Identified risk factors can guide medical professionals in their attempts to increase the likelihood of patient adherence to drug treatment regimens.

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Objective: Certain modifiable risk factors lead to higher health care costs and reduced worker productivity. A predictive return-on-investment (ROI) model was applied to an obesity management intervention to ($311,755), 59% were attributed to reduced health care expenditures ($184,582) and 41% resulted from productivity improvements ($127,173) 2008;50:981-990) T he benefits to employers of having a healthy workforce are widely acknowledged as a means of lowering an organization's medical costs and achieving higher levels of worker productivity.1-12 Nevertheless, the decision by employers to invest in health improvement programs often requires an economic justification that includes an estimate of the return-on-investment (ROI) from such programs. 13 In addition, after the programs have been in place for some time, program sponsors may increasingly require evidence that health improvements have produced measurable cost savings, and that these savings outweigh program expenses.14,15 Of particular interest to employers are programs aimed at managing overweight and obesity among workers.16 Employers instituting these programs are requiring health management program managers to demonstrate that these interventions achieve health improvements and a positive ROI. 17Previous examples of the application of ROI forecasting models to estimate program savings associated with risk reduction in employed populations are found in studies conducted at The Dow Chemical Company, Motorola, and Union Pacific Railroad.18 -20 The ROI models applied were based on the research conducted by Goetzel et al 21 for the Health Enhancement Research Organization (HERO). This research found that employees with certain modifiable risk factors were more costly for employers when compared to employees lacking the targeted risk factors. In this article, we apply an adaptation of previously developed ROI models to estimate cost savings and ROI realized from an obesity management program implemented at several employer sites. Results for 890 workers enrolled over a 6-to 12-month period in the program were analyzed and input into the ROI model.

Prognostic Level III. See instructions for Authors for a complete description of levels of evidence.

This experiment assesses the impact of watching television with an older sibling on preschoolers' reactions to a suspenseful movie scene. Preschoolers viewed one of two versions of the scene (normal, dream) either alone or with an older sibling. The two versions were identical except that the dream version included a prologue and an epilogue. Both the prologue and epilogue used standard production techniques to convey that the story was just a dream. Results revealed that coviewing had both negative and positive effects. Unexpectedly, watching with an older sibling tended to reduce preschoolers' ability to recognize the dream in the program. However, preschoolers who viewed with an older sibling were less emotionally aroused and liked the program more than did those who watched alone. Mediating processes during coviewing, such as the nature of the talk and visual attention to the television, were used to explain the results.

BackgroundState policy approaches designed to provide opioid treatment options have received significant attention in addressing the opioid epidemic in the United States. In particular, expanded availability of naloxone to reverse overdose, Good Samaritan laws intended to protect individuals who attempt to provide or obtain emergency services for someone experiencing an opioid overdose, and expanded coverage of medication-assisted treatment (MAT) for individuals with opioid abuse or dependence may help curtail hospital readmissions from opioids. The objective of this retrospective cohort study was to evaluate the association between the presence of state opioid treatment policies—naloxone standing orders, Good Samaritan laws, and Medicaid medication-assisted treatment (MAT) coverage—and opioid-related hospital readmissions.MethodsWe used 2013–2015 hospital inpatient discharge data from 13 states from the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project. We examined the relationship between state opioid treatment policies and 90-day opioid-related readmissions after a stay involving an opioid diagnosis.ResultsOur sample included 383,334 opioid-related index hospitalizations. Patients treated in states with naloxone standing-order policies at the time of the index stay had higher adjusted odds of an opioid-related readmission than did those treated in states without such policies; however, this relationship was not present in states with Good Samaritan laws. Medicaid methadone coverage was associated with higher odds of readmission among all insurance groups except Medicaid. Medicaid MAT coverage generosity was associated with higher odds of readmission among the Medicaid group but lower odds of readmission among the Medicare and privately insured groups. More comprehensive Medicaid coverage of substance use disorder treatment and a greater number of opioid treatment programs were associated with lower odds of readmission.ConclusionsDifferences in index hospitalization rates suggest that states with opioid treatment policies had a higher level of need for opioid-related intervention, which also may account for higher rates of readmission. More research is needed to understand how these policies can be most effective in influencing acute care use.

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Under the Hospital Readmissions Reduction Program (HRRP) of the Centers for Medicare & Medicaid Services (CMS), hospitals with excess readmissions for select conditions and procedures are penalized. However, readmission rates are not risk adjusted for socioeconomic status (SES) or race/ethnicity. We examined how adding SES and race/ethnicity to the CMS risk-adjustment algorithm would affect hospitals’ excess readmission ratios and potential penalties under the HRRP. For each HRRP measure, we compared excess readmission ratios with and without SES and race/ethnicity included in the CMS standard risk-adjustment algorithm and estimated the resulting effects on overall penalties across a number of hospital characteristics. For the 5 HRRP measures (heart failure, acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and total hip or knee arthroplasty), we used data from the Healthcare Cost and Utilization Project’s State Inpatient Databases for 2011-2012 to calculate the excess readmission ratio with and without SES and race/ethnicity included in the model. With these ratios, we estimated the impact on HRRP penalties and found that risk adjusting for SES and race/ethnicity would affect Medicare payments for 83.8% of hospitals. The effect on the size of HRRP penalties ranged from −14.4% to 25.6%, but the impact on overall Medicare base payments was small—ranging from −0.09% to 0.06%. Including SES and race/ethnicity in the calculation had a disproportionately favorable effect on safety-net and rural hospitals. Any financial effects on hospitals and on the Medicare program of adding SES and race/ethnicity to the HRRP risk-adjustment calculation likely would be small.