County-level Estimates Research Articles

Housing investment, sea level rise, and climate change beliefs

We investigate the association of new housing construction with sea level rise exposure and climate change beliefs. We combine U.S. Census construction permits data, sea level rise projections, county-level climate change belief estimates (Howe et al., 2015), and standard housing controls. The results suggest that (i) sea level rise vulnerability is associated with significantly reduced construction in areas with high climate change beliefs, but that (ii) this relationship is significantly attenuated in more skeptical areas, suggesting that climate skepticism may be delaying adaptation.

Multilevel Regression for Small-Area Estimation of Mammography Use in the United States, 2014.

The U.S. Preventive Services Task Force recommends biennial screening mammography for average-risk women aged 50-74 years. County-level information on population measures of mammography use can inform targeted intervention to reduce geographic disparities in mammography use. County-level estimates for mammography use nationwide are rarely presented. We used data from the 2014 Behavioral Risk Factor Surveillance System (BRFSS; n = 130,289 women), linked it to the American Community Survey poverty data, and fitted multilevel logistic regression models with two outcomes: mammography within the past 2 years (up-to-date), and most recent mammography 5 or more years ago or never (rarely/never). We poststratified the data with U.S. Census population counts to run Monte Carlo simulations. We generated county-level estimates nationally and by urban-rural county classifications. County-level prevalence estimates were aggregated into state and national estimates. We validated internal consistency between our model-based state-specific estimates and urban-rural estimates with BRFSS direct estimates using Spearman correlation coefficients and mean absolute differences. Correlation coefficients were 0.94 or larger. Mean absolute differences for the two outcomes ranged from 0.79 to 1.03. Although 78.45% (95% confidence interval, 77.95%-78.92%) of women nationally were up-to-date with mammography, more than half of the states had counties with >15% of women rarely/never using a mammogram, many in rural areas. We provided estimates for all U.S. counties and identified marked variations in mammography use. Many states and counties were far from the 2020 target (81.1%). Our results suggest a need for planning and resource allocation on a local level to increase mammography uptake.

Data triangulation to estimate age-specific coverage of voluntary medical male circumcision for HIV prevention in four Kenyan counties.

BackgroundKenya is 1 of 14 priority countries in Africa scaling up voluntary medical male circumcision (VMMC) for HIV prevention following the recommendations of the World Health Organization and the Joint United Nations Programme on HIV/AIDS. To inform VMMC target setting, we modeled the impact of circumcising specific client age groups across several Kenyan geographic areas.MethodsThe Decision Makers’ Program Planning Tool, Version 2 (DMPPT 2) was applied in Kisumu, Siaya, Homa Bay, and Migori counties. Initial modeling done in mid-2016 showed coverage estimates above 100% in age groups and geographic areas where demand for VMMC continued to be high. On the basis of information obtained from country policy makers and VMMC program implementers, we adjusted circumcision coverage for duplicate reporting, county-level population estimates, migration across county boundaries for VMMC services, and replacement of traditional circumcision with circumcisions in the VMMC program. To address residual inflated coverage following these adjustments we applied county-specific correction factors computed by triangulating model results with coverage estimates from population surveys.ResultsA program record review identified duplicate reporting in Homa Bay, Kisumu, and Siaya. Using county population estimates from the Kenya National Bureau of Statistics, we found that adjusting for migration and correcting for replacement of traditional circumcision with VMMC led to lower estimates of 2016 male circumcision coverage especially for Kisumu, Migori, and Siaya. Even after addressing these issues, overestimation of 2016 male circumcision coverage persisted, especially in Homa Bay. We estimated male circumcision coverage in 2016 by applying correction factors. Modeled estimates for 2016 circumcision coverage for the 10- to 14-year age group ranged from 50% in Homa Bay to approximately 90% in Kisumu. Results for the 15- to 19-year age group suggest almost complete coverage in Kisumu, Migori, and Siaya. Coverage for the 20- to 24-year age group ranged from about 80% in Siaya to about 90% in Homa Bay, coverage for those aged 25–29 years ranged from about 60% in Siaya to 80% in Migori, and coverage in those aged 30–34 years ranged from about 50% in Siaya to about 70% in Migori.ConclusionsOur analysis points to solutions for some of the data issues encountered in Kenya. Kenya is the first country in which these data issues have been encountered because baseline circumcision rates were high. We anticipate that some of the modeling methods we developed for Kenya will be applicable in other countries.

Benchmarking a triplet of official estimates

The publication of official statistics at different levels of aggregation requires a benchmarking step. Difficulties arise when a benchmarking method needs to be applied to a triplet of related estimates, at multiple stages of aggregation. For ratios of totals, external benchmarking constraints for the triplet (numerator, denominator, ratio) are that the weighted sum of denominator/numerator/ratio estimates equals to a constant. The benchmarking weight, applied to the ratio estimates, is a function of the denominator estimates. For example, the United States Department of Agriculture’s National Agricultural Statistics Service’s county-level, end-of-season acreage, production and yield estimates need to aggregate to the corresponding agricultural statistics district-level estimates, which also need to aggregate to the corresponding prepublished state-level values. Moreover, the definition of yield, as the ratio of production to harvested acreage, needs to hold at the county level, the agricultural statistics district level and the state level. We discuss different methods of applying benchmarking constraints to a triplet (numerator, denominator, ratio), at multiple stages of aggregation, where the denominator and the ratio are modeled and the numerator is derived. County-level and agricultural statistics district-level, end-of-season acreage, production and yield estimates are constructed and compared using the different methods. Results are illustrated for 2014 corn and soybean in Indiana, Iowa and Illinois.

Not Just a Pretty Picture: The U.S. Cancer Statistics Data Visualization Tool

Background: The United States Centers for Disease Prevention and Control (CDC) National Program of Cancer Registries (NPCR) works to disseminate cancer surveillance data to multiple audiences in accessible, discoverable, and usable formats. To this end, CDC released the official federal cancer statistics, U.S. Cancer Statistics (USCS), in a data visualization tool available at www.cdc.gov/cancer/dataviz . We made further enhancements to the online tool since its initial release in 2017. Aim: We describe the process and enhancements made to the USCS data visualization tool's content, graphical displays, and sharing capabilities. Methods: CDC partnered with Agency for Toxic Substances and Disease Registry's Geospatial Research, Analysis, and Services Program to further work initiated by a group of cancer registrars, program planners, epidemiologists, computer programmers, and communication specialists to improve the visual presentation of USCS cancer incidence and mortality data. We conducted usability testing and implemented changes to the Web site's layout and added content, including county-level data, survival data, and prevalence estimates. Results: New features include county statistics, survival, prevalence, Puerto Rico data, and tobacco-, alcohol-, and obesity-related cancers data displays. The tool was also enhanced to better display on mobile devices. Data displays on national and state incidence, mortality and trends are available as maps and bar charts with interpretive text when users scroll over each graphic. Users can customize displays of overall and cancer-specific statistics, download data tables, and share each page via social media. Conclusion: Surveillance data are fundamental to measure progress and target action. CDC's interactive USCS data visualization tool is designed to make cancer data more accessible and usable to multiple users, including the general public, media, policy makers and planners. We will continue to improve the tool's accessibility and usefulness to facilitate the interpretation and sharing of cancer data.

Structural change and economic growth in the British economy before the Industrial Revolution, 1500-1800.

Structural transformation is a key indicator of economic development. We present the first time series of male labour sectoral shares for England and Wales before 1800, using a large sample of probate and apprenticeship data to produce national and county-level estimates. England experienced a rapid decline in the share of workers in agriculture between the early seventeenth and the beginning of the eighteenth centuries, associated with rising agricultural and especially industrial productivity; Wales saw few changes. Our results show that England experienced unusually early structural change and highlight the mid-seventeenth century as a turning point.

Model-Based County Level Crop Estimates Incorporating Auxiliary Sources of Information

SummaryIn 2011, the US Department of Agriculture's National Agricultural Statistics Service started the complete implementation of the County Agricultural Production Survey (CAPS). The CAPS is an annual survey to provide accurate county level acreage and production estimates of approved federal and state crop commodities. The current top down method of producing official county level estimates that satisfy the county–district–state benchmarking constraint is an expert assessment incorporating multiple sources of information. We propose a model-based method that combines the CAPS acreage data with auxiliary data and improves county level survey estimation, while providing measures of uncertainty for the county level acreage estimates. Auxiliary sources of information include remote sensing data, weather data and planted acreage administrative data from other US agencies. A hierarchical Bayesian subarea level model is proposed and implemented, with an additional hierarchical level for the sampling variances. County level, model-based acreage estimates have lower coefficients of variation than the corresponding county level survey acreage estimates. Top down benchmarking methods are investigated and the final acreage estimates satisfy the county–district–state benchmarking constraint.

Water Fluoridation and Dental Caries in U.S. Children and Adolescents

Fluoridation of America’s drinking water was among the great public health achievements of the 20th century. Yet there is a paucity of studies from the past 3 decades investigating its dental health benefits in the U.S. population. This cross-sectional study sought to evaluate associations between availability of community water fluoridation (CWF) and dental caries experience in the U.S. child and adolescent population. County-level estimates of the percentage of population served by CWF (% CWF) from the Centers for Disease Control and Prevention’s Water Fluoridation Reporting System were merged with dental examination data from 10 y of National Health and Nutrition Examination Surveys (1999 to 2004 and 2011 to 2014). Dental caries experience in the primary dentition (decayed and filled tooth surfaces [dfs]) was calculated for 7,000 children aged 2 to 8 y and in the permanent dentition (decayed, missing, and filled tooth surfaces [DMFS]) for 12,604 children and adolescents aged 6 to 17 y. Linear regression models estimated associations between % CWF and dental caries experience with adjustment for sociodemographic characteristics: age, sex, race/ethnicity, rural-urban location, head-of-household education, and period since last dental visit. Sensitivity analysis excluded counties fluoridated after 1998. In unadjusted analysis, caries experience in the primary dentition was lower in counties with ≥75% CWF (mean dfs = 3.3; 95% confidence limit [CL] = 2.8, 3.7) than in counties with <75% CWF (mean dfs = 4.6; 95% CL = 3.9, 5.4), a prevented fraction of 30% (95% CL = 11, 48). The difference was also statistically significant, although less pronounced, in the permanent dentition: mean DMFS (95% CL) was 2.2 (2.0, 2.4) and 1.9 (1.8, 2.1), respectively, representing a prevented fraction of 12% (95% CL = 1, 23). Statistically significant associations likewise were seen when % CWF was modeled as a continuum, and differences tended to increase in covariate-adjusted analysis and in sensitivity analysis. These findings confirm a substantial caries-preventive benefit of CWF for U.S. children and that the benefit is most pronounced in primary teeth.

Modeling the Irrigation Withdrawals Over the Coterminous US Using a Hierarchical Modeling Approach

AbstractStudies focusing on national/global water scarcity require water availability and water use to quantify the imbalance. In this regard, annual irrigation withdrawal data reported by the USGS every 5 years provide a valuable information on the water use patterns over the United States. This study develops an empirical model to estimate annual irrigation water withdrawal using irrigated area, climate information, and population as predictors. Given the hierarchy in the data sources, we propose a predictive linear hierarchical regression model to develop annual irrigation water withdrawal models using varying intercepts (VI) and varying intercepts and slopes (VIS) approaches. Estimates from hierarchical models are compared with pooled and unpooled classical regression models. Overall, both hierarchical models outperform the classical models with the adjusted R2 between USGS‐reported and modeled withdrawal estimates being above 0.6 in most states using county and climate division level data. However, due to the spatial difference between the supply (rural areas) and demand (urban areas) for agriculture products, climate division level estimates exhibit a higher adjusted R2 than county level estimates. The VIS model is able to capture local effects better, particularly for states whose irrigation withdrawal patterns significantly differ from the national pattern. The performance of the models is also validated by leaving out the entire nation's water‐use data out (i.e., leave‐one‐out cross‐validation) to ensure the reported skill is not due to overfitting. Split‐sample validation in predicting 2010 irrigation withdrawal also shows the potential of the developed hierarchical model in estimating the annual irrigation withdrawals for the years with no data within the once in 5 year USGS database.

"Complete Streets" and Adult Bicyclist Fatalities: Applying G-Computation to Evaluate an Intervention That Affects the Size of a Population at Risk.

"Complete streets" policies require transportation engineers to make provisions for pedestrians, bicyclists, and mass transit users. These policies may make bicycling safer for individual cyclists while increasing the overall number of bicycle fatalities if more people cycle due to improved infrastructure. We merged county-level records of complete streets policies with Fatality Analysis Reporting System counts of cyclist fatalities occurring between January 2000 and December 2015. Because comprehensive county-level estimates of numbers of cyclists were not available, we used bicycle commuter estimates from the American Community Survey and the US Census as a proxy for the cycling population and limited analysis to 183 counties (accounting for over half of the US population) for which cycle commuting estimates were consistently nonzero. We used G-computation to estimate the effect of complete streets policies on overall numbers of cyclist fatalities while also accounting for potential policy effects on the size of the cycling population. Over a period of 16 years, 5,254 cyclists died in these counties, representing 34 fatalities per 100,000 cyclist-years. We estimated that complete streets policies made cycling safer, averting 0.6 fatalities per 100,000 cyclist-years (95% confidence interval: -1.0, -0.3) by encouraging a 2.4% increase in cycling but producing only a 0.7% increase in cyclist fatalities. G-computation is a useful tool for understanding the impact of policy on risk and exposure.

County-Level Trends in Suicide Rates in the U.S., 2005–2015

Understanding the geographic patterns of suicide can help inform targeted prevention efforts. Although state-level variation in age-adjusted suicide rates has been well documented, trends at the county-level have been largely unexplored. This study uses small area estimation to produce stable county-level estimates of suicide rates to examine geographic, temporal, and urban-rural patterns in suicide from 2005 to 2015. Using National Vital Statistics Underlying Cause of Death Files (2005-2015), hierarchical Bayesian models were used to estimate suicide rates for 3,140 counties. Model-based suicide rate estimates were mapped to explore geographic and temporal patterns and examine urban-rural differences. Analyses were conducted in 2016-2017. Posterior predicted mean county-level suicide rates increased by >10% from 2005 to 2015 for 99% of counties in the U.S., with 87% of counties showing increases of >20%. Counties with the highest model-based suicide rates were consistently located across the western and northwestern U.S., with the exception of southern California and parts of Washington. Compared with more urban counties, more rural counties had the highest estimated suicide rates from 2005 to 2015, and also the largest increases over time. Mapping county-level suicide rates provides greater granularity in describing geographic patterns of suicide and contributes to a better understanding of changes in suicide rates over time. Findings may inform more targeted prevention efforts as well as future research on community-level risk and protective factors related to suicide mortality.

Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA

Aboveground biomass (AGB) estimates for regional-scale forest planning have become cost-effective with the free access to satellite data from sensors such as Landsat and MODIS. However, the accuracy of AGB predictions based on passive optical data depends on spatial resolution and spatial extent of target area as fine resolution (small pixels) data are associated with smaller coverage and longer repeat cycles compared to coarse resolution data. This study evaluated various spatial resolutions of Landsat-derived predictors on the accuracy of regional AGB models at three different sites in the eastern USA: Maine, Pennsylvania-New Jersey, and South Carolina. We combined national forest inventory data with Landsat-derived predictors at spatial resolutions ranging from 30–1000 m to understand the optimal spatial resolution of optical data for large-area (regional) AGB estimation. Ten generic models were developed using the data collected in 2014, 2015 and 2016, and the predictions were evaluated (i) at the county-level against the estimates of the USFS Forest Inventory and Analysis Program which relied on EVALIDator tool and national forest inventory data from the 2009–2013 cycle and (ii) within a large number of strips (~1 km wide) predicted via LiDAR metrics at 30 m spatial resolution. The county-level estimates by the EVALIDator and Landsat models were highly related (R2 > 0.66), although the R2 varied significantly across sites and resolution of predictors. The mean and standard deviation of county-level estimates followed increasing and decreasing trends, respectively, with models of coarser resolution. The Landsat-based total AGB estimates were larger than the LiDAR-based total estimates within the strips, however the mean of AGB predictions by LiDAR were mostly within one-standard deviations of the mean predictions obtained from the Landsat-based model at any of the resolutions. We conclude that satellite data at resolutions up to 1000 m provide acceptable accuracy for continental scale analysis of AGB.

Early-life Medicaid Coverage and Intergenerational Economic Mobility.

New data reveal significant variation in economic mobility outcomes across U.S. localities. This suggests that social structures, institutions, and public policies-particularly those that influence critical early-life environments-play an important role in shaping mobility processes. Using new county-level estimates of intergenerational economic mobility for children born between 1980 and 1986, we exploit the uneven expansions of Medicaid eligibility across states to isolate the causal effect of this specific policy change on mobility outcomes. Instrumental-variable regression models reveal that increasing the proportion of low-income pregnant women eligible for Medicaid improved the mobility outcomes of their children in adulthood. We find no evidence that Medicaid coverage in later childhood years influences mobility outcomes. This study has implications for the normative evaluation of this policy intervention as well as our understanding of mobility processes in an era of rising inequality.

Estimating county-level tobacco use and exposure in South Carolina: a spatial model-based small area estimation approach

PurposeLocal health statistics are increasingly requested for policy-making and programmatic purposes; however, population-based surveys are often inadequate to support direct estimation for small areas. Model-based estimation techniques can be used to create local estimates for public health outcomes. Using the 2014–2015 South Carolina (SC) Adult Tobacco Survey, we examined tobacco-related outcomes at the county level using a spatial multilevel, poststratification approach. MethodsTo create county-level tobacco estimates, we used a two-level model with a spatially intrinsic conditional autoregressive random intercept. Stratum-specific (race, age, and sex) estimates for each county were then created and averaged based on population data obtained from the U.S. Census. ResultsThe estimated prevalence of current smoking in SC counties among adults ranged from 7.4% to 35.1%, and the percentage reporting ever trying an e-cigarette ranged from 4.2% to 30.2%. Model validation showed considerable agreement between direct and indirect estimates (Pearson and Spearman correlations all >0.75) that varied by the sample size of the outcome, as hypothesized. ConclusionsData from the SC Adult Tobacco Survey were used to develop county-level estimates of multiple tobacco-related outcomes using a spatial multilevel, poststratification approach. The results showed heterogeneity in smoking behaviors across the state along with marked spatial correlation.

Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA

The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (fRET) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, fRET, and fRET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input fRET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting fRET alignment to a regionally reported emergence date prior to anomaly computation improves r2 correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning fRET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models.

Geographic Variations in Arthritis Prevalence, Health-Related Characteristics, and Management - United States, 2015.

Problem/ConditionDoctor-diagnosed arthritis is a common chronic condition affecting an estimated 23% (54 million) of adults in the United States, greatly influencing quality of life and costing approximately $300 billion annually. The geographic variations in arthritis prevalence, health-related characteristics, and management among states and territories are unknown. Therefore, public health professionals need to understand arthritis in their areas to target dissemination of evidence-based interventions that reduce arthritis morbidity.Reporting Period2015.Description of SystemThe Behavioral Risk Factor Surveillance System is an annual, random-digit–dialed landline and cellular telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. Self-reported data are collected from the 50 states, the District of Columbia, Guam, and Puerto Rico. Unadjusted and age-standardized prevalences of arthritis, arthritis health-related characteristics, and arthritis management were calculated. County-level estimates were calculated using a validated statistical modeling method.ResultsIn 2015, in the 50 states and the District of Columbia, median age-standardized prevalence of arthritis was 23.0% (range: 17.2%–33.6%). Modeled prevalence of arthritis varied considerably by county (range: 11.2%–42.7%). In 13 states that administered the arthritis management module, among adults with arthritis, the age-standardized median percentage of participation in a self-management education course was 14.5% (range: 9.1%–19.0%), being told by a health care provider to engage in physical activity or exercise was 58.5% (range: 52.3%–61.9%), and being told to lose weight to manage arthritis symptoms (if overweight or obese) was 44.5% (range: 35.1%–53.2%). Respondents with arthritis who lived in the quartile of states with the highest prevalences of arthritis had the highest percentages of negative health-related characteristics (i.e., arthritis-attributable activity limitations, arthritis-attributable severe joint pain, and arthritis-attributable social participation restriction; ≥14 physically unhealthy days during the past 30 days; ≥14 mentally unhealthy days during the past 30 days; obesity; and leisure-time physical inactivity) and the lowest percentage of leisure-time walking.InterpretationThe prevalence, health-related characteristics, and management of arthritis varied substantially across states. The modeled prevalence of arthritis varied considerably by county.Public Health ActionThe findings highlight notable geographic variability in prevalence, health-related characteristics, and management of arthritis. Targeted use of evidence-based interventions that focus on physical activity and self-management education can reduce pain and improve function and quality of life for adults with arthritis and thus might reduce these geographic disparities.

Multilevel Small-Area Estimation of Colorectal Cancer Screening in the United States.

Background: The U.S. Preventive Services Task Force recommends routine screening for colorectal cancer for adults ages 50 to 75 years. We generated small-area estimates for being current with colorectal cancer screening to examine sociogeographic differences among states and counties. To our knowledge, nationwide county-level estimates for colorectal cancer screening are rarely presented.Methods: We used county data from the 2014 Behavioral Risk Factor Surveillance System (BRFSS; n = 251,360 adults), linked it to the American Community Survey poverty data, and fitted multilevel logistic regression models. We post-stratified the data with the U.S. Census population data to run Monte Carlo simulations. We generated county-level screening prevalence estimates nationally and by race/ethnicity, mapped the estimates, and aggregated them into state and national estimates. We evaluated internal consistency of our modeled state-specific estimates with BRFSS direct state estimates using Spearman correlation coefficients.Results: Correlation coefficients were ≥0.95, indicating high internal consistency. We observed substantial variations in current colorectal cancer screening estimates among the states and counties within states. State mean estimates ranged from 58.92% in Wyoming to 75.03% in Massachusetts. County mean estimates ranged from 40.11% in Alaska to 79.76% in Florida. Larger county variations were observed in various race/ethnicity groups.Conclusions: State estimates mask county variations. However, both state and county estimates indicate that the country is far behind the "80% by 2018" target.Impact: County-modeled estimates help identify variation in colorectal cancer screening prevalence in the United States and guide education and enhanced screening efforts in areas of need, including areas without BRFSS direct-estimates. Cancer Epidemiol Biomarkers Prev; 27(3); 245-53. ©2018 AACR.

Urban-Rural County and State Differences in Chronic Obstructive Pulmonary Disease - United States, 2015.

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.† 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.

The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates*

We estimate the causal effect of each county in the United States on children’s incomes in adulthood. We first estimate a fixed effects model that is identified by analyzing families who move across counties with children of different ages. We then use these fixed effect estimates to (i) quantify how much places matter for intergenerational mobility, (ii) construct forecasts of the causal effect of growing up in each county that can be used to guide families seeking to move to opportunity, and (iii) characterize which types of areas produce better outcomes. For children growing up in low-income families, each year of childhood exposure to a one standard deviation (std. dev.) better county increases income in adulthood by 0.5%. There is substantial variation in counties’ causal effects even within metro areas. Counties with less concentrated poverty, less income inequality, better schools, a larger share of two-parent families, and lower crime rates tend to produce better outcomes for children in poor families. Boys’ outcomes vary more across areas than girls’ outcomes, and boys have especially negative outcomes in highly segregated areas. Areas that generate better outcomes have higher house prices on average, but our approach uncovers many “opportunity bargains”—places that generate good outcomes but are not very expensive.

Calculating census tract-based life expectancy in New York state: a generalizable approach

BackgroundLife expectancy at birth (LE) has been calculated for states and counties. LE estimates at these levels mask health disparities in local communities. There are no nationwide estimates at the sub-county level. We present a stepwise approach for calculating LE using census tracts in New York state to identify health disparities.MethodsOur study included 2751 census tracts in New York state, but excluded New York City. We used population data from the 2010 United States Census and 2008–2010 mortality data from the state health department. Tracts were assigned to 99.97% of the deaths. We removed tracts which had a majority of people living in group quarters. Deaths in these tracts are often recorded elsewhere. Of the remaining 2679 tracts, 6.6% of the tracts had standard errors ≥ 2 years. A geographic aggregation tool was used to aggregate tracts with fewer than 60 deaths, and then aggregate areas that had standard errors of ≥ 2 years.ResultsAggregation resulted in a 9.9% reduction in the number of areas. Tracts with < 2% of population living below the poverty level had a LE of 82.8 years, while tracts with a poverty level ≥ 25% had a LE of 75.5. We observed differences in LE in border areas, of up to 10.4 years, when excluding or including deaths of study area residents that occurred outside the study area. The range and standard deviation at the county level (77.5–82.8, SD = 1.2 years) were smaller than our final sub-county areas (64.7–92.0, SD = 3.3 years). The correlation between LE and poverty were similar and statistically significant (p < 0.0001) at the county (r = − 0.58) and sub-county level (r = − 0.58). The correlations between LE and percent African-American at the county level were (r = 0.11, p = 0.43) and at the sub-county level (r = − 0.25, p < 0.0001).ConclusionThe proposed approach for geocoding and aggregation of mortality and population data provides a solution for health departments to produce stable empirically-derived LE estimates using data coded to the tract. Reliable estimates within sub-county areas are needed to aid public health officials in focusing preventive health programs in areas where health disparities would be masked by county level estimates.