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. Author manuscript; available in PMC: 2022 Jul 1.
Published in final edited form as: Womens Health Issues. 2021 Mar 30;31(4):353–365. doi: 10.1016/j.whi.2021.02.005

Age and Gender Differences in Trends and Impact of Depression on Quality of Life in the United States, 2008-2016

Ronald Anguzu 1, Sneha Nagavally 2,3, Aprill Z Dawson 2,3, Rebekah J Walker 2,3, Leonard E Egede 2,3
PMCID: PMC8260438  NIHMSID: NIHMS1677958  PMID: 33810952

Abstract

Background:

We aimed to examine age and gender differences in the relationship between depression and quality of life among United States adults.

Methods:

Using Medical Expenditure Panel Survey data for 2008–2016 on 227,663 adults were analyzed. The dependent variable, quality of life (QoL), included physical component summary (PCS) scores and mental component summary (MCS) scores from the Short Form Health Survey (SF-12). The key independent variable, depression, was measured using the 2-item Patient Health Questionnaire. General linear regression models examined the relationship between QoL and depression. Models were adjusted for individual and environmental characteristics, symptom status, functional and biological status, and health perceptions and were stratified by gender and age.

Results:

In adjusted models, MCS scores were significantly lower among those with depression compared to those without depression (β=−0.39, 95% CI: 0.38, −1.16) and lower among women compared to men (β=−0.10, 95% CI: 0.10, −1.31). Models stratified by gender and age found women with depression ages 40–64 (β=−0.07, 95% CI: 0.07, −0.20) and ≥65 (β=−0.08, 95% CI: 0.08, −0.24) had significantly lower PCS scores compared to those without depression. Among men with depression, those ages 18–39 (β=−0.03, 95% CI: 0.03, −0.10) and 40–64 (β=−0.09, 95% CI: 0.08, −0.26) had lower PCS scores compared to those without depression. Women and men of all ages with depression had significantly lower MCS scores compared to those without depression.

Conclusions:

Public health interventions and clinical approaches to address depression in women and men should target functional status in men and perceptions of health in women.

Keywords: Gender Differences, Depression, Quality of Life, Trends analysis

Background

Depression is one of the most commonly diagnosed mental health disorders and the leading cause of global disease burden, contributing 2.5% of total global Disability Adjusted Life Years (Ferrari et al., 2013). The prevalence of clinical depression in the United States (US) is 8% among US adults (Cao et al., 2020). Studies have consistently shown that there are gender differences in the burden of depression, with depression being more prevalent in women compared to men both globally and in the US (Breslau et al., 2017; Cao et al., 2020; Labaka, Goñi-Balentziaga, Lebeña, & Pérez-Tejada, 2018). Although comorbid depression contributes to increased disability related costs (Stewart, Ricci, Chee, Hahn, & Morganstein, 2003), major depressive disorders (MDDs) are responsible for a 21.5% increase in the economic burden of depression. Costs incurred by adults with MDD increased from 173 to 210.5 billion US dollars from 2005 to 2010 (Greenberg, Fournier, Sisitsky, Pike, & Kessler, 2015). In addition to direct costs, depression reduces household and workplace productivity through absenteeism (Schultz & Joish, 2009), difficulties performing social roles, and psychosomatic symptoms that worsen quality of life (QoL) (Kessler & Bromet, 2013).

Depression negatively impacts one’s general wellbeing, resulting in a reduction in role functioning, role transitioning, and overall quality of life. Additionally, depression increases one’s risk of suicidal ideation and other secondary disorders (Kessler & Bromet, 2013). QoL is a multidimensional, subjective assessment of the physical, social, and psychological health and satisfaction of an individual. The Wilson and Cleary model of QoL classifies and links different measures of health outcomes into five levels: biological and physiological; symptom status; functional status; general health perceptions; and overall QoL (Wilson & Cleary, 1995). Validated QoL measures and depression show strong correlations (Wisniewski et al., 2007) in individuals experiencing physical pain or activity limitation, and physical distress has been associated with having a mental health impairment, including depression (Strine, Chapman, Kobau, Balluz, & Mokdad, 2004). However, many studies have failed to account for the multiple aspects of health as outlined by Wilson and Cleary, which limits the understanding of how depression and QoL are related.

According to the National Center for Health Statistics, there are gender differences in the burden of depression, with depression being more prevalent in women compared to men in the US, at 10.4% and 5.5%, respectively (Brody, Pratt, & Hughes, 2018). Women with depression have also been found to have higher medical expenditures (Dagher, McGovern, Dowd, & Gjerdingen, 2012) and are more likely to have undiagnosed depression (Li et al., 2009) when compared men.

While much of the literature has focused on gender differences based on increased burden in women, evidence suggests that men with depression have higher rates of suicide, alcohol abuse, and substance abuse (Cochran, 2001; Good & Brooks, 2005). In addition, men have been shown to have a higher risk of first onset major depression (Addis, 2008). Although several studies investigating depression differences by gender focus on the higher burden among women of childbearing age (Breslau et al., 2017; Kokras & Dalla, 2017; Labaka et al., 2018; Mackenzie, Visperas, Ogrodniczuk, Oliffe, & Nurmi, 2019; Rydberg Sterner et al., 2020; Skovlund, Kessing, Mørch, & Lidegaard, 2017; Vetter et al., 2020), there is paucity of research specific to differences by age and gender in the relationship between depression and QoL. Given a lack of evidence with comprehensive measures to understand possible differences by age and gender in the relationship between depression and QoL, the aim of this study was to use nationally representative data and a theoretical behavioral model to guide examination of age and gender differences in the influence of depression on QoL among adults in the US from 2008 to 2016.

Methods

Study population

Using the Medical Expenditure Panel Survey-Household component (MEPS-HC) consolidated files for 2008 to 2016, a pooled sample of 227,663 adult women and men aged ≥ 18 years was analyzed. MEPS is an ongoing national household survey for the civilian non-institutionalized U.S. population, with oversampling for Black and Hispanic individuals (AHRQ, 2013). Survey weights were applied to account for complex survey design, making our study sample generalizable to the adult US population (AHRQ, 2013). Data are collected through in-person interviews and include detailed information on demographic characteristics, health perceptions, use of medical services, charges and sources of payment, access to care, satisfaction with care, health insurance coverage, income, and employment for each person in the household.

Study measures

Dependent variable

The dependent variable was quality of life (QoL) as measured by the physical component summary (PCS) scores and mental component summary (MCS) scores of the Short Form – 12 Version 2 (SF −12v2) (Ware, Kosinski, Turner-Bowker, & Gandek, 2002). PCS scores and MCS scores were continuous measures assessed using a 12-item scale (Resnick & Parker, 2001; J. Ware Jr, Kosinski, & Keller, 2002). The test-retest reliability estimates for internal consistency of PCS scores and MCS scores in the general US population were 0.890 and 0.760, respectively (Ware Jr, Kosinski, & Keller, 1996).

PCS score measurement

In the MEPS-HC dataset, the PCS scores weighted responses more heavily to the following items:

  • 1

    In general, would you say your health is: 1) Excellent, 2) Very good, 3) Good, 4) Fair, or 5) Poor.

  • 2

    During a typical day, does your health now limit you in these moderate activities such as moving a table, pushing a vacuum cleaner, bowling, or playing golf? 1) Yes, limited a lot, 2) Yes, limited a little, or 3) No, not limited at all.

  • 3

    During a typical day, does your health now limit you in climbing several flights of stairs? 1) Yes, limited a lot, 2) Yes, limited a little, or 3) No, not limited at all.

  • 4

    During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of your physical health? Accomplished less than you would like: 1) Yes or 2) No.

  • 5

    During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of your physical health? Were limited in the kind of work or other activities: 1) Yes or 2) No.

  • 6

    During the past 4 weeks, how much did pain interfere with your normal work (including work outside the home and housework)? 1) Not at all 2) A little bit 3) Moderately 4) Quite a bit 5) Extremely.

MCS measurement

Similarly, in the MEPS-HC dataset, the MCS scores weighted responses more heavily to the following questions:

  • 7

    How much of the time during the past 4 weeks have you felt downhearted and depressed? 1) All of the time 2) Most of the time 3) A good bit of the time 4) Some of the time 5) A little of the time 6) None of the time.

  • 8

    How much of the time during the past 4 weeks have you felt calm and peaceful? 1) All of the time 2) Most of the time 3) A good bit of the time 4) Some of the time 5) A little of the time 6) None of the time.

  • 9

    During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)? Did work or activities less carefully than usual: 1) Yes, 2) No.

  • 10

    During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)? Accomplished less than you would like: 1) Yes, 2) No.

  • 11

    During the past 4 weeks, how much of the time has your physical health or emotional problems interfered with your social activities (like visiting friends, relatives, etc.)? 1) All of the time, 2) Most of the time, 3) A good bit of the time, 4) Some of the time, 5) A little of the time or 6) None of the time.

PCS and MCS scores were computed from the respective items according to standard algorithms and data imputation for missing data as previously described (Campbell, Bishu, Walker, & Egede, 2017). Algorithmic scoring generated continuous variables for PCS and MCS, with higher values indicating greater quality of life. Models were run separately for physical quality of life and mental quality of life as separate outcomes.

Primary independent variable

The primary independent variable was depression measured with the PHQ-2, a self-report of depressive symptomatology. Survey respondents were asked if they were bothered by “having little interest or pleasure in doing things” and “feeling, down, depressed, or hopeless” during the last two weeks. The measure was summed (0 – 6), and a cut point of <3 or ≥ 3 was used to dichotomize the variable as the presence/absence of depressive symptoms. The PHQ-2 has been shown to have high sensitivity and specificity for major depressive disorder, at 87% and 78%, respectively, and for any depressive disorder at 79% and 86%, respectively (Löwe, Kroenke, & Gräfe, 2005).

Covariates

A modified version of the Wilson and Cleary Model of QoL (Wilson & Cleary, 1995) underpinned the selection of covariates that were included in the final adjusted model. The variables were entered in five sequential blocks that were identified as: (1) individual characteristics, (2) environmental characteristics, (3) symptom status, (4) functional and biological status, and (5) health perceptions. Individual characteristics were: age (18 – 34, 35 – 44, 45 – 64, ≥ 65 years), race/ethnicity (Hispanic, Non-Hispanic White, Non-Hispanic Black, Other), family income to poverty ratio (high [≥400%], middle [<400%-≥200%], low [<200%-≥125%], near poor [<125%->100%], or poor [<100%]), region (Northeast, Midwest, South, West), insurance (any private insurance, public only, uninsured), year (2008/10, 2011/13, 2014/16) and education level (<bachelor’s degree, ≥bachelor’s degree). Environmental characteristics included: difficulty accessing the usual source of care (USC) provider (yes/no) and access to USC provider. Access to USC provider was measured using two categories:1) being unable or delay in getting treatment (yes/no) and 2) being unable or delay in getting prescription (yes/no). Symptom status was measured as the number of self-reported comorbid conditions, including: cancer, diabetes, cholesterol, high blood pressure, and heart disease (a diagnosis of coronary heart disease, angina diagnosis, heart attack, or other heart disease). Functional or biological status was measured as the number of workdays or school days missed due to illness, injury, or mental/emotional problems. Health perceptions were measured as perceived physical and perceived mental health status as continuous variables. Perceived physical and mental health status were both assessed on a continuous scale from 1 to 5, with 1 being excellent and 5 being poor.

Statistical Analysis

First, descriptive statistics using frequencies, percentages, and means with corresponding standard deviations (SD) were computed for data from 2008 to 2016. The prevalence of depression was analyzed over time based on three-year time frames (2008–2010, 2011–2013, and 2014–2016). Second, linear trends in QoL (MCS and PCS scores) over time among men and women with depression were examined. Cochran Armitage test was used to test for significant differences in trend lines. Two general linear regression models were analyzed with 1) MCS and 2) PCS scores as dependent variables and depression as primary independent variable to investigate trend overall unadjusted relationships. Two sets of interaction terms were run to test whether the relationship between depression and QoL (MCS and PCS scores) differed by gender and age, respectively, as described below.

Interaction between gender and depression on QoL

Prior to adjusting, interactions between depression and gender on MCS and PCS scores were assessed. The interaction between depression and gender was significant for PCS scores (p=0.01), but not for MCS scores (p=0.37); therefore, models were stratified by gender for PCS scores as an outcome, while models for MCS scores as an outcome were not stratified.

Hierarchical modeling was used for both MCS and PCS score outcomes, with covariates added to the models in five blocks: 1) Individual characteristics (age, race, gender, poverty level, region insurance coverage, education, time period), 2) environmental characteristics (having US provider, unable/delay to treatment, unable/delay to prescription), 3) symptom status (comorbidity count), 4) functional and biological status (missed work days and missed school days), and 5) Health perceptions (perceived physical and mental health).

Interaction between age and depression on QoL

The second interaction terms were run between age and depression on MCS and PCS scores. The interaction terms between depression and age on MCS scores and PCS scores, respectively, were statistically significant. Therefore, fully adjusted models were stratified by age groups for both MCS and PCS scores as outcomes while controlling for covariates. These covariates were: age, race, gender, poverty level, region, insurance coverage, education, time period, having US provider, unable/delay to treatment, unable/ delay to prescription, comorbidity count, missed work days, missed school days, perceived physical and mental health.

The analysis was weighted for US population using the survey package from CRAN repository (CRAN, 2019) and was executed in R version-4.0.0 statistical software (Team, 2019). Statistical significance was determined based on p<0.05.

Results

Table 1 shows the US population characteristics for 2008 to 2016 of 227,663 adult respondents. Among those who had depression, 40% were aged 45–64 years, 61% were female, and 42% were Non-Hispanic White. In addition, among those with depression, 42% were from the South, 34% had incomes below the federal poverty level, 44% had public insurance coverage only, and 84% had less than bachelor’s degree.

Table 1.

Socio-demographic characteristics for time period, MEPS-HC 2008–2016

Overall n (%) Depression n (%)
Characteristic Yes No
Individual characteristics
Age (years)
 18–34 75,029 (33%) 4,409 (24%) 56,565 (32%)
 35–44 40,841 (18%) 3,082 (17%) 32,459 (18%)
 45–64 75,563 (33%) 7,224 (40%) 58,557 (33%)
 ≥ 65 36,230 (16%) 3,346 (19%) 28,659 (16%)
Gender
 Men 105,808 (46%) 7,053 (39%) 82,096 (47%)
 Women 121,855 (54%) 11,008 (61%) 94,144 (53%)
Race and ethnicity
 Hispanic 62,471 (27%) 4,497 (25%) 48,145 (27%)
 Non-Hispanic White 97,981 (43%) 7,618 (42%) 77,537 (44%)
 Non-Hispanic Black 44,672 (20%) 4,500 (25%) 33,052 (19%)
 Other 22,539 (10%) 1,446 (8%) 17,506 (10%)
Region
 Northeast 36,212 (16%) 2,763 (15%) 27,931 (16%)
 Midwest 43,470 (19%) 3,396 (19%) 33,771 (19%)
 South 86,081 (38%) 7,553 (42%) 66,414 (38%)
 West 61,900 (27%) 4,349 (24%) 48,124 (27%)
Family Income to Poverty Ratio
 High income [>=400%] 66,206 (29%) 2,417 (13%) 54,475 (31%)
 Middle Income [>=200% & <400%] 68,321 (30%) 4,368 (24%) 53,627 (30%)
 Low Income [>=125% & <200%] 37,626 (17%) 3,557 (20%) 28,385 (16%)
 Near Poor [>=100% & <125%] 13,566 (6%) 1,607 (9%) 10,034 (6%)
 Poor [<100%] 41,944 (18%) 6,112 (34%) 29,719 (17%)
Insurance Coverage
 Any Private 129,900 (57%) 6,756 (37%) 105,758 (60%)
 Public only 52,815 (23%) 7,942 (44%) 37,265 (21%)
 Uninsured 44,948 (20%) 3,363 (19%) 33,217 (19%)
Education
 Less than Bachelors 143,906 (72%) 13,355 (84%) 109,402 (71%)
 Bachelor’s degree or more 54,868 (28%) 2,534 (16%) 45,560 (29%)
Year
 2008–2010 72,625 (32%) 6,313 (35%) 56,583 (32%)
 2011–2013 79,362 (35%) 6,597 (37%) 62,575 (36%)
 2014–2016 75,676 (33%) 5,151 (29%) 57,082 (32%)
Environmental characteristics
Access to Care
Have a usual care provider
 Yes 139,111 (72.5%) 13,869 (77.8%) 12,5242 (72.0%)
 No 52,636 (27.5%) 3,966 (22.2%) 48,670 (28.0%)
Unable/delay to treatment
 Yes 10,354 (5.3%) 2,533 (14.1%) 7,821 (4.4%)
 No 183,563 (94.7%) 15,474 (85.9%) 168,089 (95.6%)
Unable/delay to prescription
 Yes 8,192 (4.2%) 2228 (12.4%) 5,964 (3.4%)
 No 185,685 (95.8%) 15,776 (87.6%) 169,909 (96.6%)
Symptom status, Mean (SD)
Comorbidity Count (0–5) 0.95 (1.2) 1.467 (1.4) 0.897 (1.2)
Functional Status, Mean (SD)
 Missed work days 1.63 (7.5) 3.464 (11.7) 1.514 (7.1)
 Missed school days 0.54 (2.5) 1.607 (5.9) 0.478 (2.1)
Health perceptions
Perception of Health Status, Mean (SD)
 Physical health (1 excellent to 5 poor) 2.40 (1.1) 3.284 (1.2) 2.306 (1.0)
 Mental health (1 excellent to 5 poor) 2.09 (1.0) 3.030 (1.2) 1.996 (0.9)
Quality of Life, Mean (SD)
 PCS 49.27 (10.6) 40.232 (13.6) 50.196 (9.8)
 MCS 51.16 (10.0) 34.403 (10.7) 52.867 (8.2)
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Figure 1 shows trends of depression prevalence from 2008–2016. The prevalence of depression did not change significantly from 2008 to 2013, and there was no significant difference in depression between men and women (p=0.93). However, men had a significant decrease in linear trend of depression (p<0.001), whereas women did not (p=0.33).

Figure 1.

Figure 1

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Gender differences in prevalence of depression, 2008–2016

Table 2 shows the linear regression model for the relationship between gender and MCS scores. After adjusting for covariates, MCS scores were significantly lower among those with depression than those without depression (β=−0.39, 95% CI: 0.38, −1.16) and lower among women compared to men (β=−0.10, 95% CI: 0.10, −0.31).

Table 2.

Relationship between Depression and Mental Quality of Life as a linear outcome, MEPS-HC 2008–2016

Characteristic β (95% CI)
Depressed −0.39 (0.38,−1.16) ***
Gender
 Men Ref
 Women −0.10 (0.10,−0.31) ***
Age −0.04 (0.04,−0.11) *
Race
 Non-Hispanic White Ref
 Hispanic 0.04 (−0.04,0.12)
 Non-Hispanic Black 0.08 (−0.08,0.24) ***
 Other 0.01 (−0.01,0.04)
Poverty Category
 High income [>=400] Ref
 Middle Income [>=200 & <400] −0.02 (0.02,−0.06)
 Low Income [>=125 & <200] −0.04 (0.04,−0.11)
 Near Poor [>=100 & <125] 0.004 (−0.004,0.03)
 Poor [<100] −0.01 (0.01,−0.04)
Region
 Northeast Ref
 Midwest 0.03 (−0.03,0.08)
 South 0.03 (−0.02,0.08)
 West −0.0003 (0.0003,−0.0008)
Insurance Coverage
 Any Private Ref
 Public only 0.0003 (−0.0003,0.0009)
 Uninsured −0.002 (0.002,−0.006)
Year
 2008–2010 Ref
 2011–2013 0.01 (−0.01,0.02)
 2014–2016 0.03 (−0.03,0.08)
Education
 Less than Bachelors Ref
 Bachelors degree or more −0.03 (0.03,−0.09)
Access to care
 Have USC Provider −0.04 (0.04,−0.12) *
 Unable/ Delay to T reatment −0.05 (0.05,−0.15) **
 Unable/ Delay to Prescription −0.03 (0.03,−0.1)
Comorbidities
 Comorbidity Count (0–5) 0.01 (−0.01,0.03)
Functional Status
 Missed work days −0.01 (0.01,−0.02)
 Missed school days −0.04 (0.04,−0.13) *
Health Status
 Physical Health (1-excellent to 5-poor) −0.02 (0.02,−0.07)
 Mental Health (1-excellent to 5-poor) −0.24 (0.24,−0.72)
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***

p < 0.001

**

p < 0.01

*

p < 0.05

Table 3 shows results from the hierarchical regression modeling for the relationship between depression and PCS scores stratified by gender. Model 1 was adjusted for individual characteristics and found women (β=−0.20, 95% CI: 0.19, −0.59) and men (β=−0.21, 95% CI: 0.21, −0.63) with depression had lower PCS scores compared to women and men who did not have depression. Model 2 was adjusted for individual characteristics and environmental characteristics and, like model 1, found that women (β=−0.17, 95% CI: 0.17, 0.52) and men (β=−0.19, 95% CI: 0.19, −0.57) with depression had lower PCS scores compared to women and men who did not have depression. Model 3 was adjusted for individual characteristics, environmental characteristics, and comorbidities, and found that women (β=−0.15, 95% CI: 0.15, −0.46) and men (β=−0.17, 95% CI: 0.17, −0.52) with depression had lower PCS scores compared to those without depression. Model 4 was adjusted for individual and environmental characteristics, comorbidities, and functional status, and found that women (β=−0.09, 95% CI: 0.09, −0.28) with depression had significantly lower PCS scores compared to women without depression. The relationship was not statistically significant for men (β= 0.01, 95% CI: −0.01, 0.03) in model 4. In the final model 5, after adjusting for individual and environmental characteristics, comorbidities, functional status, and health perceptions, the relationship between depression and PCS scores was no longer statistically significant for women (β= −0.08, 95% CI: 0.08, −0.24) or men (β= 0.01, 95% CI: −0.01, 0.04).

Table 3.

Relationship between Depression and Physical Health Related Quality of Life as a Linear Outcome stratified by gender, MEPS-HC 2008–2016

OUTCOME: PCS
Model 1 Model 2 Model 3 Model 4 Model 5
Men Women Men Women Men Women Men Women Men Women
Characteristic β (95%CI) β (95%CI) β (95%CI) β (95%CI) β (95%CI) β (95%CI) β (95%CI) β (95%CI) β (95%CI) β (95%CI)
Depression −0.21 (0.21,−0.63) *** −0.20 (0.19,−0.59) *** −0.19 (0.19,−0.57) *** −0.17 (0.17,−0.52) *** −0.17 (0.17,−0.52) *** −0.15 (0.15,−0.46) *** 0.01 (−0.01,0.03) −0.09 (0.09,−0.28) * 0.01 (−0.01,0.04) −0.08 (0.08,−0.24)
Age −0.38 (0.37,−1.13) *** −0.38 (0.37,−1.12) *** −0.36 (0.35,−1.06) *** −0.36 (0.35,−1.08) *** −0.22 (0.21,−0.65) *** −0.22 (0.21,−0.65) *** 0.03 (−0.02,0.07) 0.03 (−0.03,0.08) 0.04 (−0.03,0.10) 0.03 (−0.03,0.10)
Race
Non-Hispanic White (Ref) - - - - - - - - - -
Hispanic 0.05 (−0.05,0.14) *** 0.03 (−0.03,0.10) *** 0.03 (−0.03,0.09) *** 0.02 (−0.02,0.06) *** 0.03 (−0.03,0.08) *** 0.02 (−0.02,0.05) ** −0.002 (0.001,−0.004) −0.06 (0.06,−0.18) * 0.01 (−0.01,0.02) −0.06 (0.06,−0.17) *
Non-Hispanic Black 0.01 (−0.01,0.02) −0.01 (0.01,−0.03) * −0.0003 (0.0003,−0.0001) −0.02 (0.01,−0.05) ** 0.03 (−0.03,0.08) −0.01 (0.01,−0.03) −0.07 (0.07,−0.22) ** −0.12 (0.12,−0.37) *** −0.07 (0.07,−0.22) *** −0.11 (0.11,−0.33) ***
Other 0.001 (−0.001,0.002) −0.01 (0.01,−0.02) −0.004 (0.004,−0.01) −0.01 (0.01,−0.04) ** −0.01 (0.01,−0.02) −0.02 (0.02,−0.05) *** −0.006 (0.006,−0.018) −0.02 (0.02,−0.06) 0.002 (−0.002,0.006) −0.02 (0.02,−0.06)
Poverty category
High income (>=400) (Ref) - - - - - - - - - -
Middle Income (>= 200 – <400) −0.05 (0.05,−0.14) *** −0.06 (0.06,−0.17) *** −0.05 (0.05,−0.15) *** −0.05 (0.05,−0.16) *** −0.05 (0.05,−0.14) *** −0.05 (0.04,−0.13) *** −0.04 (0.04,−0.12) 0.002 (−0.002,0.005) −0.02 (−0.02,0.06) −0.004 (0.004,−0.013)
Low Income (>=125 – <200) −0.07 (0.07,−0.21) *** −0.08 (0.08,−0.23) *** −0.07 (0.07,−0.21) *** −0.08 (0.07,−0.23) *** −0.07 (0.07,−0.20) *** −0.07 (0.06,−0.20) *** −0.02 (0.02,−0.07) −0.01 (0.01,−0.02) −0.002 (0.001,−0.004) 0.001 (−0.001,0.002)
Near Poor (>=100 – <125) −0.06 (0.06,−0.19) *** −0.07 (0.07,−0.21) *** −0.06 (0.06,−0.19) *** −0.07 (0.07,−0.20) *** −0.06 (0.06,−0.18) *** −0.06 (0.06,−0.17) *** −0.01 (0.01,−0.04) −0.04 (0.04,−0.13) 0.002 (−0.002,0.007) −0.04 (0.04,−0.11)
Poor (<100) −0.10 (0.10,−0.31) *** −0.12 (0.12,−0.37) *** −0.09 (0.09,−0.29) *** −0.12 (0.12,−0.35) *** −0.09 (0.09,−0.28) *** −0.10 (0.10,−0.31) *** −0.02 (0.02,−0.06) −0.04 (0.04,−0.13) 0.02 (−0.02,0.07) −0.03 (0.03,−0.10)
Region
Northeast (Ref) - - - - - - - - - -
Midwest −0.03 (0.03,−0.09) *** −0.02 (0.02,−0.05) * −0.03 (0.03,−0.10) *** −0.02 (0.02,−0.05) ** −0.03 (0.03,−0.09) *** −0.01 (0.01,−0.04) −0.02 (0.02,−0.05) 0.05 (−0.05,0.16) −0.02 (0.02,−0.07) 0.05 (−0.05,0.15)
South −0.06 (0.06,−0.17) *** −0.04 (0.04,−0.13) *** −0.06 (0.06,−0.19) *** −0.05 (0.05,−0.14) *** −0.05 (0.05,−0.16) *** −0.03 (0.03,−0.10) *** −0.01 (0.01,−0.03) 0.07 (−0.07,0.2) −0.01 (0.01,−0.04) 0.07 (−0.07,0.21)
West −0.03 (0.03,−0.08) *** −0.01 (0.01,−0.04) * −0.03 (0.03,−0.08) *** −0.02 (0.02,−0.05) * −0.03 (0.03,−0.08) *** −0.01 (0.01,0.04) * −0.03 (0.03,−0.09) 0.06 (−0.06,0.19) −0.03 (0.03,−0.08) 0.07 (−0.07,0.22)
Insurance coverage
Any Private (Ref) - - - - - - - - - -
Public only −0.13 (0.13,−0.40) *** −0.15 (0.14,−0.44) *** −0.13 (0.13,−0.39) *** −0.14 (0.14,−0.43) *** −0.12 (0.12,−0.35) *** −0.13 (0.13,−0.38) *** −0.12 (0.12,−0.37) ** −0.10 (0.10,−0.31) ** −0.13 (0.13,−0.38) −0.07 (0.07,−0.21)
Uninsured 0.02 (−0.02,0.05) ** 0.003 (−0.003,0.009) 0.01 (−0.01,0.02) 0.003 (−0.003,0.008) −0.01 (0.01,−0.02) −0.01 (0.01,−0.02) −0.05 (0.05,−0.16) −0.03 (0.03,−0.10) −0.05 (0.05,−0.15) −0.01 (0.01,−0.04)
Year
2008–2010 (Ref)
2011–2013 0.004 (−0.004,0.012) 0.003 (−0.003,0.009) 0.01 (−0.01,0.02) 0.004 (−0.003,0.010) 0.01 (−0.01,0.02) ** 0.001 (−0.001,0.003) 0.01 (−0.01,0.02) −0.002 (0.002,−0.007) 0.001 (−0.001,0.002) *** −0.01 (0.01,−0.02) *
2014–2016 0.01 (−0.01,0.04) 0.02 (−0.02,0.07) *** 0.01 (−0.01,0.04) * 0.02 (−0.02,0.07) *** 0.01 (−0.01,0.04) 0.02 (−0.02,0.07) *** 0.01 (−0.01,0.03) 0.05 (−0.05,0.14) −0.01 (0.01,−0.02) 0.04 (−0.04,0.12)
Education
Less than Bachelors (Ref) - - - - - - - - - -
Bachelors degree or more 0.09 (−0.09,0.29) *** 0.07 (−0.07,0.19) *** 0.09 (−0.09,0.29) *** 0.07 (−0.07,0.21) *** 0.09 (−0.08,0.26) *** 0.06 (−0.05,0.16) *** −0.03 (0.03,−0.08) −0.03 (0.03,−0.09) −0.02 (0.02,−0.07) −0.04 (0.04,−0.11)
Access to care
Have USC Provider −0.09 (0.08,−0.26) *** −0.07 (0.07,−0.21) *** −0.05 (0.05,−0.15) *** −0.05 (0.05,−0.14) *** −0.02 (0.02,−0.06) −0.02 (0.02,−0.06) −0.02 (0.02,−0.05) −0.01 (0.01,−0.04)
Unable/ Delay to Treatment −0.08 (0.08,−0.24) *** −0.09 (0.09,−0.26) *** −0.07 (0.07,−0.22) *** −0.08 (0.08,−0.24) *** −0.01 (0.01,−0.04) −0.06 (0.06,−0.19) * −0.02 (0.02,−0.05) −0.05 (0.05,−0.14)
Unable/ Delay to Prescription −0.07 (0.07,−0.21) *** −0.07 (0.07,−0.21) *** −0.05 (0.05,−0.16) *** −0.06 (0.05,−0.17) *** −0.07 (0.07,−0.2) −0.06 (0.06,−0.18) −0.06 (0.06,−0.19) −0.06 (0.06,−0.18)
Comorbidities
Comorbidity Count (0–5) −0.25 (0.24,−0.74) *** −0.26 (0.25,−0.78) *** −0.08 (0.08,−0.24) *** −0.02 (0.02,−0.05) −0.06 (0.06,−0.18) ** 0.01 (−0.01,0.03)
Functional Status
Missed work days −0.06 (0.06,−0.18) −0.08 (0.08,−0.25) −0.06 (0.06,−0.19) −0.07 (0.07,−0.21)
Missed school days −0.07 (0.07,−0.20) −0.05 (0.05,−0.15) −0.04 (0.04,−0.12) −0.02 (0.02,−0.07)
Health Status
Physical Health (1-excellent to 5-poor) −0.31 (0.30,−0.93) *** −0.31 (0.30,−0.92) ***
Mental Health (1-excellent to 5-poor) 0.17 (−0.16,0.5) *** 0.14 (−0.14,0.41) ***
Open in a new tab

Note:

*

p <0.05

**

p <0.01

***

p <0.001

B: Standardized beta coefficient

Model 1: Individual characteristics + depression

Model 2: Individual characteristics + depression + Access to Care

Model 3: Individual characteristics + depression + Access to Care + Comorbidity

Model 4: Individual characteristics + depression + Access to Care + Comorbidity + Functional status

Model 5: Individual characteristics + depression + Access to Care + Comorbidity + Functional status + Health status

Table 4 shows results from the adjusted regression models for the relationship between depression and PCS scores stratified by age. Women with depression ages 40 – 64 years (β= −0.07, 95% CI: 0.07, −0.20) and ≥65 years (β= −0.08, 95% CI: 0.08, −0.24) had significantly lower PCS scores compared to those without depression. However, men with depression aged 18 – 39 years (β= −0.03, 95% CI: 0.03, −0.10) and ages 40 – 64 years (β= −0.09, 95% CI: 0.08, −0.26) had significantly lower PCS scores compared to those without depression.

Table 4.

Relationship between depression and physical component summary (PCS) scores of quality of life as a linear outcome stratified by age-group, MEPS-HC 2008–2016

OUTCOME: PCS
Women Men
Age group, years Model I Model II Model III Model IV Model V Model VI
18–39 40–64 ≥65 18–39 40–64 ≥65
β (95%CI) β (95%CI) β (95%CI) β (95%CI) β (95%CI) β (95%CI)
N 24,281 25,330 2,598 25,088 25,512 2,513
Characteristic
Depression −0.02 (0.02,−0.06) −0.07 (0.07,−0.20) *** −0.08 (0.08,−0.24) *** −0.03 (0.03,−0.10) * −0.09 (0.08,−0.26) *** −0.05 (0.05,−0.15)
Race
Non-Hispanic White (ref)
Hispanic −0.01 (0.01,−0.02) 0.04 (−0.04,0.12) *** 0.09 (−0.09,0.27) *** 0.004 (−0.004,0.013) 0.07 (−0.06,0.19) *** 0.08 (−0.08,0.23) ***
Non-Hispanic Black −0.05 (0.04,−0.14) *** 0.02 (−0.02,0.05) * 0.10 (−0.10,0.31) *** −0.03 (0.03,−0.09) *** 0.01 (−0.01,0.04) 0.04 (−0.03,0.11)
Other −0.03 (0.03,−0.10) *** −0.02 (0.02,−0.01) 0.04 (−0.03,0.11) −0.05 (0.04,−0.13) *** 0.01 (−0.01,0.03) 0.05 (−0.05,0.14) *
Poverty Category
High income (>=400) (ref)
Middle Income (>=200 & <400) −0.01 (0.01,−0.04) * −0.01 (0.01,−0.03) * −0.02 (0.02,−0.05) *** −0.02 (0.02,−0.06) ** −0.03 (0.03,−0.08) *** −0.02 (0.02,−0.05)
Low Income (>=125 & <200) −0.02 (0.02,−0.06) −0.02 (0.02,−0.07) −0.05 (0.05,−0.14) −0.04 (0.04,−0.13) * −0.02 (0.02,−0.06) −0.05 (0.05,−0.14)
Near Poor (>=100 & <125) −0.01 (0.01,−0.04) * −0.02 (0.01,−0.04) ** −0.05 (0.05,−0.16) −0.02 (0.02,−0.06) *** −0.01 (0.01,−0.04) * −0.01 (0.01,−0.04)
Poor (<100) −0.03 (0.03,−0.08) −0.02 (0.02,−0.07) −0.13 (0.13,−0.39) −0.03 (0.03,−0.09) * −0.04 (0.04,−0.12) ** −0.03 (0.03,−0.10)
Region
Northeast (ref)
Midwest 0.01 (−0.01,0.03) −0.02 (0.01,−0.04) −0.02 (0.02,−0.05) −0.01 (0.01,−0.04) −0.03 (0.03,−0.08) ** −0.05 (0.05,−0.14)
South 0.01 (−0.01,0.04) −0.04 (0.04,−0.12) *** −0.08 (0.07,−0.23) ** −0.04 (0.04,−0.12) *** −0.05 (0.05,−0.16) *** −0.06 (0.06,−0.17)
West 0.01 (−0.01,0.04) −0.03 (0.03,−0.09) ** −0.06 (0.06,−0.17) * −0.02 (0.02,−0.06) −0.03 (0.02,−0.07) * −0.06 (0.06,−0.18)
Insurance Coverage
Any Private (ref)
Public only −0.06 (0.06,−0.18) *** −0.06 (0.06,−0.19) *** −0.02 (0.02,−0.07) −0.05 (0.05,−0.15) *** −0.06 (0.06,−0.18) *** −0.02 (0.02,−0.06)
Uninsured −0.01 (0.01,−0.04) −0.01 (0.01,−0.03) 0.05 (−0.05,0.15) *** −0.01 (0.01,−0.03) −0.0001 (0.0001,−0.0002) −0.01 (0.01,−0.04)
Year
2008–2010 (ref)
2011–2013 −0.02 (0.02,−0.05) * 0.003 (−0.003,0.008) 0.05 (−0.05,0.16) * −0.01 (0.01,−0.02) 0.02 (−0.02,0.05) −0.01 (0.01,−0.02)
2014–2016 0.03 (−0.03,0.10) *** 0.03 (−0.03,0.09) *** 0.08 (−0.08,0.23) ** 0.01 (−0.01,0.04) 0.04 (−0.04,0.12) *** 0.01 (−0.01,0.04)
Education
Less than Bachelors (ref)
Bachelors degree or more 0.03 (−0.03,0.10) *** 0.04 (−0.04,0.11) *** −0.01 (0.01,−0.02) 0.05 (−0.05,0.14) *** 0.06 (−0.06,0.18) *** 0.05 (−0.05,0.14)
Access to care
Have USC Provider −0.03 (0.02,−0.07) ** −0.05 (0.05,−0.14) *** −0.01 (0.01,−0.04) −0.04 (0.04,−0.12) *** −0.04 (0.04,−0.13) *** −0.02 (0.02,−0.07)
Unable/ Delay to Treatment −0.06 (0.06,−0.19) *** −0.08 (0.07,−0.23) *** −0.04 (0.04,−0.11) −0.06 (0.05,−0.17) *** −0.08 (0.08,−0.25) *** −0.08 (0.07,−0.22) ***
Unable/ Delay to Prescription −0.04 (0.04,−0.12) *** −0.04 (0.04,−0.12) *** −0.03 (0.03,−0.09) −0.02 (0.02,−0.07) −0.04 (0.04,−0.13***) −0.03 (0.03,−0.09)
Comorbidities
Comorbidity Count (0–5) −0.09 (0.08,−0.25) *** −0.14 (0.14,−0.42) *** −0.12 (0.12,−0.36) *** −0.06 (0.06,−0.18) *** −0.13 (0.12,−0.38) *** −0.17 (0.17,−0.51) ***
Functional Status
Missed work days −0.10 (0.10,−0.31) *** −0.11 (0.11,−0.34) *** −0.03 (0.03,−0.10) −0.11 (0.11,−0.32) *** −0.13 (0.13,−0.40) *** −0.10 (0.10,−0.31) ***
Health Status
Physical Health (1-excellent to 5-poor) −0.34 (0.33,−1.02) *** −0.43 (0.41,−1.27) *** −0.48 (0.47,−1.44) *** −0.32 (0.31,−0.94) *** −0.37 (0.36,−1.11) *** −0.36 (0.35,−1.07) ***
Mental Health (1-excellent to 5-poor) 0.10 (−0.10,0.3) *** 0.08 (−0.08,0.25) *** 0.06 (−0.06,0.18) * 0.08 (−0.07,0.22) *** 0.06 (−0.06,0.18) *** −0.05 (0.05,−0.14)
Open in a new tab

Note:

***

p < .001

**

p < .01

*

p < .05

Table 5 highlights the adjusted regression models for the relationship between depression and MCS scores between men and women, stratified by age. Women with depression aged 18 – 39 years (β= −0.42, 95% CI: 0.41, −1.25), 40 – 64 years (β= −0.45, 95% CI: 0.43, −1.33), and ≥65 years (β= −0.36, 95% CI: 0.36, −1.08) all had significantly lower MCS scores compared to those without depression. Similarly, men with depression aged 18 – 39 years (β= −0.38, 95% CI: 0.37, −1.14), 40 – 64 years (β= −0.40, 95% CI: 0.39, −1.20), and ≥65 years (β= −0.34, 95% CI: 0.33, −1.01) all had significantly lower MCS scores compared to those without depression.

Table 5.

Relationship between depression and mental component summary (MCS) scores of quality of life as a linear outcome stratified by age-group, MEPS-HC 2008–2016

OUTCOME: MCS
Women Men
Model I Model II Model III Model IV Model V Model VI
Age group, years 18–39 40–64 ≥65 18–39 40–64 ≥65
β (95%CI) β (95%CI) β (95%CI) β (95%CI) β (95%CI) β (95%CI)
N 24,290 25,339 2,600 25,097 25,529 2,513
Depressed −0.42 (0.41,−1.25) *** −0.45 (0.43,−1.33) *** −0.36 (0.36,−1.08) *** −0.38 (0.37,−1.14) *** −0.40 (0.39,−1.20) *** −0.34 (0.33,−1.01) ***
Race
Non-Hispanic White (ref)
Hispanic 0.05 (−0.05,0.16) *** 0.04 (−0.04,0.11) *** −0.01 (0.01,−0.02) 0.04 (−0.04,0.13) *** 0.02 (−0.02,0.05) * −0.03 (0.03,−0.08)
Non-Hispanic Black 0.07 (−0.07,0.21) *** 0.05 (−0.05,0.16) *** 0.03 (−0.03,0.08) 0.06 (−0.06,0.17) *** 0.05 (−0.05,0.14) *** 0.04 (−0.04,0.13) *
Other 0.04 (−0.04,0.12) *** 0.03 (−0.03,0.08) *** −0.03 (0.03,−0.1) 0.04 (−0.04,0.12) *** 0.02 (−0.02,0.05) * 0.03 (−0.03,0.08)
Poverty Category
High income [>=400] (ref)
Middle Income [>=200 & <400] −0.01 (0.01,−0.04) *** −0.02 (0.02,−0.06) ** 0.002 (−0.002,0.007) −0.004 (0.004,−0.012) ** −0.01 (0.01,−0.04) *** −0.02 (0.02,−0.07) *
Low Income [>=125 & <200] −0.01 (0.01,−0.03) * −0.04 (0.04,−0.11) ** −0.01 (0.01,−0.04) −0.03 (0.03,−0.08) −0.02 (0.02,−0.06) −0.02 (0.02,−0.07) **
Near Poor [>=100 & <125] −0.02 (0.02,−0.06) −0.02 (0.02,−0.07) *** −0.02 (0.02,−0.06) −0.01 (0.01,−0.03) ** −0.01 (0.01,−0.02) ** −0.06 (0.06,−0.17)
Poor [<100] −0.04 (0.04,−0.11) −0.03 (0.03,−0.08) ** −0.01 (0.01,−0.03) −0.03 (0.03,−0.09) −0.03 (0.03,−0.1) −0.07 (0.07,−0.20)
Region
Northeast (ref)
Midwest −0.01 (0.01,−0.03) 0.01 (−0.01,0.02) 0.02 (−0.02,0.07) 0.002 (−0.002,0.007) −0.002 (0.002,−0.005) 0.03 (−0.03,0.08)
South −0.03 (0.03,−0.09) * 0.003 (−0.003,0.010) 0.004 (−0.004,0.013) 0.03 (−0.03,0.1) * −0.001 (0.001,−0.003) 0.003 (−0.003,0.009)
West −0.04 (0.04,−0.12) ** −0.02 (0.02,−0.05) −0.04 (0.04,−0.13) −0.01 (0.01,−0.02) −0.02 (0.02,−0.06) −0.01 (0.01,−0.04)
Insurance Coverage
Any Private (ref)
Public only 0 (0,0.01) −0.03 (0.02,−0.08) ** −0.03 (0.03,−0.08) −0.001 (0.001,−0.003) −0.01 (0.01,−0.01) 0.02 (−0.02,0.06)
Uninsured 0.01 (−0.01,0.04) 0.003 (−0.003,0.008) 0.02 (−0.02,0.05) 0.01 (−0.01,0.02) −0.01 (0.01,−0.03) −0.005 (0.005,−0.014)
Year
2008–2010 (ref)
2011–2013 0.03 (−0.03,0.09) *** 0.01 (−0.01,0.02) −0.04 (0.04,−0.12) 0.03 (−0.03,0.08) *** 0.003 (−0.003,0.010) −0.003 (0.003,−0.009)
2014–2016 0.05 (−0.05,0.15) *** 0.03 (−0.03,0.1) *** −0.03 (0.03,−0.1) 0.05 (−0.05,0.16) *** 0.04 (−0.04,0.12) *** 0.02 (−0.02,0.07)
Education
Less than Bachelors (ref)
Bachelor’s degree or more −0.02 (0.02,−0.07) ** −0.02 (0.02,−0.07) *** 0.002 (−0.002,0.004) −0.04 (0.04,−0.13) *** −0.05 (0.05,−0.16) *** −0.03 (0.03,−0.08)
Access to care
Have USC Provider −0.02 (0.02,−0.05) * −0.003 (0.023,−0.009) *** −0.01 (0.01,−0.03) −0.02 (0.02,−0.05) ** 0.01 (−0.01,0.03) 0.04 (−0.04,0.13)
Unable/ Delay to Treatment −0.06 (0.06,−0.17) *** −0.04 (0.04,−0.12) *** −0.06 (0.06,−0.19) −0.05 (0.05,−0.15) *** −0.04 (0.04,−0.13) *** −0.03 (0.03,−0.08)
Unable/ Delay to Prescription −0.03 (0.03,−0.09) *** −0.03 (0.03,−0.10) 0.02 (−0.02,0.05) −0.04 (0.04,−0.13) *** −0.02 (0.02,−0.06) ** −0.01 (0.01,−0.03)
Comorbidities
Comorbidity Count (0–5) −0.02 (0.02,−0.07) ** 0.004 (−0.004,0.012) −0.04 (0.04,−0.12) −0.04 (0.04,−0.11) *** −0.01 (0.01,−0.03) −0.03 (0.03,−0.08)
Functional Status
Missed work days −0.01 (−0.02,0.01) −0.01 (0.01,−0.02) −0.01 (0.01,−0.04) −0.02 (0.02,−0.06) * −0.01 (0.01,−0.04) −0.03 (0.03,−0.1)
Health Status
Physical Health (1-excellent to 5-poor) −0.02 (0.02,−0.06) * −0.03 (0.03,−0.1) ** 0.01 (−0.01,0.04) −0.04 (0.04,−0.11) *** −0.03 (0.03,−0.1) *** −0.08 (0.08,−0.25) *
Mental Health (1-excellent to 5-poor) −0.26 (0.26,−0.78) *** −0.24 (0.23,−0.71) *** −0.25 (0.24,−0.74) −0.22 (0.21,−0.65) *** −0.22 (0.22,−0.67) *** −0.19 (0.19,−0.57) ***
Open in a new tab

Note:

***

p < .001

**

p < .01

*

p < .05

Discussion

Overall, using a nationally representative dataset, this study revealed the following key findings. First, we found that MCS scores were significantly negatively associated with depression, and lower for women compared to men after adjusting for possible confounders. There was a differential relationship between depression and PCS scores by gender. Secondly, by using a series of hierarchical models and selecting variables based on theory, we found that functional status factors explained the relationship between depression and PCS scores for men, whereas health perceptions explained the relationship for women. Thirdly, women with depression aged 40 – 64 years and ≥65 years had significantly lower PCS scores compared to those without depression, while men with depression aged 18 – 39 years and 40 – 64 years had significantly lower PCS scores compared to those without depression. Fourth, women and men with depression in all age groups had significantly lower MCS scores compared to those without depression.

This study presents new knowledge for clinical and public health interventions by providing a comprehensive analysis of the relationship between depression and QoL. Namely, this study underscores the importance and need to further investigate how gender differences influence QoL in women and men with depression. We found that there was no differential influence between men and women in the relationship between depression and MCS scores, though women did have lower MCS scores overall. It is known that individuals with lower MCS scores have higher risks of suicide (Hoertel et al., 2018), obesity (Farhat, lannotti, & Summersett-Ringgold, 2015), post-traumatic stress disorders (Sareen et al., 2007), developing cardiovascular diseases or stroke (Crichton, Bray, McKevitt, Rudd, & Wolfe, 2016), and work productivity loss (Dewa, Hoch, Nieuwenhuijsen, Parikh, & Sluiter, 2019). These negative outcomes contribute to the burden of healthcare costs and reduction of human capital, negatively impacting the economy. Based on these results, efforts to address mental health quality of life may not need differentiation by gender.

However, clinical and public health efforts to improve physical QoL may need to focus on different aspects of quality of life for men and women with depression, specifically, health perceptions in women and functional status in men. While the relationship between PCS scores and depression was significant for both men and women when individual characteristics, environmental characteristics, and comorbidities were added to the model, significance of the relationship was removed for men when functional status was added to the model. Factors that influence missed work or school days may explain the relationship that was seen between PCS scores and depression for men. Depression is known to be associated with reduction in role functioning such as poor work performance, unstable employment, reduced earnings (Parker & Brotchie, 2010), more work-days missed (Booker et al., 2020; Negi, Swanberg, Clouser, & Harmon-Darrow, 2020), and work impairment (Kessler, Greenberg, Mickelson, Meneades, & Wang, 2001). Our findings are line with evidence from prior research that showed that major depressive disorders manifested as impairment in physical wellbeing and occupational performance (Lewinsohn, Rohde, Seeley, Klein, & Gotlib, 2003). Based on these results, interventions to address depression should include a component that targets physical function or wellbeing in men. In women, perceptions of health were more important in explaining the relationship between depression and quality of life. This finding is important because of the possibility that depressive symptoms lead to physical symptoms by lowering an individual’s mood and influencing how they view their physical health. This in turn may lead to worsening depression symptoms and physical QoL due to reduced physical activity, reduced adherence to anti-depressant treatment, or increased severity of depressive symptoms (Heesch, van Gellecum, Burton, van Uffelen, & Brown, 2015, 2016).

Our study showed that depression lowered physical QoL much more in older women aged 65 years or more than it lowered physical QoL among middle-aged counterparts aged 40 to 64 years. One review highlighted similar evidence showing that depression severity was associated with poorer QoL in older persons and this relationship persisted over time (Sivertsen, Bjørkløf, Engedal, Selbæk, & Helvik, 2015). Prior research highlights the negative effect of comorbid depression on QoL in populations with multimorbidity (Williams & Egede, 2016) or chronic conditions such as diabetes (Dismuke, Hernandez-Tejada, & Egede, 2014; Egede & Hernandez-Tejada, 2013) and stroke (Ellis, Grubaugh, & Egede, 2013). This finding is of public health importance because depression in older women may result from prolonged exposure to high-magnitude stressors (Seib et al., 2014).

Conversely, we found that physical QoL was lower among middle-aged men (40–64 years) with depression compared to elderly men (≥65 years) with depression. Stressful life events such as financial constraints and work-related events are known to have persistent negative effects on functioning among middle-aged patients (Oh & Hwang, 2017). Therefore, managing stressors may help to improve physical QoL among middle-aged men (Sherbourne, Meredith, Rogers, & Ware, 1992).

Regarding mental QoL, both women and men across all age groups with depression had lower mental QoL compared to those without depression; however, the strength of association was highest among middle-aged men and women. Prior research by Monteiro and colleagues arrived at similar findings demonstrating that middle-aged patients with fewer depressive symptoms had higher QoL, including physical QoL (Monteiro, Canavarro, & Pereira, 2016). Our findings are line with findings from prior research that showed that depressive symptoms worsened menstrual specific QoL among middle-aged women (Sohn, 2018). Among middle-aged women and men, it is likely that shorter duration of somatic depressive symptoms may contribute to better physical QoL and subsequent healthy aging.

Older women and men with depression experienced the least reduction in mental QoL. Evidence shows that suicidal rates associated with depression among older populations are declining, possibly because older adults with depression are less likely to present with affective symptoms and more likely to present with somatic symptoms (Fiske, Wetherell, & Gatz, 2009). In addition, mental wellbeing in older women and men with depression may be higher compared to younger counterparts with depression because of spousal support through caregiver or treatment support roles.

While our study’s use of a nationally representative dataset is a strength, it also has two noteworthy limitations. First, comorbidities and depression were assessed by self-report, which may be subject to recall bias. However, questions used by MEPS have been based on validated questions and therefore are reliable as measures of the overall constructs being assessed. Second, changes in physiological status of women like pregnancy or postpartum periods, which are known to increase the risk of depression, were not included as covariates in the models. Future studies should collect information specific to men and women to better understand the differential relationship specifically on physical related QoL. Thirdly, the MEPS-HC dataset only identifies participants as women or men, so our results cannot be generalized to people with other gender identities. Finally, MEPS data is cross-sectional, and as such we cannot comment on causality. The relationship between depression and QoL needs investigation using longitudinal data to better understand if causal or recursive relationships exist.

Implications for practice and/or policy

Depression was associated with poor mental QoL in the general adult US population of women and men. This is clinically important because of the opportunity to assess mental QoL in women and men diagnosed with depression. Addressing the severity of depressive symptoms may improve mental wellbeing and is likely to reduce the risk of medical comorbidities, especially among patients diagnosed with depression (Barnes, Murphy, Fowler, & Rempfer, 2012).

Clinical assessment of women with depression for mental and physical health perceptions may improve performance in their activities of daily living such as in the workplace or school (Furegato, Santos, & Silva, 2008). Similarly, evaluating the functional status among men with depression may provide an opportunity to integrate clinic- and workplace-based tailored interventions that may improve physical wellbeing of men with depression in the general adult population.

Although current United States Preventive Services Task Force (USPSTF) guidelines recommend accurate diagnostic screening for depression as well as provision of effective treatment and appropriate follow-up (USPSTF, 2016), screening for quality of life is not addressed. Our findings indicate a clinically relevant rationale for mental QoL screening in middle-aged populations of women and men with and without depression. In addition, our findings suggest the importance of health perceptions screening among women with depression and functional status screening among men with depression in order to improve their physical wellbeing. Future research is needed that uses longitudinal study designs to investigate whether any causal pathways exist in this gender- and age- differential relationship between depression and physical QoL.

Conclusions

In conclusion, this study found that depression was associated with lower MCS and PCS scores in both women and men, though women had lower MCS scores compared to men. In addition, functional status explained the relationship between depression and PCS scores for men, whereas health perceptions explained the relationship between depression and PCS scores for women. Interventions to address depression should include additional components that target functional status and perceptions of health among men and women, respectively, to improve both depressive symptoms and the influence on QoL. Considerations for clinical practice should include addressing the age- and gender related differences in the association between depression and QoL.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgements

We would like to thank the Center for Advancing Population Sciences (CAPS) team and the CAPS manuscript writing group for collective support during discussions.

Funding

Effort for this project was partially supported by the National Institute of Diabetes and Digestive and Kidney Diseases (K24DK093699, R01DK118038, R01DK120861, PI: Egede); NIH/NIMHD (R01MD013826, PI: Egede/Walker) and the American Diabetes Association (1-19-JDF-075, PI: Walker). Funding organizations had no role in the analysis, interpretation of data, or writing of the manuscript.

Abbreviations

CI

Confidence Interval

MCS

Mental Component Summary

MEPS-HC

Medical Expenditure Panel Survey-Household Component

PCS

Physical Component Summary

PHQ-2

Patient Health Questionnaire-2

QoL

Quality of Life

Footnotes

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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