Random property allocation: A novel geographic imputation procedure based on a complete geocoded address file

Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization

Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM_2.5) and respiratory hospitalizations in New York State (2000-2005). We observed non-significantly higher PM_2.5 exposures, high accuracy of binary exposure assignment (89 to 99%), and marginally different hazard ratios (HRs) (-0.2 to 0.7%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.

GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology

Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location-based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health-specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision-making and policy, ultimately aimed at reducing the burden of cancer.

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.

Geographic Imputation of Missing Activity Space Data from Ecological Momentary Assessment (EMA) GPS Positions

This research presents a pilot study to develop and compare methods of geographic imputation for estimating the location of missing activity space data collected using geographic ecological momentary assessment (GEMA). As a demonstration, we use data from a previously published analysis of the effect of neighborhood disadvantage, captured at the U.S. Census Bureau tract level, on momentary psychological stress among a sample of 137 urban adolescents. We investigate the impact of listwise deletion on model results and test two geographic imputation techniques adapted for activity space data from hot deck and centroid imputation approaches. Our results indicate that listwise deletion can bias estimates of place effects on health, and that these impacts are mitigated by the use of geographic imputation, particularly regarding inflation of the standard errors. These geographic imputation techniques may be extended in future research by incorporating approaches from the non-spatial imputation literature as well as from conventional geographic imputation and spatial interpolation research that focus on non-activity space data.

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.