Primary Care Provider Encounter Cadence and HbA1c Control in Older Patients With Diabetes.

INTRODUCTION: Primary care provider encounters are associated with health and well-being; however, limited evidence guides optimal primary care provider rate of visit, referred to as encounter cadence. This study measures associations between primary care provider encounter cadence and diabetes outcomes among individuals newly diagnosed with type 2 diabetes mellitus. METHODS: In this retrospective cohort study, 7,106 people enrolled in Medicare Advantage and newly diagnosed with type 2 diabetes mellitus between July 1, 2012 and June 30, 2013 were identified and followed for 36 months. Two methods measured primary care provider encounter cadence: total primary care provider encounters (frequency) and quarters with primary care provider encounter (regularity). Logistic regression measured relationships between primary care provider encounter cadence and non-insulin diabetes medication adherence, HbA1c control, emergency department visits, and inpatient admissions. Non-insulin diabetes medication adherence was defined according to the National Committee for Quality Assurance, Healthcare Effectiveness Data and Information Set specifications and measured using healthcare claims data. Post-hoc models examined adherence and diabetes control among those nonadherent (n=5,212) and with noncontrolled HbA1c (n=326) during the encounter/cadence period. Data were extracted and analyzed in 2017. RESULTS: Adjusted models indicated that both frequency (AOR=1.08, 95% CI=1.06, 1.10) and regularity (AOR=1.18, 95% CI=1.13, 1.22) of primary care provider encounters were associated with increased odds of adherence. Post-hoc analyses indicated that more frequent (AOR=1.12, 95% CI=1.10, 1.15) and regular (AOR=1.27, 95% CI=1.22, 1.33) primary care provider encounters were associated significantly with adherence and were associated directionally with HbA1c control. CONCLUSIONS: More frequent and regular primary care provider encounters are associated with an increased likelihood of non-insulin diabetes medication adherence. These findings contribute to data needed to establish evidence-based guidelines for primary care provider encounter cadence for those newly diagnosed with type 2 diabetes mellitus.

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.

A Bold Goal: More Healthy Days Through Improved Community Health.

Humana, a large health care company, has set a goal of 20% improvement in health in the communities it serves by 2020. The metric chosen for the Bold Goal initiative was the HRQOL-4 version of the Centers for Disease Control and Prevention (CDC) Healthy Days survey. This paper presents the methods for measuring progress, reports results for the first year of tracking, and describes Humana's community-based interventions. Across 7 specially designated "Bold Goal" communities, mean unhealthy days declined from 10.98 in 2015 to 10.64 in 2016, which represented a 3.1% relative, or 0.34 absolute, decline. This compares with a 0.17 absolute unhealthy days decline in Humana's national population overall. The paper also describes how additional work identifying associations between social determinants of health (SDOH) and Healthy Days is influencing Humana's strategy. Lastly, a strategy of community engagement is illustrated through 2 case examples: San Antonio and Knoxville. In the San Antonio area, the community in which Humana has been involved the longest, unhealthy days dropped by 9.0% (-0.95 absolute) from a mean 10.52 to 9.57 unhealthy days. In Knoxville, one of the newer areas of engagement, mean unhealthy days declined by 4.8% (-0.61 absolute), representing declines in both physically and mentally unhealthy days. Overall, results are encouraging, and Humana expects declines to accelerate over time as initiatives are launched and scaled in Bold Goal communities.

A health plan's investigation of Healthy Days and chronic conditions.

OBJECTIVES: To investigate whether self-reported unhealthy days are related to 6 chronic conditions and other health indicators by using administrative claims. STUDY DESIGN: Cross-sectional study using Healthy Days survey data linked to administrative claims. METHODS: Survey respondents 65 years or older with Medicare Advantage coverage in November or December 2014 and 12 months continuous presurvey enrollment were identified. Mean physically and mentally unhealthy days were reported by chronic condition subgroups. Mean incremental unhealthy days were calculated for individuals in chronic condition subgroups and those exhibiting noncompliance with 2014 quality measures after adjusting for age, gender, provider/insurer contractual relationship, dual Medicaid/Medicare eligibility, and sum of chronic conditions. The relationship between the unhealthy days category and adjusted mean resource utilization (inpatient and outpatient visits) and total healthcare costs for the year prior to the survey was also described. RESULTS: The population averages for physically and mentally unhealthy days were 7.24 and 4.05, respectively. After adjustment, all 6 chronic conditions were associated with significantly more physically unhealthy days, and chronic obstructive pulmonary disease, depression, and diabetes were associated with significantly more mentally unhealthy days (P <.001 vs not having the condition). After adjustment, quality measure noncompliance was generally associated with incremental increases in unhealthy days. Utilization and cost generally increased with increasing unhealthy days. CONCLUSIONS: This is the first study to use administrative claims to demonstrate a relationship between Healthy Days and chronic conditions, related healthcare quality measures, utilization, and costs. Our findings underscore the validity of using Healthy Days to supplement traditional health measures in assessing health status in this population.

The Toronto outcome measure for craniofacial prosthetics: reliability and validity of a condition-specific quality-of-life instrument.

PURPOSE: The purpose of this study was to refine the Toronto Outcome Measure for Craniofacial Prosthetics (TOMCP), present evidence for its reliability and validity, and use the instrument to explore differences in quality of life between prostheses made with chlorinated polyethylene (CPE) (experimental) and silicone (control). MATERIALS AND METHODS: As part of a multicenter prospective controlled randomized double-blind single-crossover clinical trial of the two materials, the TOMCP was administered at the start and end of two 4-month study arms, during which 42 patients wore prostheses made from one material then the other. Reliability was assessed at the crossover. To determine validity of the TOMCP, the Linear Analogue Self-Assessment (LASA-12) and the Short-Form 8 (SF-8) were also administered with the TOMCP. The TOMCP was reduced by removing items that were unreliable, had poorly distributed answers, showed increased internal consistency after their removal, or were too highly correlated with more than one other item. The tests of reliability and validity were then repeated. Finally, the reduced instrument was used to test for differences in quality of life between prostheses made of the two materials. RESULTS: The item reduction tactics pared the 52-item instrument down to 27 items. The correlations of both TOMCP versions with the LASA-12 and the SF-8 were found to be statistically significant, providing evidence of the validity of the TOMCP. The instrument revealed significantly better quality of life with silicone rather than CPE prostheses. CONCLUSIONS: Both versions of the TOMCP were found to be reliable and valid. The instrument was able to show differences in quality of life between two materials.