Abstract
BackgroundLike many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which often uses the inherently identifiable variable of participant address, must balance reproducibility with participant privacy. In this study, we assess the impact of several different data perturbation methods on key spatial statistics and patient privacy.MethodsWe analyzed the impact of perturbation on spatial patterns in the full set of address-level mortality data from Lawrence, MA during the period from 1911 to 1913. The original death locations were perturbed using seven different published approaches to stochastic and deterministic spatial data anonymization. Key spatial descriptive statistics were calculated for each perturbation, including changes in spatial pattern center, Global Moran’s I, Local Moran’s I, distance to the k-th nearest neighbors, and the L-function (a normalized form of Ripley’s K). A spatially adapted form of k-anonymity was used to measure the privacy protection conferred by each method, and its compliance with HIPAA and GDPR privacy standards.ResultsRandom perturbation at 50 m, donut masking between 5 and 50 m, and Voronoi masking maintain the validity of descriptive spatial statistics better than other perturbations. Grid center masking with both 100 × 100 and 250 × 250 m cells led to large changes in descriptive spatial statistics. None of the perturbation methods adhered to the HIPAA standard that all points have a k-anonymity > 10. All other perturbation methods employed had at least 265 points, or over 6%, not adhering to the HIPAA standard.ConclusionsUsing the set of published perturbation methods applied in this analysis, HIPAA and GDPR compliant de-identification was not compatible with maintaining key spatial patterns as measured by our chosen summary statistics. Further research should investigate alternate methods to balancing tradeoffs between spatial data privacy and preservation of key patterns in public health data that are of scientific and medical importance.
Highlights
Like many scientific fields, epidemiology is addressing issues of research reproducibility
The COVID-19 pandemic has shown the crucial role of understanding the determinants of fine-scale spatial variation in infection outcomes, as such data are key for understanding differential risks of mortality by age, socioeconomic status and as a function of neighborhood environments
Point center: Affine shear moved the median of the spatial distribution the farthest Euclidean distance, followed by grid center masking with 100 × 100 m cells, and grid center masking with 250 × 250 m cells, which moved the median 123 m, 42 m, and 33 m, respectively
Summary
Epidemiology is addressing issues of research reproducibility. Broen et al Int J Health Geogr (2021) 20:3 and research subject privacy must be considered ahead of the public health and scientific benefits of reproducibility These issues are acute for spatially referenced disease and health data which may reveal the identity but the spatial location of individuals with sensitive health conditions, e.g. HIV infection, or behavioral risks such as injection drug use [2]. The COVID-19 pandemic has shown the crucial role of understanding the determinants of fine-scale spatial variation in infection outcomes, as such data are key for understanding differential risks of mortality by age, socioeconomic status and as a function of neighborhood environments This has created an unprecedented amount of interest in making individual level case data publicly available, with multiple sources producing maps of case and testing rates [8,9,10,11]. More granular maps have suppressed data for zip codes with limited numbers of cases, but there are no standardized limits for data release [10]
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