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The American Journal of Managed Care October 2017
Low-Value Antibiotic Prescribing and Clinical Factors Influencing Patient Satisfaction
Adam L. Sharp, MD, MS; Ernest Shen, PhD; Michael H. Kanter, MD; Laura J. Berman, MPH; and Michael K. Gould, MD, MS
Countywide Physician Organization Learning Collaborative and Changes in Hospitalization Rates
Brent D. Fulton, PhD, MBA; Susan L. Ivey, MD, MHSA; Hector P. Rodriguez, PhD, MPH; and Stephen M. Shortell, PhD, MPH, MBA
Boosting Workplace Wellness Programs With Financial Incentives
Alison Cuellar, PhD; Amelia M. Haviland, PhD; Seth Richards-Shubik, PhD; Anthony T. LoSasso, PhD; Alicia Atwood, MPH; Hilary Wolfendale, MA; Mona Shah, MS; and Kevin G. Volpp, MD, PhD
Use of Patient-Reported Outcomes and Satisfaction for Quality Assessments
Anne P. Ehlers, MD, MPH; Sara Khor, MS; Amy M. Cizik, PhD, MPH; Jean-Christophe A. Leveque, MD; Neal S. Shonnard, MD; Rod J. Oskouian, Jr, MD; David R. Flum, MD, MPH; and Danielle C. Lavallee, PharmD, PhD
Trends in Hospital–Physician Integration in Medical Oncology
Jeffrey D. Clough, MD, MBA; Michaela A. Dinan, PhD; and Kevin A. Schulman, MD
Improving Care Transitions: Complex High-Utilizing Patient Experiences Guide Reform
Nancy Ambrose Gallagher, PhD, APRN-BC; Donna Fox, RN; Carrie Dawson, MS, RN; and Brent C. Williams, MD, MPH
The Option Value of Innovative Treatments for Non–Small Cell Lung Cancer and Renal Cell Carcinoma
Julia Thornton Snider, PhD; Katharine Batt, MD, MSc; Yanyu Wu, PhD; Mahlet Gizaw Tebeka, MS; and Seth Seabury, PhD
Statewide Data Infrastructure Supports Population Health Management: Diabetes Case Study
Craig Jones, MD; Mary Kate Mohlman, PhD; David Jorgenson, MS; Karl Finison, MA; Katie McGee, MS; and Hans Kastensmith
Is Higher Patient Satisfaction Associated With Better Stroke Outcomes?
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A Health Plan's Investigation of Healthy Days and Chronic Conditions
Tristan Cordier, MPH; S. Lane Slabaugh, PharmD; Eric Havens, MA; Jonathan Pena, MS; Gil Haugh, MS; Vipin Gopal, PhD; Andrew Renda, MD; Mona Shah, PhD; and Matthew Zack, MD

A Health Plan's Investigation of Healthy Days and Chronic Conditions

Tristan Cordier, MPH; S. Lane Slabaugh, PharmD; Eric Havens, MA; Jonathan Pena, MS; Gil Haugh, MS; Vipin Gopal, PhD; Andrew Renda, MD; Mona Shah, PhD; and Matthew Zack, MD
Linking administrative claims to health-related quality of life measured in Healthy Days provides a new vision into the health of populations.

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.

Am J Manag Care. 2017;23(10):e323-e330
Humana Inc selected the CDC’s quality of life instrument, Healthy Days, to measure progress toward a goal of improving the health of the communities it serves by 20% by 2020. In 2014, Humana began collecting Healthy Days data through an annual telephone-administered voice-activated technology survey and linking the data with administrative claims to measure health. This is the first published study combining Healthy Days with administrative claims to investigate whether physically and mentally unhealthy days are related to chronic condition prevalence and other health indicators. In a Medicare Advantage population, this cross-sectional analysis found that:
  • Patients with chronic conditions reported higher mean physically and mentally unhealthy days than the population average. 
  • Patients who were compliant with certain quality measures related to chronic conditions reported fewer unhealthy days after statistical adjustment. 
  • Patients who reported more unhealthy days had higher adjusted mean total healthcare costs and healthcare utilization. 
  • Healthy Days is a valid tool that can supplement traditional measures in assessing overall population health.
Health-related quality of life (HRQoL) is a valid measure of disease burden and population health encompassing physical, mental, emotional, and social functioning.1-3 HRQoL is associated with traditional health measures, including morbidity, mortality, and healthcare costs.4-8 Despite these relationships, health plans have not traditionally prioritized quality of life (QoL) outcomes in decision making, possibly due to a lack of confidence in the ability to quantify the relationship between HRQoL and clinical status or healthcare utilization. The shift toward population health and value-based care is challenging health plans to look beyond traditional health improvement measures. HRQoL can supplement these measures by providing insight into vulnerable populations, identifying specific diseases that negatively impact a person’s holistic health view, and highlighting opportunities to reduce the burden of disease. HRQoL may also indicate perceived health benefits after clinical interventions or population-based health improvement efforts.9

Although several available HRQoL instruments have demonstrated validity and reliability, they vary in scope, intended purpose, applicability, and ease of use. The ideal instrument for use by a health plan would be holistic, easy to administer, tied to a single measure that reflects the individual’s perspective, and understandable to healthcare providers and the general public alike. For these reasons, Humana Inc selected the QoL survey instrument, Healthy Days, to measure progress toward a goal of improving the health of the communities it serves by 20% by 2020.10 

Healthy Days (formally, CDC-HRQOL-4), developed by the CDC in the early 1990s, contains 4 questions that ask about a person’s perceived health: 1) would you say that, in general, your health is excellent, very good, good, fair, or poor? 2) Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good? 3) Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good? 4) During the past 30 days, approximately how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?11 

Answers to the second and third questions are used to develop a summary index of unhealthy days for each individual. Reported Healthy Days are typically expressed as means or dichotomized as above or below 14 days.9 Healthy Days data have been collected extensively in national surveys, such as the Behavioral Risk Factor Surveillance System and the National Health and Nutrition Examination Survey, as well as the Medicare Health Outcomes Survey, a longitudinal, patient-reported outcomes measure that CMS requires all Medicare Advantage plans to collect.9,12 

Several published studies have utilized large, national survey responses to assess the relationship between HRQoL, measured in Healthy Days, and various chronic conditions.12-20 To date, only 1 such study has linked self-reported Healthy Days data to claims-based diagnoses. That study evaluated Healthy Days in an elderly Pennsylvania population with arthritis.21 No other studies have utilized administrative claims data to assess the relationship between Healthy Days and other objective health measures. Furthermore, no studies have evaluated the association with quality measure compliance or healthcare utilization and self-reported Healthy Days. The objective of this study was to explore the relationship between HRQoL (assessed using Healthy Days) and the presence of 6 chronic conditions, condition-related quality measures, healthcare utilization, and costs. The 6 chronic conditions—coronary artery disease (CAD), congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), depression, diabetes, and hypertension—were identified by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and were chosen based on their prevalence in the plan population, the ability to improve disease outcomes through intervention, and their plausible relationship to HRQoL. Understanding the relationship between Healthy Days and health indicators can further validate the use of Healthy Days as a health status measure and highlight key drivers of unhealthy days at the population level, thus informing downstream strategies for improving population health.


Healthy Days Survey

A cross-sectional survey using Healthy Days questions was administered to individuals with health plan coverage from Humana Inc, a health and well-being company serving millions of enrollees across the United States through Medicare Advantage, standalone prescription drug plans, and commercial plan offerings.22 Voice-activated technology (VAT) surveys were conducted in a stratified random sample of all individuals across multiple Humana medical coverage plans (employer group, Medicare, and individual) from November 24 to December 24, 2014, via a computer-operated phone system. The random survey sample was stratified to ensure representation of various geographic locations (25 geographies based on county codes), medical coverage plans, and chronic conditions of the larger plan population (Figure 1). Individuals were removed from the survey sample if they did not have a viable phone number or did not respond after 3 consecutive calls. Only individuals who responded to both physically and mentally unhealthy days questions were included in the survey sample. Of the 4.1 million eligible medical plan enrollees, contact was attempted for 870,791 individuals. Of those, 618,984 did not have a viable phone number or did not respond and 118,034 did not answer both types of Healthy Days questions, leaving 133,773 individuals (15%) with complete survey responses. 

Survey analysis was performed using SAS survey procedures that accounted for sample design and respondent weighting. Weighting was used to standardize the survey respondent profile to that of the entire health plan population as of September 30, 2014, using an iterative proportional fitting (raking) algorithm to account for differences between respondents and nonrespondents in gender, age (6 categories: 18-34, 35-44, 45-54, 55-64, 65-74, ≥75 years), and the presence of 5 diagnosed conditions (CAD, CHF, COPD, diabetes, and hypertension, identified using ICD-9-CM codes obtained from claims). 

Study Population

From those with complete survey responses, this study included only the 83,732 individuals (63%) with Medicare Advantage insurance. Survey results were linked to administrative, medical, pharmacy, and enrollment data. The 55,681 survey respondents who met the following further criteria were selected for the study population: 1) 65 years or older at the beginning of the study period and 2) continuous health plan enrollment for 12 months prior to the survey date. 

Measurements and Analyses

This study explored the relationship between Healthy Days and the following health indicators: 1) a chronic condition (ie, CAD, CHF, COPD, depression, diabetes, or hypertension), 2) condition-related quality measures captured in the Medicare Star Ratings for calendar year 2014 (Healthcare Effectiveness Data and Information Set [HEDIS] and Pharmacy Quality Alliance [PQA] measures), and 3) healthcare resource utilization (inpatient admissions and outpatient visits) and total healthcare costs for the 12 months prior to the Healthy Days survey. Responses to Healthy Days questions were extracted from the VAT survey. Total unhealthy days were calculated individually for questions 2 and 3 and combined and capped at 30 days per CDC methodology.9,11 

The 6 chronic conditions were identified using ICD-9-CM codes and pharmacy claims in the 12 to 24 months prior to the VAT survey (eAppendix [available at]). Chronic condition subgroups were not mutually exclusive. To account for comorbidities within each chronic condition subgroup, the mean numbers of chronic conditions of interest were reported. The mean numbers of physically and mentally unhealthy days were reported for the total study population and for each of the chronic condition subgroups. Multiple linear regression accounting for survey design was used to calculate the incremental mean number of unhealthy days for individuals with a condition of interest, adjusting for age, gender, provider payment relationship with Humana (fee-for-service vs capitated), dual Medicaid/Medicare eligibility, and the additional number of conditions of interest excluding the specified condition subgroup. 

HEDIS and PQA measures related to conditions of interest that were included in Medicare Star Ratings in 2014 were analyzed for quality measures. PQA measures were obtained from claims, and HEDIS quality measures were obtained from a National Committee for Quality Assurance–certified vendor that evaluates medical and lab claims to determine HEDIS measure eligibility and compliance. HEDIS measures included cardiovascular care (low-density lipoprotein cholesterol [LDL-C] screening) and diabetes care (glycated hemoglobin [A1C] screening, LDL-C screening, yearly eye exams, nephropathy screening, A1C <9, and LDL-C <100). PQA measures included medication adherence for statins, oral diabetes medications, and the combined group of angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs), calculated as the proportion of days covered (PDC). Adherence was defined as a PDC of 80% or greater. The incremental mean number of physically and mentally unhealthy days for individuals noncompliant with HEDIS and PQA measures was assessed using multiple linear regression accounting for survey design. Covariates adjusted for in the regression were age, gender, provider payment relationship with Humana (fee-for-service vs capitated), dual Medicaid/Medicare eligibility, and the sum of chronic conditions of interest (0, 1, 2, 3, ≥4).

Utilization and total medical and pharmacy costs (sum of plan- and patient-paid) were identified from claims for the 12 months before the VAT survey date. The mean number of inpatient admissions and outpatient visits per 1000 individuals annually within each category of unhealthy days was calculated using multiple linear regression accounting for survey design. Linear regression was also used to calculate total per-person-per-month (PPPM) cost for each additional unhealthy day. Cost analyses were only completed for 29,994 individuals (54%) whose primary physician was not part of a shared-risk arrangement, as claims data for patients with primary physicians in shared-risk arrangements do not include cost information. Utilization and cost results were adjusted for the same covariates used to adjust quality measures.

All analyses were completed using SAS Enterprise Guide 5.4 (SAS Institute; Cary, North Carolina). The statistical significance level was set a priori at 0.05. This study was conducted as a part of Humana’s normal quality improvement operations and did not meet HHS’s regulatory definition of research under 45 Code of Federal Regulations 46.102(d).


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