Fine Particulate Matter and Parkinson Disease Risk Among Medicare Beneficiaries.

BACKGROUND AND OBJECTIVES: Numerous studies suggest that environmental exposures play a critical role in Parkinson disease (PD) pathogenesis, and large, population-based studies have the potential to advance substantially the identification of novel PD risk factors. We sought to study the nationwide geographic relationship between PD and air pollution, specifically PM2.5 (particulate matter with a diameter <2.5 micrometers), using population-based US Medicare data. METHODS: We conducted a population-based geographic study of Medicare beneficiaries aged 66-90 years geocoded to US counties and zip+4. We used integrated nested Laplace approximation to create age, sex, race, smoking, and health care utilization-adjusted relative risk (RR) at the county level for geographic analyses with PM2.5 as the primary exposure of interest. We also performed an individual-level analysis using logistic regression with cases and controls with zip+4 centroid PM2.5. We adjusted a priori for the same covariates and verified no confounding by indicators of socioeconomic status or neurologist density. RESULTS: Among 21,639,190 Medicare beneficiaries, 89,390 had incident PD in 2009. There was a nationwide association between average annual PM2.5 and PD risk whereby the RR of PD was 56% (95% CI 47%-66%) greater for those exposed to the median level of PM2.5 compared with those with the lowest level of PM2.5. This association was linear up to 13 µg/m3 corresponding to a 4.2% (95% CI 3.7%-4.8%) greater risk of PD for each additional µg/m3 of PM2.5 (p trend < 0.0001). We identified a region with high PD risk in the Mississippi-Ohio River Valley, where the risk of PD was 19% greater compared with the rest of the nation. The strongest association between PM2.5 and PD was found in a region with low PD risk in the Rocky Mountains. PM2.5 was also associated with PD in the Mississippi-Ohio River Valley where the association was relatively weaker, due to a possible ceiling effect at average annual PM2.5 levels of ∼13 µg/m3. DISCUSSION: State-of-the-art geographic analytic techniques revealed an association between PM2.5 and PD that varied in strength by region. A deeper investigation into the specific subfractions of PM2.5 may provide additional insight into regional variability in the PM2.5-PD association.

Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward.

The Health Insurance Portability and Accountability Act (HIPAA) was an important milestone in protecting the privacy of patient data; however, the HIPAA provisions specific to geographic data remain vague and hinder the ways in which epidemiologists and geographers use and share spatial health data. The literature on spatial health and select legal and official guidance documents present scholars with ambiguous guidelines that have led to the use and propagation of multiple interpretations of a single HIPAA safe harbor provision specific to geographic data. Misinterpretation of this standard has resulted in many entities sharing data at overly conservative levels, whereas others offer definitions of safe harbors that potentially put patient data at risk. To promote understanding of, and adherence to, the safe harbor rule, this paper reviews the HIPAA law from its creation to the present day, elucidating common misconceptions and presenting straightforward guidance to scholars. We focus on the 20,000-person population threshold and the 3-digit zip code stipulation of safe harbors, which are central to the confusion surrounding how patient location data can be shared. A comprehensive examination of these 2 stipulations, which integrates various expert perspectives and relevant studies, reveals how alternative methods for safe harbors can offer researchers better data and better data protection. Much has changed in the 20 years since the introduction of the safe harbor provision; however, it continues to be the primary source of guidance (and frustration) for researchers trying to share maps, leaving many waiting for these rules to be revised in accordance with the times.

Regionalization with Self-Organizing Maps for Sharing Higher Resolution Protected Health Information.

This paper addresses the challenge of sharing finer-scale Protected Health Information (PHI) while maintaining patient privacy by using regionalization to create higher resolution HIPAA-compliant geographical aggregations. We compare four regionalization approaches in terms of their fitness for analysis and display: max-p-regions, REDCAP, and self-organizing maps (SOM) variants of each. Each method is used to create a configuration of regions that aligns with census boundaries, optimizes intra-unit homogeneity, and maximizes the number of spatial units while meeting the minimum population threshold required for sharing PHI under HIPAA guidelines. The relative utility of each configuration was assessed with measures of model-fit, compactness, homogeneity, and resolution. Adding the SOM procedure to max-p-regions resulted in statistically significant improvements for nearly all assessment measures whereas the addition of SOM to REDCAP primarily degraded these measures. These differences can be attributed to the different impacts of SOM on top-down and bottom-up regionalization procedures. Overall, we recommend REDCAP which outperformed on most measures. The SOM variant of max-p-regions (MSOM) may also be recommended as it provided the highest resolution while maintaining suitable performance on all other measures.

Use of a Geographic Information System to create treatment groups for group-randomized community trials: The Minnesota Heart Health Program.

BACKGROUND: Group-randomized trials of communities often rely on the convenience of pre-existing administrative divisions, such as school district boundaries or census entities, to divide the study area into intervention and control sites. However, these boundaries may include substantial heterogeneity between regions, introducing unmeasured confounding variables. This challenge can be addressed by the creation of exchangeable intervention and control territories that are equally weighted by pertinent socio-demographic characteristics. The present study used territory design software as a novel approach to partitioning study areas for The Minnesota Heart Health Program's "Ask about Aspirin" Initiative. METHODS: Twenty-four territories were created to be similar in terms of age, sex, and educational attainment, as factors known to modify aspirin use. To promote ease of intervention administration, the shape and spread of the territories were controlled. Means of the variables used in balancing the territories were assessed as well as other factors that were not used in the balancing process. RESULTS: The analysis demonstrated that demographic characteristics did not differ significantly between the intervention and control territories created by the territory design software. CONCLUSIONS: The creation of exchangeable territories diminishes geographically based impact on outcomes following community interventions in group-randomized trials. The method used to identify comparable geographical units may be applied to a wide range of population-based health intervention trials. TRIAL REGISTRATION: National Institutes of Health (Clinical Trials.gov), Identifier: NCT02607917 . Registered on 16 November 2015.

Geo-spatial Informatics in International Public Health Nursing Education.

This poster describes results of an undergraduate nursing informatics experience. Students applied geo-spatial methods to community assessments in two urban regions of New Zealand and the United States. Students used the Omaha System standardized language to code their observations during a brief community assessment activity and entered their data into a mapping program developed in Esri ArcGIS Online, a geographic information system. Results will be displayed in tables and maps to allow comparison among the communities. The next generation of nurses can employ geo-spatial informatics methods to contribute to innovative community assessment, planning and policy development.