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General Hospital Psychiatry 33 (2011) 238–245

Multimorbidity and depression treatmentAmi Vyas, B.Pharm., M.B.A.a,⁎, Usha Sambamoorthi, Ph.D.a,b

aDepartment of Pharmaceutical Systems & Policy, School of Pharmacy, West Virginia University, Morgantown, WV 26506-9510, USAbDepartment of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, GA, USA

Received 25 October 2010; accepted 18 February 2011

Abstract

Objectives: We compare treatment for depression among individuals with multiple chronic physical conditions to those with single chronicphysical condition, after controlling for demographic, socioeconomic, access to care and the number of outpatient visits.Methods: Using a cross-sectional study design, we analyzed data on 1,376 adults age above 21 years, with depression and at least onechronic physical condition in the following clusters: cardiometabolic (diabetes or heart disease or hypertension), respiratory (chronicobstructive pulmonary disease or asthma) and musculoskeletal (arthritis or osteoporosis) from the 2007 Medical Expenditure Panel Surveyfor depression treatment.Results: Overall, 56.2% used antidepressants, 21.4% had psychotherapy and 22.5% reported no depression treatment. After adjusting forfactors, there were no statistically significant differences in the likelihood of type of depression treatment.Conclusion: Individuals with multiple conditions are as likely as those with single condition to report treatment for depression perhaps dueto increased contact with the health care system. Our findings suggest that competing demands due to multiple chronic conditions may notaffect depression treatment.© 2011 Elsevier Inc. All rights reserved.

Keywords: Multimorbidity; Depression; Antidepressants; Psychotherapy; Medical Expenditure Panel Survey

1. Introduction

Multimorbidity defined as the concurrent presence of twoor more chronic conditions in an individual is common [1–3].Individuals with multimorbidity face a number of challenges[4] and a multitude of negative health consequences. A re-view of studies published between 1990 and 2003 concludedthat there was an inverse relationship between number ofmedical conditions and quality of life measured by instru-ments such as short-form health surveys—example SF-36[5]. Studies from Sweden reported that the number of diag-nosed conditions was associated with the increased numberof inpatient admissions [6], and the risk of functional declineincreased with the number of medical conditions [7]. Indi-viduals with multimorbidity have an increased risk for mor-tality [8]. In addition, health care expenditures increase withan increase in the number of chronic conditions, and nearly

⁎ Corresponding author. Tel.: +1 304 293 8194; fax: +1 304 293 2529.E-mail address: avyas@hsc.wvu.edu (A. Vyas).

0163-8343/$ – see front matter © 2011 Elsevier Inc. All rights reserved.doi:10.1016/j.genhosppsych.2011.02.009

80% of Medicare expenditures are for individuals with fouror more chronic conditions [9]. The annual Medicare pay-ment amounts for Medicare beneficiaries with two conditionswere twice, and those with three or more conditions, it was4.5 times compared with those with only one condition [3].

One of the emerging areas of multimorbidity is thepresence of depression. In fact, the high prevalence ofdepression and its negative impact on health care have beendocumented in many chronic conditions such as cardiometa-bolic [heart disease [10], hypertension [11], diabetes mellitus[12,13]), respiratory (chronic obstructive pulmonary disease(COPD) [14], asthma [15]] and musculoskeletal diseases(rheumatoid arthritis [16], osteoporosis [17]). However, inmany of these studies, the primary interest has been on theprevalence of depression within a single chronic physicalcondition. Indeed, many studies on multimorbidity excludepsychiatric conditions [5]. It is important to note that a studyfrom Canada reported a relationship between psychologicalstress and multimorbidity [18]. In another study using dataon veterans in the United States and cluster analysis, it wasfound that over half of the participants in each cluster of

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chronic physical conditions had serious depression andanxiety [19].

Given that depression is highly prevalent in individualswith multimorbidity, it is important to analyze depressioncare among individuals with multimorbidity. Clinical trialshave shown that in individuals with diabetes or heart dis-ease, depression treatment provides relief from depression inthese individuals [20,21]. In a recent study, collaborativecare for depression is shown to be effective in individualswith poorly controlled diabetes and/or coronary heart disease[22]. While this study has shown that collaborative care iseffective in individuals with diabetes and/or coronary heartdisease, studies on the rates and correlates of depressiontreatment in individuals with multimorbidity are scarce. It ispossible that depression treatment may be neglected inindividuals with multimorbidity due to competing demands[23,24]. In these individuals, treatment for depression withantidepressants may be less preferred due to drug–drug in-teractions [25] and drug–disease interactions [26,27]. Cog-nitive behavioral psychotherapy may be a favorablealternative. It is also possible that individuals with multi-morbidity may have more frequent contact with medicalproviders that may lead to higher likelihood of depressiontreatment. There is some evidence that multimorbidity maybe associated with better quality of care in other areas [28].Another study [29] also reported that individuals with morechronic medical comorbidities are treated for depression withgreater intensity. With regard to depression treatment, thelikelihood of treatment may depend on the type of chroniccondition. In one study, which compared depression treat-ment in four chronic conditions (diabetes, hypertension, heartdisease and arthritis), it was found that individuals withhypertension were more likely to receive any depressiontreatment compared with those without hypertension. Nostatistically significant differences in depression treatmentwere found for heart disease, diabetes, and arthritis [30].

All these studies suggest that multimorbidity may affectboth, any treatment for depression and type of treatment.Therefore, the primary objective of the current study is tocompare the rates and types of depression treatment amongindividuals with multiple chronic physical conditions ver-sus those with single chronic physical condition. We alsoexamine the relationship between multimorbidity and de-pression treatment within in a multivariate framework aftercontrolling for demographic, socioeconomic, access to care,health status and the number of visits to either office-basedprovider or outpatient hospital clinics.

2. Methods

2.1. Study design

For the purpose of the present study, we used a cross-sectional study design using data from the Medical Expen-diture Panel Survey (MEPS), nationally representativeestimates of health care use, expenditures, source of payment

and health insurance coverage for the US noninstitutional-ized civilian population, which is sponsored by Agency forHealthcare Research & Quality [31]. The MEPS HouseholdComponent also provides estimates of participants' healthstatus, demographic and socioeconomic factors, employ-ment, access to care and satisfaction with health care [31].The panel design of the survey includes five rounds ofinterviews in two full calendar years. Each year a new panelof sample households is selected [31]. In the MedicalProvider Component of MEPS, data are collected from thesample of medical providers on dates of visit, diagnosis andprocedure codes, charges and payments. This component istypically used to supplement and/or replace the data onhousehold reported expenditure [31].

2.2. Data

Data for the study were drawn from multiple files fromthe 2007 MEPS: household, medical conditions, prescribedmedicines, outpatient visits and office-based medical pro-vider visits files to derive multimorbidity, depression treat-ment and other independent variables.

2.3. Study sample

Our study sample consisted of living adults over 21 yearsof age as of the end of year 2007, reported having depressionand had at least one chronic physical condition in thefollowing clusters: cardiometabolic consisting of diabetesor heart disease or hypertension, respiratory consisting ofCOPD or asthma and musculoskeletal consisting of osteo-arthritis or rheumatoid arthritis or osteoporosis. We alsoexcluded very few individuals who were uninsured in the agegroup 65 years and above to ensure the consistency ofnationally representative sample as most Americans over65 years old are enrolled in Medicare. At the final stage, oursample consisted of 1,376 adults with depression in one ofthe chronic condition clusters. These individuals representedapproximately 14 million individuals of the US population.

2.4. Measures

2.4.1. Dependent variableDepression treatment: Antidepressants. We used MEPS

2007 prescribed medicines file to identify individuals whoused antidepressant medications. Therapeutic class and sub-class codes were used to identify antidepressants. MEPS-prescribed medicine files contain information on therapeuticclasses through linkage of Multum Lexicon database (http://www.meps.ahrq.gov/mepsweb/data_files/publications/st163/stat163.pdf). Therapeutic class code of TC1S1 andsubclass code of 249 represented antidepressants (http://www.multum.com/Lexicon.htm). Antidepressant use is de-fined as one or more purchases of a prescribed antidepressantduring year 2007.

Depression treatment: Psychotherapy. We derived psy-chotherapy visits from the outpatient visits and office-basedmedical provider visits files. These files contain information

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on visit details such as treatments and procedures obtainedduring the visit. Among other therapies received during thevisit, psychotherapy/counseling was also indicated (http://www.meps.ahrq.gov/mepsweb/data_stats/download_data/pufs/h110g/h110gdoc.pdf). We considered individuals withat least one visit for psychotherapy/counseling treatment asreceiving psychotherapy for depression. Such procedureshave been used to identify psychotherapy in prior researchusing MEPS data [30].

We combined antidepressant and psychotherapy use toclassify depression treatment variable into four categories:(1) no depression treatment, (2) antidepressants only, (3)psychotherapy only and (4) psychotherapy with antidepres-sants. Because very few individuals had only psychotherapy,we combined psychotherapy with or without antidepres-sants into one group yielding three categories for depres-sion treatment.

2.4.2. Key independent variable: MultimorbidityWe selected the following seven chronic conditions from

medical conditions file due to their high prevalence, cost,morbidity and mortality (http://meps.ahrq.org/mepsweb/data_files/publications/st167/stat167.shtml): diabetes, heartdisease, hypertension, COPD, asthma, arthritis and osteopo-rosis [32]. Prior research has reported that combining dis-eases based on specific organ domains may be one way ofclustering chronic diseases and a meaningful summarizationof multimorbidity [33]. Therefore, out of these seven chronicconditions, we defined three very common clinicallymeaningful disease clusters, which are (1) cardiometabolicconditions (diabetes or heart disease or hypertension), (2)respiratory conditions (asthma or COPD), and (3) musculo-skeletal conditions (arthritis or osteoporosis). Clustering alsofollows our prior research that has grouped conditions basedon synergism in treatment plans and self-managementstrategies [34].

These clusters were grouped to yield two mutuallyexclusive categories of multimorbidity as (1) single clusterof conditions consisting of cardiometabolic or respiratory ormusculoskeletal and (2) multiple cluster of conditionsconsisting of cardiometabolic and respiratory conditions orcardiometabolic and musculoskeletal conditions or respira-tory and musculoskeletal conditions or all three clusters.From here on, the term single condition represents indi-viduals having only conditions that are in each cluster, andmultiple conditions represent individuals having multiplechronic conditions from different clusters.

It has to be noted that in MEPS, the conditions reportedby the respondent were recorded by the interviewer asverbatim text, which was then coded by professional codersto fully specified International Classification of Diseases(ICD)-9-CM codes. In addition MEPS, these ICD-9 codeswere aggregated into clinically meaningful categories thatgroup similar conditions using the Clinical ClassificationSystem software (http://meps.ahrq.org/mepsweb/data_files/publications/st167/stat167.shtml). We identified chronic

conditions listed above, using a combination of ICD-9 andcondition codes.

2.4.3. Other independent variablesDemographic variables consisted of gender (women,

men), race/ethnicity (white, African American, Latino andother) age (22-39, 40-49, 50-64, 65 years and +) and maritalstatus (married, widowed, separated/divorced and nevermarried). Socioeconomic characteristics were measured byeducation (less than high school, high school and above highschool), area of residence (metro and nonmetro), employ-ment and poverty status. Access to care was measured usingthe variables for health insurance coverage and usual sourceof health care. Health status was assessed with variablesindicating perceived physical and mental health status(excellent/very good, good, fair/poor). In addition, we alsocontrolled for the total number of visits to either office-basedprovider or outpatient hospital clinics as a proxy for contactwith health care system.

2.4.4. Statistical techniquesχ2 statistics were used to examine the significant dif-

ferences between the depression treatment categories acrossall the independent variables. We analyzed treatment versusno treatment for depression using logistic regressions andtype of treatment among those who reported any treatment.We also performed multinomial logistic regressions toanalyze the relationship between multimorbidity and typeof depression treatment, after controlling for demographic,socioeconomic, health status and number of visits to eitheroffice-based provider or outpatient hospital clinics. Weentered variables in sequential blocks. Model 1 includedonly “single versus multiple conditions” as an independentvariable. Therefore, this could be considered as anunadjusted model. In all other models, in addition tomultimorbidity variable, we entered other independentvariables in blocks. Model 2 added demographic character-istics (gender, race/ethnicity, age, metro status, maritalstatus) and socioeconomic characteristics (education, em-ployment, poverty status). Model 3 added access to carefactors (health insurance and usual source of care) in additionto the variables specified in Model 2. Model 4 includedhealth status (perceived physical and mental health) and totalnumber of visits in addition to the variables specified inModel 3. In all these regressions, “no depression treatment”was used as the reference group for the dependent variable.From multinomial logistic regression, the parameter esti-mates were transformed to odds ratios, and their corre-sponding 95% confidence intervals were examined. Wediscuss findings that were significant, with P values less than.05 levels. All analyses used the strata and weights providedin the MEPS data to control for clustering and unequalprobability design. For the present study, all analyses wereconducted in survey procedures using SAS 9.2 to appropri-ately handle study weights and clustering.

able 1escription of study sample with depression and chronic illnesses, Medicalxpenditure Panel Survey, 2007

N Wt. n Wt.%

ll 1,376 14,269,129 100.0ultimorbiditySingle condition 738 7,903,933 55.4Multiple conditions 638 6,365,196 44.6enderWomen 951 9,408,373 65.9Men 425 4,860,756 34.1ace/EthnicityWhite 927 11,363,606 79.6African American 176 1,127,563 7.9Latino 213 1,236,309 8.7Other 60 541,650 3.8ge (years)22–39 208 2,193,397 15.440–49 266 2,555,447 17.950–64 551 5,544,850 38.9≥65 351 3,975,435 27.9arital statusMarried 669 7,162,386 50.2Widowed 175 1,775,781 12.4Divorced/separated 364 3,480,044 24.4Never married 168 1,850,919 13.0etroMetro 1,102 11,490,725 80.5Nonmetro 274 2,778,404 19.5ducationLT HS 375 2,782,650 19.5HS 437 4,719,649 33.2Above HS 560 6,734,860 47.3overty statusPoor 312 2,267,221 15.9Near poor 318 3,077,651 21.6Middle income 369 3,925,561 27.5High income 377 4,998,696 35.0ealth insurancePrivate 716 8,689,636 60.9Public 514 4,259,851 29.9Uninsured 146 1,319,642 9.2sual source of careYes 1,234 12,887,782 90.9No 135 1,293,194 9.1erceived healthExcellent/very good 314 3,620,402 25.4Good 461 5,039,389 35.3Fair/poor 601 5,609,337 39.3ental healthExcellent/very good 393 4,394,301 30.8Good 525 5,715,668 40.1Fair/Poor 458 4,159,159 29.1

ote: Based on 1,376 adults alive at the end of 2007, reported havingepression and at least one chronic condition in the cardiometabolic,spiratory or musculoskeletal clusters. All analyses accounted for complexrvey design of the Medical Expenditure Panel Survey. Wt, weighted; HS,igh school; LT, less than.

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2.5. Findings

We describe our study sample of 1,376 adults above 21years of age, alive, with depression and at least one of thecardiometabolic, respiratory and musculoskeletal conditionsin year 2007 in Table 1. Forty-five percent of the studysample with depression had at least two conditions, while55% with depression had single condition only. Womencomprised 66% of our study sample. Our study sample wasoverwhelmingly white (80%). Thirty-nine percent of ourstudy sample was between 50 and 64 years of age. Amongthose with chronic conditions and depression, 9.2% did nothave any health insurance throughout the year.

Group differences in depression treatment by multi-morbidity and demographic, socioeconomic, access to careand health status are shown in Table 2. Among those withdepression and at least one condition in 2007, 56.2% wereusing antidepressants only, 21.4% had psychotherapy withor without antidepressants and 22.5% had no depressiontreatment. All the subgroups were significant in χ2 analysis,at the .01% level, with the exception of perceived healthstatus. With regard to multimorbidity, higher proportion ofsample in both the conditions groups reported use ofantidepressants. Without any adjustment for other indepen-dent variables (data not shown in tabular form), comparedwith individuals with single condition, individuals withmultiple conditions were more likely to be on antidepressanttreatment as well as psychotherapy with or without anti-depressants. The odds ratios were 1.69 (95% CI, 1.28–2.22)for antidepressants only and 1.38 (95% CI, 1.02–1.87) forpsychotherapy with or without antidepressants. In the secondstage, when we controlled for demographic and socioeco-nomic factors, the adjusted odds ratios remained at 1.38 forantidepressants and 1.55 for psychotherapy with and withoutantidepressants. When access to care in terms of healthinsurance and usual source of care were entered in the model,the adjusted odds ratio for antidepressants only was nolonger statistically significant. However, psychotherapy withor without antidepressants was more likely among those withmultiple conditions. The adjusted odds ratio for psycho-therapy in this model was 1.47 (95% CI, 1.03–2.08). In thefully adjusted model including the total number of outpatientvisits (Table 3), we found that compared with individualshaving single conditions, those with multiple conditionswere more likely to report antidepressants only and psycho-therapy with or without antidepressant than no treatment fordepression. However, the adjusted odds ratios were not sta-tistically significant suggesting that there were no significantdifferences for depression treatment between the individualswith single and multiple clusters.

Among other independent variables, women were morelikely to report antidepressants compared with men. Theadjusted odds ratio was 1.68 (95% CI, 1.19–2.36). However,there were no significant differences between men andwomen in psychotherapy visits with or without antidepres-sants. Similarly, older individuals in the age group 65+ years

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were more likely to be on antidepressants than youngerindividuals in the age group 22–39 years. We also foundthat African Americans (adjusted odds ratio, 0.33; 95% CI,0.22–0.52) were less likely to be on antidepressants only aswell as psychotherapy with or without antidepressants.

Table 2Description of study sample by depression treatment, Medical ExpenditurePanel Survey, 2007

No Tx AD only Psyc Tx Significance

n Wt.% n Wt. % n Wt. %

All 340 22.5 738 56.2 298 21.4Multimorbidity ***Single condition 212 26.1 366 52.3 160 21.7Multiple conditions 128 18.0 372 61.1 138 21.0

Gender **Women 213 19.5 532 59.2 206 21.3Men 127 28.1 206 50.3 92 21.5

Race/ethnicity ***White 174 18.1 555 61.1 198 20.8African American 56 35.0 75 37.9 45 27.2Latino 83 39.5 87 38.1 43 22.3Other 27 48.8 21 32.9 12 18.3

Age (years) ***22–39 73 33.9 72 33.9 63 32.340–49 79 28.8 124 47.7 63 23.550–64 119 18.4 312 59.9 120 21.7≥65 69 17.7 230 68.8 52 13.5

Marital status ***Married 146 19.3 401 63.4 122 17.3Widowed 50 23.4 97 61.8 28 14.7Divorced/separated 90 24.6 188 52.1 86 23.2Never married 54 29.6 52 30.4 62 39.9

Metro **Metro 282 23.3 564 53.5 256 23.2Nonmetro 58 19.2 174 67.2 42 13.6

Education ***LT HS 114 27.8 189 54.0 72 18.2HS 114 23.9 246 60.0 77 16.0Above HS 112 19.3 299 54.2 149 26.5

Poverty status **Poor 91 28.3 138 44.9 83 26.8Near poor 85 23.3 163 56.6 70 20.0Middle income 96 25.2 207 56.5 66 18.3High income 68 17.1 230 60.8 79 22.1

Health insurance ***Private 149 19.8 419 59.4 148 20.7Public 122 21.1 260 53.4 132 25.5Uninsured 69 44.1 59 43.7 18 12.1

Usual source of care ***Yes 272 20.1 692 58.3 270 21.6No 65 43.8 43 36.8 27 19.3

Perceived healthExcellent/very good 79 21.6 169 56.9 66 21.5Good 111 21.7 251 55.9 99 22.4Fair/poor 150 23.7 318 56.0 133 20.3

Mental health ***Excellent/very good 93 20.7 237 63.7 63 15.6Good 147 26.3 280 55.1 98 18.6Fair/poor 100 19.1 221 49.7 137 31.3

Note: Based on 1,376 adults alive at the end of 2007, reported havingdepression and at least one chronic condition in the cardiometabolic,respiratory or musculoskeletal clusters. All analyses accounted for complexsurvey design of the Medical Expenditure Panel Survey. Wt, Weighted. HS,high school; Tx, treatment; AD, antidepressants; Psyc Tx, psychotherapywith or without antidepressants; LT, less than.Asterisks represent statistically significant group differences based on χ2

tests: ***Pb.001; **001bPb.01 ; *.01bPb.05.

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Similar findings were noted for Latinos and other minorityracial groups.

Among access to care variables, not having healthinsurance was associated with lower likelihood of depressiontreatment. The adjusted odds ratios were 0.49 (95% CI,0.31–0.78) for antidepressants only and 0.35 (95% CI, 0.20–0.62) for psychotherapy with or without antidepressants. Thetotal number of outpatient visits was associated with in-creased likelihood of both antidepressants only and psycho-therapy with or without antidepressants.

3. Discussion

Our study compared the treatment rates and type ofdepression treatment in individuals with chronic physicalcondition in the following clusters: cardiometabolic, respi-ratory, and musculoskeletal conditions. Although thereexists studies in terms of comparing depression treatmentpatterns among the elderly with and without a specificchronic condition (i.e., with and without diabetes, with andwithout hypertension and so forth) [35] and comparing acollaborative model of depression care to care as usualdefined as use of any primary care or specialty mental healthservices available in usual care [36] or among individualswith depression with or without comorbid physical condi-tions [37], our study contributed to the literature by includinga variety of co-existing chronic physical conditions.

In general, these studies indicated that the number ofchronic conditions do not affect treatment for depression orresponse to treatment for depression [35–37]. Ani et al. [36]also reported that co-occurrence of chronic medical condi-tions is not associated with the diagnosis, treatment andfollow-up care of depression. Consistent with the findings ofthese studies, our study found that the presence of multi-morbidity in the fully adjusted model including total numberof outpatient visits was not associated with depressiontreatment. However, in the models that did not include totaloutpatient visits, we did find a significant associationbetween those with multiple chronic condition clusters andthose with only single cluster of chronic conditions. Thesefindings suggest that individuals with multiple chroniccondition clusters were more likely to report depressiontreatment perhaps due to increased contact with the healthcare system.

Thus, our study findings do not support other studiessuggesting that competing demands due to presence ofchronic conditions may affect depression treatment [23,24].When multiple conditions coexist, especially when theseconditions can be discordant (i.e., when treatment goals ofdiseases differ), it is possible that multiple chronic conditionsmay exert a negative impact on depression treatment [38]. Ithas to be noted that depression is often considered a dis-cordant condition [38]. However, a recent single-blindrandomized controlled trial [22] in 14 primary care clinicscomprising 214 individuals with poorly controlled diabetes

Table 3Adjusted odd ratios and 95% confidence interval from multinomial logistic regression on depression treatment, Medical Expenditure Panel Survey, 2007

AD only Psyc Tx

AOR 95%CI Significance AOR 95% CI Significance

MultimorbidityMultiple conditions 1.27 0.91–1.75 1.10 0.74–1.63Single conditionGenderWomen 1.68 1.19–2.36 ** 1.31 0.89–1.93MenRace/ethnicityWhiteAfrican American 0.34 0.22–0.53 *** 0.62 0.39–1.00 *Latino 0.36 0.21–0.60 *** 0.52 0.31–0.87 *Other 0.20 0.11–0.40 *** 0.29 0.13–0.62 **Age (years)22-3940–49 1.45 0.87–2.42 1.08 0.64–1.8450–64 2.71 1.71–4.32 *** 1.34 0.81–2.23≥65 2.72 1.54–4.79 *** 0.72 0.35–1.46Marital statusMarriedWidowed 0.46 0.30–0.72 *** 0.73 0.37–1.46Divorced/separated 0.74 0.51–1.09 1.14 0.71–1.81Never married 0.55 0.33–0.93 * 1.32 0.75–2.33MetroMetroNonmetro 1.55 1.00–2.41 1.03 0.60–1.79EducationLT HS 0.89 0.60–1.33 0.60 0.34–1.06HS 0.99 0.67–1.46 0.58 0.38–0.87 **Above HSPoverty statusPoor 0.88 0.51–1.52 1.07 0.56–2.05Near poor 0.89 0.52–1.52 1.15 0.60–2.21Middle income 0.83 0.54–1.26 0.76 0.44–1.33High incomeHealth insurancePrivatePublic 1.14 0.76–1.70 1.40 0.88–2.20Uninsured 0.49 0.31–0.78 ** 0.35 0.20–0.62 ***Usual source of careYesNo 0.45 0.28–0.72 ** 0.61 0.33–1.13Perceived healthExcellent/very goodGood 1.15 0.70 –1.88 0.78 0.47–1.29Fair/Poor 0.85 0.53–1.34 0.34 0.20–0.58 ***Mental healthExcellent/very goodGood 0.68 0.45–1.02 1.23 0.74–2.05Fair/poor 1.18 0.73–1.92 3.90 2.24–6.79 ***Total no. of visits 1.02 1.00–1.03 * 1.05 1.04–1.07 ***

Note: Based on 1,376 adults alive at the end of 2007, reported having depression and at least one chronic condition in the cardiometabolic, respiratory ormusculoskeletal clusters. The regressions also include intercept terms and parameter estimates for other variables controlled are not presented. “No depressiontreatment” is the reference group for the dependent variable. Tx, treatment; AD, antidepressants; Psyc Tx, psychotherapy with or without antidepressants; AOR,adjusted odds ratio; HS, high school; LT, less than.Asterisks represent statistically significant group differences compared with the reference group: ***Pb.001; **.001bPb.01 ; *.01bPb.05.

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and/or heart disease concluded that collaborative care forpatients with depression and chronic illnesses improvedoutcomes. This study provided evidence that despite com-peting demands, a collaborative care model that supportedboth the patient and the primary care team may be a feasible

approach to improve outcomes when depression coexistswith multiple conditions.

In our study among those with chronic conditions, nearlyquarter (22.5%) reported no treatment for depression. Onecould speculate several reasons for our findings. It is possible

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that providers may do watchful waiting before treatingdepression because few clinical trials in individuals withdepression and medical illnesses such as diabetes and heartdisease have not shown improvement in the chronic out-comes but have demonstrated that compared with notreatment (i.e., placebo), treatment for depression relievesdepressive symptoms [21,39,40–42]. In addition, a recentmeta-analysis of antidepressants versus placebo for depres-sion treatment concluded the minimal benefits of antide-pressants in patients with mild or moderate depression [43].Although our study did not control for severity of depres-sion, it is possible that individuals with multiple chronicconditions may have severe form of depression. It has beenreported that those with either single or multiple chronicconditions have severe depression [10–19]. Given the bene-ficial effects of treatment with antidepressants for those withsevere depression [43] and individuals with chronic condi-tions who may have severe forms of depression, furtherresearch is needed as to the reasons for nonreceipt of treat-ment for depression in individuals with multimorbidity. Suchstudies will help target intervention efforts to promotedepression treatment.

All findings with respect to women, race/ethnicity, ageand access to care were consistent with those reported in theliterature. As reported in other studies of individuals withchronic illness, we found that women were more likely touse antidepressants than men [44]. However, in somespecial populations such as veteran clinic users withdiabetes, no gender differences in guideline-consistentdepression treatment were observed [45] perhaps becauseall the veterans in the study had insurance coverage throughthe veteran administration. A systematic literature review ofover 2,300 articles reported that African Americans andethnic minorities are less likely to be treated for depression[46], which is consistent with our study findings. Similarly,lack of insurance has been found to affect depressiontreatment [47].

Resolving these insurance and racial/ethnic disparities indepression treatment may require interventions that maketreatment affordable. Evidence from the largest and longeststudy on depression treatment in real-world settings, theSequenced Treatment Alternatives to Relieve Depression,suggests that after enrollment into programs that enhancedthe affordability of medications reduced the impact ofnonclinical factors such as gender, race/ethnic, education andinsurance status on use of depression treatment [48].

Our study has many advantages such as the use ofnationally representative sample of adults and the large sam-ple size allowing for good generalizability of our findings. Italso includes the comprehensive list of independent variablesand therefore able to better control for variations in depressiontreatment. However, the results of the study should beinterpreted with caution due to several limitations. The studyis only cross-sectional and cannot explain the interaction oftreatments for depression and chronic care, thereby makingcausal inferences impossible. All the measures in the study

are self-reported and therefore subject to recall bias.However, recall bias may be minimized by conducting atleast five rounds of interviews. Our study is onlyobservational and suffers from selection bias introduceddue to its uncontrolled nature. It likely that there may besystematic differences in various factors that are not ob-served, as in those who use antidepressants may differ fromthose who do not take antidepressants in preferences, atti-tudes and other unobservable factors. In addition, we selectedonly seven chronic conditions and excluded individuals withother chronic conditions, which may have introducedselection biases in terms of depression severity and treatmentconsiderations. The use of only diagnostic codes to identifydepression and chronic conditions is a limitation. These datado not define the severity of depression, which affects ourfindings because it is reported that use and benefits ofantidepressants are higher in patients with severe depressionthan those with mild depression [43].

Despite all these limitations, our study contributes to thegrowing literature of depression care in individuals withmultimorbidity and highlights possible future areas ofresearch exploration. Our study did not support the con-clusion that competing demands due to presence of multipleconditions may be a barrier to depression treatment.

Acknowledgments

This study received partial grant support from Agency forHealthcare Research and Quality Grant No. R24HS018622-01.

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