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.

Prevalence, pattern and correlates of work-related musculoskeletal disorders among school teachers in Enugu, Nigeria.

Introduction. There is insufficient literature on the prevalence of and risk factors for work-related musculoskeletal disorders (MSDs) among teachers in Nigeria. This study aimed to investigate the prevalence of work-related MSDs and their associations with occupational and socio-demographic factors of Nigerian-based school teachers. Materials and methods. Socio-demographic and occupational characteristics, prevalence and pattern of work-related MSDs of 352 school teachers (age 24-60 years) were investigated using a standard Nordic questionnaire. Data were summarized with descriptive statistics. Univariate and multivariate analyses were used to assess predictors of work-related MSDs. Results. The results showed that 70.2% of the teachers had work-related MSDs predominantly in the shoulder (62.3%) and neck (57.9%) regions. Most of the studied socio-demographic features and anthropometric and occupational characteristics were significantly associated (p < 0.05) with MSDs in at least one body region. Conclusions. There was a prevalence of work-related MSDs among the teachers. Advanced educational qualification, elevated teaching boards, teaching experience over 10 years and age range of 35-50 years were the major predictors of MSDs.

Secure surveillance of antimicrobial resistant organism colonization or infection in Ontario long term care homes.

BACKGROUND: There is stigma attached to the identification of residents carrying antimicrobial resistant organisms (ARO) in long term care homes, yet there is a need to collect data about their prevalence for public health surveillance and intervention purposes. OBJECTIVE: We conducted a point prevalence study to assess ARO rates in long term care homes in Ontario using a secure data collection system. METHODS: All long term care homes in the province were asked to provide colonization or infection counts for methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), and extended-spectrum beta-lactamase (ESBL) as recorded in their electronic medical records, and the number of current residents. Data was collected online during the October-November 2011 period using a Paillier cryptosystem that allows computation on encrypted data. RESULTS: A provably secure data collection system was implemented. Overall, 82% of the homes in the province responded. MRSA was the most frequent ARO identified at 3 cases per 100 residents, followed by ESBL at 0.83 per 100 residents, and VRE at 0.56 per 100 residents. The microbiological findings and their distribution were consistent with available provincial laboratory data reporting test results for AROs in hospitals. CONCLUSIONS: We describe an ARO point prevalence study which demonstrated the feasibility of collecting data from long term care homes securely across the province and providing strong privacy and confidentiality assurances, while obtaining high response rates.