De-identification methods for open health data: the case of the Heritage Health Prize claims dataset.

BACKGROUND: There are many benefits to open datasets. However, privacy concerns have hampered the widespread creation of open health data. There is a dearth of documented methods and case studies for the creation of public-use health data. We describe a new methodology for creating a longitudinal public health dataset in the context of the Heritage Health Prize (HHP). The HHP is a global data mining competition to predict, by using claims data, the number of days patients will be hospitalized in a subsequent year. The winner will be the team or individual with the most accurate model past a threshold accuracy, and will receive a US $3 million cash prize. HHP began on April 4, 2011, and ends on April 3, 2013. OBJECTIVE: To de-identify the claims data used in the HHP competition and ensure that it meets the requirements in the US Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. METHODS: We defined a threshold risk consistent with the HIPAA Privacy Rule Safe Harbor standard for disclosing the competition dataset. Three plausible re-identification attacks that can be executed on these data were identified. For each attack the re-identification probability was evaluated. If it was deemed too high then a new de-identification algorithm was applied to reduce the risk to an acceptable level. We performed an actual evaluation of re-identification risk using simulated attacks and matching experiments to confirm the results of the de-identification and to test sensitivity to assumptions. The main metric used to evaluate re-identification risk was the probability that a record in the HHP data can be re-identified given an attempted attack. RESULTS: An evaluation of the de-identified dataset estimated that the probability of re-identifying an individual was .0084, below the .05 probability threshold specified for the competition. The risk was robust to violations of our initial assumptions. CONCLUSIONS: It was possible to ensure that the probability of re-identification for a large longitudinal dataset was acceptably low when it was released for a global user community in support of an analytics competition. This is an example of, and methodology for, achieving open data principles for longitudinal health data.

Compulsive hoarding: overview and implications for community health nurses.

Hoarding is a serious public health hazard that poses significant health and safety risks for individuals, families, and communities. Research indicates that compulsive hoarding is a growing social and environmental problem across the United States, affecting as many as 2-5% of the population. Hoarders often live chaotically in their own private clutter until it spreads so far that it evolves into medical emergencies or can only be contained by the law. Visiting clients in their homes provides community health nurses with the unique opportunity to identify cases of hoarding and connect individuals and their families with appropriate resources, potentially preventing catastrophic outcomes. Interventions such as case-finding, outreach, case management, community education, and advocacy may be implemented by the community health nurse to improve the health of this very vulnerable population. The aim of this article is to inform community health nurses of the complexity of this emerging phenomenon, its symptoms, treatments, and impacts on individuals, their families, and their communities.