Exploring the Link between Altitude of Residence and Smoking Patterns in the United States.

INTRODUCTION: Smoking-related diseases affect 16 million Americans, causing approximately 480,000 deaths annually. The prevalence of cigarette smoking varies regionally across the United States, and previous research indicates that regional rates of smoking-related diseases demonstrate a negative association with altitude. The purpose of this study was to determine the relationship between altitude and the prevalence of cigarette smoking by county (N = 3106) in the United States. We hypothesized that smoking prevalence among adults would be negatively associated with mean county altitude. METHODS: A multivariate linear regression was performed to examine the relationship between county-level mean altitude and county smoking rate. Covariates were individually correlated with 2020 smoking data, and significant associations were included in the final model. RESULTS: The multivariate linear regression indicated that the county-level smoking rates are significantly reduced at high altitudes (p < 0.001). The model accounted for 89.5% of the variance in smoking prevalence, and for each 1000-foot increase in altitude above sea level, smoking rates decreased by 0.143%. Based on multivariate linear regression, the following variables remained independently and significantly associated: race, sex, educational attainment, socioeconomic status, unemployment, physical inactivity, drinking behavior, mental distress, and tobacco taxation. CONCLUSIONS: Our results indicate that smoking rates are negatively associated with altitude, which may suggest that altitude affects the pharmacokinetics, pharmacodynamics, and mechanistic pathways involved in cigarette use. Further research is needed to explore the relationship between altitude and smoking and how altitude may serve as a protective factor in the acquisition and maintenance of tobacco use disorders.

A county-level cross-sectional analysis of positive deviance to assess multiple population health outcomes in Indiana.

OBJECTIVE: To test a positive deviance method to identify counties that are performing better than statistical expectations on a set of population health indicators. DESIGN: Quantitative, cross-sectional county-level secondary analysis of risk variables and outcomes in Indiana. Data are analysed using multiple linear regression to identify counties performing better or worse than expected given traditional risk indicators, with a focus on 'positive deviants' or counties performing better than expected. PARTICIPANTS: Counties in Indiana (n=92) constitute the unit of analysis. MAIN OUTCOME MEASURES: Per cent adult obesity, per cent fair/poor health, low birth weight per cent, per cent with diabetes, years of potential life lost, colorectal cancer incidence rate and circulatory disease mortality rate. RESULTS: County performance that outperforms expectations is for the most part outcome specific. But there are a few counties that performed particularly well across most measures. CONCLUSIONS: The positive deviance approach provides a means for state and local public health departments to identify places that show better health outcomes despite demographic, social, economic or behavioural disadvantage. These places may serve as case studies or models for subsequent investigations to uncover best practices in the face of adversity and generalise effective approaches to other areas.

Determinants of U.S. Prescription Drug Utilization using County Level Data.

Prescription drugs are the third largest component of U.S. healthcare expenditures. The 2006 Medicare Part D and the 2010 Affordable Care Act are catalysts for further growths in utilization becuase of insurance expansion effects. This research investigating the determinants of prescription drug utilization is timely, methodologically novel, and policy relevant. Differences in population health status, access to care, socioeconomics, demographics, and variations in per capita number of scripts filled at retail pharmacies across the U.S.A. justify fitting separate econometric models to county data of the states partitioned into low, medium, and high prescription drug users. Given the skewed distribution of per capita number of filled prescriptions (response variable), we fit the variance stabilizing Box-Cox power transformation regression models to 2011 county level data for investigating the correlates of prescription drug utilization separately for low, medium, and high utilization states. Maximum likelihood regression parameter estimates, including the optimal Box-Cox λ power transformations, differ across high (λ = 0.214), medium (λ = 0.942), and low (λ = 0.302) prescription drug utilization models. The estimated income elasticities of -0.634, 0.031, and -0.532 in high, medium, and low utilization models suggest that the economic behavior of prescriptions is not invariant across different utilization levels.

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties.

Temperature changes are known to have significant impacts on human health. Accurate estimates of population-weighted average monthly air temperature for US counties are needed to evaluate temperature's association with health behaviours and disease, which are sampled or reported at the county level and measured on a monthly-or 30-day-basis. Most reported temperature estimates were calculated using ArcGIS, relatively few used SAS. We compared the performance of geostatistical models to estimate population-weighted average temperature in each month for counties in 48 states using ArcGIS v9.3 and SAS v 9.2 on a CITGO platform. Monthly average temperature for Jan-Dec 2007 and elevation from 5435 weather stations were used to estimate the temperature at county population centroids. County estimates were produced with elevation as a covariate. Performance of models was assessed by comparing adjusted R2, mean squared error, root mean squared error, and processing time. Prediction accuracy for split validation was above 90% for 11 months in ArcGIS and all 12 months in SAS. Cokriging in SAS achieved higher prediction accuracy and lower estimation bias as compared to cokriging in ArcGIS. County-level estimates produced by both packages were positively correlated (adjusted R2 range=0.95 to 0.99); accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and SAS are reliable for U.S. county-level temperature estimates; However, ArcGIS's merits in spatial data pre-processing and processing time may be important considerations for software selection, especially for multi-year or multi-state projects.