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.

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.

Spatiotemporal aggregation for temporally extensive international microdata.

We describe a strategy for regionalizing subnational administrative units in conjunction with harmonizing changes in unit boundaries over time that can be applied to provide small-area geographic identifiers for census microdata. The availability of small-area identifiers blends the flexibility of individual microdata with the spatial specificity of aggregate data. Regionalizing microdata by administrative units poses a number of challenges, such as the need to aggregate individual scale data in a way that ensures confidentiality and issues arising from changing spatial boundaries over time. We describe a regionalization and harmonization strategy that creates units that satisfy spatial and other constraints while maximizing the number of units in a way that supports policy and research use. We describe this regionalization strategy for three test cases of Malawi, Brazil, and the United States. We test different algorithms and develop a semi-automated strategy for regionalization that meets data restrictions, computation, and data demands from end users.

Modeling the effect of social networks on adoption of multifunctional agriculture.

Rotational grazing (RG) has attracted much attention as a cornerstone of multifunctional agriculture (MFA) in animal systems, potentially capable of producing a range of goods and services of value to diverse stakeholders in agricultural landscapes and rural communities, as well as broader societal benefits. Despite these benefits, global adoption of MFA has been uneven, with some places seeing active participation, while others have seen limited growth. Recent conceptual models of MFA emphasize the potential for bottom-up processes and linkages among social and environmental systems to promote multifunctionality. Social networks are critical to these explanations but how and why these networks matter is unclear. We investigated fifty-three farms in three states in the United States (New York, Wisconsin, Pennsylvania) and developed a stylized model of social networks and systemic change in the dairy farming system. We found that social networks are important to RG adoption but their impact is contingent on social and spatial factors. Effects of networks on farmer decision making differ according to whether they comprise weak-tie relationships, which bridge across disparate people and organizations, or strong-tie relationships, which are shared by groups in which members are well known to one another. RG adoption is also dependent on features of the social landscape including the number of dairy households, the probability of neighboring farmers sharing strong ties, and the role of space in how networks are formed. The model replicates features of real-world adoption of RG practices in the Eastern US and illustrates pathways toward greater multifunctionality in the dairy landscape. Such models are likely to be of heuristic value in network-focused strategies for agricultural development.

Delineating West Nile Virus Transmission Cycles at Various Scales: The Nearest Neighbor Distance-Time Model.

Various approaches are used to identify West Nile virus (WNV) exposure areas, including unusual sightings of infected dead birds, mosquito pools or human cases both prospectively and retrospectively. A significant and largely unmet need in WNV research is to incorporate the temporal characterization of virus spread and locational information of the three components of transmission cycle-i.e., birds (reservoir), mosquitoes (vector), and humans (host)-on a localized scale. Exposure areas containing all three components of the WNV cycle in close proximity have higher potential to amplify an outbreak as compared to exposure areas delineated by a single component. In this paper, we introduce a novel approach, termed 'Nearest Neighbor Distance Time' or NNDT, to identify and retrospectively monitor WNV transmission cycles on various scales in the Twin Cities Metropolitan area of Minnesota. The NNDT model was implemented in a geographic information system using data from the period 2002 to 2006. The results indicated that 2002 and 2003 had three such WNV cycles, followed by one, two, and four respectively in 2004, 2005, and 2006. The NNDT method can be useful in locating chronically exposed areas and generating hypotheses about the transmission of WNV.

Robust Principal Component Analysis and Geographically Weighted Regression: Urbanization in the Twin Cities Metropolitan Area of Minnesota.

In this paper, we present a hybrid approach, robust principal component geographically weighted regression (RPCGWR), in examining urbanization as a function of both extant urban land use and the effect of social and environmental factors in the Twin Cities Metropolitan Area (TCMA) of Minnesota. We used remotely sensed data to treat urbanization via the proxy of impervious surface. We then integrated two different methods, robust principal component analysis (RPCA) and geographically weighted regression (GWR) to create an innovative approach to model urbanization. The RPCGWR results show significant spatial heterogeneity in the relationships between proportion of impervious surface and the explanatory factors in the TCMA. We link this heterogeneity to the "sprawling" nature of urban land use that has moved outward from the core Twin Cities through to their suburbs and exurbs.

Case studies, cross-site comparisons, and the challenge of generalization: comparing agent-based models of land-use change in frontier regions.

Cross-site comparisons of case studies have been identified as an important priority by the land-use science community. From an empirical perspective, such comparisons potentially allow generalizations that may contribute to production of global-scale land-use and land-cover change projections. From a theoretical perspective, such comparisons can inform development of a theory of land-use science by identifying potential hypotheses and supporting or refuting evidence. This paper undertakes a structured comparison of four case studies of land-use change in frontier regions that follow an agent-based modeling approach. Our hypothesis is that each case study represents a particular manifestation of a common process. Given differences in initial conditions among sites and the time at which the process is observed, actual mechanisms and outcomes are anticipated to differ substantially between sites. Our goal is to reveal both commonalities and differences among research sites, model implementations, and ultimately, conclusions derived from the modeling process.

Agent-based modeling of deforestation in southern Yucatan, Mexico, and reforestation in the Midwest United States.

We combine mixed-methods research with integrated agent-based modeling to understand land change and economic decision making in the United States and Mexico. This work demonstrates how sustainability science benefits from combining integrated agent-based modeling (which blends methods from the social, ecological, and information sciences) and mixed-methods research (which interleaves multiple approaches ranging from qualitative field research to quantitative laboratory experiments and interpretation of remotely sensed imagery). We test assumptions of utility-maximizing behavior in household-level landscape management in south-central Indiana, linking parcel data, land cover derived from aerial photography, and findings from laboratory experiments. We examine the role of uncertainty and limited information, preferences, differential demographic attributes, and past experience and future time horizons. We also use evolutionary programming to represent bounded rationality in agriculturalist households in the southern Yucatán of Mexico. This approach captures realistic rule of thumb strategies while identifying social and environmental factors in a manner similar to econometric models. These case studies highlight the role of computational models of decision making in land-change contexts and advance our understanding of decision making in general.