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. Author manuscript; available in PMC: 2020 Dec 14.
Published in final edited form as: Int J Geriatr Psychiatry. 2016 Jul 1;32(8):909–921. doi: 10.1002/gps.4546

Impact of comorbid mental health needs on racial/ethnic disparities in general medical care utilization among older adults

Daniel E Jimenez 1, Andrew C Schmidt 1, Giyeon Kim 2, Benjamin Le Cook 3
PMCID: PMC7734612  NIHMSID: NIHMS996657  PMID: 27363866

Abstract

Objective:

The objective is to apply the Institute of Medicine definition of healthcare disparities in order to compare (1) racial/ethnic disparities in general medical care use among older adults with and without comorbid mental health need and (2) racial/ethnic disparities in general medical care use within the group with comorbid mental health need.

Methods:

Data were obtained from the Medical Expenditure Panel Survey (years 2004–2012). The sample included 21,263 participants aged 65+ years (14,973 non-Latino Caucasians, 3530 African–Americans, and 2760 Latinos). Physical illness was determined by having one of the 11 priority chronic health illnesses. Comorbid mental health need was defined as having one of the chronic illnesses plus a Kessler-6 Scale >12, or two-item Patient Health Questionnaire >2. General medical care use refers to receipt of non-mental health specialty care. Two-part generalized linear models were used to estimate and compare general medical care use and expenditures among older adults with and without a comorbid mental health need.

Results:

Racial/ethnic disparities in general medical care expenditures were greater among those with comorbid mental health need compared with those without. Among those with comorbid mental health need, non-Latino Caucasians had significantly greater expenditures on prescription drug use than African–Americans and Latinos.

Conclusions:

Expenditure disparities reflect differences in the amount of resources provided to African–Americans and Latinos compared with non-Latino Caucasians. This is not equivalent to disparities in quality of care. Interventions and policies are needed to ensure that racial/ethnic minority older adults receive equitable services that enable them to manage effectively their comorbid mental and physical health needs. Copyright © 2016 John Wiley & Sons, Ltd.

Keywords: racial/ethnic disparities, older adults, comorbidities

Introduction

Comorbidity with physical illness is the hallmark of late-life depression, distinguishing this condition from depression in younger patients (Kelley, 2003). The comorbid state of mental illness incrementally worsens health compared with mental illness alone, with any chronic disease alone, and with any combination of chronic diseases without mental illness (Moussavi et al., 2007). Previous studies suggest that comorbid mental illnesses are associated with a high degree of functional impairment, decreased quality of life, and mortality (Yang and George, 2006; Gallegos-Carrillo et al., 2009; Fiske et al., 2009). With a growing older adult population, and the associated increase in prevalence of chronic medical conditions, the prevalence of mental illness is expected to rise among older adults concomitantly (Kelley, 2003; U.S. Census Bureau, 2012; Vogeli et al., 2007). The increasing prevalence of comorbid physical and mental illnesses leads to the question of how comorbidities influence general medical care use among older adults.

Studies have consistently shown that older adults with comorbid mental and physical illnesses have increased use of general medical care compared with those with a chronic physical illness alone (Himelhoch et al., 2004; Unützer et al., 2009). Comparing age groups, comorbid mental illness has been found to play a more important role in determining frequent attendance in primary care among older adults compared with younger adult patients (Menchetti et al., 2006). However, the degree to which these comorbid states are associated with general medical care use among racial/ethnic minority older adults and the association between a comorbid mental illness and general medical care disparities has not been reported (Jimenez et al., 2013).

According to the Institute of Medicine (IOM), disparities in healthcare are defined as racial or ethnic differences in the quality of health care related to the operation of healthcare systems but not differences due to clinical needs, preferences, and appropriateness of interventions (Institute of Medicine, 2002). In previous studies, differences due to need-related variables have been adjusted for and not included in the measurement of treatment disparities, whereas differences that provide evidence of the differential treatment by the healthcare system (i.e., differential treatment of low-income individuals) are included in the disparity calculation (McGuire et al., 2006; Stockdale et al., 2008; Alegría et al., 2008). Deciding how to categorize comorbid illness when measuring disparities is challenging because comorbidity may represent both differences due to clinical need—the presence of comorbidity worsens the prognosis of an illness, justifying more intensive treatment (Vogeli et al., 2007)—and differential treatment by the healthcare system—individuals with comorbid mental illness are more likely to be recognized and treated for mental illness because of greater exposure to the healthcare system (Cook et al., 2011). In this paper, we implement the IOM definition of healthcare disparities by adjusting for variables related to clinical appropriateness and need, but we make comorbidities our main variable of interest by which we make comparisons.

The purpose of this study is twofold, that is, to apply the IOM definition of healthcare disparities in order to compare (1) racial/ethnic disparities in general medical care use among older adults with and without comorbid mental health need and (2) racial/ethnic disparities in general medical care use within the group with comorbid mental health need. Our focus on older adults rather than the general population in the present study was primarily due to higher risk of functional impairment and comorbidity among older adults compared with other age groups, relatively higher prevalence of general medical care use in the older adult population, and little research on disparities in general medical care use among comorbid older adults. Our study separately assesses disparities in any general medical care and expenditures by type of services. General medical care was defined as general medical provider care (primary care physician), prescription drug care, emergency department visits, or inpatient hospitalization for a non-mental health condition, that is, any care that was not related to specialty mental health care (psychiatrist, psychologist, counselor, or social worker), or general medical provider care, emergency department visits, or inpatient hospitalization specifically linked to ICD-9 codes related to mood, anxiety, psychotic, substance use, personality, behavioral, and developmental disorders (codes 291, 292, or 295–314) (Machlin et al., 2009). This method of identifying mental health diagnoses has been shown to have strong sensitivity (88%) to provider reports (Methodology Report #23, Design, Methods, and Field Results of the Medical Expenditure Panel Survey Medical Provider Component, 2006) and is used here to distinguish general medical care from mental health care. To our knowledge, this may be the first study that assesses the association between comorbid mental health need and racial/ethnic disparities in general medical care using a solely geriatric (65+) sample in the USA.

Methods

Study population

We use a nationally representative sample of the non-institutionalized civilian population of the USA from the 2004–2012 Medical Expenditure Panel Survey (MEPS). Approximately 22% of our weighted sample was missing on one or more variables, reducing our sample from 27,150 to a final sample of 21,263 adults aged 65+ years (14,973 non-Latino Caucasians, 3530 African–Americans, and 2760 Latinos). To account for differential missingness by race/ethnicity, we reweighted the included individuals to represent their propensity to be like individuals with missing values with probable mental health need (Brick and Kalton, 1996; Wooldridge, 2002). Details regarding the characteristics of those participants who were excluded have been included in the Appendix. Native Americans (n=20) and Asians (n=6) were excluded because of small sample sizes.

Independent variables

Racial/ethnic categories (non-Latino Caucasians, African–American, and Latino) were based on the US Census definitions (U.S. Census Bureau, 2011). Physical illness was determined by having one of the 11 priority chronic health illnesses (diabetes, asthma, stroke, emphysema, joint pain, coronary heart disease, angina, myocardial infarction, other heart disease, high blood pressure, and obesity). Participants with mental health need were defined as a score greater than 12 on the Kessler 6 Scale (K6) (Kessler et al., 2002)—indicating nonspecific, clinically significant psychological distress (Kessler et al., 2002; Furukawa et al., 2003)—or a score greater than 2 on the two-item Patient Health Questionnaire (Kroenke et al., 2003)—indicating probable depressive disorder. Participants with comorbid mental health need were defined as those with mental health need plus one or more of the 11 priority illnesses listed previously. We also included income, education, health insurance, participation in a health maintenance organization, region of the country, employment status, and residence in a metropolitan statistical area.

Dependent variables

Dependent variables were a dichotomized measure of any general medical care use and a continuous measure of general medical care expenditures. Expenditures include all direct payments for general medical care provided, including out-of-pocket payments and payments by private insurance, Medicaid, Medicare, and other sources.

Dataset

The MEPS is an annual survey of approximately 15,000 households that has been conducted since 1996. It produces annual estimates and behavioral and economic analyses of healthcare use, expenditures, insurance coverage, sources of payment, access to care, and healthcare quality. Data are collected in five rounds of computer-assisted personal interviews that cumulatively cover a consecutive 2-year period. Respondents are interviewed about all household members’ sociodemographic information, clinical characteristics, and healthcare use and expenditures. For example, to measure self-rated mental health, respondents are asked: “In general, would you say that (‘Person’s’) mental health is excellent, very good, good, fair, or poor?” Responses ranged from 1 (excellent) through 5 (poor). Information regarding healthcare use and expenditures is verified directly from individual user’s medical care providers and pharmacy records by the Agency for Healthcare Research and Quality. Records provided by hospitals, health maintenance organizations, office-based providers, home care agencies, and pharmacies were reviewed by staff trained to abstract the core data elements for each provider type. Individual respondent information on expenditures is always replaced by provider information as the provider information is considered to be more complete and less prone to reporting errors (Methodology Report #23, Design, Methods, and Field Results of the Medical Expenditure Panel Survey Medical Provider Component, 2006). Trained staff resolved other discrepancies at their discretion (Methodology Report #23, Design, Methods, and Field Results of the Medical Expenditure Panel Survey Medical Provider Component, 2006).

For our purposes, we compared general medical care expenditures between those with and without comorbid mental health need among a population with one or more of the 11 priority chronic health illnesses that were specifically queried in the household component of the MEPS interview. All study methods and protocols were approved by the Institutional Review Board of Cambridge Health Alliance.

Statistical analyses

We estimate racial/ethnic and comorbidity differences in general medical care use and expenditures that are not due to differences in severity of illness by conducting the following four steps: (1) estimate a regression model of any mental health care (logistic regression for dichotomous-dependent variables and generalized linear models—log link and gamma distribution for the variance—for continuous-dependent variables), adjusting for all independent variables described previously; (2) transform distributions of need variables described previously to be equal across racial/ethnic groups using a rank and replace method (Cook et al., 2010; Cook et al., 2009); (3) estimate a prediction of the rate or mean of the dependent variable of interest for each comorbidity and racial/ethnic group by multiplying the coefficient from the original model by the independent variable values (transformed in the case of need variables) and averaging the predictions across the two groups; and (4) compare predicted general medical care use between racial/ethnic and comorbidity groups.

Statistical differences between groups and standard errors were calculated using nonparametric bootstrap re-sampling techniques with 100 replications to ensure parameter stability (Davidson and MacKinnon, 2004). Coefficients from the regression models described in step 2 provide an independent effect of race/ethnicity and comorbidities, adjusting for all independent variables. Comparisons of mean-predicted probabilities by comorbidity after adjustment for only mental health need variables (step 4) provide disparity results that are concordant with the IOM definition of healthcare disparities (Institute of Medicine, 2002). Both types of results are presented.

Results

Background characteristics of the sample

Table 1 summarizes health status and sociodemographic characteristics. African–Americans and Latinos had significantly higher rates of mental health comorbidities than non-Latino Caucasians. Latinos had significantly higher two-item Patient Health Questionnaire and K6 scores than non-Latino Caucasians, indicating greater mental health need or severity of mental illness among Latinos. African–Americans and Latinos overall reported having lower income, lower education, and higher likelihood of being enrolled in Medicaid and were more likely to live in an urban area than older non-Latino Caucasians.

Table 1.

Health status and sociodemographic characteristics of non-Latino Caucasians, African–American, and Latino sample (aged 65+ years with and without comorbid mental health needa; N = 21,263)

Non-Latino Caucasians (n = 14,973) African-Americans (n = 3530) Latinos (n = 2760)
Comorbidity status, %
 Comorbid mental health need 10.7 15.5d 23.7d
 Non-comorbid 89.3 84.5d 76.3d
Mental health status
 Mental health component of SF-12b 52.6 (9.8) 51.2 (10.3)d 48.2 (11)d
 PHQ-2 scoreb 0.75 (1.3) 0.89 (1.4)d 1.3 (1.7)d
 Kessler 6 Scale scoreb 3.5 (4.2) 3.6 (4.4) 5.4 (5.8)d
 Self-rated mental health, % Excellent 28.2 22.8d 19.5d
Very good 32 25.2d 26.3d
Good 30.4 37.5d 36.9d
Fair 7.5 11.9d 14.5d
Poor 1.9 2.6c 2.8c
Health status
 Diabetes, % 19.6 35.1d 36.5d
 Asthma, % 9.9 10.4 9.9
 High blood pressure, % 73.1 86.8d 77.2d
 Coronary heart disease, % 20.7 15.9d 17.7d
 Angina, % 10.2 6.5d 8.5c
 Myocardial infarction, % 13.6 11.4c 10d
 Other heart disease, % 28.1 19.5d 16.1d
 Stroke, % 12.5 14.8d 10.6c
 Emphysema, % 7.5 4.1d 3.6d
 Arthritis, % 64.2 65.1 59.3d
 Body mass indexb 27.6 (5.6) 29.1 (6.1)d 28.5 (6.4)d
 Physical health component of SF-12b 40.9 (12) 39.4 (12.1)d 39.2 (12.3)d
 Self-rated physical health, % Excellent 16.7 10d 7.8d
Very good 29.9 22d 17.6d
Good 32.1 33 32.8
Fair 15.6 26.1d 31.9d
Poor 5.7 8.9d 9.9d
 Any work limitation, % 61.3 62.9 57.9d
Sex, %
 Male 43.6 39d 41.5
 Female 56.4 61d 58.5
Age, %
 65–74 50.9 58.3d 56.8d
 75+ 49.1 41.7d 43.2d
Marital Status, %
 Married 56.6 35d 47.6d
 Single 43.4 65d 52.4d
SES, %
 Percentage of federal poverty level <100 7.4 20d 17.5d
100–124 6.1 10.2d 12.2d
125–199 17.9 24.4d 25.7d
200–399 30.8 27.2d 28.8
400+ 37.8 18.2d 15.8d
Education, %
< High school 18.4 40.6d 60.9d
High school graduate 37.4 30.5d 20.1d
Any college 20.2 15.8d 10.7d
College graduate 24 13.1d 8.3d
Health insurance, %e
Medicaid 5.6 23.9d 36.2d
Medicare 99.3 98.5d 96.8d
Uninsured 0.1 0.4d 2.2d
Region, %
Northeast 20.3 17.8d 14d
Midwest 25 18.1d 5.8d
South 35.8 55.2d 41.1d
West 18.9 8.9d 39.1d
Urbanicity, % Live in metropolitan statistical area 78 86.8d 92.2d
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PHQ-2, two-item Patient Health Questionnaire; SF-12, medical outcomes study 12-item short-form survey.

a

Participants with a comorbid mental health need were defined as those with a Kessler-6 Scale >12, or PHQ-2 > 2 plus one or more of the 11 priority illnesses (diabetes, asthma, stroke, emphysema, joint pain, coronary heart disease, angina, myocardial infarction, other heart disease, high blood pressure, and obesity).

b

Data from K6, PHQ-2, and SF-12 reflect mean scores, not percentages, and standard deviations are presented in parentheses

c

Significantly different from non-Latino Caucasians at the α < 0.05 level.

d

Significantly different from non-Latino Caucasians at the α < 0.01 level.

e

Enrollment in Medicare and Medicaid was not mutually exclusive.

Disparities in general medical care use and expenditures

Table 2 illustrates disparities in general medical care use and expenditures among racial/ethnic minority older adults with and without comorbid mental health need. Once general medical care was accessed, significant racial/ethnic disparities in expenditures were found regardless of comorbidity status.

Table 2.

Institute of Medicine-concordant disparities in general medical care and expenditures

General medical care (%)a Healthcare expenditures ($)b
Comorbidc Noncomorbid Difference in differencec Comorbidc Noncomorbid Difference in differenced
Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE
Non-Latino Caucasians 98.8 0.3 98.9 0.1 17,784.64 581.82 9,982.32 181.76
African-American 98.3 0.6 96.7 0.4 14,347.87 976.48 8,804.85 350.58
Latino 98.3 0.7 96 0.6 10,807.48 749.53 7,101.20 274.33
W-AA disparity 0.5 0.7 2.3 0.4 −1.8 0.8 3,436.77 1,166.21 1,177.47 378.46 2,259.30 1,149.67
W-L disparity 0.4 0.7 2.9 0.6 −2.5 0.9 6,977.16 937.00 2,881.12 315.81 4,096.04 1,028.08
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Numbers in bold represent significant disparities at p < 0.05 level.

a

General medical care is defined as engaging in medical provider care (primary care medical doctor), prescription drug care, emergency room, or inpatient hospitalization.

b

Among individuals who engaged in general medical care.

c

Participants with a comorbid mental health need were defined as those with a Kessler-6 Scale >12, or two-item Patient Health Questionnaire >2 plus one or more of the 11 priority illnesses (diabetes, asthma, stroke, emphysema, joint pain, coronary heart disease, angina, myocardial infarction, other heart disease, high blood pressure, and obesity).

d

Difference in difference is defined as the difference in general medical service use and expenditures between non-Latino Caucasians with and without a comorbid mental health need compared with the difference in general medical service use and expenditures between African–Americans and Latinos with and without a comorbid mental health need.

Assessing interactions of comorbidity and racial/ethnic disparities, the non-Latino White-African–American disparity in expenditures was greater for those with comorbid mental health need ($3436.77) compared with those without ($1177.47) a significant difference in disparity ($2259.30). The non-Latino White-Latino disparity in expenditures was greater for those with comorbid mental health need ($6977.16) compared with those without ($2881.12), and the difference in disparity ($4096.04) was significant.

Disparities in types of general medical services used

Table 3 illustrates disparities in the types of general medical services used among only those with a comorbid mental illness. Non-Latino Caucasians spent significantly more on outpatient services ($3553.57) and inpatient hospitalizations ($18,056.82) compared with Latinos ($2293.72 and $12,250.48, respectively). Furthermore, non-Latino Caucasians had significantly greater expenditures on prescription drug fills ($3877.15) compared with African–Americans ($2932.94) and Latinos ($2868.37).

Table 3.

Patterns of service use and expendituresa among individuals with comorbid mental health needb

Outpatient carec Prescription drug filld Inpatient hospitalizatione Emergency departmentf
Engagement (%) Expenditures ($) Engagement (%) Expenditures ($) Engagement (%) Expenditures ($) Engagement (%) Expenditures ($)
Estimate (SE)
Non-Latino Caucasians 95.7 (0.6) 3,553.57 (205.41) 97.5 (0.5) 3,877.15 (137.85) 19.5 (1.2) 18,056.82 (1,305.04) 19 (1.2) 1,169.04 (100.03)
African-American 91.8 (1.6) 3,492.18 (812.06) 95.1 (1.4) 2,932.94 (182.75) 19.7 (2.2) 16,129.25 (1,591.85) 21.5 (2.2) 1,191.13 (228.80)
Latino 93.8 (1.4) 2,293.72 (297.61) 95 (1.3) 2,868.37 (196.03) 13 (1.7) 12,250.48 (1,559.36) 13 (1.5) 1,713.97 (391.14)
W-AA disparityg 3.9 (1.7) 61.39 (478.43) 2.4 (1.5) 944.21 (216.03) −0.2 (2.5) 1,927.57 (1,909.97) −2.5 (2.4) −22.09 (219.89)
W-L disparityh 1.9 (1.5) 1,259.85 (261.33) 2.5 (1.4) 1,008.78 (198.50) 6.5 (2.2) 5,806.34 (1,955.22) 6 (1.8) −544.93 (4.4.96)
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Numbers in bold represent significant disparities at p<0.05 level.

a

Among individuals who engaged in either outpatient care, prescription drug care, inpatient hospitalization, or emergency department care.

b

Participants with comorbid mental health need were defined as those with mental health need (Kessler-6 Scale >12, or two-item Patient Health Questionnaire >2) plus one or more of the 11 priority illnesses (diabetes, asthma, stroke, emphysema, joint pain, coronary heart disease, angina, myocardial infarction, other heart disease, high blood pressure, and obesity).

c

Outpatient care is defined as an office-based provider visit that includes primary care provider or specialist healthcare provider (services received from a cardiologist, endocrinologist, dermatologist, etc.).

d

Prescription drug care is a prescribed medicine refill without an outpatient or office-based visit to assess the progress of the medications.

e

Inpatient hospitalization defined as hospitalization for a non-mental health condition.

f

Emergency department visit was for non-mental health emergencies.

g

Difference in service use and expenditures estimates between non-Latino Whites and African-Americans, after adjustment for clinical appropriateness and need.

h

Difference in service use and expenditure estimates between non-Latino Caucasians and Latinos, after adjustment for clinical appropriateness and need.

Regression model results

As displayed in Table 4, indicators of African-American and Latino race/ethnicity were significant negative predictors of general medical care use and expenditures after adjusting for all (both need and system level) covariates. Of the other covariates, having diabetes, asthma, high blood pressure, heart disease, arthritis, and experiencing any work limitations were predictive of increased general medical care use and expenditures.

Table 4.

Coefficient estimates from logistic regression models of general medical care and expenditures among older adults with and without comorbid mental health need

General medical carea (N = 21,887) $ Expendituresb (n = 21,206)
Coefficient SE p Coefficient SE p
Race/ethnicity African-American −0.75 0.19 ** −0.07 0.04
(Referent = non-Latino Caucasian) Latino −0.58 0.21 ** −0.22 0.05 **
Mental health condition Comorbid mental health needc −1.6 0.38 ** 0.02 0.06
Interactions African-American mental health 1.0 0.50 * 0.02 0.08
Latino mental health 1.0 0.52 * −0.05 0.08
Mental health status Mental health component of SF-12 −0.01 0.01 −0.01 <0.01 **
PHQ-2 score 0.35 0.11 ** <−0.01 0.02
Kessler 6 Scale score −0.03 0.04 −0.02 0.01 **
Self-rated mental health Very good −0.04 0.18 −0.05 0.03
Good −0.12 0.19 −0.02 0.03
Fair −0.23 0.34 −0.08 0.05
Poor −0.16 0.53 0.13 0.11
Physical health status Physical health component of SF-12 −0.02 0.01 ** −0.03 <0.01 **
Priority chronic health illnesses Diabetes 1.9 0.26 ** 0.20 0.03 **
Asthma 1.1 0.29 ** 0.13 0.04 **
High blood pressure 1.7 0.14 ** 0.10 0.03 **
Coronary heart disease 0.65 0.29 * 0.26 0.04 **
Angina 0.09 0.37 0.06 0.04
Myocardial infarction 0.26 0.29 0.08 0.04 *
Other heart disease 0.95 0.26 ** 0.18 0.03 **
Stroke 0.62 0.29 * 0.13 0.03 **
Emphysema −0.11 0.28 0.04 0.04
Arthritis 0.62 0.14 ** 0.04 0.03
Obesity −0.02 0.01 −0.01 <0.01 **
Self-rated physical health Very good 0.10 0.19 0.06 0.04
Good 0.26 0.19 0.10 0.04 **
Fair <0.01 0.27 0.21 0.05 **
Poor 0.19 0.41 0.51 0.07 **
Any work limitation 0.48 0.15 ** 0.24 0.03 **
Other covariates Female 0.47 0.15 ** −0.01 0.02
Age 75 0.48 0.15 ** −0.02 0.03
Marital status Married 0.44 0.14 ** −0.03 0.03
Percentage of federal poverty level 100–124 −0.34 0.25 0.09 0.05
125–199 −0.15 0.22 0.08 0.04
200–399 0.04 0.21 0.10 0.04 **
400+ 0.46 0.26 0.14 0.04 **
Education High school graduate 0.47 0.16 ** 0.10 0.03 **
Any college 0.96 0.24 ** 0.15 0.04 **
College graduate 1.6 0.26 ** 0.25 0.04 **
Health insurance Medicaid −0.44 0.21 * 0.08 0.04 **
Medicare 0.72 0.49 0.12 0.11
Uninsured −1.6 0.62 ** −1.2 0.25 **
Region Midwest 0.10 0.22 0.03 0.03
South 0.04 0.19 −0.07 0.03 *
West 0.37 0.22 −0.06 0.04
Urbanicity Live in metropolitan statistical area 0.21 0.16 0.04 0.03
Constant _cons 2.1 1.2 10.2 0.21 **
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SE, standard error; SF-12, medical outcomes study 12-item short-form survey; PHQ-2, two-item Patient Health Questionnaire.

a

General medical care is defined as medical provider care (primary care medical doctor), prescription drug care, emergency department, or inpatient hospitalization for non-mental health conditions.

b

Among individuals who engaged in general medical care.

c

Participants with comorbid mental health need were defined as those with mental health need (Kessler-6 Scale >12, or PHQ-2 >2) plus one or more of the 11 priority illnesses (diabetes, asthma, stroke, emphysema, joint pain, coronary heart disease, angina, myocardial infarction, other heart disease, high blood pressure, and obesity).

*

p < 0.05.

**

p < 0.01.

***

p < 0.001.

Table 5 illustrates coefficient estimates from logistic regression models of patterns of general medical care use and expenditures among only individuals with a comorbid mental health need. After adjusting for need and system-level covariates, indicators of African–American and Latino race/ethnicity were significant negative predictors of outpatient and prescription drug use and expenditures. Diabetes, asthma, high blood pressure, heart disease, arthritis, and experiencing any work limitations were predictive of increased outpatient and prescription drug use and expenditures. In addition, we ran a test of multicollinearity. The variance inflation factor for each of the variables included in the analyses is below 3 indicating a low degree of collinearity between the variables used in the regression (data not shown) (O’Brien, 2007).

Table 5.

Coefficient estimates from logistic regression models of patterns of general medical carea and expendituresb among individuals with comorbid mental health need

Outpatient visitc Prescription drug visitd Inpatient hospitalizatione Emergency departmentf
Engagement Expenditures Engagement Expenditures Engagement Expenditures Engagement Expenditures
Coefficient (standard error)
Race/ethnicity African-American −0.74 (0.11)** −0.05 (0.07) −0.67 (0.12)** −0.18 (0.03)** 0.02 (0.08) −0.01 (0.08) 0.13 (0.08) 0.18 (0.13)
(Referent non-Latino Caucasian) Latino −0.25 (0.13)** −0.30 (0.06)** −0.26 (0.15) −0.18 (0.04)** −0.09 (0.11) 0.10 (0.13) −0.09 (0.11) 0.32 (0.19)
Mental health condition Comorbid mental health needg −0.40 (0.21) 0.04 (0.08) −0.37 (0.25) 0.03 (0.06) −0.15 (0.14) −0.12 (0.14) −0.14 (0.14) −0.21 (0.16)
Interactions African-American mental health 0.34 (0.27) 0.20 (0.16) 0.35 (0.40) −0.01 (0.08) 0.19 (0.17) −0.05 (0.15) 0.23 (0.16) −0.08 (0.23)
Latino mental health 0.24 (0.28) 0.05 (0.12) −0.01 (0.33) 0.01 (0.08) −0.14 (0.21) −0.47 (0.18)** −0.16 (0.20) 0.16 (0.30)
Mental health status Mental health component of SF-12 0.01 (0.01) −0.01 (<0.01) −0.01 (0.01) <−0.01 (<0.01)* −0.01 (<0.01)** −0.01 (<0.01)** −0.02 (<0.01)** <0.01 (0.01)
PHQ-2 score 0.10 (0.06) 0.01 (0.02) 0.07 (0.07) <0.01 (0.02) 0.04 (0.04) −0.05 (0.04) 0.02 (0.04) 0.06 (0.05)
Kessler 6 Scale score −0.01 (0.02) −0.01 (0.01)* −0.02 (0.02) −0.01 (0.01)* <−0.01 (0.01) <0.01 (0.01) 0.01 (0.01) −0.01 (0.01)
Self-rated mental health Very good 0.12 (0.10) −0.03 (0.04) 0.06 (0.10) 0.03 (0.03) −0.12 (0.08) −0.06 (0.08) −0.04 (0.08) 0.07 (0.10)
Good −0.01 (0.11) <0.01 (0.04) 0.01 (0.12) 0.07 (0.03)** −0.08 (0.08) −0.06 (0.08) −0.01 (0.08) 0.01 (0.10)
Fair −0.03 (0.18) −0.19 (0.06)** 0.03 (0.21) 0.10 (0.05)* −0.18 (0.11) −0.15 (0.11) −0.10 (0.11) −0.04 (0.14)
Poor −0.60 (0.27)* −0.30 (0.13)* −0.05 (0.39) 0.51 (0.23)* −0.50 (0.18)** −0.12 (0.18) −0.52 (0.18)** −0.03 (0.17)
Physical health status Priority Chronic health illnesses Physical health component of SF-12 −0.02 (0.01)** −0.02 (<0.01)** −0.03 (0.01)** −0.01 (<0.01)** −0.03 (<0.01)** −0.01 (<0.01)** −0.01 (<0.01)** −0.01 (<0.01)
Diabetes 0.72 (0.11)** 0.12 (0.04)** 1.8 (0.17)** 0.43 (0.02)** 0.24 (0.06)** −0.03 (0.06) 0.16 (0.06)** −0.07 (0.07)
Asthma 0.63 (0.15)** 0.09 (0.05) 1.0 (0.19)** 0.33 (0.05)** 0.10 (0.08) −0.12 (0.07) 0.17 (0.08) −0.23 (0.09)*
High blood pressure 0.81 (0.08)** 0.02 (0.03) 1.5 (0.09)** 0.21 (0.03)** 0.22 (0.07)** 0.06 (0.07) 0.31 (0.07)** 0.18 (0.08)*
Coronary heart disease 0.09 (0.13) 0.11 (0.04)* 0.50 (0.16)** 0.26 (0.04)** 0.39 (0.07)** 0.23 (0.07)** 0.29 (0.07)** 0.27 (0.09)**
Angina 0.11 (0.20) <−0.01 (0.04) 0.11 (0.21) 0.03 (0.03) −0.02 (0.09) −0.07 (0.09) 0.08 (0.08) −0.03 (0.10)
Myocardial infarction −0.08 (0.14) −0.05 (0.05) 0.16 (0.17) 0.07 (0.03)* 0.12 (0.08) 0.01 (0.07) 0.06 (0.07) 0.03 (0.09)
Other heart disease 0.73 (0.11)** 0.24 (0.03)** 0.76 (0.13)** 0.10 (0.02)** 0.42 (0.06)** −0.02 (0.06) 0.49 (0.06)** −0.01 (0.07)
Stroke 0.06 (0.14) −0.05 (0.04) 0.42 (0.20)* 0.10 (0.03)** 0.37 (0.07)** 0.04 (0.07) 0.45 (0.07)** <−0.01 (0.08)
Emphysema 0.17 (0.17) −0.09 (0.06) 0.47 (0.22)* 0.27 (0.05)** 0.28 (0.10)** −0.10 (0.08) 0.14 (0.10) 0.05 (0.12)
Arthritis 0.45 (0.08)** 0.05 (0.04) 0.33 (0.09)** 0.01 (0.03) 0.18 (0.06)** 0.06 (0.06) 0.16 (0.06)** −0.20 (0.08)*
Obesity <−0.01 (0.01) −0.01 (<0.01)** 0.01 (<−0.01) <0.01 (<0.01)* −0.01 (<0.01)** <−0.01 (0.01) −0.02 (0.01)** −0.01 (0.01)
Self-rated physical health Very good 0.21 (0.11) 0.14 (0.05)** 0.32 (0.11)** 0.14 (0.03)** −0.04 (0.11) −0.14 (0.10) −0.20 (0.10) −0.01 (0.12)
Good 0.21 (0.11) 0.20 (0.05)** 0.35 (0.12)** 0.24 (0.03)** −0.07 (0.11) −0.23 (0.10)* −0.13 (0.10) 0.08 (0.13)
Fair 0.23 (0.15) 0.27 (0.06)** 0.19 (0.17) 0.35 (0.04)** 0.06 (0.12) −0.09 (0.11) −0.12 (0.12) 0.10 (0.14)
Poor −0.10 (0.21) 0.59 (0.09)** 0.56 (0.30) 0.38 (0.05)** 0.23 (0.14) 0.14 (0.14) 0.07 (0.14) 0.54 (0.18)
Any work limitation 0.26 (0.08)** 0.16 (0.03)** 0.19 (0.09)* 0.12 (0.03)** 0.17 (0.07)* 0.19 (0.07)** 0.18 (0.07) −0.03 (0.09)
Other covariates Female 0.30 (0.08)** −0.06 (0.03) 0.36 (0.10)** 0.10 (0.02)** −0.08 (0.06) −0.10 (0.06) −0.05 (0.06) 0.06 (0.08)
Age 75 0.38 (0.08)** −0.11 (0.03)** 0.22 (0.10)* −0.06 (0.02)** 0.04 (0.06) −0.10 (0.06) 0.06 (0.06) −0.09 (0.07)
Marital status Married 0.15 (0.08) 0.05 (0.03) 0.22 (0.09)* 0.01 (0.02) −0.14 (0.06)* 0.10 (0.06) −0.19 (0.06)** −0.10 (0.07)
Federal poverty level 100–124 −0.13 (0.15) 0.22 (0.07)** −0.10 (0.19) <0.01 (0.04) 0.06 (0.12) −0.15 (0.11) −0.04 (0.11) 0.04 (0.14)
125–199 <−0.01 (0.12) 0.11 (0.05)* −0.23 (0.15) 0.03 (0.04) 0.12 (0.09) −0.15 (0.09) −0.01 (0.09) −0.08 (0.10)
200–399 0.38 (0.12)** 0.21 (0.05)** 0.05 (0.14) 0.07 (0.03)* <−0.01 (0.09) −0.12 (0.08) −0.02 (0.09) 0.20 (0.11)
400+ 0.45 (0.13)** 0.25 (0.05)** 0.20 (0.16) 0.10 (0.03)** −0.06 (0.10) −0.15 (0.09) −0.12 (0.10) 0.30 (0.12)*
Education High school graduate 0.25 (0.09)** 0.14 (0.04)** 0.29 (0.11)** 0.08 (0.03)** 0.11 (0.07) <−0.01 (0.07) 0.06 (0.07) −0.09 (0.09)
Any college 0.52 (0.12)** 0.24 (0.05)** 0.65 (0.14)** 0.13 (0.03)** 0.13 (0.09) −0.10 (0.08) 0.10 (0.08) 0.16 (0.11)
College graduate 1.1 (0.14)** 0.37 (0.05)** 0.64 (0.14)** 0.18 (0.03)** 0.16 (0.09) 0.08 (0.09) <−0.01 (0.09) −0.10 (0.11)
Health insurance Medicaid −0.35 (0.12)** −0.10 (0.06) −0.29 (0.15) 0.19 (0.04)** −0.09 (0.08) −0.02 (0.09) −0.02 (0.08) <0.01 (0.12)
Medicare 0.51 (0.29) 0.28 (0.11)* 1.2 (0.26)** −0.12 (0.13) 0.42 (0.33) −0.02 (0.17) −0.28 (0.28) −0.06 (0.22)
Uninsured −1.8 (0.42)** −0.15 (0.39) −0.36 (0.41) −1.3 (0.20)** 0.18 (0.69) −0.93 (0.27)** <0.01 (<0.01) <0.01 (<0.01)
Region Midwest 0.06 (0.13) 0.04 (0.04) 0.14 (0.13) −0.07 (0.03)* 0.01 (0.08) 0.02 (0.07) −0.07 (0.08) −0.11 (0.10)
South 0.02 (0.10) −0.11 (0.04)** 0.14 (0.12) −0.02 (0.03) −0.04 (0.08) −0.10 (0.07) −0.25 (0.07)** −0.07 (0.10)
West 0.02 (0.12) 0.03 (0.05) −0.03 (0.14) 0.20 (0.04)** −0.18 (0.09) 0.18 (0.09) −0.15 (0.09) 0.08 (0.12)
Urbanicity Live in metropolitan statistical area 0.16 (0.09) 0.13 (0.04)** 0.05 (0.10) 0.02 (0.03) 0.04 (0.07) 0.07 (0.06) −0.03 (0.07) −0.01 (0.08)
Constant _cons 0.75 (0.73) 8.1 (0.25)** 0.96 (0.80) 7.6 (0.19)** −1.07 (0.54)* 10.9 (0.39)** −0.26 (0.49) 7.2 (0.50)**
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PHQ-2, two-item Patient Health Questionnaire; SF-12, medical outcomes study 12-item short-form survey.

a

General medical care is defined as medical provider care (primary care medical doctor), prescription drug care, emergency room, or inpatient hospitalization for non-mental health conditions.

b

Among individuals who engaged in either outpatient care, prescription drug care, inpatient hospitalization, or emergency department care.

c

Outpatient care is defined as an office-based provider visit that includes primary care provider or specialist health care provider (services received from a cardiologist, endocrinologist, dermatologist, etc.).

d

Prescription drug care is a prescribed medicine refill without an outpatient or office-based visit to assess the progress of the medications.

e

Inpatient hospitalization defined as hospitalization for a non-mental health condition.

f

Emergency department visit was for non-mental health emergencies.

g

Participants with comorbid mental health need were defined as those with a Kessler-6 Scale >12, or PHQ-2 >2 plus one or more of the 11 priority illnesses (diabetes, asthma, stroke, emphysema, joint pain, coronary heart disease, angina, myocardial infarction, other heart disease, high blood pressure, and obesity).

*

p<0.05.

**

p<0.01.

***

p<0.001.

Interpreting the significance of race/ethnicity coefficients in Tables 4 and 5 is not an IOM-concordant method of identifying disparities. However, they tell us about racial/ethnic differences after adjusting for need and system-level factors. The significance of other covariates also provides information regarding the underlying pathways by which disparities arise.

Discussion

Our findings indicate that the presence of comorbid mental health need was not associated with racial/ethnic disparities in accessing general medical care—nearly all individuals reporting a chronic physical health condition accessed general healthcare services across racial/ethnic groups and comorbidity status. However, we identified that, among those accessing care, there were significant racial/ethnic disparities in general health expenditures and that these disparities in general health expenditures were actually exacerbated among the group with comorbid mental health need. These latter results provide preliminary evidence that general medical care for older African–Americans and Latinos with a comorbid mental health need is not being provided equally even after they have accessed the healthcare system and that having a comorbid mental health need may be especially detrimental to older African–Americans and Latinos.

Although structural inequalities (e.g., income, education, language, and insurance status) contribute to differential access to and utilization of general medical services, disparities are unlikely to be ameliorated without equal attention to the healthcare systems where racial/ethnic minority older adults seek treatment. Racial/ethnic minority older adults have limited access to higher quality medical facilities (Baicker et al., 2004). As a result, they are less likely to receive new, costlier, high-technology medical procedures (Groeneveld et al., 2005). Additionally, hospitals with larger racial/ethnic minority populations provide new procedures less frequently to all patients and are particularly less likely to provide these procedures to racial/ethnic minority patients (Groeneveld et al., 2005). Another study among adults of all ages identifies that physicians treating minority patients are less likely than other physicians to be board certified, to practice evidence-based medicine, to have access to important clinical resources, and to refer to specialty care (Bach et al., 2004). Taken together, racial/ethnic minority older adults who receive care in healthcare systems that serve larger numbers of racial/ethnic minority older adult patients are doubly disadvantaged in terms of equitable access, referral, and utilization of medical care.

Furthermore, we found racial/ethnic disparities in prescription drug expenditures among those with a comorbid mental health need. These disparities may reflect differential prescribing behaviors by physicians. There is evidence that patient sociodemographic characteristics independently influence physician expectations and perceptions towards patients (Agency for Healthcare Research and Quality, 2012). African–American and Latino patients are significantly more likely than non-Latino White patients to be asked about possible problems paying for their medications when prescribing, even after adjusting for risk factors such as annual income, pharmacy benefits, number of medications, and whether they actually reported medication cost-related burdens (van Ryn, 2002). Racial/ethnic minority patients are also more likely to be rated as non-adherent to their medications than their non-Latino White counterparts (Agency for Healthcare Research and Quality, 2012). Physicians who know their patients cannot afford high prescription costs or believe that their patients will be non-adherent may be less likely to prescribe costly but effective medications (Heisler et al., 2004). Thus, they are less likely to receive new drugs that offer potential benefits of higher efficacy and effectiveness and fewer or less severe side effects compared with older drugs but are more expensive than the older drugs they replace (Wang et al., 2007).

It is important to note that expenditure disparities reflect a disparity in the amount of resources that are provided to African–Americans and Latinos compared with non-Latino Caucasians. This is not necessarily equivalent to disparities in quality of care (Fowler et al., 2008). It is possible that many of the non-Latino Caucasians with comorbid mental health need are “over-serviced but underserved”, that is, they may be subjected to inappropriate and expensive diagnostic procedures and treatments for medical illnesses, while their actual problem is undetected or undertreated mental illness (Fischer et al., 1997). Therefore, in order to enhance value to patient care, clinicians and researchers must find ways to provide constant quality in patient outcomes, safety, and satisfaction while reducing costs by eliminating unnecessary diagnostic tests and therapeutic interventions (Fowler et al., 2008). Addressing disparities and achieving equity are the perfect target areas to recoup value, and doing so will pave the way for high-value general medical care (Betancourt, 2014). Thus, future studies that incorporate quality and expenditure measures are needed. Because it was signed into law on 23 March 2010, the Patient Protection and Affordable Care Act (ACA) has been implemented in a gradual manner, with the majority of the provisions being enacted in 2012 and 2013 (Kaiser Family Foundation, n.d.). Given that the goals of the ACA include controlling cost and healthcare expenditures, increasing quality of care, and reducing disparities (Kaiser Family Foundation, n.d.), future research should explore the effect of the ACA on disparities in general care expenditure.

Our results are novel and provide evidence that the healthcare system is not providing equitable treatments to racial/ethnic minority older adults with comorbid mental health need. In addition, these results may also indicate that the healthcare system is not adequately retaining racial/ethnic minority older adult patients with comorbid mental health need in services that would enable them to manage their chronic diseases—hemoglobin A1c levels for those with diabetes, blood pressure medications and checkups for those with heart disease, and so on. As we move to implement healthcare reform and payment reform, it is critical to assure that our healthcare system is culturally competent and has the capacity to deliver high-quality care for all while eliminating disparities and assuring equity (Betancourt et al., 2014).

There are a number of limitations in the current study’s design that future researchers would be prudent to consider. First, because we are using cross-sectional data, we cannot determine whether having a comorbid mental health need causes greater expenditures, only that these factors are associated. Because mental illness tends to be chronic, it is difficult to conclude that there is a logical sequence of cause and effect. The question of whether mental illness leads to healthcare utilization or if it is a consequence of medical illness remains. Second, we do not have information on quality of care. Therefore, expenditure disparities can only be construed as disparities in resources expended not quality of care. Third, because of the sample size limitations, we were unable to include Asians or Native Americans. The exclusion of participants from these racial/ethnic groups limits the external validity of the study. Fourth, mental health need was not determined by structured diagnostic, but instead by two brief scales/measures of symptom severity and distress. This broad definition may mask important individual differences because underserved racial/ethnic groups with severe mental illness (e.g., schizophrenia and bipolar disorders) face higher rates of obesity and chronic illness and lower rates of general medical care utilization than non-Latino Caucasians with severe mental illness (Druss et al., 2011). Fifth, our definition of physical illness was limited to having one or more of the 11 priority chronic illnesses measured in the MEPS. Therefore, our results may not be generalizable to older adults with other physical illnesses (e.g., cancer).

Despite high prevalence and associated disease burden, little is known about utilization of general medical care among chronically ill racial/ethnic minority older adults with and without comorbid physical and mental health needs. It is with these realities in mind that we sought out what effect, if any, does comorbid mental health need have on these aforementioned disparities, as well as comparing the overall patterns of general medical care utilization in racial/ethnic minority older adults with comorbid needs. When taken as a whole, our findings illustrate that while there is most certainly the desire to seek treatment as evidenced by similar rates of engagement in the general medical services, there is a marked disparity in expenditures. Interventions and policies are needed to retain racial/ethnic minority older adult patients in services that would enable them to manage effectively their comorbid mental and physical health needs. Potentially, innovative programs to improve the recognition and treatment of mental illness, such as integrated physical and behavioral health care, nurse case management, mental illness education, group therapy, or special counseling programs, might be “paid for” by reducing expenditures for unneeded general medical services. In the end, if we are to be successful in our pursuit of value, we must make efficient use of scarce resources in order to deliver high-quality care to an increasingly diverse older adult population.

Supplementary Material

Appendix A1

Table A1. Descriptive characteristics of those participants that were included and excluded.

Acknowledgements

This research was supported by grants K23 MH098025 from the National Institute of Mental Health, K01 AG045342 from the National Institute on Aging, and the Harvard T. H. Chan School of Public Health Program on the Global Demography of Aging funded by P30 AG024409 from the National Institute on Aging. None of the contributing authors has any potential conflicts of interest, including specific financial interests and relationships, relevant to the subject of this manuscript. Each author has made a substantial contribution in the design, data analysis, and in the interpretation of the results of the paper.

Footnotes

Supporting information

Additional supporting information may be found in the online version of this article at the publisher’s web site:

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Supplementary Materials

Appendix A1

Table A1. Descriptive characteristics of those participants that were included and excluded.

NIHMS996657-supplement-Appendix_A1.docx (17.4KB, docx)

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