Health Care Utilization and Medical Cost Outcomes from a Digital Diabetes Prevention Program in a Medicare Advantage Population.

This study examined the effects of a digital diabetes prevention program (DPP) on health care costs and utilization among Medicare Advantage participants. Patients (n = 501) received access to a plan-sponsored, digitally-delivered DPP accessible through computer, tablet, or smartphone. Prior research demonstrated a 7.5% reduction in body weight at 12 months. A comparison group who did not participate in the DPP was constructed by matching on demographic, health plan, health status, and health care costs and utilization. The authors assessed effects on cost and utilization outcomes using difference-in-differences regressions, controlling for propensities to participate and engage in the DPP, in the 12 months prior to DPP enrollment and 24 months after. Though post-enrollment data showed trends in decreased drug spending and emergency department use, increased inpatient utilization, and no change in total nondrug costs or outpatient utilization, the findings did not reach statistical significance, potentially because of sample size. The population had low costs and utilization at baseline, which may be responsible for the lack of observed effects in the short time frame. This study demonstrates the challenges of studying the effectiveness of preventive programs in a population with low baseline costs and the importance of using a large enough sample and follow-up period, but remains an important contribution to exploring the effects of digital DPPs in a real-world sample of individuals who were eligible and willing to participate.

Charlson Comorbidity Index: ICD-9 Update and ICD-10 Translation.

BACKGROUND: The original Charlson Comorbidity Index (CCI) encompassed 19 categories of medical conditions that were identifiable in medical records. Subsequent publications provided scoring algorithms based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. The recent adoption of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes in the United States created a need for a new scoring scheme. In addition, a review of existing claims-based scoring systems suggested 3 areas for improvement: the lack of explicit identification of secondary diabetes, the lack of differentiation between HIV infection and AIDS, and insufficient guidance on scoring hierarchy. In addition, addressing the third need raised the issue of disease severity in renal disease. OBJECTIVES: This initiative aimed to create an expanded and refined ICD-9 scoring system for CCI, addressing the classification of issues noted above, create a corresponding ICD-10 system, assess the comparability of ICD-9- and ICD-10-based scores, and validate the new scoring scheme. METHODS: We created ICD-9 and ICD-10 code tables for 19 CCI medical conditions. The new scoring scheme was labeled CDMF CCI and was tested using claims-based data for individuals aged ≥65 years who participated in a Humana Medicare Advantage plan during at least 1 of 3 consecutive 12-month periods. Two 12-month periods were during the ICD-9 era and the third 12-month period was during the ICD-10 era. Because many individuals were counted in more than one 12-month period, we described the study population as comprising 3 panels. We used regression models to analyze the association between the CCI score and same-year inpatient admissions and near-term (90-day) mortality. Additional testing was done by comparing the mean CCI score or disease prevalence in the 3 subpopulations of people with HIV/AIDS, renal disease, or diabetes. Finally, we calculated area under the receiver operating characteristics (AUC-ROC) curve values by applying the Deyo system and our ICD-9 and ICD-10 scoring systems. RESULTS: The CDMF ICD-9 and ICD-10 scoring scheme yielded comparable scores across the 3 panels, and inpatient admissions and mortality rates consistently increased in each panel as the CCI score increased. Comparisons of the performance of the Deyo system and our proposed CDMF ICD-9 system in the 3 key subpopulations showed that the CDMF ICD-9 system produced a lower CCI score in the presence of HIV infection without AIDS, achieved similar detection ability of diabetes, and allowed good differentiation between mild-to-moderate and severe renal disease. AUC-ROC values were similar between the CDMF ICD-9 coding system and the Deyo system. CONCLUSION: Our results support the implementation of the CDMF CCI scoring instrument to triage individual patients for disease- and care-management programs. In addition, the CDMF scheme allows for a more precise understanding of chronic disease at a population level, thus allowing health systems and plans to design services and benefits to meet multifactorial clinical needs. Preliminary validation sets the stage for further testing using long-term follow-up data and for the adaptation of this coding scheme to a chart review instrument.

Outcomes of a Digital Health Program With Human Coaching for Diabetes Risk Reduction in a Medicare Population.

OBJECTIVE: To examine the outcomes of a Medicare population who participated in a program combining digital health with human coaching for diabetes risk reduction. METHOD: People at risk for diabetes enrolled in a program combining digital health with human coaching. Participation and health outcomes were examined at 16 weeks and 6 and 12 months. RESULTS: A total of 501 participants enrolled; 92% completed at least nine of 16 core lessons. Participants averaged 19 of 31 possible opportunities for weekly program engagement. At 12 months, participants lost 7.5% ( SD = 7.8%) of initial body weight; among participants with clinical data, glucose control improved (glycosylated hemoglobin [HbA1c] change = -0.14%, p = .001) and total cholesterol decreased (-7.08 mg/dL, p = .008). Self-reported well-being, depression, and self-care improved ( p < .0001). DISCUSSION: This Medicare population demonstrated sustained program engagement and improved weight, health, and well-being. The findings support digital programs with human coaching for reducing chronic disease risk among older adults.