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The American Journal of Managed Care August 2015
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Differential Impact of Mental Health Multimorbidity on Healthcare Costs in Diabetes
Leonard E. Egede, MD, MS; Mulugeta Gebregziabher, PhD; Yumin Zhao, PhD; Clara E. Dismuke, PhD; Rebekah J. Walker, PhD; Kelly J. Hunt, PhD, MSPH; and R. Neal Axon, MD, MSCR
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Differential Impact of Mental Health Multimorbidity on Healthcare Costs in Diabetes

Leonard E. Egede, MD, MS; Mulugeta Gebregziabher, PhD; Yumin Zhao, PhD; Clara E. Dismuke, PhD; Rebekah J. Walker, PhD; Kelly J. Hunt, PhD, MSPH; and R. Neal Axon, MD, MSCR
Assessment of prevalence and specific costs associated with discrete multimorbid mental health disease clusters in adults with diabetes.
ABSTRACT

Objectives: This study assessed the prevalence and specific costs associated with discrete multimorbid mental health disease clusters in adults with diabetes mellitus (DM).

Study Design: Longitudinal analysis of a retrospective cohort.

Methods: We performed a 5-year longitudinal analysis of a retrospective cohort of 733,071 patients with DM from the US Veterans Health Administration (VHA) between 2002 and 2006. The mental health comorbidities (MHCs) examined included depression, substance abuse, and psychosis. Our primary outcomes of interest were total inpatient, outpatient, and pharmacy costs measured in 2012 US$ from the perspective of the VHA.

Results: DM was present with comorbid depression, substance abuse, and psychosis in 12.1%, 3.7%, and 4.2% of patients, respectively. Overall, 13.5% of patients had 1 MHC, 2.5% had 2 MHCs, and 0.5% had all MHCs. Total inpatient ($1,435,651,415), outpatient ($366,137,435), and pharmacy ($90,064,725) costs were highest for patients with DM and comorbid depression alone. At the per-patient level, DM plus psychosis and substance abuse had the highest inpatient costs ($35,518), DM plus all MHCs had the highest outpatient costs ($6962), and DM plus depression and psychosis had the highest pharmacy costs ($1753).

Conclusions: DM with comorbid depression is the most prevalent MHC combination and is associated with the highest total VHA healthcare costs. However, other comorbidity clusters are associated with higher mean per patient costs, and may therefore benefit from more intensive intervention. Analysis of healthcare expenditures by multimorbid disease clusters can be a useful tool for healthcare policy planning.

Am J Manag Care. 2015;21(8):535-544
Take-Away Points

This study assessed the prevalence and specific costs associated with discrete mul-timorbid mental health disease clusters in adults with diabetes mellitus using data from the Veterans Health Administration.
  • Depression, substance use, and psychosis were highly prevalent in this population.
  • Diabetes mellitus with comorbid depression was the most prevalent comorbid mental health condition and was associated with the highest total healthcare cost. However, other comorbidity clusters were also associated with higher mean per patient costs.
  • Analysis of healthcare expenditures by multimorbid disease clusters may be a useful tool for healthcare policy planning.
Diabetes mellitus (DM) is a global health problem that affected 285 million adults, or 6.4% of the worldwide adult population in 2010.1 By 2030, DM prevalence is projected to increase by 69%, creating additional health burdens in both developing and developed countries.1

The United States has been similarly affected by epidemic DM spread, with 25.8 million people affected in 2012 and a projected 29 million to be affected by 2050.2 DM is a costly disease, with new estimates indicating that the total 2012 DM-related expenditures in the United States were $245 billion3— a 41% increase over estimates from 2007—and costs are expected to continue to rise.3,4 The largest component of national DM-related expenditures is attributed to inpatient costs, with additional costs related to medication and physician office visits.3 Currently, more than 1 in 10 US healthcare dollars are spent directly on DM and its complications.5

Multimorbidity, defined as the coexistence of 2 or more chronic diseases in a given patient, has been found to be highly prevalent among primary care populations in recent years, and prevalence appears to be increasing as the world-wide population ages.6,7 A recent cross-sectional study of 1.7 million Scottish primary care patients found that 42% of subjects had 2 or more of 40 chronic conditions.8 Similarly, 35% of veterans in the US Veterans Health Administration (VHA) were reported to have 3 or more of 29 chronic conditions.9 Research indicates that certain common multimorbid disease clusters involve both somatic and psychiatric illnesses and are associated with increased healthcare utilization, increased costs, and worse health outcomes.8,10 Among these, DM is the most commonly cited chronic medical illness.6

Compared with the general population, rates of major depression are 60% higher and rates of generalized anxiety disorder are 123% higher among patients with DM.11 Individuals with DM are also more likely to have schizophrenia and bipolar disorder than those without DM.12,13 Poor disease control in either DM or psychiatric disorders has beenshown to complicate and aggravate the other.14 The addition of comorbid mental health disorders to DM has been associated with poorer glycemic control, lower medication adherence, higher rates of diabetic complications, higher levels of disability, and diminished quality of life.12,15 Proposed mechanisms for these effects include altered physiological pathways and abnormal behavioral pathways that result in decreased self-care.14

Relatively few studies have examined the impact of different clusters of mental health comorbidities (MHCs) on healthcare utilization and costs among patients with DM. Most completed studies to date have focused solely on depression, have been limited by cross-sectional design, have had a relatively small sample size, or have used costing methodologies that failed to analyze costs in relevant categories.12,16-19 Most studies only analyzed total costs, or analyzed cost categories in separate models. The purpose of this study was to evaluate the impact of multiple combinations of psychiatric comorbidity on healthcare utilization and cost in adults with DM using a novel methodology that allows estimation of 3 categories of cost simultaneously.

We examined a national cohort of US veterans with DM cared for in the VHA—an integrated national healthcare system in the United States—over a 5-year period. Veterans have a high prevalence of chronic medical conditions as well as MHCs,20 but rates of chronic conditions have increased over time in the general US population as well.21 Importantly, few data sources that are not from the VHA system have comprehensive information on inpatient, outpatient, and pharmacy costs across age ranges and insurance carriers.

METHODS

Data Source and Sample

The study involves 733,071 veterans from a national cohort of patients with DM cared for in the VHA from 2002 to 2006. The cohort was created by linking patient and administrative files from the VHA National Patient Care (NPC) and Pharmacy Benefits Management (PBM) databases. Veterans in the DM cohort were included in the study if they had: 1) DM defined by 2 or more International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for DM (250, 357.2, 362.0, and 366.41) in the previous 24 months (in 2000 and 2001); 2) ICD-9-CM codes for DM from inpatient stays and/or outpatient visits on separate days (excluding codes from lab tests and other nonclinician visits) in 2002; and 3) prescriptions for insulin or oral hypoglycemic agents in 2002 based on a previously validated algorithm.22 Veterans identified as having DM by ICD-9-CM codes were excluded from the cohort if they did not have prescriptions for DM medications in 2002 (see Egede et al [2012]).23 The DM cohort included 740,195 veterans who were followed through December 2006 or until death or loss to follow-up. Data sets were linked using scrambled patient Social Security numbers, and annual patient-specific Veterans Administration (VA) healthcare costs were calculated through VA Decision Support System (DSS) data, which extract costs from the VA payroll and general ledger. Costs were then classified into inpatient, outpatient (including emergency department [ED]), and pharmacy categories. We excluded less than 1% of the patients (n = 7124) from the DM cohort because of missing data on all 3 cost outcomes, resulting in n = 733,071 subjects. Our statistical models account for patients with missing cost data, assuming they are missing at random. The study was approved by our Institutional Review Board and local VA Research and Development Committee.

Study Variables

Outcome variables. The primary outcome variables were 3 cost types (pharmacy, inpatient, and outpatient) measured in 2006 US$, and the perspective was that of the fed-eral payer. DSS cost data were applied to encounter codes (diagnosis or procedure) from the VA. Prescription drug costs were identified from the PBM system and summed from the price per dispensed unit. Costs for each study year were represented in person-years to account for censoring.

Covariates. The primary covariate was the number of MHCs modeled as continuous and categorical (0-1, 2, 3+). MHCs of interest included ICD-9-CM diagnosis of: substance abuse (292.0, 292.82-292.89, 292.9, 304.x, 305.2-305.9, 648.3); depression (300.4, 301.12, 309.0, 309.1, 311); or psychosis (295.x-298.x, 299.1), as defined by Elixhauser’s Agency for Healthcare Research and Quality ICD-9-CM coding algorithm, reported by Quan et al.24 It was also modeled as MHC types (ie, none, depression, psychosis, substance abuse, and their 2-way and 3-way interactions). Other covariates included in the full model were age, gender, marital status, race/ethnicity, residence, region, and comorbidities. Marital status was defined as single or mar-ried. Race/ethnicity was classified as non-Hispanic white (NHW), non-Hispanic black (NHB), Hispanic, and other/unknown/missing. Location of residence was defined as urban and rural/highly rural, and hospital region was defined by the 5 geographic regions of the country based on VHA Veterans Integrated Service Networks (VISNs): northeast (VISNs 1, 2, 3), mid-Atlantic (VISNs 4, 5, 6, 9, 10), south (VISNs 7, 8, 16, 17), midwest (VISNs 11, 12, 15, 19, 23), and west (VISNs 18, 20, 21, 22).25

Comorbidity variables included anemia, cancer, cerebrovascular disease, congestive heart failure, cardiovascular disease, fluid and electrolyte disorders, hypertension, hypothyroidism, liver disease, lung disease, obesity, peripheral vascular disease, and other diseases (ie, AIDS, rheumatoid arthritis, renal failure, peptic ulcer disease and bleeding, and weight loss), and were defined based on ICD-9-CM codes at entry into the cohort based on previously validated algorithms.24

Statistical Analyses

Descriptive measures including mean and median costs were computed for each cost type for each MHC group. Preliminary analysis included plotting the unadjusted mean costs in each cost type (inpatient, outpatient, phar-macy) over time by MHC, with time on the x-axis and unadjusted mean cost on the y-axis. The plots helped to examine trends over time in each source of cost by MHC before adjusting for any other covariates. To model the relationship between the 3 cost categories and covariates, a joint model based on a multivariate generalized linear mixed model (mGLMM) approach with shared random intercept was used (see Gebregziabher et al).26

Since the response variable is a vector of 3 correlated cost outcomes, mGLMM with a random intercept and slope was implemented in SAS Proc GLIMMIX (SAS In-stitute, Cary, North Carolina). To account for the skewness in the observed cost data, a log-normal distribution with an identity link was used. Therefore, the exponent of the parameter estimates can be interpreted as the percent change in each type of cost as a function of unit change in the covariates. Comparison with analysis results based on fitting separate models for each outcome was also performed. In the joint models, the random intercept shared by the 3 cost outcomes captures the association in the natural heterogeneity among the individual subjects’ baseline inpatient, outpatient, and pharmacy costs, while the random slope captures the correlation in the trajectory over time of cost outcomes. Goodness-of-fit of models was assessed using pseudo-AIC-type statistics and using residual plots.

Costs to the VHA of individual and combinations of MHCs were estimated by examining the adjusted 5-year mean least-square cost of each individual MHC and all possible combinations of MHCs by the number of veterans in every MHC category, and multiplying by the number of veterans diagnosed with each individual or combination of MHCs. The estimate of the medical component of the Consumer Price Index (CPI) from the Bureau of Labor Statistics (within the Department of Labor) was used to convert all 2002-2005 costs to 2006 US$ prior to model estimation. The CPI was also used to convert total costs in the VHA from 2006 US$ to December 31, 2012, dollar values using the 1.138856 multiplier. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, North Carolina).

RESULTS

Population Characteristics by Number of Mental Health Comorbidities

The final study sample consisted of 733,071 veterans with a diagnosis of DM during 2002 and followed through 2006 or until death or loss to follow-up (Table 1). The prevalence of depression, substance abuse, and psychosis were 12.1%, 3.7%, and 4.2%, respectively. Moreover, 13.5% had 1 MHC, 2.5% had 2 MHCs, and 0.5% had all 3 MHCs, while 83.5% had 0 MHCs. Across the 5 years studied, the mean number of mental health visits per year ranged from 26.6 in those with 3 MHCs to 0.9 in those without a MHC. Those who had at least 1 MHC were younger, more likely to be female, less likely to be married, and less likely to have a rural residence than those without a MHC.

Longitudinal Cost Associations With Mental Health Comorbidity Status

 
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