Which modifiable health risks are associated with changes in productivity costs?

The purpose of this retrospective, longitudinal study was to assess longitudinal associations between modifiable health risks and workplace absenteeism and presenteeism and to estimate lost productivity costs. Across the 4-year study period (2007-2010), 17,089 unique employees from a large US computer manufacturer with a highly technical workforce completed at least 1 health risk assessment. Generalized estimating equation models were used to estimate the mean population-level absenteeism and presenteeism for 11 modifiable health risks and adjust for 9 sociodemographic and employment-related factors. Because patient age was highly correlated with several other variables, the analysis was stratified by age (<45 vs. ≥45 years). For all ages, poor emotional health, inadequate exercise, tobacco use, and having a body mass index (BMI) greater than 35 (all P<.05) were consistently associated with both absenteeism and presenteeism. Having a BMI over 35 and poor emotional health were associated with the largest impact in absenteeism (0.46 days) and presenteeism (4.03 days), respectively. Younger and older workers had similar associations between health risks and presenteeism; however, hypertension, blood sugar, inadequate exercise, and alcohol were associated (P⋜.01) with greater absenteeism among older but not younger workers. The results suggest that productivity loss is strongly related to emotional health and obesity-related health risks (eg, BMI, exercise) but differs by age. These findings could help prioritize preventive health programs offered by employers at their worksite health centers. Given the aging of the US workforce, keeping older workers healthy and productive will be crucial to remaining competitive in the global economy. (Population Health Management 2015;18:30-38).

The role of matched controls in building an evidence base for hospital-avoidance schemes: a retrospective evaluation.

OBJECTIVE: To test whether two hospital-avoidance interventions altered rates of hospital use: "intermediate care" and "integrated care teams." DATA SOURCES/STUDY SETTING: Linked administrative data for England covering the period 2004 to 2009. STUDY DESIGN: This study was commissioned after the interventions had been in place for several years. We developed a method based on retrospective analysis of person-level data comparing health care use of participants with that of prognostically matched controls. DATA COLLECTION/EXTRACTION METHODS: Individuals were linked to administrative datasets through a trusted intermediary and a unique patient identifier. PRINCIPAL FINDINGS: Participants who received the intermediate care intervention showed higher rates of unscheduled hospital admission than matched controls, whereas recipients of the integrated care team intervention showed no difference. Both intervention groups showed higher rates of mortality than did their matched controls. CONCLUSIONS: These are potentially powerful techniques for assessing impacts on hospital activity. Neither intervention reduced admission rates. Although our analysis of hospital utilization controlled for a wide range of observable characteristics, the difference in mortality rates suggests that some residual confounding is likely. Evaluation is constrained when performed retrospectively, and careful interpretation is needed.

Predicting who will use intensive social care: case finding tools based on linked health and social care data.

BACKGROUND: the costs of delivering health and social care services are rising as the population ages and more people live with chronic diseases. OBJECTIVES: to determine whether predictive risk models can be built that use routine health and social care data to predict which older people will begin receiving intensive social care. DESIGN: analysis of pseudonymous, person-level, data extracted from the administrative data systems of local health and social care organisations. SETTING: five primary care trust areas in England and their associated councils with social services responsibilities. SUBJECTS: people aged 75 or older registered continuously with a general practitioner in five selected areas of England (n = 155,905). METHODS: multivariate statistical analysis using a split sample of data. RESULTS: it was possible to construct models that predicted which people would begin receiving intensive social care in the coming 12 months. The performance of the models was improved by selecting a dependent variable based on a lower cost threshold as one of the definitions of commencing intensive social care. CONCLUSIONS: predictive models can be constructed that use linked, routine health and social care data for case finding in social care settings.