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BackgroundAmerican Indians and Alaska Natives (AI/AN) have the highest diabetes prevalence among any racial/ethnic group in the United States. Among AI/AN, diabetes accounts for 69% of new cases of end-stage renal disease (ESRD), defined as kidney failure treated with dialysis or transplantation. During 1982–1996, diabetes-related ESRD (ESRD-D) in AI/AN increased substantially and disproportionately compared with other racial/ethnic groups.MethodsData from the U.S. Renal Data System, the Indian Health Service (IHS), the National Health Interview Survey, and the U.S. Census were used to calculate ESRD-D incidence rates by race/ethnicity among U.S. adults aged ≥18 years during 1996–2013 and in the diabetic population during 2006–2013. Rates were age-adjusted based on the 2000 U.S. standard population. IHS clinical data from the Diabetes Cares and Outcomes Audit were analyzed for diabetes management measures in AI/AN.ResultsAmong AI/AN adults, age-adjusted ESRD-D rates per 100,000 population decreased 54%, from 57.3 in 1996 to 26.5 in 2013. Although rates for adults in other racial/ethnic groups also decreased during this period, AI/AN had the steepest decline. Among AI/AN with diabetes, ESRD-D incidence decreased during 2006–2013 and, by 2013, was the same as that for whites. Measures related to the assessment and treatment of ESRD-D risk factors also showed more improvement during this period in AI/AN than in the general population.Conclusion and implications for public health practiceDespite well-documented health and socioeconomic disparities among AI/AN, ESRD-D incidence rates among this population have decreased substantially since 1996. This decline followed implementation by the IHS of public health and population management approaches to diabetes accompanied by improvements in clinical care beginning in the mid-1980s. These approaches might be a useful model for diabetes management in other health care systems, especially those serving populations at high risk.

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 R 2 , 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 R 2 range=0.95 to 0.99); accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and * Corresponding author: WEI Liang. LWei1@cdc.gov.. § These authors contributed equally to this work.Note: the findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. HHS Public AccessAuthor manuscript J Resour Ecol. Author manuscript; available in PMC 2015 July 09. Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript 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.Keywords temperature estimation; county data; ArcGIS; SAS; cokriging BackgroundSpatial data analysis has received considerable attention and played an important role in disciplines of environmental science and socio-economic science due to the rapid development of Geographic Information Systems (GIS) in recent years. The need for reliable environmental geospatial databases is fast-growing (Croner et al. 1996). Ecology is the scientific study of the relations that people have with respect to each other and their natural environment. The environment is dynamically interlinked, imposed upon and constrains people at any time throughout their life. Meteorological measurements such as temperature and precipitation are needed to assess links between the environment and diseases in the population. Various spatial interpolation methods including inverse distance weighting (IDW), multiple regression, thin plate smoothing spline (TPSS), kriging and cokriging...

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