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The American Journal of Managed Care May 2014
Survival and Cost-Effectiveness of Hospice Care for Metastatic Melanoma Patients
Jinhai Huo, PhD, MD, MPH; David R. Lairson, PhD; Xianglin L. Du, MD, PhD; Wenyaw Chan, PhD; Thomas A. Buchholz, MD; and B. Ashleigh Guadagnolo, MD, MPH
Treatment of Diabetes Mellitus: The Urgent Need for Multifactorial Interventions
Charles H. Hennekens, MD; Marc A. Pfeffer, MD; John W. Newcomer, MD; Paul S. Jellinger, MD, MACE; and Alan Garber, MD, PhD
Patient-Centered Medical Home Features and Expenditures by Medicare Beneficiaries
Erica L. Stockbridge, MA; Lindsey M. Philpot, PhD, MPH; and José Pagán, PhD
Wait Times, Patient Satisfaction Scores, and the Perception of Care
Clifford Bleustein, MD, MBA; David B. Rothschild, BS; Andrew Valen, MHA; Eduardas Valaitis, PhD; Laura Schweitzer, MS; and Raleigh Jones, MD
Out-of-Pocket Healthcare Expenditure Burdens Among Nonelderly Adults With Hypertension
Didem Minbay Bernard, PhD; Patrik Johansson, MD, MPH; and Zhengyi Fang, MS
The SAFER Guides: Empowering Organizations to Improve the Safety and Effectiveness of Electronic Health Records
Dean F. Sittig, PhD; Joan S. Ash, PhD, MLS, MBA; and Hardeep Singh, MD, MPH
Effect of Management Strategies and Clinical Status on Costs of Care for Advanced HIV
Paul G. Barnett, PhD; Adam Chow, BA; Vilija R. Joyce, MS; Ahmed M. Bayoumi, MD, MSc; Susan C. Griffin, MSc, BSc, PhD; Huiying Sun, PhD; Mark Holodniy, MD; Sheldon T. Brown, MD; D. William Cameron, MD; Mark Sculpher, PhD; Mike Youle, MB, ChB; Aslam H. Anis, PhD; and Douglas K. Owens, MD, MS
Assessment and Potential Determinants of Compliance and Persistence to Antiosteoporosis Therapy in Italy
Manuela Casula, PhD; Alberico Luigi Catapano, PhD; Rossana Piccinelli, PharmD; Enrica Menditto, PhD; Lamberto Manzoli, MD, MPH; Luisa De Fendi, BSc; Valentina Orlando, PharmD; Maria Elena Flacco, MD; Marco Gambera, PharmD; Alessandro Filippi, MD; and Elena Tragni, PhD
Pharmacogenetic-Guided Psychiatric Intervention Associated With Increased Adherence and Cost Savings
Jesen Fagerness, JD; Eileen Fonseca, MS; Gregory P. Hess, MD, MBA, MSc; Rachel Scott, PharmD; Kathryn R. Gardner, MS; Michael Koffler, MBA; Maurizio Fava, MD; Roy H. Perlis, MD, MSc; Francis X. Brennan, PhD; and Jay Lombard, DO
Currently Reading
Healthcare Utilization and Costs in Persons With Insomnia in a Managed Care Population
Louise H. Anderson, PhD; Robin R. Whitebird, PhD, MSW; Jennifer Schultz, PhD; Charlene E. McEvoy, MD, MPH; Mary Jo Kreitzer, PhD, RN; and Cynthia R. Gross, PhD

Healthcare Utilization and Costs in Persons With Insomnia in a Managed Care Population

Louise H. Anderson, PhD; Robin R. Whitebird, PhD, MSW; Jennifer Schultz, PhD; Charlene E. McEvoy, MD, MPH; Mary Jo Kreitzer, PhD, RN; and Cynthia R. Gross, PhD
Patients with an insomnia diagnosis have higher healthcare utilization and costs than a matched control group, both before and after the diagnosis.

To better understand the direct costs of insomnia. Our study aimed to compare healthcare costs and utilization of patients diagnosed with insomnia who received care in a managed care organization with a set of matched controls.


Our observational, retrospective cohort study compared 7647 adults with an insomnia diagnosis with an equally sized matched cohort of health plan members without an insomnia diagnosis between 2003 and 2006. We also compared a subset of patients diagnosed with and treated for insomnia with those diagnosed with insomnia but not treated.


A large Midwestern health plan with more than 600,000 members.


Multivariate analysis was used to estimate the association between insomnia diagnosis and costs, controlling for covariates, in the baseline and follow-up periods. Although we cannot conclude a causal relationship between insomnia and healthcare costs, our analysis found that insomnia diagnosis was associated with 26% higher costs in the baseline and 46% in the 12 months after diagnosis. When comorbidities were recognized, the insomnia cohort had 80% higher costs, on average, than the matched control cohort.


These outcomes suggest the need to look beyond the direct cost of insomnia to how its interaction with comorbid conditions drives healthcare cost and utilization.

Am J Manag Care. 2014;20(5):e157-e165
Increasing costs for treated and untreated insomnia suggest the need to look beyond the cost of insomnia to how its interaction with comorbid conditions drives healthcare cost and utilization: 
  • At baseline prior to diagnosis, insomnia was associated with 26% higher healthcare costs than matched controls.

  • In the 12 months following diagnosis, insomnia was associated with 46% higher costs.

  • Predicted follow-up costs for the insomnia cohort were 80% higher than the controls, reflecting the relatively greater increase in morbidity in the insomnia cohort.

  • Of those diagnosed with insomnia, 75% received pharmaceutical treatment.

  • Those treated for insomnia had higher utilization and costs than those not treated. n Insomnia and its treatment may be indications of more serious underlying conditions.
Chronic insomnia, difficulty falling or staying asleep or experiencing poor-quality sleep, is a growing health problem with significant consequences for both individuals and the healthcare system.1,2 Chronic insomnia is associated with a host of physical, psychosocial, and emotional problems, including premature mortality, depression, anxiety, and poor quality of life.3-7 Chronic insomnia can also exacerbate comorbidities.8,9 In addition to the personal toll of chronic insomnia on the individual, major economic consequences for the healthcare system include increased direct medical costs and healthcare utilization.6,10,11 Research examining the economic consequences associated with chronic insomnia has highlighted the burgeoning indirect societal costs of insomnia, such as workplace absenteeism, lost productivity, and increased workplace errors and accidents.12-16 Less attention has been focused on direct healthcare costs and increased utilization associated with chronic insomnia.

Direct costs to the healthcare system associated with chronic insomnia are difficult to assess due to complexities and gaps in available data.17 Estimates of the aggregate costs of insomnia vary widely depending on the costs considered. 15,18,19 In earlier work, Simon and Von Korff20 interviewed patients in primary care clinics to measure insomnia prevalence, associated functional impairment, lost productivity, and comorbidities. They found that 10% of patients reported insomnia, which was associated with functional impairment, disability, and increased use of health services. A more recent study estimated the direct and indirect costs of untreated insomnia and found that direct and indirect costs for adults younger than 65 years with insomnia were $1253 greater than for subjects without insomnia for the 6-month study period.10

The most common treatment for insomnia is pharmacotherapy, with 2.5% of Americans taking prescription drugs to treat insomnia each year, and about 1 in 4 of them continuing treatment for 4 months or longer.21-24 Commonly prescribed US Food and Drug Administration (FDA)–approved hypnotics include benzodiazepines such as temazepam and triazolam, and drugs that act as agonists to benzodiazepine receptors such as zolpidem.25 These agents are efficacious in the short-term management of insomnia, but adverse effects include residual daytime sedation, cognitive impairment, motor incoordination, dependence, and rebound insomnia.26 A variety of cognitive and behavioral therapy (CBT) programs are as efficacious as approved sleep medications in the short term, side effects are nil, and benefits are more durable.27,28 There is, however, limited access to these programs due to a shortage of trained professionals.26,29-31 A 2002 national survey estimated that 2.2% of Americans use complementary or alternative therapies for insomnia.32,33 However, herbals, dietary supplements, over the counter medications such as diphenhydramine, and alcoholic beverages are not recommended for insomnia treatment due to lack of efficacy data, potential for adverse effects, lack of standardization, or a combination of these concerns.26

The purpose of this study was to estimate the healthcare costs associated with insomnia diagnosis by comparing costs and utilization of patients who have been diagnosed with insomnia to a set of matched controls. We compared costs in the baseline period before an insomnia diagnosis, in the 12-month follow-up period postdiagnosis, and the change in cost from baseline to follow-up. We also compared a subset of patients who received a diagnosis of insomnia and associated treatment with patients who received a diagnosis of insomnia but no treatment. This research will contribute to the scarce literature currently available on the direct costs and health services used by patients with chronic insomnia.


Study Population

We conducted a retrospective observational study using data from a large Midwestern health plan with more than 600,000 members. A study cohort of 7647 adults with an insomnia diagnosis during 2003 to 2006 was identified and compared with an equally sized matched cohort of health plan members without an insomnia diagnosis. All health plan members were eligible for the study if they met the following criteria: (1) at least 18 months of continuous enrollment from January 1, 2003, to December 31, 2006; (2) aged 18 years or older; (3) continuous pharmacy coverage; and (4) Medicaid or commercial insurance coverage.

Members were considered to have insomnia if they had a qualifying insomnia diagnosis code from January 1, 2004, to December 31, 2005, preceded by a 6-month period free of an insomnia diagnosis (baseline period) and if they remained in the plan for at least 12 months after the diagnosis (follow-up period). Qualifying insomnia cohort International Classification of Diseases, Ninth Revision codes were: 307.41 (transient disorder of initiating or maintaining sleep), 307.42 (persistent disorder of initiating or maintaining sleep), and 780.52 (insomnia unspecified). This research was reviewed and approved by the local Institutional Review Board.

Propensity Score Matching

A propensity score–matching method was used to create a matched control group that had similar covariate values as the insomnia cohort and therefore reduce confounding by covariate differences.34 First, a 10:1 age- and gender-matched sample of members without an insomnia diagnosis was created. Members were then matched with controls during the 6-month baseline period and 12-month follow-up period. Second, a logistic model was used to produce propensity scores for all members with an insomnia diagnosis and the 10:1 matched cohort. The dependent variable in the model was an indicator of a qualifying insomnia diagnosis, and the predictor variables were age, sex, insurance type, and specific individual baseline physical comorbidities (diabetes, cardiovascular disease, chronic obstructive pulmonary disorder, chronic heart failure, myocardial infarction, peripheral vascular disease, connective tissue disorder, ulcer disease, mild liver disease, moderate to severe renal disease, acquired immunodeficiency disorder, tumor, hemiplegia, leukemia or lymphoma, metastatic solid tumor) and specific mental health comorbidities (alcohol use disorder, opioid and substance use disorder, major depression, anxiety, bipolar disorder, posttraumatic stress disorder, dementia, schizophrenia, organic mental disorders, other psychotic disorders, affective disorders, personality disorders, or adjustment disorders). Finally, propensity scores from the model were used to identify a 1:1 cohort, matched on propensity score. This 1:1 propensity score–matched cohort was the comparison group to evaluate differences in healthcare costs and utilization. Multivariate regression methods were used to adjust for differences that remained between the cohorts after matching.

To compare members with an insomnia diagnosis with and without pharmacological treatment, pharmacological treatment was defined as a prescription fill for benzodiazepines or nonbenzodiazepines, categories of medications that have been approved by the FDA for the treatment of insomnia,27,35 or prescriptions for medications frequently used off label for treatment of insomnia, including: trazodone, ziprasidone, amitriptyline, mirtazapine (with no depression diagnosis), doxepin, quetiapine fumarate (Seroquel), and diphenhydramine (Benadryl).36

Healthcare Utilization and Cost Data

Healthcare cost and utilization data were obtained from the health plan’s administrative databases. They included type of insurance coverage, diagnostic codes for insomnia and other chronic conditions, plan reimbursed amounts, and member-paid amounts. These data were used to identify major medical comorbidities and total direct medical costs, including inpatient, outpatient, pharmacy, urgent care and emergency department services, for all members. Cost was defined as plan reimbursed amounts plus member-paid.


Multivariate analysis was used to estimate the association between study cohort and costs, controlling for covariates. The outcome was total healthcare costs, which captures the effect of insomnia on the use of healthcare services for all reasons. Estimates of the percentage of cost associated with an insomnia diagnosis were made at 3 times: the baseline period, the follow-up period, and the change from baseline to follow-up. For the baseline and follow-up analyses, a 2-part Heckman estimator was used because of the number of members in the control cohort that had no healthcare costs. First, the association between cohort and positive cost was determined by logistic regression. Second, the association between cohort and costs was estimated for those with positive costs. Because the outcomes were heavily skewed, a log transformation with Duan’s smearing estimator was used.37 For the analysis of change from baseline to follow-up, multivariate linear regression analysis was used and the dependent variable was untransformed dollars.

The covariates were age, sex, insurance type, Charlson Comorbidity Index (CCI) score, psychiatric medication use, and mental health diagnoses. We included interaction and polynomial terms that improved model performance. Comorbidities were identified using clinical classification software maintained by the Agency for Healthcare Research and Quality or defined by the health plan’s algorithms, which were summarized as a CCI score.38,39 The CCI includes 19 medical conditions, each with a weight of 1, 2, 3, or 6. The CCI score is the sum of the weights for the conditions identified as comorbidities. Total scores can range from 0 (no conditions) to 37 (all 19 conditions).40

The parameter for insomnia diagnosis in the regression models for baseline and follow-up costs was used to estimate the percentage of costs that were associated with insomnia. To estimate the impact of insomnia and comorbidities on total costs, we used regression results to estimate predicted costs for each cohort in the follow-up period, with covariate values set at the mean value for each cohort.


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