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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.
A total of 55,681 individuals were included in the study (Figure 1). The study population had a mean age of 75.5 years, was 56.6% female, was 87.4% white, and resided primarily in the South Atlantic, Mountain, East North Central, and West South Central Medicare regions of the United States; a small percentage (7%) were eligible for both Medicare and Medicaid (Table 1). Hypertension was the most prevalent condition (73%) and CHF was least prevalent (12%). The mean number of chronic conditions of interest ranged from 2.2 for patients with hypertension to 3.6 for those with CHF. 

Chronic Conditions and Healthy Days

The study population reported a mean number of 7.2 physically unhealthy days (95% CI, 7.1-7.4) and 4.1 mentally unhealthy days (95% CI, 3.9-4.2). Approximately one-third (34.7%; 95% CI, 34.0%-35.4%) reported 0 unhealthy days (0 physically unhealthy days: 41.3%; 95% CI, 40.5%-42.0%; 0 mentally unhealthy days: 62.0%; 95% CI, 61.3%-62.7%). Individuals diagnosed with CAD, CHF, COPD, depression, diabetes, and hypertension reported more unadjusted mean physically and mentally unhealthy days than the total study population (Figure 2). After adjusting for covariates, all chronic conditions were associated with significantly more physically unhealthy days, and COPD, depression, and diabetes with significantly more mentally unhealthy days (Table 2). COPD and depression had the biggest incremental impacts with 2.93 and 2.41 excess physically unhealthy days and 1.08 and 4.02 excess mentally unhealthy days, respectively (P <.001 vs not having these conditions) (Table 2). 

Quality Measure Compliance

Table 2 shows the adjusted incremental physically and mentally unhealthy days for individuals noncompliant with quality measures. Noncompliance with diabetic eye exams, A1C control, and LDL-C screening in diabetes was associated with significantly more physically unhealthy days, whereas only noncompliance with diabetic eye exams and LDL-C control in diabetes was associated with significantly more mentally unhealthy days. Individuals noncompliant with diabetic nephropathy screening or noncompliant with LDL-C screening in cardiovascular disease did not differ from compliant individuals in unhealthy days. Nonadherence with ACE inhibitors/ARBs and oral diabetes medications was associated with significantly more physically and mentally unhealthy days, whereas nonadherence with statins was associated with only significantly more mentally unhealthy days. 

Healthcare Resource Utilization and Cost

Adjusted utilization and cost results indicate that inpatient admissions and outpatient visits generally increased with increasing total unhealthy days (Figure 3). Those reporting 0 unhealthy days had an adjusted annual mean of 193 inpatient admissions and 5724 outpatient visits per 1000 individuals. Peak adjusted mean inpatient visits (377 admissions per 1000 individuals) corresponded to 21 to 25 unhealthy days, whereas adjusted mean outpatient visits (8386 visits per 1000 individuals) peaked at 16 to 20 unhealthy days. The adjusted PPPM cost of each additional unhealthy day in the entire study population was $15.64 (95% CI, $13.16-$18.11).

DISCUSSION

Several studies have utilized Healthy Days to describe the impact of various disease states on HRQoL, but they have relied heavily on survey and clinical data collection or on self-reported clinical information. This is the first study to use administrative claims data to describe the relationship between self-reported Healthy Days and various claims-based health indicators. 

Our study validates previous findings, showing that individuals diagnosed with chronic conditions report more unhealthy days. Previous studies in individuals diagnosed with similar chronic conditions reported comparable numbers of physically unhealthy days (8.4-9.3) but slightly fewer mentally unhealthy days (3.0-4.5) than those reported in this study.12,14-18 One notable exception is a study of patients with serious psychological distress (SPD) and similar comorbidities to those studied here.13 The authors reported fewer physically unhealthy days in patients with SPD and comorbid depression (5.9), diabetes (6.6), and a diagnosis of respiratory disease (6.0), and more mentally unhealthy days in patients with comorbid depression (8.2), which may reflect differences in the studied population (ie, California residents with SPD). Because these other studies used Healthy Days surveys spanning 6 or 12 months whereas our survey was administered only in November and December, seasonality may account for differences in these findings. 

Importantly, our analysis adds to current evidence by showing higher incremental unhealthy days for individuals with CHF, COPD, and depression, which supports the results of other studies showing increased odds of reporting unhealthy days in patients with certain chronic conditions, such as COPD and diabetes.14,16 Continuous efforts to prevent and manage chronic conditions are needed given their negative impact on QoL. Moreover, understanding correlations between management of depression and reported mentally unhealthy days is paramount to guiding mental health treatment decisions. These combined findings highlight the potential of using Healthy Days as a marker for higher disease burden and identification of participants in whom chronic condition management interventions may be most beneficial.

Compliance with HEDIS and PQA measures is an important indicator of healthcare quality. Of the 6 HEDIS measures studied in those with diabetes, individuals noncompliant with diabetic eye exams, A1C <9, and LDL-C screening reported significantly more unhealthy days than compliant individuals. Although a causal relationship cannot be established using a cross-sectional study design, this is the first report of any relationship between healthcare quality measures and better HRQoL. Future research to understand this relationship—whether poor compliance is a symptom, cause, or result of poor QoL—would provide insight into the ultimate benefit to be gained from quality compliance initiatives. Additionally, patients not adherent to ACE inhibitor/ARB and oral diabetes medications reported significantly more mentally and physically unhealthy days, and those not adherent to statins reported significantly more mentally unhealthy days. Given the consistent and significant association of medication nonadherence and higher mentally unhealthy days, additional studies exploring Healthy Days as a reflection of medication adherence may be warranted. 

Finally, this is also the first study to explore the relationship between unhealthy days and healthcare utilization and cost. Utilization varied directly with total unhealthy days after accounting for certain covariates, and individuals reporting more unhealthy days exhibited higher utilization compared with those reporting fewer unhealthy days. Increased utilization paralleled an incremental PPPM cost increase of $15.63 per additional unhealthy day. Although these findings are not surprising, they highlight Healthy Days as a potential indicator for identifying individuals in greatest need of multifaceted interventions aimed at improving both actual and perceived health. Validation of our findings could support the use of Healthy Days as an early marker of changes in health outcomes prior to the manifestation of such changes in claims data. Lastly, evaluating whether interventions that improve health and decrease utilization and cost exert a corresponding decrease on reported unhealthy days is an important area for future research. 

Limitations

Certain limitations impact interpretation of study results. Healthy Days survey data are subject to response and recall bias. Selection bias due to survey nonresponse was adjusted for by weighting responses so that they were applicable to the mix of demographic characteristics and distribution of key chronic conditions in the study sample; however, residual bias may remain. Administrative claims data have inherent limitations, including coding errors and missing data. Potential confounders, such as socioeconomic characteristics, employment, and marital status, were not available in claims and could not be adjusted for in the analysis. Given the potential impact of seasons on depression, measuring Healthy Days during the winter (November through December) may overestimate mentally unhealthy days. This study was conducted in a Medicare Advantage population heavily concentrated in the southern United States, and results may not be generalizable to a younger, commercially insured population residing elsewhere. Finally, the analysis of unhealthy days and chronic conditions did not differentiate between individuals with mild versus severe disease. 

CONCLUSIONS

This is the first study linking Healthy Days data with health indicators from administrative claims, and results should be corroborated in other populations. The findings of higher incremental unhealthy days in the presence of certain chronic conditions and noncompliance with certain quality measures underscore the validity of using Healthy Days to supplement traditional health measures in assessing health status in this population. Humana is creating population-level healthcare interventions that can potentially reduce the burden of chronic disease and improve overall well-being. Future studies will use Healthy Days data to investigate the impact of such interventions.

Acknowledgments

The authors would like to thank Laura Happe, PharmD, and Charron Long, PharmD, for their contributions to manuscript development.

Author Affiliations: Humana Inc (TC, SLS, EH, JP, GH, VG, AR), Louisville, KY; Robert Wood Johnson Foundation (MS), Princeton, NJ; Centers for Disease Control and Prevention (MZ), Atlanta, GA.

Source of Funding: None.

Author Disclosures: Mr Cordier, Dr Slabaugh, Mr Havens, Mr Pena, Mr Haugh, Dr Gopal, and Dr Renda are employees of Humana Inc. Dr Slabaugh and Dr Gopal own stock in Humana Inc. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC.

Authorship Information: Concept and design (TC, SLS, EH, JP, GH, VG AR); acquisition of data (TC, EH, JP, GH); analysis and interpretation of data (TC, SLS, EH, JP, GH, AR, MS, MZ); drafting of the manuscript (TC, SLS, EH, GH, AR); critical revision of the manuscript for important intellectual content (TC, SLS, EH, JP, GH, VG, AR, MS, MZ); statistical analysis (TC, EH, GH, MS, MZ); administrative, technical, or logistic support (EH); and supervision (JP, VG).

Address Correspondence to: Tristan Cordier, MPH, Humana Inc, 500 W Main St, Louisville, KY 40202. E-mail: tcordier1@humana.com. 
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