Improved Geocoding of Cancer Registry Addresses in Urban and Rural Oklahoma.

BACKGROUND: Between 1997 and 2013 (the included study years), approximately 23% of addresses in the Oklahoma Central Cancer Registry (OCCR) were not geocoded to the address level. Addresses in rural counties were geocoded with poorer quality, preventing the instructive geographic research that informs policymaking. METHODS: To improve the accuracy of the geocodes, we first utilized the United States Postal Service's LACSLink database to correct addresses; specifically, to convert old rural route-based addresses to modernized Enhanced 911 (E911) addresses. We created custom geocoders using regional E911 reference data sets and used existing national scope geocoders of NAVTEQ and the North American Association of Central Cancer Registries. We attempted to geocode 5,102 addresses, which are either regular street addresses or rural route addresses. In the process, we evaluated and tabulated performances of the address correction. Accordingly, we first tabulated how well each geocoder could geocode original and LACSLink corrected addresses. We then documented the overall performances of geocoders based on pairwise comparisons. RESULTS: We were able to geocode 1,945 addresses out of this data set using 5 distinct geocoders. We observed that the LACSLink correction and E911 data were useful in the specific purpose of geocoding rural addresses, as found in the literature. CONCLUSIONS: We conclude that both LACSLink correction and E911 data were useful for improving geocoding of cancer records, many of which were in rural areas. Future directions include further validation of the geocoding and plans to conduct spatial exploratory data analysis to generate hypotheses related to the distribution of cancer in Oklahoma.

A geospatial environmental concentrations database of Oklahoma, United States.

Environmental factors can affect human health throughout the lifespan. Reliable and accurate data are needed to understand and establish relationships between environmental factors and health outcomes. In this article, spatiotemporal data (across time and space) on environmental concentrations were compiled in a database for the State of Oklahoma, United States. Data were collected from local, state, and federal government agencies, and organized into a metadata document, which includes spatial extent (information on the area covered), attributes (i.e., variables such as chemical concentration), and temporal extent (time period) of the dataset, among others. Data have been cataloged for concentrations found in water (n = 53 files), air (n = 15 files), land (n = 7 files), and industry (n = 3 files). Data also included physical characteristics (i.e., data on location, geology, and features of waterways, watersheds, and lakes, among others, n = 31 files) and administrative datasets (i.e., data on location and distribution of county boundaries and tribal statistical areas and reservations for federally recognized tribes in Oklahoma, n = 4 files). The main result is a collection of a wide range of spatially-resolved concentration data. This spatiotemporal database will assist in future epidemiologic investigations and assessment of the geographic and temporal distribution of environmental exposures in Oklahoma.

Spatiotemporal Analysis of Oklahoma Tobacco Helpline Registrations Using Geoimputation and Joinpoint Analysis.

OBJECTIVE: Tobacco quitlines provide free smoking cessation telephone services to smokers interested in quitting tobacco. We aimed to explore spatial and temporal analyses of registrations to the Oklahoma Tobacco Helpline including those of any racial group and American Indians (AI) from January 1, 2006, to June 30, 2017. This will allow tribal and community organizations, such as the Oklahoma Tribal Epidemiology Center, to better implement and evaluate public health prevention efforts at a smaller geographic area using the larger geographic units that are publicly available. DESIGN: Retrospective, descriptive study. SETTING: Oklahoma. PARTICIPANTS: Registrants to the Oklahoma Tobacco Helpline. MAIN OUTCOME MEASURES: To evaluate the spatial distribution of Helpline participants using geoimputation methods and evaluate the presence of time trends measured through annual percent change (APC). RESULTS: We observed increased density of participants in the major population centers, Oklahoma City and Tulsa. Density of AI registrations was higher in the rural areas of Oklahoma where there is a larger tribal presence compared with participants of any racial group. For all racial groups combined, we identified 3 significant trends increasing from July 2008 to March 2009 (APC: 10.9, 95% confidence interval [CI], 0.8-21.9), decreasing from March 2009 to May 2014 (APC: -0.8, 95% CI: -1.1 to -0.4), and increasing from May 2014 to June 2017 (APC: 0.8, 95% CI: 0.0-1.6). The number of AI registrations to the Helpline increased significantly from July 2008 to March 2009 (APC: 12.0, 95% CI: 2.0-22.9) and decreased from March 2009 to June 2014 (APC: -0.7, 95% CI: -1.0 to -0.3). CONCLUSIONS: Results of this project will allow the Helpline to efficiently identify geographic areas to increase registrations and reduce commercial tobacco use among the AI population in Oklahoma through existing programs at the Oklahoma Tribal Epidemiology Center.

Evaluation of geoimputation strategies in a large case study.

BACKGROUND: Health data usually has missing or incomplete location information, which impacts the quality of research. Geoimputation methods are used by health professionals to increase the spatial resolution of address information for more accurate analyses. The objective of this study was to evaluate geo-imputation methods with respect to the demographic and spatial characteristics of the data. METHODS: We evaluated four geoimputation methods for increasing spatial resolution of records with known locational information at a coarse level. In order to test and rigorously evaluate two stochastic and two deterministic strategies, we used the Texas Sex Offender registry database with over 50,000 records with known demographic and coordinate information. We reduced the spatial resolution of each record to a census block group and attempted to recover coordinate information using the four strategies. We rigorously evaluated the results in terms of the error distance between the original coordinates and recovered coordinates by studying the results by demographic sub groups and the characteristics of the underlying geography. RESULTS: We observed that in estimating the actual location of a case, the weighted mean method is the most superior for each demographic group followed by the maximum imputation centroid, the random point in matching sub-geographies and the random point in all sub-geographies methods. Higher accuracies were observed for minority populations because minorities tend to cluster in certain neighborhoods, which makes it easier to impute their location. Results are greatly affected by the population density of the underlying geographies. We observed high accuracies in high population density areas, which often exist within smaller census blocks, which makes the search space smaller. Similarly, mapping geoimputation accuracies in a spatially explicit manner reveals that metropolitan areas yield higher accuracy results. CONCLUSIONS: Based on gains in standard error, reduction in mean error and validation results, we conclude that characteristics of the estimated records such as the demographic profile and population density information provide a measure of certainty of geographic imputation.