Andrew Renda

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.

Impact of comorbid conditions in COPD patients on health care resource utilization and costs in a predominantly Medicare population

Background Patients with chronic obstructive pulmonary disease (COPD) often have multiple underlying comorbidities, which may lead to increased health care resource utilization (HCRU) and costs. Objective To describe the comorbidity profiles of COPD patients and examine the associations between the presence of comorbidities and HCRU or health care costs. Methods A retrospective cohort study utilizing data from a large US national health plan with a predominantly Medicare population was conducted. COPD patients aged 40–89 years and continuously enrolled for 12 months prior to and 24 months after the first COPD diagnosis during the period of January 01, 2009, through December 31, 2010, were selected. Eleven comorbidities of interest were identified 12 months prior through 12 months after COPD diagnosis. All-cause and COPD-related hospitalizations and costs were assessed 24 months after diagnosis, and the associations with comorbidities were determined using multivariate statistical models. Results Ninety-two percent of 52,643 COPD patients identified had at least one of the 11 comorbidities. Congestive heart failure (CHF), coronary artery disease, and cerebrovascular disease (CVA) had the strongest associations with all-cause hospitalizations (mean ratio: 1.56, 1.32, and 1.30, respectively; P<0.0001); other comorbidities examined had moderate associations. CHF, anxiety, and sleep apnea had the strongest associations with COPD-related hospitalizations (mean ratio: 2.01, 1.32, and 1.21, respectively; P<0.0001); other comorbidities examined (except chronic kidney disease [CKD], obesity, and osteoarthritis) had moderate associations. All comorbidities assessed (except obesity and CKD) were associated with higher all-cause costs (mean ratio range: 1.07–1.54, P<0.0001). CHF, sleep apnea, anxiety, and osteoporosis were associated with higher COPD-related costs (mean ratio range: 1.08–1.67, P<0.0001), while CVA, CKD, obesity, osteoarthritis, and type 2 diabetes were associated with lower COPD-related costs. Conclusion This study confirms that specific comorbidities among COPD patients add significant burden with higher HCRU and costs compared to patients without these comorbidities. Payers may use this information to develop tailored therapeutic interventions for improved management of patients with specific comorbidities.

Glycemic Control Outcomes After Canagliflozin Initiation: Observations in a Medicare and Commercial Managed Care Population in Clinical Practice

PURPOSE Although the efficacy of canagliflozin has been well established in clinical trials, research regarding its use and impact on outcomes in clinical practice has been limited by the availability of data on observations up to and beyond 6 months after the initial use of canagliflozin. The purpose of this study was to evaluate changes in glycemic control after the initiation of canagliflozin use in a managed care population. METHODS A retrospective cohort analysis in adults with type 2 diabetes mellitus was conducted using medical and pharmacy claims data and laboratory results from the Humana Research Database. The differences between hemoglobin (Hb) A1c levels pre- and postindex were assessed. Changes from pre- to postindex in the percentages of patients achieving glycemic control (eg, HbA1c <7% or <8%) were evaluated. HbA1c levels were also observed during days 31 to 90, 91 to 180, 181 to 270, and 271 to 360 postindex relative to preindex to assess the durability of HbA1c change over time. Analyses were conducted in the full cohort and in 3 subgroups: (1) HbA1c ≥7% at baseline; (2) age ≥65 years; (3) and Medicare members age ≥65 years and HbA1c ≥7% at baseline. FINDINGS Among the 1562 patients meeting the study criteria, the mean HbA1c values pre- and postindex were 8.6% and 7.9%, respectively (P < 0.0001); in the subgroup with HbA1c ≥7% at baseline, these values were 8.9% and 8.0%; in the subgroup aged ≥65 years, 8.5% and 7.9%; and in the subgroup aged ≥65 years with HbA1c ≥7% at baseline, 8.8% and 8.1% (all subgroups, P < 0.001). The percentages of patients meeting glycemic-control thresholds (HbA1c <7%, <8%) were significantly greater at postindex in the full study cohort and in all 3 subgroups (all, P < 0.001). Based on longitudinal HbA1c results in the postindex periods, HbA1c reduction appeared durable across 12 months. IMPLICATIONS The findings from this study suggest that treatment with canagliflozin is associated with improved glycemic control, as evidenced by HbA1c reduction and glycemic goal attainment. Even though not all patients had valid HbA1c measurements available in each quarter during the follow-up period, the reductions in mean HbA1c appeared durable across the postindex intervals. The observations from this majority Medicare Advantage with Prescription Drug sample and, more specifically, in the subgroups limited to patients aged ≥65 years are particularly informative for payers and providers managing or caring for patients of this age with diabetes.

COPD exacerbations associated with the modified Medical Research Council scale and COPD assessment test among Humana Medicare members

Background The Global initiative for chronic Obstructive Lung Disease guidelines recommend assessment of COPD severity, which includes symptomatology using the modified Medical Research Council (mMRC) or COPD assessment test (CAT) score in addition to the degree of airflow obstruction and exacerbation history. While there is great interest in incorporating symptomatology, little is known about how patient reported symptoms are associated with future exacerbations and exacerbation-related costs. Methods The mMRC and CAT were mailed to a randomly selected sample of 4,000 Medicare members aged >40 years, diagnosed with COPD (≥2 encounters with International Classification of Dis eases-9th Edition Clinical Modification: 491.xx, 492.xx, 496.xx, ≥30 days apart). The exacerbations and exacerbation-related costs were collected from claims data during 365-day post-survey after exclusion of members lost to follow-up or with cancer, organ transplant, or pregnancy. A logistic regression model estimated the predictive value of exacerbation history and symptomatology on exacerbations during follow-up, and a generalized linear model with log link and gamma distribution estimated the predictive value of exacerbation history and symptomatology on exacerbation-related costs. Results Among a total of 1,159 members who returned the survey, a 66% (765) completion rate was observed. Mean (standard deviation) age among survey completers was 72.0 (8.3), 53.7% female and 91.2% white. Odds ratios for having post-index exacerbations were 3.06, 4.55, and 16.28 times for members with 1, 2, and ≥3 pre-index exacerbations, respectively, relative to members with 0 pre-index exacerbations (P<0.001 for all). The odds ratio for high vs low symptoms using CAT was 2.51 (P<0.001). Similarly, exacerbation-related costs were 73% higher with each incremental pre-index exacerbation, and over four fold higher for high-vs low-symptom patients using CAT (each P<0.001). The symptoms using mMRC were not statistically significant in either model (P>0.10). Conclusion The patient-reported symptoms contribute important information related to future COPD exacerbations and exacerbation-related costs beyond that explained by exacerbation history.

Association between adherence to medications for COPD and medications for other chronic conditions in COPD patients

Background Patients with COPD often have multiple comorbidities requiring use of multiple medications, and adherence rates for maintenance COPD (mCOPD) medications are already known to be suboptimal. Presence of comorbidities in COPD patients, and use of medications used to treat those comorbidities (non-COPD medications), may have an adverse impact on adherence to mCOPD medications. Objective The objective of the study was to evaluate the association between non-adherence to mCOPD medications and non-COPD medications in COPD patients. Methods COPD patients were identified using a large administrative claims database. Selected patients were 40–89 years old and continuously enrolled for 12 months prior to and 24 months after the first identified COPD diagnosis (index date) during January 1, 2009 to December 31, 2010. Patients were required to have ≥1 prescription for a mCOPD medication within 365 days of the index date and ≥1 prescription for one of 12 non-COPD medication classes within ±30 days of the first COPD prescription. Adherence (proportion of days covered [PDC]) was measured during 365 days following the first COPD prescription. The association between non-adherence (PDC <0.8) to mCOPD and non-adherence to non-COPD medications was determined using logistic regression, controlling for baseline patient characteristics. Results A total of 14,117 patients, with a mean age of 69.9 years, met study criteria. Of these, 40.9% were males and 79.2% were non-adherent to mCOPD medications with a mean PDC of 0.47. Non-adherence to mCOPD medications was associated with non-adherence to 10 of 12 non-COPD medication classes (odds ratio 1.38–1.78, all P<0.01). Conclusion Adherence to mCOPD medications is low. Non-adherence (or adherence) to mCOPD medications is positively related to non-adherence (or adherence) to non-COPD medications, implying that the need to take medications prescribed for comorbid conditions does not adversely impact adherence to mCOPD medications.

Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data

Background Patients with COPD often experience severe exacerbations involving hospitalization, which accelerate lung function decline and reduce quality of life. This study aimed to develop and validate a predictive model to identify patients at risk of developing severe COPD exacerbations using administrative claims data, to facilitate appropriate disease management programs. Methods A predictive model was developed using a retrospective cohort of COPD patients aged 55–89 years identified between July 1, 2010 and June 30, 2013 using Humana’s claims data. The baseline period was 12 months postdiagnosis, and the prediction period covered months 12–24. Patients with and without severe exacerbations in the prediction period were compared to identify characteristics associated with severe COPD exacerbations. Models were developed using stepwise logistic regression, and a final model was chosen to optimize sensitivity, specificity, positive predictive value (PPV), and negative PV (NPV). Results Of 45,722 patients, 5,317 had severe exacerbations in the prediction period. Patients with severe exacerbations had significantly higher comorbidity burden, use of respiratory medications, and tobacco-cessation counseling compared to those without severe exacerbations in the baseline period. The predictive model included 29 variables that were significantly associated with severe exacerbations. The strongest predictors were prior severe exacerbations and higher Deyo–Charlson comorbidity score (OR 1.50 and 1.47, respectively). The best-performing predictive model had an area under the curve of 0.77. A receiver operating characteristic cutoff of 0.4 was chosen to optimize PPV, and the model had sensitivity of 17%, specificity of 98%, PPV of 48%, and NPV of 90%. Conclusion This study found that of every two patients identified by the predictive model to be at risk of severe exacerbation, one patient may have a severe exacerbation. Once at-risk patients are identified, appropriate maintenance medication, implementation of disease-management programs, and education may prevent future exacerbations.

Association of obesity with healthcare resource utilization and costs in a commercial population

Abstract Objective: To examine the association of obesity with healthcare resource utilization (HRU) and costs among commercially insured individuals. Methods: This retrospective observational cohort study used administrative claims from 1 January 2007 to 1 December 2013. The ICD-9-CM status codes (V85 hierarchy) from 2008 to 2012 classified body mass index (BMI) into the World Health Organizations’ BMI categories. The date of first observed BMI code was defined as the index date and continuous eligibility for one year pre- and post- index date was ensured. Post-index claims determined individuals’ HRU and costs. Sampling weights developed using the entropy balance method and National Health and Nutrition Examination Survey data ensured representation of the US adult commercially insured population. Baseline characteristics were described across BMI classes and associations between BMI categories, and outcomes were examined using multivariable regression. Results: The cohort included 9651 individuals with BMI V85 codes. After weighting, the BMI distribution was: normal (31.1%), overweight (33.4%), obese class I (22.0%), obese class II (8.1%) and obese class III (5.4%). Increasing BMI was associated with greater prevalence of cardiometabolic conditions, including hypertension, type 2 diabetes and metabolic syndrome. The use of antihypertensives, antihyperlipidemics, antidiabetics, analgesics and antidepressants rose with increasing BMI. Greater BMI level was associated with increased inpatient, emergency department and outpatient utilization, and higher total healthcare, medical and pharmacy costs. Conclusions: Increasing BMI was associated with higher prevalence of cardiometabolic conditions and higher HRU and costs. There is an urgent need to address the epidemic of obesity and its clinical and economic impacts.

Positive predictive value between medical-chart body-mass-index category and obesity versus codes in a claims-data warehouse

Abstract Objective: To evaluate the positive predictive value of claims-based V85 codes for identifying individuals with varying degrees of BMI relative to their measured BMI obtained from medical record abstraction. Methods: This was a retrospective validation study utilizing administrative claims and medical chart data from 1 January 2009 to 31 August 2015. Randomly selected samples of patients enrolled in a Medicare Advantage Prescription Drug (MAPD) or commercial health plan and with a V85 claim were identified. The claims-based BMI category (underweight, normal weight, overweight, obese class I–III) was determined via corresponding V85 codes and compared to the BMI category derived from chart abstracted height, weight and/or BMI. The positive predictive values (PPVs) of the claims-based BMI categories were calculated with the corresponding 95% confidence intervals (CIs). Results: The overall PPVs (95% CIs) in the MAPD and commercial samples were 90.3% (86.3%–94.4%) and 91.1% (87.3%–94.9%), respectively. In each BMI category, the PPVs (95% CIs) for the MAPD and commercial samples, respectively, were: underweight, 71.0% (55.0%–87.0%) and 75.9% (60.3%–91.4%); normal, 93.8% (85.4%–100%) and 87.8% (77.8%–97.8%); overweight, 97.4% (92.5%–100%) and 93.5% (84.9%–100%); obese class I, 96.9 (90.9%–100%) and 97.2% (91.9%–100%); obese class II, 97.0% (91.1%–100%) and 93.0% (85.4%–100%); and obese class III, 85.0% (73.3%–96.1%) and 97.1% (91.4%–100%). Conclusions: BMI categories derived from administrative claims, when available, can be used successfully particularly in the context of obesity research.

Association of obesity with healthcare utilization and costs in a Medicare population

Abstract Objectives: To examine the association of obesity with healthcare resource utilization and costs in a Medicare population. Methods: This study was a retrospective cohort study using Humana Medicare Advantage (MA) claims data. Body mass index (BMI) was assessed using ICD-9-CM status codes (V85 hierarchy) that have been validated in the data source to classify patients into BMI categories: normal (N), overweight (Ow), obese class I (ObI), obese class II (ObII), and obese class III (ObIII). Healthcare resource utilization (HRU) and costs were determined based on claims data. Descriptive statistics were used to examine baseline characteristics and HRU across BMI classes. Multivariable analysis was used to examine the association between BMI class and outcome measures. Results: Among the 172,866 patients aged ≥65 years that were identified, BMI distribution was: N, 21%; Ow 37%; ObI, 24%, ObII, 10%; and ObIII, 9%. Inpatient, emergency department and outpatient utilization increased with greater BMI level, and greater BMI level was associated with higher total healthcare, medical and pharmacy costs. Greater prevalence of several cardiometabolic conditions, total medication use, and use of specific medication classes was observed with increasing BMI class. Conclusions: Greater BMI was associated with greater HRU and costs and observed increase in prevalence of cardiometabolic conditions. These results reflect an urgent need to address the epidemic of obesity and the resulting excessive clinical and economic burden on the healthcare system.