Evaluating the performance of a predictive modeling approach to identifying members at high-risk of hospitalization.

Aims: To evaluate the risk-of-hospitalization (ROH) models developed at Blue Cross Blue Shield of Louisiana (BCBSLA) and compare this approach to the DxCG risk-score algorithms utilized by many health plans.Materials and Methods: Time zero for this study was December 31, 2016. BCBSLA members were eligible for study inclusion if they were fully insured; aged 80 years or younger; and had continuous enrollment starting on or before June 1, 2016, through time zero. Up to 2 years of historical claims data from time zero per patient was included for model development. Members were excluded if they had cancer, renal failure, or were admitted for hospice. The Blue Cross ROH models were developed using (1) regularized logistic regression and (2) random decision forests (a tree ensemble learning classification method). All models were generated using Scikit-learn: Machine Learning in Python. Prognostic capabilities of DxCG risk-score algorithms were compared to those of the Blue Cross models.Results: When stratifying by the top 0.1% of members with the highest ROH, the Blue Cross logistic regression model had the highest area under the receiving operator characteristics curve (0.862) based on the result of 10-fold cross-validation. The Blue Cross random decision forests model had the highest positive predictive value (49.0%) and positive likelihood ratio (61.4), but sensitivity, specificity, negative predictive values, and negative likelihood ratios were similar across all four models.Limitations: The Blue Cross ROH models were developed and evaluated using BCBSLA data, and predictive power may fluctuate if applied to other databases.Conclusions: The predictability of the Blue Cross models show how member-specific, regional data can be used to accurately identify patients with a high ROH, which may allow healthcare workers to intervene earlier and subsequently reduce the healthcare burden for patients and providers.

An outbreak of Clostridium perfringens and the enforcement of food safety standards.

Investigation and management of a possible foodborne outbreak notified to the Brisbane Northside Population Health Unit aimed to determine the likely source of the outbreak and prevent the same scenario from recurring. Environmental health officers inspected the implicated premises and collected legal samples prior to the 1st outbreak control team meeting. Interview evidence was carefully documented. Inspection revealed large quantities of meat dishes being allowed to cool at room temperature overnight. Microbiological results implicated the meat dishes as a source of Clostridium perfringens, consistent with the cause of illness in notified cases. When educational measures failed to alter food handling practices, the restaurant owner was successfully prosecuted under the Food Act 2006. Education and voluntary compliance with food safety standards must form the foundation of sustainable behaviour change among food handlers. When these fail, prosecution is justified to mitigate the risk to public health. Immediate inspection, sampling left over food, and attention to formal interview technique and evidence collection can assist the investigation of outbreaks of foodborne illness and help to ensure any necessary court proceedings are a cost effective use of resources.