 to 2010 Behavioral Risk Factor Surveillance System surveys. RESULTS: Among ages 18 to 64 years, 1.1% reported recent active duty and 7.1% had past service; among ages ≥65 years, 0.6% reported recent and 24.6% had past service. Among ages 18 to 64 years, prevalence of daily insufficient sleep was 13.7% among those reporting recent duty, 12.6% for those with past service, and 11.2% for those with no service. Insufficient sleep did not vary significantly with active duty status among ages ≥65 years. After adjustment for sociodemographic characteristics, health behaviors, and frequent mental distress in multivariate logistic regression models, respondents aged 18 to 64 years with recent active duty were 34% more likely and those with past service were 23% more likely to report daily insufficient sleep than those with no service (p < 0.05, both). CONCLUSIONS: Adults with either recent or past active duty have a greater risk for daily insufficient sleep.

A variety of small-area statistical models have been developed for health surveys, but none are sufficiently flexible to generate small-area estimates (SAEs) to meet data needs at different geographic levels. We developed a multilevel logistic model with both state- and nested county-level random effects for chronic obstructive pulmonary disease (COPD) using 2011 data from the Behavioral Risk Factor Surveillance System. We applied poststratification with the (decennial) US Census 2010 counts of census-block population to generate census-block-level SAEs of COPD prevalence which could be conveniently aggregated to all other census geographic units, such as census tracts, counties, and congressional districts. The model-based SAEs and direct survey estimates of COPD prevalence were quite consistent at both the county and state levels. The Pearson correlation coefficient was 0.99 at the state level and ranged from 0.88 to 0.95 at the county level. Our extended multilevel regression modeling and poststratification approach could be adapted for other geocoded national health surveys to generate reliable SAEs for population health outcomes at all administrative and legislative geographic levels of interest in a scalable framework.

BACKGROUND: Current asthma prevalence among adults in the United States has reached historically high levels. Although national-level estimates indicate that asthma prevalence among adults increased by 33% from 2000 to 2009, state-specific temporal trends of current asthma prevalence and their contributing risk factors have not been explored. METHODS: We used 2000-2009 Behavioral Risk Factor Surveillance System data from all 50 states and the District of Columbia (D.C.) to estimate state-specific current asthma prevalence by 2-year periods (2000-2001, 2002-2003, 2004-2005, 2006-2007, 2008-2009). We fitted a series of four logistic-regression models for each state to evaluate whether there was a statistically significant linear change in the current asthma prevalence over time, accounting for sociodemographic factors, smoking status, and weight status (using body mass index as the indicator). RESULTS: During 2000-2009, current asthma prevalence increased in all 50 states and D.C., with significant increases in 46/50 (92%) states and D.C. After accounting for weight status in the model series with sociodemographic factors, and smoking status, 10 states (AR, AZ, IA, IL, KS, ME, MT, UT, WV, and WY) that had previously shown a significant increase did not show a significant increase in current asthma prevalence. CONCLUSIONS: There was a significant increasing trend in state-specific current asthma prevalence among adults from 2000 to 2009 in most states in the United States. Obesity prevalence appears to contribute to increased current asthma prevalence in some states.

OBJECTIVE: Automobile dependency and longer commuting are associated with current obesity epidemic. We aimed to examine the urban-rural differential effects of neighborhood commuting environment on obesity in the US. METHODS: The 1997-2005 National Health Interview Survey (NHIS) were linked to 2000 US Census data to assess the effects of neighborhood commuting environment: census tract-level automobile dependency and commuting time, on individual obesity status. RESULTS: Higher neighborhood automobile dependency was associated with increased obesity risk in urbanized areas (large central metro (OR 1.11[1.09, 1.12]), large fringe metro (OR 1.17[1.13, 1.22]), medium metro (OR 1.22[1.16, 1.29]), small metro (OR 1.11[1.04, 1.19]), and micropolitan (OR 1.09[1.00, 1.19])), but not in non-core rural areas (OR 1.00[0.92, 1.08]). Longer neighborhood commuting time was associated with increased obesity risk in large central metro (OR 1.09[1.04, 1.13]), and less urbanized areas (small metro (OR 1.08[1.01, 1.16]), micropolitan (OR 1.06[1.01, 1.12]), and non-core rural areas (OR 1.08[1.01, 1.17])), but not in (large fringe metro (OR 1.05[1.00, 1.11]), and medium metro (OR 1.04[0.98, 1.10])). CONCLUSION: The link between commuting environment and obesity differed across the regional urbanization levels. Urban and regional planning policies may improve current commuting environment and better support healthy behaviors and healthy community development.

INTRODUCTION: Traditional survey methods for obtaining nationwide small-area estimates (SAEs) of childhood obesity are costly. This study applied a geocoded national health survey in a multilevel modeling framework to estimate prevalence of childhood obesity at the census block-group level. METHODS: We constructed a multilevel logistic regression model to evaluate the influence of individual demographic characteristics, zip code, county, and state on the childhood obesity measures from the 2007 National Survey of Children's Health. The obesity risk for a child in each census block group was then estimated on the basis of this multilevel model. We compared direct survey and model-based SAEs to evaluate the model specification. RESULTS: Multilevel models in this study explained about 60% of state-level variances associated with childhood obesity, 82.8% to 86.5% of county-level, and 93.1% of zip code-level. The 95% confidence intervals of block- group level SAEs have a wide range (0.795-20.0), a low median of 2.02, and a mean of 2.12. The model-based SAEs of childhood obesity prevalence ranged from 2.3% to 54.7% with a median of 16.0% at the block-group level. CONCLUSION: The geographic variances among census block groups, counties, and states demonstrate that locale may be as significant as individual characteristics such as race/ethnicity in the development of the childhood obesity epidemic. Our estimates provide data to identify priority areas for local health programs and to establish feasible local intervention goals. Model-based SAEs of population health outcomes could be a tool of public health assessment and surveillance.

BACKGROUND: Local data are often used to plan and evaluate public health interventions and policy. With increasingly fewer public resources to collect sufficient data to support direct estimation of local outcomes, methods for deriving small area estimates are vital. The purpose of this study is to describe the county-level geographic distribution of human papillomavirus (HPV) vaccine coverage among adolescent females in Texas using multilevel small area estimation. METHODS: Multilevel (individual, county, public health region) random-intercept logit models were fit to HPV vaccination data (≥1 dose Gardasil) from the 2008 Behavioral Risk Factor Surveillance System. Using the parameter estimates from the final model, we simulated 10,000 data sets for each regression coefficient from the normal distribution and applied them to the logit model to estimate HPV vaccine coverage in each county. RESULTS: County-level coverage estimates ranged from 7% to 29%, compared with the state average of 18% (95% confidence interval [CI], 13.59-21.88). Many Southwestern border and metropolitan counties exhibited high coverage estimates. Low coverage estimates were noted in the Panhandle, Southeastern border region, and Northeast. Significant correlations were observed between HPV vaccination and Hispanic ethnicity, county poverty, and public health region poverty. CONCLUSION: Harnessing the flexibility of multilevel small area models to estimate HPV vaccine coverage at the county level, we have provided data that may inform the development of health education programs/policies, the provision of health services, and the planning of new research studies. Additionally, we have provided a framework for modeling other health outcomes at the county level using national survey data.

This paper investigates spatiotemporal interpolation methods for the application of air pollution assessment. The air pollutant of interest in this paper is fine particulate matter PM(2.5). The choice of the time scale is investigated when applying the shape function-based method. It is found that the measurement scale of the time dimension has an impact on the quality of interpolation results. Based upon the result of 10-fold cross validation, the most effective time scale out of four experimental ones was selected for the PM(2.5) interpolation. The paper also estimates the population exposure to the ambient air pollution of PM(2.5) at the county-level in the contiguous U.S. in 2009. The interpolated county-level PM(2.5) has been linked to 2009 population data and the population with a risky PM(2.5) exposure has been estimated. The risky PM(2.5) exposure means the PM(2.5) concentration exceeding the National Ambient Air Quality Standards. The geographic distribution of the counties with a risky PM(2.5) exposure is visualized. This work is essential to understanding the associations between ambient air pollution exposure and population health outcomes.