National Public Health Surveillance of Corporations in Key Unhealthy Commodity Industries - A Scoping Review and Framework Synthesis.

BACKGROUND: Corporations in unhealthy commodity industries (UCIs) have growing influence on the health of national populations through practices that lead to increased consumption of unhealthy products. The use of government-led public health surveillance is best practice to better understand any emerging public health threat. However, there is minimal systematic evidence, generated and monitored by national governments, regarding the scope of UCI corporate practices and their impacts. This study aims to synthesise current frameworks that exist to identify and monitor UCI influence on health to highlight the range of practices deployed by corporations and inform future surveillance efforts in key UCIs. METHODS: Seven biomedical, business and scientific databases were searched to identify literature focused on corporate practices that impact human health and frameworks for monitoring or assessment of the way UCIs impact health. Content analysis occurred in three phases, involving (1) the identification of framework documents in the literature and extraction of all corporate practices from the frameworks; (2) initial inductive grouping and synthesis followed by deductive synthesis using Lima and Galea's 'vehicles of power' as a heuristic; and (3) scoping for potential indicators linked to each corporate practice and development of an integrated framework. RESULTS: Fourteen frameworks were identified with 37 individual corporate practices which were coded into five different themes according the Lima and Galea 'Corporate Practices and Health' framework. We proposed a summary framework to inform the public health surveillance of UCIs which outlines key actors, corporate practices and outcomes that should be considered. The proposed framework draws from the health policy triangle framework and synthesises key features of existing frameworks. CONCLUSION: Systematic monitoring of the practices of UCIs is likely to enable governments to mitigate the negative health impacts of corporate practices. The proposed synthesised framework highlights the range of practices deployed by corporations for public health surveillance at a national government level. We argue there is significant precedent and great need for monitoring of these practices and the operationalisation of a UCI monitoring system should be the object of future research.

Predicting all-cause unplanned readmission within 30 days of discharge using electronic medical record data: A multi-centre study.

OBJECTIVE: To develop a predictive model for identifying patients at high risk of all-cause unplanned readmission within 30 days after discharge, using administrative data available before discharge. MATERIALS AND METHODS: Hospital administrative data of all adult admissions in three tertiary metropolitan hospitals in Australia between July 01, 2015, and July 31, 2016, were extracted. Predictive performance of four mixed-effect multivariable logistic regression models was compared and validated using a split-sample design. Diagnostic details (Charlson Comorbidity Index CCI, components of CCI, and primary diagnosis categorised into International Classification of Diseases chapters) were added gradually in the clinically simplified model with socio-demographic, index admission, and prior hospital utilisation variables. RESULTS: Of the total 99 470 patients admitted, 5796 (5.8%) were re-admitted through the emergency department of three hospitals within 30 days after discharge. The clinically simplified model was as discriminative (C-statistic 0.694, 95% CI [0.681-0.706]) as other models and showed excellent calibration. Models with diagnostic details did not exhibit any substantial improvement in predicting 30-days unplanned readmission. CONCLUSION: We propose a 10-item predictive model to flag high-risk patients in a diverse population before discharge using readily available hospital administrative data which can easily be integrated into the hospital information system.