Shared Risk Factors for Depressive Disorder Among Older Adult Couples in Korea

Ji Won Han; Hee Won Yang; Jong Bin Bae; Dae Jong Oh; Dong Gyu Moon; Eunji Lim; Jin Shin; Bong Jo Kim; Dong Woo Lee; Jeong Lan Kim; Jin Hyeong Jhoo; Joon Hyuk Park; Jung Jae Lee; Kyung Phil Kwak; Seok Bum Lee; Seok Woo Moon; Seung-Ho Ryu; Shin Gyeom Kim; Ki Woong Kim

doi:10.1001/jamanetworkopen.2023.8263

. 2023 Apr 14;6(4):e238263. doi: 10.1001/jamanetworkopen.2023.8263

Shared Risk Factors for Depressive Disorder Among Older Adult Couples in Korea

Ji Won Han ^1,², Hee Won Yang ³, Jong Bin Bae ^1,², Dae Jong Oh ⁴, Dong Gyu Moon ¹, Eunji Lim ⁵, Jin Shin ¹, Bong Jo Kim ⁶, Dong Woo Lee ⁷, Jeong Lan Kim ⁸, Jin Hyeong Jhoo ⁹, Joon Hyuk Park ¹⁰, Jung Jae Lee ¹¹, Kyung Phil Kwak ¹², Seok Bum Lee ¹¹, Seok Woo Moon ¹³, Seung-Ho Ryu ¹⁴, Shin Gyeom Kim ¹⁵, Ki Woong Kim ^1,^2,^16,^✉

¹Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea

²Department of Psychiatry, Seoul National University, College of Medicine, Seoul, South Korea

³Department of Psychiatry, Chungnam National University Hospital, Daejeon, South Korea

⁴Workplace Mental Health Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea

⁵Department of Neuropsychiatry, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea

⁶Department of Psychiatry, Gyeongsang National University, School of Medicine, Jinju, South Korea

⁷Department of Neuropsychiatry, Inje University Sanggye Paik Hospital, Seoul, South Korea

⁸Department of Psychiatry, School of Medicine, Chungnam National University, Daejeon, South Korea

⁹Department of Neuropsychiatry, Kangwon National University Hospital, Chuncheon, South Korea

¹⁰Department of Neuropsychiatry, Jeju National University Hospital, Jeju, South Korea

¹¹Department of Psychiatry, Dankook University Hospital, Cheonan, South Korea

¹²Department of Psychiatry, Dongguk University Gyeongju Hospital, Gyeongju, South Korea

¹³Department of Psychiatry, School of Medicine, Konkuk University and Konkuk University Chungju Hospital, Chungju, South Korea

¹⁴Department of Psychiatry, School of Medicine, Konkuk University and Konkuk University Medical Center, Seoul, South Korea

¹⁵Department of Neuropsychiatry, Soonchunhyang University Bucheon Hospital, Bucheon, South Korea

¹⁶Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, South Korea

Accepted for Publication: February 22, 2023.

Published: April 14, 2023. doi:10.1001/jamanetworkopen.2023.8263

^✉

Corresponding Author: Ki Woong Kim, MD, PhD, Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumiro, 173 Beongil Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea (kwkimmd@snu.ac.kr).

Author Contributions: Drs Han and K. W. Kim had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Han, Yang, Lim, B. J. L. Kim, Jhoo, J. J. Lee, K. W. Kim.

Acquisition, analysis, or interpretation of data: Han, Yang, Bae, Oh, D. Moon, Shin, D. W. Lee, J. L. Kim, Park, J. J. Lee, Kwak, S. B. Lee, S. W. Moon, Ryu, S. G. Kim, K. W. Kim.

Drafting of the manuscript: Han, Yang, Lim, Shin, B. J. L. Kim, D. W. Lee, Jhoo, S. W. Moon, K. W. Kim.

Critical revision of the manuscript for important intellectual content: Han, Yang, Bae, Oh, D. Moon, J. L. Kim, Park, J. J. Lee, Kwak, S. B. Lee, Ryu, S. G. Kim, K. W. Kim.

Statistical analysis: Han, Yang, Shin.

Obtained funding: K. W. Kim.

Administrative, technical, or material support: Han, Bae, Lim, B. J. Kim, D. W. Lee, J. L. Kim, Jhoo, Park, S. W. Moon, K. W. Kim.

Supervision: Oh, D. W. Lee, K. W. Kim.

Conflict of Interest Disclosures: None reported.

Funding/Support: This research was supported by research project 2019-ER6201-01 from the Korea National Institute of Health and grant HI09C1379 (A092077) from the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (Dr K. W. Kim, principal investigator).

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See the Supplement.

^✉

Corresponding author.

PMCID: PMC10105310 PMID: 37058304

Key Points

Question

Do the risk factors shared between older couples mediate their shared risk of depressive disorders?

Findings

In this cohort study of 956 elderly couples, social-emotional support, chronic illness burden, and the presence of a cognitive disorder that were shared between couples mediated almost one-third of the spousal risk of depressive disorder.

Meaning

These findings suggest that identification and intervention of the shared risk factors of depressive disorders within older couples may reduce the risk of depressive disorders in the spouses of people with depression.

This cohort study of elderly couples examines shared risk factors for depressive disorders and whether these factors mediate the shared risk of depressive disorders.

Abstract

Importance

Although couples may share many risk factors for depressive disorders in their lifetime, whether these factors mediate the shared risk of depressive disorders has rarely been investigated.

Objectives

To identify the shared risk factors for depressive disorder in couples and investigate their mediating roles in the shared risk of depressive disorders among older adult couples.

Design, Setting, and Participants

This nationwide, multicenter, community-based cohort study assessed 956 older adults from the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD) and a cohort of their spouses (KLOSCAD-S) between January 1, 2019, to February 28, 2021.

Exposures

Depressive disorders of the KLOSCAD participants.

Main Outcomes and Measures

The mediating roles of shared factors in couples on the association between one spouse’s depressive disorder and the other’s risk of depressive disorders was examined using structural equation modeling.

Results

A total of 956 KLOSCAD participants (385 women [40.3%] and 571 men [59.7%]; mean [SD] age, 75.1 [5.0] years) and their spouses (571 women [59.7%] and 385 men [40.3%]; mean [SD] age, 73.9 [6.1] years) were included. The depressive disorders of the KLOSCAD participants were associated with an almost 4-fold higher risk of depressive disorders in their spouses in the KLOSCAD-S cohort (odds ratio, 3.89; 95% CI, 2.06-7.19; P < .001). Social-emotional support mediated the association between depressive disorders in the KLOSCAD participants and their spouses’ risk of depressive disorders by itself (β = 0.012; 95% CI, 0.001-0.024; P = .04; mediation proportion [MP] = 6.1%) and through chronic illness burden (β = 0.003; 95% CI, 0.000-0.006; P = .04; MP = 1.5%). Chronic medical illness burden (β = 0.025; 95% CI, 0.001-0.050; P = .04; MP = 12.6%) and presence of a cognitive disorder (β = 0.027; 95% CI, 0.003-0.051; P = .03; MP = 13.6%) mediated the association.

Conclusions and Relevance

The risk factors shared by older adult couples may mediate approximately one-third of the spousal risk of depressive disorders. Identification of and intervention in the shared risk factors of depression among older adult couples may reduce the risk of depressive disorders in the spouses of older adults with depression.

Introduction

Depression is a significant public health problem for older adults and is associated with a substantial individual and societal burden.¹ Depression in older adults is associated with decreased functional status, low perceived quality of life, increased use of medical services, decreased adherence to treatment plans, and higher admission rates to long-term care facilities.^1,2 Thus, identifying individuals at high risk for geriatric depression and implementing preventive strategies may be important in this aspect of the public health burden of late-life depression.¹

Previous studies^3,4,5,6,7 have shown that having a partner with depression may increase an individual’s risk of depression^3,4,5,6 (1.58-fold³ to 2.08-fold⁷). This spousal concordance of depression has been explained by assortative mating, cohabitation effects,^6,8 and emotional contagion.⁹ Assortative mating refers to the tendency of mate selection based on the similarity of individual characteristics, including values and personality traits.⁶ Cohabitation effects explicate the significance of common household environmental influences, including the sharing of numerous lifestyle aspects after marriage.^6,9,10,11,12 Emotional contagion shows that the mood of one person can lead to similar affect in another.^6,9 In older adult couples, cohabitation effects may be more influential according to the duration of marriage, which may explain the difference in mechanism between older adult couple depression and that of young couples. Social interaction,^13,14 physical health,^14,15,16 or marital quality¹³ are known to affect depressive symptoms among older adult couples. These factors shared within couples may act as a mediator that links depression in couples. However, to our knowledge, no study has directly investigated the mediation of shared risk factors in couples regarding the risk of depressive disorders associated with spousal depressive disorders in the older adult population. Our objective was to identify the shared risk factors in couples and examine their mediating roles in the shared risk of depressive disorders among elderly couples in a population-based couple cohort study.

Methods

Study Design and Participants

We acquired data from the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD).¹⁷ The KLOSCAD is an ongoing, nationwide, multicenter prospective cohort study of 6818 community-dwelling Koreans who were randomly sampled from the residents of 13 districts across South Korea. The inclusion criterion was age of 60 years or older, and there were no exclusion criteria. The baseline assessment was conducted from November 2010 to October 2012 with biannual follow-ups. At the fourth follow-up assessment (from January 1, 2019, to February 28, 2021), we constructed a spousal cohort (KLOSCAD-S) that consisted of the spouses of the KLOSCAD participants. The spouses of the 863 KLOSCAD participants responded. In the case of the 121 couples included in the original KLOSCAD cohort, 93 couples also completed their fourth follow-up assessment. We assigned these 93 participants who were enrolled later as spouses, resulting in a total of 956 participants in KLOSCAD-S.¹⁸ We introduced the KLOSCAD-S in a previous study,¹⁸ which overlaps in study design, covariates, and statistical analysis with this study. The study protocol was explained to all participants, each of whom provided written informed consent. The study protocol was approved by the institutional review board of the Seoul National University Bundang Hospital. This report followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for observational studies.

Assessment of Covariates

Research nurses evaluated the participants’ demographic characteristics (age, sex, and years of formal education). A history of heavy alcohol use was defined as the average lifetime amount of alcohol use over 21 standard units per week. Physical inactivity was defined as less than 2.5 hours of moderate activity per week and less than 1.25 hours of vigorous activity per week according to the World Health Organization’s recommendations on the minimum amount of activity that confers health benefits.¹⁹ We evaluated the burden of comorbid chronic medical illnesses, including hearing loss, smoking, hypertension, diabetes, dyslipidemia, cerebrovascular accident, history of head injury, and obesity using the Cumulative Illness Rating Scale (CIRS),²⁰ which combines the morbidity of chronic medical problems of 14 organ systems into a single comprehensive score. The CIRS has been reported to be a valid indicator of health status in geriatric patients.²¹

We evaluated social-emotional and tangible support using the Medical Outcomes Study Social Support Survey (MOS-SSS), which is a brief, self-administered, multidimensional social support survey developed from the Medical Outcomes Study.²² On the basis of the 2-factor model,^23,24 we defined social-emotional support using the items of emotional, informational, affectionate support, and positive social interaction. We defined tangible support using items of tangible support of the MOS-SSS. Each support has a score ranging from 0 to 100, with higher scores indicating better social support.²⁵

Geriatric psychiatrists conducted face-to-face standardized diagnostic interviews and performed physical and neurologic examinations of every participant using the Korean version of the Consortium to Establish a Registry for Alzheimer Disease (CERAD-K) Assessment Packet Clinical Assessment Battery.²⁶ Trained research neuropsychologists administered neuropsychological tests by means of the CERAD-K Neuropsychological Assessment Battery, which includes the Mini-Mental State Examination.²⁷ We defined cognitive disorder as having mild cognitive impairment or dementia. Through diagnostic consensus conferences, a panel of geriatric psychiatrists diagnosed dementia and mild cognitive impairment according to the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) (DSM-IV) criteria²⁸ and the consensus criteria proposed by the International Working Group,²⁹ respectively.

Assessment of Depression

Geriatric psychiatrists assessed all participants through face-to-face standardized diagnostic interviews using the Korean version of the Mini International Neuropsychiatric Interview (MINI-K).¹⁰ A panel of geriatric psychiatrists diagnosed major depressive disorder (MDD) and minor depressive disorder (mDD) using the DSM-IV criteria.²⁸ They also diagnosed subsyndromal depression (SSD) using the operational criteria.³⁰ The operational diagnostic criteria for SSD were as follows: (1) the occurrence of 2 or more symptoms of depression listed in criterion A for a major depressive episode in the DSM-IV within the same 2-week period; (2) the presence of at least a depressed mood or anhedonia; (3) each depressive symptom should be present for more than a half day or more than 7 days during the 2-week period; (4) participants must not fulfill the criteria for the diagnosis of MDD or mDD; (5) the symptoms must not be due to the direct physiologic effects of a substance or a general medical condition; (6) the symptoms must not be attributable to bereavement, dementia, or schizophrenia and other psychotic disorders; and (7) there should not be a history of the occurrence of a manic or hypomanic episode. We defined depressive disorder as having MDD, mDD, or SSD. History of mood disorders, MDD, mDD, SSD, or bipolar disorder was evaluated by participants’ self-report and by previous MINI-K diagnoses during the follow-up period (in the case of KLOSCAD participants). We also evaluated the severity of depressive symptoms using the revised Korean version of the Geriatric Depression Scale (GDS).³¹

Statistical Analysis

In a cross-sectional analysis, we compared the demographic and clinical characteristics between couples in whom the KLOSCAD participants (index participants) had depressive disorders and couples in whom the KLOSCAD participants did not have a depressive disorder, using Pearson χ² tests for categorical variables and 2-tailed, unpaired t tests for continuous variables. We examined the agreement of the demographic and clinical characteristics in couples using the intraclass correlation coefficient for continuous variables and the κ coefficient for categorical variables.

We used binary logistic regression analyses to examine the association between index participants’ depressive disorders and the risk of depressive disorders among their spouses. We added spouses’ clinical characteristics with significant concordance in couples to the logistic regression model as independent variables. Next, we examined an additional binary logistic regression model to investigate the differential association of index participants’ depressive disorders with and without past mood history.

We then evaluated the mediating role of the significant factors shared in couples (from the logistic regression model) on the association between index participants’ depressive disorder and the risk of their spouses’ depressive disorders and depressive symptoms using structural equation modeling as follows:

Y = c + εX + β₁M₁ + β₂M₂ + β₃M₃ + … + β_kM_k

M_k = d + α_kX for k = 1, …, K,

where X, M, and Y are the exposure, mediators, and outcomes, respectively. We included mediator-mediator interactions in the model if the mediators affect one another; for 2 mediators with interactions, the model becomes as follows:

M_i = d + α_iX

M_j = d + α_jX + γijMi.

We then estimated the indirect and direct effects of exposure X on outcome Y as follows:

Direct effect = ε

Indirect effect = α₁β₁+ α₂β₂+ α₃β₃+ ∙∙∙ α_iγijβ_j+ α_kβ_k.

Significant indirect effects were confirmed with 95% CIs using the adjusted bootstrap percentile method. All statistical analyses were performed using the lavaan package in R software, version 4.2.2 (R Foundation for Statistical Computing). P values were 2-sided, with P < .05 considered statistically significant.

Results

The demographic and clinical characteristics of the index and spousal participants are given in Table 1. All couples were heterosexual. Compared with the index participants, the spouse participants were younger (mean [SD] age, 75.1 [5.0] years; age range, 67-94 years for index participants; mean [SD] age, 73.9 [6.1] years; age range, 55-94 years for spouses; P < .001) but equally educated (mean [SD], 10.2 [5.0] years of education for index participants; mean [SD], 9.9 [5.0] years of education for spouses; P = .16). Among the 956 index participants, 82 (8.6%) had depressive disorders. As summarized in Table 2, age, educational level, social-emotional support, and Mini-Mental State Examination scores were highly concordant within couples regardless of whether the index participants had depressive disorders. However, a history of heavy alcohol use, physical inactivity, CIRS score, tangible support, cognitive disorder, and GDS score were concordant in couples only when the index participants did not have depressive disorders.

Table 1. Demographic and Clinical Characteristics of the Participants^a.

Characteristic	Index participants			Spouse participants
Characteristic	IDD+ (n = 82)	IDD− (n = 874)	P value^b	SDD+ (n = 82)	SDD− (n = 874)	P value^b
Age, mean (SD), y	76.3 (5.3)	75.0 (5.0)	.02	75.4 (6.3)	73.8 (6.1)	.02
Educational level, mean (SD), y	9.9 (5.0)	10.3 (5.0)	.50	9.9 (5.2)	9.9 (5.0)	.94
Sex
Female	40 (48.8)	345 (39.5)	.13	42 (51.2)	529 (60.5)	.13
Male	42 (51.2)	529 (60.5)	.13	40 (48.8)	345 (39.5)	.13
Lifetime amount of alcohol use, mean (SD), SU/wk	2.1 (8.4)	2.5 (7.3)	.65	3.3 (9.7)	2.5 (9.1)	.43
Current alcohol use	20 (24.4)	272 (31.1)	.25	27 (32.9)	219 (25.1)	.15
History of heavy alcohol use^c	8 (9.8)	109 (12.5)	.59	6 (7.3)	76 (8.7)	.83
MET, mean (SD), h/wk	14.9 (22.0)	23.8 (34.5)	.001	19.9 (24.6)	21.2 (29.7)	.65
Physical inactivity^d	47 (57.3)	278 (31.8)	<.001	34 (41.5)	314 (35.9)	.38
Social-emotional support score, mean (SD)^e	59.6 (27.7)	77.6 (21.2)	<.001	70.6 (23.7)	77.6 (21.8)	.007
Tangible support score, mean (SD)^f	73.5 (24.9)	84.0 (19.7)	<.001	75.0 (25.0)	82.3 (20.8)	.01
CIRS score, mean (SD)	9.4 (3.6)	6.4 (3.2)	<.001	6.7 (2.8)	5.7 (3.2)	.01
MMSE score, mean (SD)	25.3 (4.3)	27.0 (2.9)	<.001	25.7 (4.4)	26.4 (3.3)	.18
Cognitive disorders^g	35 (42.7)	129 (14.8)	<.001	34 (41.5)	194 (22.2)	<.001
GDS score, mean (SD)^h	18.4 (6.1)	7.2 (5.3)	<.001	11.7 (6.8)	8.1 (6.1)	<.001
History of mood disordersⁱ	66 (80.5)	146 (16.7)	<.001	17 (20.7)	72 (8.2)	<.001
Current depressive disorders
Major depressive disorder	7 (8.5)	0 (0.0)	<.001	1 (1.2)	5 (0.6)	<.001
Minor depressive disorder	12 (14.6)	0 (0.0)		2 (2.4)	7 (0.8)
Subsyndromal depression	63 (76.8)	0 (0.0)		19 (23.2)	57 (6.5)

Open in a new tab

Abbreviations: CIRS, Cumulative Illness Rating Scale; GDS, Geriatric Depression Scale; IDD+, index participants with depressive disorders; IDD−, index participants without depressive disorders; MET, metabolic equivalent task; MMSE, Mini-Mental State Examination; SDD+, spouses of the IDD+ participants; SDD−, spouses of the IDD− participants; SU, standard unit.

^{^a}

Data are presented as number (percentage) of study participants unless otherwise indicated.

^{^b}

The 2-tailed, unpaired t test was used for continuous variables and the χ² test for categorical variables.

^{^c}

Mean lifetime amount of alcohol use of 21 SU/wk or more.

^{^d}

Less than 2.5 hours/wk of moderate activity and less than 1.25 hours/wk of vigorous activity.

^{^e}

Sum of the emotional support, informational support, positive social interaction, and affectionate support scores of the Medical Outcomes Study Social Support Survey.

^{^f}

Tangible support from the Medical Outcomes Study Social Support Survey.

^{^g}

Mild cognitive impairment or dementia.

^{^h}

Missing values: 2 from the IDD+ group, 1 from the IDD− group, 2 from the SDD+ group, and 3 from the SDD− group.

^ⁱ

Major depressive disorder, minor depressive disorder, subsyndromal depression, or bipolar disorder.

Table 2. Concordance of Demographic and Clinical Characteristics Within Couples.

Characteristic	Between IDD+ and SDD+			Between IDD− and SDD−
Characteristic	Difference^a	Concordance^b	P value	Difference^a	Concordance^b	P value
Age, y	0.94 (5.37)	0.571	<.001	1.17 (4.63)	0.636	<.001
Educational level, y	0.00 (5.10)	0.505	<.001	0.35 (4.70)	0.561	<.001
Lifetime amount of alcohol use, SU/wk	−1.15 (13.29)	−0.078	.76	0.07 (12.00)	−0.060	.96
Current alcohol use	−7.0 (−8.5)	−0.035	.75	53.0 (6.0)	−0.018	.60
History of heavy alcohol use^c	2.0 (2.5)	−0.091	.40	33.0 (3.8)	−0.078	.02
MET, h/wk	−4.97 (30.85)	0.124	.13	2.62 (39.80)	0.236	<.001
Physical inactivity^d	13.0 (15.8)	0.120	.26	−36.0 (−4.1)	0.220	<.001
Social-emotional support score^e	−9.92 (31.54)	0.212	.02	−0.05 (25.20)	0.312	<.001
Tangible support score^f	−1.51 (33.00)	0.126	.14	1.64 (25.83)	0.190	<.001
CIRS score	2.73 (4.52)	−0.001	.51	0.69 (4.17)	0.157	<.001
MMSE score	−0.40 (4.85)	0.386	<.001	0.68 (3.78)	0.241	<.001
Cognitive disorders^g	1.0 (1.2)	−0.126	.26	−65.0 (−7.4)	0.153	<.001
GDS score	6.84 (8.37)	0.104	.12	−0.87 (7.23)	0.201	<.001

Open in a new tab

^{^a}

Data are presented as mean (SD) for continuous variables and number (percentage) for categorical variables.

^{^b}

Intraclass correlation coefficient for continuous variables and κ coefficients for categorical variables.

^{^c}

Mean lifetime amount of alcohol use of 21 SU/wk or more.

^{^d}

Less than 2.5 hours/wk of moderate and less than 1.25 hours/wk of vigorous activity.

^{^e}

Sum of the emotional support, informational support, positive social interaction, and affectionate support scores of the Medical Outcomes Study Social Support Survey.

^{^f}

Tangible support from Medical Outcomes Study Social Support Survey.

^{^g}

Mild cognitive impairment or dementia.

The spouses of index participants with depressive disorder were older, were more likely to have a higher medical illness burden and cognitive disorder, and had less social-emotional and tangible support than the spouses of participants without depressive disorder. The spouses of participants with depressive disorder exhibited higher symptoms of depression and were more likely to have depressive disorders than the spouses of participants without depressive disorder (22 [26.8%] vs 69 [7.9%], P < .001) (Table 1), indicating that having a depressive disorder may be associated with an increased risk of depressive disorder in one’s spouse.

In the logistic regression models, the spouses of participants with depressive disorder had an approximately 4 times higher risk of depressive disorders than the spouses of participants without depressive disorder (model 2; odds ratio [OR], 3.89; 95% CI, 2.06-7.19; P < .001) (Table 3). This association remained significant when the factors that were concordant in couples were adjusted. Among the factors that were concordant in couples, social-emotional support score, cognitive disorder, and CIRS score were independently associated with the risk of depressive disorders in spousal participants (model 2) (Table 3).

Table 3. Association of Depressive Disorders of Index Participants on the Risk of Depressive Disorders of Their Spouses.

Factor	Model 1^a		Model 2^a
Factor	OR (95% CI)	P value	OR (95% CI)	P value
Depressive disorder of index participants^b	4.60 (2.59-7.98)	<.001	3.89 (2.06-7.19)	<.001
Shared factors
Heavy alcohol use^c	NA	NA	1.86 (0.70-4.66)	.20
Physical inactivity^d	NA	NA	1.19 (0.71-1.99)	.50
Social-emotional support^e	NA	NA	0.97 (0.96-0.99)	<.001
Tangible support^f	NA	NA	2.40 (0.56-10.59)	.24
Cognitive disorders^g	NA	NA	2.73 (1.65-4.51)	<.001
CIRS, total	NA	NA	1.26 (1.17-1.37)	<.001

Open in a new tab

Abbreviations: CIRS, Cumulative Illness Rating Scale; NA, not applicable; OR, odds ratio; SU, standard unit.

^{^a}

Binary logistic regression analyses adjusted for age, sex, and educational level.

^{^b}

Major depressive disorder, minor depressive disorder, or subsyndromal depression.

^{^c}

Mean lifetime amount of alcohol use of 21 SU/wk or more.

^{^d}

Less than 2.5 hours/wk of moderate activity and less than 1.25 hours/wk of vigorous activity.

^{^e}

Sum of the emotional support, informational support, positive social interaction, and affectionate support scores of the Medical Outcomes Study Social Support Survey.

^{^f}

Tangible support from the Medical Outcomes Study Social Support Survey.

^{^g}

Mild cognitive impairment or dementia.

Among the 82 index participants with depressive disorders, 16 (19.5%) were newly diagnosed with depressive disorders without a history of depressive disorders, and 66 (80.5%) had a history of depressive disorders. Both index participants with depressive disorders with and without a history of depressive disorders were associated with the risk of depressive disorders in their spouses (without a history: OR, 6.40; 95% CI, 2.10-18.10; P < .001; with a history: OR, 4.20; 95% CI, 2.20-7.70; P < .001), and these ORs were not significantly different from each other. Thus, we did not consider the influence of the mood history of index participants on spouses’ depression in subsequent analyses.

In the structural equation model with multiple mediators (Figure, A), social-emotional support mediated the association between depressive disorder in the index participants and their spouses’ risk of depressive disorder by itself (standardized coefficient β = 0.012; 95% CI, 0.001-0.024; P = .04; mediation proportion [MP] = 6.1%) and by lowering the CIRS score (β = 0.003; 95% CI, 0.000-0.006; P = .04; MP = 1.5%). Spousal participants’ cognitive disorder and CIRS score also mediated the association between depressive disorder in the index participants and their spouses’ risk of depressive disorders (cognitive disorders: β = 0.027; 95% CI, 0.003-0.051; P = .03; MP = 13.6%; CIRS score: β = 0.025; 95% CI, 0.001-0.050; P = .04; MP = 12.6%). The direct and total effects of index participants’ depressive disorders on the risk of spouses’ depressive disorders were also significant (β = 0.122; 95% CI, 0.036-0.209; P = .005 for direct effect [61.6% of total effect]; β = 0.198; 95% CI, 0.116-0.280; P < .001 for total effect). In the association between depressive disorders of the index participants and their spouses’ GDS score, social-emotional support and cognitive disorder mediated the association. The CIRS score mediated the association by being influenced by social-emotional support (Figure, B).

Discussion

This study found that index participants’ depressive disorders were associated with spouses’ depressive disorders and that this association was mediated by social-emotional support, a cognitive disorder, and cumulative chronic illness burden, which were shared in couples. In addition, having a history of mood episodes did not differentially affect the risk of depressive disorders of spouses compared with incident depression.

Our results showed a relatively higher OR for depression associated with spousal depression (OR, 3.89) than previous studies^6,7 that did not limit the study population to old age (ORs, 2.08 for depression⁷ and 2.91 for mental disorders⁶). It is possible that not only old age itself but also the unique mechanism of depression in older adult couples may have played a role in contributing to an increased risk of mental disorders.

In the older adult population, reduced social networks due to physical, economic, and social factors are associated with limited social support.¹⁴ As a person ages, social networks and connectedness may be limited to family members,¹⁴ particularly the spouse, who serves as the main provider of social-emotional support. When a person has a depressive disorder, the social-emotional support he or she provides to his or her spouse may be reduced, which may influence the occurrence of depressive disorder in the spouse. Moreover, if the spouse of a person with a depressive disorder is the caregiver,^6,20 the spouse’s social network external to his or her family may not be as accessible. Interestingly, tangible support was not associated with spousal depressive disorder when considered with social-emotional support in the model (Table 3). This finding may suggest that the pivotal targets for depression prevention in older adult couples may be formal social-emotional support outside the family and the coping strategies of an informal caregiver,^6,32,33 which may be more efficacious when added to tangible support (material aid or behavioral assistance), such as long-term care services.

Social-emotional support mediated the association of couple depression by itself and by increasing the CIRS score. The CIRS score also independently mediated the association of couple depression. In older adult couples, shared environmental factors, diet,³⁴ lifestyle,³⁵ and health-related behaviors may influence the concurrence of chronic medical conditions in couples.^36,37 Examples include coronary heart disease,^38,39 hypertension,³⁸ hyperlipidemia,⁷ lung cancer,⁴⁰ diabetes,⁴¹ musculoskeletal health,⁴² and mental health.^36,43,44 These chronic medical conditions are also known to be associated with the risk of late-life depression.^6,45,46 Furthermore, one partner’s depressive disorder may affect the other partner’s chronic medical condition as one of the stressors (to the spouse) or by the loss of proper management, which may influence the occurrence of depressive disorder in the spouse. Therefore, lowering the burden of chronic medical illness in couples and proper management of chronic medical illness may be important to reduce the co-occurrence of depression in older adult couples.

Cognitive disorders mediated couple depression in the largest proportion among significant mediating variables. Recently, the Rotterdam study⁴⁷ reported that higher cognitive reserve, defined as the common variance across cognitive tests, may be a protective factor for late-life depression, which is in line with several previous studies^48,49,50 that suggested that low cognitive reserve may increase the risk of late-life depression. Cognitive and brain reserve coping mechanisms are particularly important in regions that are related to neuromodulation of the serotonin system.⁴⁸ In our study, the mediating role of cognitive disorder between couple depression may reflect the impaired coping or adaptation ability of the person against spousal depression,⁴⁷ which may also relate to one’s own depression. Of course, we should consider the cross-sectional design of this study, which may limit the interpretation of the directionality between cognitive disorders and depression.

These 3 shared risk factors mediated approximately one-third of the spousal risk of depressive disorders. Marital environments, including household income, marital quality,¹³ negative life events, and actual caregiving status,⁵¹ and individual factors, such as perceived stress, coping strategies,⁵² other psychiatric morbidity,³ personality,³ medication use, and shared genetic disposition,⁴ may serve as other potential mediators.

To our knowledge, this is the first study to reveal how the association among depressive disorders and shared risk factors is structured within community-dwelling older adult couples based on psychiatrist diagnosis. We diagnosed depressive disorders with psychiatrists’ diagnostic interviews based on the MINI-K, which may confirm the diagnostic validity. Previous studies that investigated couple depression in large population samples usually adopted a survey,^5,16,53 scale,^{4,9,13,14,15,16,54} or medical records^3,6,36 as diagnostic confirmation. In contrast with previous studies^3,5,53 focusing on syndromal depression (MDD or mDD), we included SSD as a depressive disorder. Subsyndromal depression has a 2.4-fold higher prevalence than that of syndromal depression, which is also associated with poor outcomes in late life.³⁰ Therefore, the current study may better reflect real-world depression in the older adult population.

Limitations

This study has several limitations. First, it used a cross-sectional design, which may not confirm causality and temporal order. However, the association of chronic or recurrent depression among the index participants with spousal depressive disorder was not significantly different from the association of first-onset depression among the index participants with spousal depression, which was why we focused on the cross-sectional association. Second, marital discord could be a contributor to depression in couples,³⁶ but we had no information on the quality of the relationship within the partnership. Some potentially mediating factors also were not considered, as discussed earlier. Third, we could not perform separate analyses for syndromal depression because of the small number of individuals with this condition. Fourth, we did not consider acute medical conditions or fluctuating health status that may contribute to current depression but are not reflected in the CIRS.

Conclusions

This cohort study may imply that the depression status of a spouse should also be treated as an indication of the risk of depression in the older adult population.^5,6 Establishing an external social-emotional support system, active prevention of cognitive disorders, and proper management of chronic medical illness for elderly couples should be considered in the policy making process to reduce the burden of geriatric depression.⁶ Because of limited social networks, multiple chronic medical comorbidities, and high prevalence of cognitive impairment in the older adult population, active intervention to address these mediating factors may be more important than in the younger population.

Supplement.

Data Sharing Statement

Click here for additional data file.^{(14.8KB, pdf)}

References

1.Zivin K, Wharton T, Rostant O. The economic, public health, and caregiver burden of late-life depression. Psychiatr Clin North Am. 2013;36(4):631-649. doi: 10.1016/j.psc.2013.08.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Vasiliadis HM, Dionne PA, Préville M, Gentil L, Berbiche D, Latimer E. The excess healthcare costs associated with depression and anxiety in elderly living in the community. Am J Geriatr Psychiatry. 2013;21(6):536-548. doi: 10.1016/j.jagp.2012.12.016 [DOI] [PubMed] [Google Scholar]
3.Joutsenniemi K, Moustgaard H, Koskinen S, Ripatti S, Martikainen P. Psychiatric comorbidity in couples: a longitudinal study of 202,959 married and cohabiting individuals. Soc Psychiatry Psychiatr Epidemiol. 2011;46(7):623-633. doi: 10.1007/s00127-010-0228-9 [DOI] [PubMed] [Google Scholar]
4.Desai S, Schimmack U, Jidkova S, Bracke P. Spousal similarity in depression: a dyadic latent panel analysis of the panel study of Belgian households. J Abnorm Psychol. 2012;121(2):309-314. doi: 10.1037/a0025575 [DOI] [PubMed] [Google Scholar]
5.Zeng Y, Navarro P, Xia C, et al. Shared genetics and couple-associated environment are major contributors to the risk of both clinical and self-declared depression. EBioMedicine. 2016;14:161-167. doi: 10.1016/j.ebiom.2016.11.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Wang JY, Hsieh MH, Lin PC, Liu CS, Chen JD. Parallel contagion phenomenon of concordant mental disorders among married couples: a nationwide cohort study. J Epidemiol Community Health. 2017;71(7):640-647. doi: 10.1136/jech-2016-208283 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Hippisley-Cox J, Coupland C, Pringle M, Crown N, Hammersley V. Married couples’ risk of same disease: cross sectional study. BMJ. 2002;325(7365):636. doi: 10.1136/bmj.325.7365.636 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Knuiman MW, Divitini ML, Welborn TA, Bartholomew HC. Familial correlations, cohabitation effects, and heritability for cardiovascular risk factors. Ann Epidemiol. 1996;6(3):188-194. doi: 10.1016/1047-2797(96)00004-X [DOI] [PubMed] [Google Scholar]
9.Butterworth P, Rodgers B. Concordance in the mental health of spouses: analysis of a large national household panel survey. Psychol Med. 2006;36(5):685-697. doi: 10.1017/S0033291705006677 [DOI] [PubMed] [Google Scholar]
10.Yoo SW, Kim YS, Noh JS, et al. Validity of Korean version of the mini-international neuropsychiatric interview. Anxiety Mood. 2006;2(1):50-55. [Google Scholar]
11.Cutrona CE. Social Support in Couples: Marriage as a Resource in Times of Stress. SAGE Publications; 1996.
12.Tower RB, Kasl SV. Depressive symptoms across older spouses and the moderating effect of marital closeness. Psychol Aging. 1995;10(4):625-638. doi: 10.1037/0882-7974.10.4.625 [DOI] [PubMed] [Google Scholar]
13.Han SH, Kim K, Burr JA. Friendship and depression among couples in later life: the moderating effects of marital quality. J Gerontol B Psychol Sci Soc Sci. 2019;74(2):222-231. doi: 10.1093/geronb/gbx046 [DOI] [PubMed] [Google Scholar]
14.Miller LM, Steele JS, Wu CY, et al. Depressive symptoms in older adult couples: associations with dyadic physical health, social engagement, and close friends. Front Psychiatry. 2022;13:989182. doi: 10.3389/fpsyt.2022.989182 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Schulz R, Beach SR, Hebert RS, et al. Spousal suffering and partner’s depression and cardiovascular disease: the Cardiovascular Health Study. Am J Geriatr Psychiatry. 2009;17(3):246-254. doi: 10.1097/JGP.0b013e318198775b [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Siegel MJ, Bradley EH, Gallo WT, Kasl SV. The effect of spousal mental and physical health on husbands’ and wives’ depressive symptoms, among older adults: longitudinal evidence from the Health and Retirement Survey. J Aging Health. 2004;16(3):398-425. doi: 10.1177/0898264304264208 [DOI] [PubMed] [Google Scholar]
17.Han JW, Kim TH, Kwak KP, et al. Overview of the Korean Longitudinal Study on Cognitive Aging and Dementia. Psychiatry Investig. 2018;15(8):767-774. doi: 10.30773/pi.2018.06.02 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Yang HW, Bae JB, Oh DJ, et al. Exploration of cognitive outcomes and risk factors for cognitive decline shared by couples. JAMA Netw Open. 2021;4(12):e2139765. doi: 10.1001/jamanetworkopen.2021.39765 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Margetts B. WHO global strategy on diet, physical activity and health. Public Health Nutr. 2004;7(3):361-363. doi: 10.1079/PHN2004622 [DOI] [PubMed] [Google Scholar]
20.Miller MD, Paradis CF, Houck PR, et al. Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale. Psychiatry Res. 1992;41(3):237-248. doi: 10.1016/0165-1781(92)90005-N [DOI] [PubMed] [Google Scholar]
21.de Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity. a critical review of available methods. J Clin Epidemiol. 2003;56(3):221-229. doi: 10.1016/S0895-4356(02)00585-1 [DOI] [PubMed] [Google Scholar]
22.Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med. 1991;32(6):705-714. doi: 10.1016/0277-9536(91)90150-B [DOI] [PubMed] [Google Scholar]
23.Yu Y, Yang JP, Shiu CS, et al. Psychometric testing of the Chinese version of the Medical Outcomes Study Social Support Survey among people living with HIV/AIDS in China. Appl Nurs Res. 2015;28(4):328-333. doi: 10.1016/j.apnr.2015.03.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Yu Y, Shiu CS, Yang JP, et al. Factor analyses of a social support scale using two methods. Qual Life Res. 2015;24(4):787-794. doi: 10.1007/s11136-014-0815-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Shin JK, Kim KW, Park JH, et al. Impacts of poor social support on general health status in community-dwelling Korean elderly: the results from the Korean Longitudinal Study on Health and Aging. Psychiatry Investig. 2008;5(3):155-162. doi: 10.4306/pi.2008.5.3.155 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Lee JH, Lee KU, Lee DY, et al. Development of the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet (CERAD-K): clinical and neuropsychological assessment batteries. J Gerontol B Psychol Sci Soc Sci. 2002;57(1):47-53. doi: 10.1093/geronb/57.1.P47 [DOI] [PubMed] [Google Scholar]
27.Seo EH, Lee DY, Lee JH, et al. Total scores of the CERAD neuropsychological assessment battery: validation for mild cognitive impairment and dementia patients with diverse etiologies. Am J Geriatr Psychiatry. 2010;18(9):801-809. doi: 10.1097/JGP.0b013e3181cab764 [DOI] [PubMed] [Google Scholar]
28.American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders. 4th ed. American Psychiatric Association; 1994. [Google Scholar]
29.Winblad B, Palmer K, Kivipelto M, et al. Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med. 2004;256(3):240-246. doi: 10.1111/j.1365-2796.2004.01380.x [DOI] [PubMed] [Google Scholar]
30.Oh DJ, Han JW, Kim TH, et al. Epidemiological characteristics of subsyndromal depression in late life. Aust N Z J Psychiatry. 2020;54(2):150-158. doi: 10.1177/0004867419879242 [DOI] [PubMed] [Google Scholar]
31.Kim JY, Park JH, Lee JJ, et al. Standardization of the Korean version of the geriatric depression scale: reliability, validity, and factor structure. Psychiatry Investig. 2008;5(4):232-238. doi: 10.4306/pi.2008.5.4.232 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Benazon NR, Coyne JC. The next step in developing an interactional description of depression? Psychol Inq. 1999;10(4):279-282. [Google Scholar]
33.Thompson G, McBride RB, Hosford CC, Halaas G. Resilience among medical students: the role of coping style and social support. Teach Learn Med. 2016;28(2):174-182. doi: 10.1080/10401334.2016.1146611 [DOI] [PubMed] [Google Scholar]
34.Pachucki MA, Jacques PF, Christakis NA. Social network concordance in food choice among spouses, friends, and siblings. Am J Public Health. 2011;101(11):2170-2177. doi: 10.2105/AJPH.2011.300282 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Li J, Loerbroks A, Angerer P. Physical activity and risk of cardiovascular disease: what does the new epidemiological evidence show? Curr Opin Cardiol. 2013;28(5):575-583. doi: 10.1097/HCO.0b013e328364289c [DOI] [PubMed] [Google Scholar]
36.Walker J, Liddle J, Jordan KP, Campbell P. Affective concordance in couples: a cross-sectional analysis of depression and anxiety consultations within a population of 13,507 couples in primary care. BMC Psychiatry. 2017;17(1):190. doi: 10.1186/s12888-017-1354-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Cummings EM, Keller PS, Davies PT. Towards a family process model of maternal and paternal depressive symptoms: exploring multiple relations with child and family functioning. J Child Psychol Psychiatry. 2005;46(5):479-489. doi: 10.1111/j.1469-7610.2004.00368.x [DOI] [PubMed] [Google Scholar]
38.Di Castelnuovo A, Quacquaruccio G, Donati MB, de Gaetano G, Iacoviello L. Spousal concordance for major coronary risk factors: a systematic review and meta-analysis. Am J Epidemiol. 2009;169(1):1-8. doi: 10.1093/aje/kwn234 [DOI] [PubMed] [Google Scholar]
39.Meyler D, Stimpson JP, Peek MK. Health concordance within couples: a systematic review. Soc Sci Med. 2007;64(11):2297-2310. doi: 10.1016/j.socscimed.2007.02.007 [DOI] [PubMed] [Google Scholar]
40.Hemminki K, Dong C, Vaittinen P. Cancer risks to spouses and offspring in the Family-Cancer Database. Genet Epidemiol. 2001;20(2):247-257. doi: [DOI] [PubMed] [Google Scholar]
41.Leong A, Rahme E, Dasgupta K. Spousal diabetes as a diabetes risk factor: a systematic review and meta-analysis. BMC Med. 2014;12(1):12. doi: 10.1186/1741-7015-12-12 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Campbell P, Shraim M, Jordan KP, Dunn KM. In sickness and in health: a cross-sectional analysis of concordance for musculoskeletal pain in 13,507 couples. Eur J Pain. 2016;20(3):438-446. doi: 10.1002/ejp.744 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Mueller B, Nordt C, Lauber C, Rueesch P, Meyer PC, Roessler W. Social support modifies perceived stigmatization in the first years of mental illness: a longitudinal approach. Soc Sci Med. 2006;62(1):39-49. doi: 10.1016/j.socscimed.2005.05.014 [DOI] [PubMed] [Google Scholar]
44.Low N, Cui L, Merikangas KR. Spousal concordance for substance use and anxiety disorders. J Psychiatr Res. 2007;41(11):942-951. doi: 10.1016/j.jpsychires.2006.11.003 [DOI] [PubMed] [Google Scholar]
45.Dinkel A, Kremsreiter K, Marten-Mittag B, Lahmann C. Comorbidity of fear of progression and anxiety disorders in cancer patients. Gen Hosp Psychiatry. 2014;36(6):613-619. doi: 10.1016/j.genhosppsych.2014.08.006 [DOI] [PubMed] [Google Scholar]
46.Ducat L, Philipson LH, Anderson BJ. The mental health comorbidities of diabetes. JAMA. 2014;312(7):691-692. doi: 10.1001/jama.2014.8040 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Zijlmans JL, Vernooij MW, Ikram MA, Luik AI. The role of cognitive and brain reserve in late-life depressive events: the Rotterdam Study. J Affect Disord. 2023;320:211-217. doi: 10.1016/j.jad.2022.09.145 [DOI] [PubMed] [Google Scholar]
48.Barnett JH, Salmond CH, Jones PB, Sahakian BJ. Cognitive reserve in neuropsychiatry. Psychol Med. 2006;36(8):1053-1064. doi: 10.1017/S0033291706007501 [DOI] [PubMed] [Google Scholar]
49.Huang CM, Fan YT, Lee SH, et al. Cognitive reserve-mediated neural modulation of emotional control and regulation in people with late-life depression. Soc Cogn Affect Neurosci. 2019;14(8):849-860. doi: 10.1093/scan/nsz054 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Ponsoni A, Damiani Branco L, Cotrena C, Milman Shansis F, Fonseca RP. The effects of cognitive reserve and depressive symptoms on cognitive performance in major depression and bipolar disorder. J Affect Disord. 2020;274:813-818. doi: 10.1016/j.jad.2020.05.143 [DOI] [PubMed] [Google Scholar]
51.Capistrant BD, Berkman LF, Glymour MM. Does duration of spousal caregiving affect risk of depression onset? evidence from the Health and Retirement Study. Am J Geriatr Psychiatry. 2014;22(8):766-770. doi: 10.1016/j.jagp.2013.01.073 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Holahan CJ, Moos RH, Moerkbak ML, Cronkite RC, Holahan CK, Kenney BA. Spousal similarity in coping and depressive symptoms over 10 years. J Fam Psychol. 2007;21(4):551-559. doi: 10.1037/0893-3200.21.4.551 [DOI] [PubMed] [Google Scholar]
53.Van Orden KA, Braithwaite S, Anestis M, et al. An exploratory investigation of marital functioning and order of spousal onset in couples concordant for psychopathology. J Marital Fam Ther. 2012;38(suppl 1):308-319. doi: 10.1111/j.1752-0606.2011.00277.x [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Pradeep N, Sutin AR. Spouses and depressive symptoms in older adulthood. Sci Rep. 2015;5:8594. doi: 10.1038/srep08594 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplement.

Data Sharing Statement

Click here for additional data file.^{(14.8KB, pdf)}

[zoi230263r1] 1.Zivin K, Wharton T, Rostant O. The economic, public health, and caregiver burden of late-life depression. Psychiatr Clin North Am. 2013;36(4):631-649. doi: 10.1016/j.psc.2013.08.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r2] 2.Vasiliadis HM, Dionne PA, Préville M, Gentil L, Berbiche D, Latimer E. The excess healthcare costs associated with depression and anxiety in elderly living in the community. Am J Geriatr Psychiatry. 2013;21(6):536-548. doi: 10.1016/j.jagp.2012.12.016 [DOI] [PubMed] [Google Scholar]

[zoi230263r3] 3.Joutsenniemi K, Moustgaard H, Koskinen S, Ripatti S, Martikainen P. Psychiatric comorbidity in couples: a longitudinal study of 202,959 married and cohabiting individuals. Soc Psychiatry Psychiatr Epidemiol. 2011;46(7):623-633. doi: 10.1007/s00127-010-0228-9 [DOI] [PubMed] [Google Scholar]

[zoi230263r4] 4.Desai S, Schimmack U, Jidkova S, Bracke P. Spousal similarity in depression: a dyadic latent panel analysis of the panel study of Belgian households. J Abnorm Psychol. 2012;121(2):309-314. doi: 10.1037/a0025575 [DOI] [PubMed] [Google Scholar]

[zoi230263r5] 5.Zeng Y, Navarro P, Xia C, et al. Shared genetics and couple-associated environment are major contributors to the risk of both clinical and self-declared depression. EBioMedicine. 2016;14:161-167. doi: 10.1016/j.ebiom.2016.11.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r6] 6.Wang JY, Hsieh MH, Lin PC, Liu CS, Chen JD. Parallel contagion phenomenon of concordant mental disorders among married couples: a nationwide cohort study. J Epidemiol Community Health. 2017;71(7):640-647. doi: 10.1136/jech-2016-208283 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r7] 7.Hippisley-Cox J, Coupland C, Pringle M, Crown N, Hammersley V. Married couples’ risk of same disease: cross sectional study. BMJ. 2002;325(7365):636. doi: 10.1136/bmj.325.7365.636 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r8] 8.Knuiman MW, Divitini ML, Welborn TA, Bartholomew HC. Familial correlations, cohabitation effects, and heritability for cardiovascular risk factors. Ann Epidemiol. 1996;6(3):188-194. doi: 10.1016/1047-2797(96)00004-X [DOI] [PubMed] [Google Scholar]

[zoi230263r9] 9.Butterworth P, Rodgers B. Concordance in the mental health of spouses: analysis of a large national household panel survey. Psychol Med. 2006;36(5):685-697. doi: 10.1017/S0033291705006677 [DOI] [PubMed] [Google Scholar]

[zoi230263r10] 10.Yoo SW, Kim YS, Noh JS, et al. Validity of Korean version of the mini-international neuropsychiatric interview. Anxiety Mood. 2006;2(1):50-55. [Google Scholar]

[zoi230263r11] 11.Cutrona CE. Social Support in Couples: Marriage as a Resource in Times of Stress. SAGE Publications; 1996.

[zoi230263r12] 12.Tower RB, Kasl SV. Depressive symptoms across older spouses and the moderating effect of marital closeness. Psychol Aging. 1995;10(4):625-638. doi: 10.1037/0882-7974.10.4.625 [DOI] [PubMed] [Google Scholar]

[zoi230263r13] 13.Han SH, Kim K, Burr JA. Friendship and depression among couples in later life: the moderating effects of marital quality. J Gerontol B Psychol Sci Soc Sci. 2019;74(2):222-231. doi: 10.1093/geronb/gbx046 [DOI] [PubMed] [Google Scholar]

[zoi230263r14] 14.Miller LM, Steele JS, Wu CY, et al. Depressive symptoms in older adult couples: associations with dyadic physical health, social engagement, and close friends. Front Psychiatry. 2022;13:989182. doi: 10.3389/fpsyt.2022.989182 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r15] 15.Schulz R, Beach SR, Hebert RS, et al. Spousal suffering and partner’s depression and cardiovascular disease: the Cardiovascular Health Study. Am J Geriatr Psychiatry. 2009;17(3):246-254. doi: 10.1097/JGP.0b013e318198775b [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r16] 16.Siegel MJ, Bradley EH, Gallo WT, Kasl SV. The effect of spousal mental and physical health on husbands’ and wives’ depressive symptoms, among older adults: longitudinal evidence from the Health and Retirement Survey. J Aging Health. 2004;16(3):398-425. doi: 10.1177/0898264304264208 [DOI] [PubMed] [Google Scholar]

[zoi230263r17] 17.Han JW, Kim TH, Kwak KP, et al. Overview of the Korean Longitudinal Study on Cognitive Aging and Dementia. Psychiatry Investig. 2018;15(8):767-774. doi: 10.30773/pi.2018.06.02 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r18] 18.Yang HW, Bae JB, Oh DJ, et al. Exploration of cognitive outcomes and risk factors for cognitive decline shared by couples. JAMA Netw Open. 2021;4(12):e2139765. doi: 10.1001/jamanetworkopen.2021.39765 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r19] 19.Margetts B. WHO global strategy on diet, physical activity and health. Public Health Nutr. 2004;7(3):361-363. doi: 10.1079/PHN2004622 [DOI] [PubMed] [Google Scholar]

[zoi230263r20] 20.Miller MD, Paradis CF, Houck PR, et al. Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale. Psychiatry Res. 1992;41(3):237-248. doi: 10.1016/0165-1781(92)90005-N [DOI] [PubMed] [Google Scholar]

[zoi230263r21] 21.de Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity. a critical review of available methods. J Clin Epidemiol. 2003;56(3):221-229. doi: 10.1016/S0895-4356(02)00585-1 [DOI] [PubMed] [Google Scholar]

[zoi230263r22] 22.Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med. 1991;32(6):705-714. doi: 10.1016/0277-9536(91)90150-B [DOI] [PubMed] [Google Scholar]

[zoi230263r23] 23.Yu Y, Yang JP, Shiu CS, et al. Psychometric testing of the Chinese version of the Medical Outcomes Study Social Support Survey among people living with HIV/AIDS in China. Appl Nurs Res. 2015;28(4):328-333. doi: 10.1016/j.apnr.2015.03.006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r24] 24.Yu Y, Shiu CS, Yang JP, et al. Factor analyses of a social support scale using two methods. Qual Life Res. 2015;24(4):787-794. doi: 10.1007/s11136-014-0815-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r25] 25.Shin JK, Kim KW, Park JH, et al. Impacts of poor social support on general health status in community-dwelling Korean elderly: the results from the Korean Longitudinal Study on Health and Aging. Psychiatry Investig. 2008;5(3):155-162. doi: 10.4306/pi.2008.5.3.155 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r26] 26.Lee JH, Lee KU, Lee DY, et al. Development of the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet (CERAD-K): clinical and neuropsychological assessment batteries. J Gerontol B Psychol Sci Soc Sci. 2002;57(1):47-53. doi: 10.1093/geronb/57.1.P47 [DOI] [PubMed] [Google Scholar]

[zoi230263r27] 27.Seo EH, Lee DY, Lee JH, et al. Total scores of the CERAD neuropsychological assessment battery: validation for mild cognitive impairment and dementia patients with diverse etiologies. Am J Geriatr Psychiatry. 2010;18(9):801-809. doi: 10.1097/JGP.0b013e3181cab764 [DOI] [PubMed] [Google Scholar]

[zoi230263r28] 28.American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders. 4th ed. American Psychiatric Association; 1994. [Google Scholar]

[zoi230263r29] 29.Winblad B, Palmer K, Kivipelto M, et al. Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med. 2004;256(3):240-246. doi: 10.1111/j.1365-2796.2004.01380.x [DOI] [PubMed] [Google Scholar]

[zoi230263r30] 30.Oh DJ, Han JW, Kim TH, et al. Epidemiological characteristics of subsyndromal depression in late life. Aust N Z J Psychiatry. 2020;54(2):150-158. doi: 10.1177/0004867419879242 [DOI] [PubMed] [Google Scholar]

[zoi230263r31] 31.Kim JY, Park JH, Lee JJ, et al. Standardization of the Korean version of the geriatric depression scale: reliability, validity, and factor structure. Psychiatry Investig. 2008;5(4):232-238. doi: 10.4306/pi.2008.5.4.232 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r32] 32.Benazon NR, Coyne JC. The next step in developing an interactional description of depression? Psychol Inq. 1999;10(4):279-282. [Google Scholar]

[zoi230263r33] 33.Thompson G, McBride RB, Hosford CC, Halaas G. Resilience among medical students: the role of coping style and social support. Teach Learn Med. 2016;28(2):174-182. doi: 10.1080/10401334.2016.1146611 [DOI] [PubMed] [Google Scholar]

[zoi230263r34] 34.Pachucki MA, Jacques PF, Christakis NA. Social network concordance in food choice among spouses, friends, and siblings. Am J Public Health. 2011;101(11):2170-2177. doi: 10.2105/AJPH.2011.300282 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r35] 35.Li J, Loerbroks A, Angerer P. Physical activity and risk of cardiovascular disease: what does the new epidemiological evidence show? Curr Opin Cardiol. 2013;28(5):575-583. doi: 10.1097/HCO.0b013e328364289c [DOI] [PubMed] [Google Scholar]

[zoi230263r36] 36.Walker J, Liddle J, Jordan KP, Campbell P. Affective concordance in couples: a cross-sectional analysis of depression and anxiety consultations within a population of 13,507 couples in primary care. BMC Psychiatry. 2017;17(1):190. doi: 10.1186/s12888-017-1354-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r37] 37.Cummings EM, Keller PS, Davies PT. Towards a family process model of maternal and paternal depressive symptoms: exploring multiple relations with child and family functioning. J Child Psychol Psychiatry. 2005;46(5):479-489. doi: 10.1111/j.1469-7610.2004.00368.x [DOI] [PubMed] [Google Scholar]

[zoi230263r38] 38.Di Castelnuovo A, Quacquaruccio G, Donati MB, de Gaetano G, Iacoviello L. Spousal concordance for major coronary risk factors: a systematic review and meta-analysis. Am J Epidemiol. 2009;169(1):1-8. doi: 10.1093/aje/kwn234 [DOI] [PubMed] [Google Scholar]

[zoi230263r39] 39.Meyler D, Stimpson JP, Peek MK. Health concordance within couples: a systematic review. Soc Sci Med. 2007;64(11):2297-2310. doi: 10.1016/j.socscimed.2007.02.007 [DOI] [PubMed] [Google Scholar]

[zoi230263r40] 40.Hemminki K, Dong C, Vaittinen P. Cancer risks to spouses and offspring in the Family-Cancer Database. Genet Epidemiol. 2001;20(2):247-257. doi: [DOI] [PubMed] [Google Scholar]

[zoi230263r41] 41.Leong A, Rahme E, Dasgupta K. Spousal diabetes as a diabetes risk factor: a systematic review and meta-analysis. BMC Med. 2014;12(1):12. doi: 10.1186/1741-7015-12-12 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r42] 42.Campbell P, Shraim M, Jordan KP, Dunn KM. In sickness and in health: a cross-sectional analysis of concordance for musculoskeletal pain in 13,507 couples. Eur J Pain. 2016;20(3):438-446. doi: 10.1002/ejp.744 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r43] 43.Mueller B, Nordt C, Lauber C, Rueesch P, Meyer PC, Roessler W. Social support modifies perceived stigmatization in the first years of mental illness: a longitudinal approach. Soc Sci Med. 2006;62(1):39-49. doi: 10.1016/j.socscimed.2005.05.014 [DOI] [PubMed] [Google Scholar]

[zoi230263r44] 44.Low N, Cui L, Merikangas KR. Spousal concordance for substance use and anxiety disorders. J Psychiatr Res. 2007;41(11):942-951. doi: 10.1016/j.jpsychires.2006.11.003 [DOI] [PubMed] [Google Scholar]

[zoi230263r45] 45.Dinkel A, Kremsreiter K, Marten-Mittag B, Lahmann C. Comorbidity of fear of progression and anxiety disorders in cancer patients. Gen Hosp Psychiatry. 2014;36(6):613-619. doi: 10.1016/j.genhosppsych.2014.08.006 [DOI] [PubMed] [Google Scholar]

[zoi230263r46] 46.Ducat L, Philipson LH, Anderson BJ. The mental health comorbidities of diabetes. JAMA. 2014;312(7):691-692. doi: 10.1001/jama.2014.8040 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r47] 47.Zijlmans JL, Vernooij MW, Ikram MA, Luik AI. The role of cognitive and brain reserve in late-life depressive events: the Rotterdam Study. J Affect Disord. 2023;320:211-217. doi: 10.1016/j.jad.2022.09.145 [DOI] [PubMed] [Google Scholar]

[zoi230263r48] 48.Barnett JH, Salmond CH, Jones PB, Sahakian BJ. Cognitive reserve in neuropsychiatry. Psychol Med. 2006;36(8):1053-1064. doi: 10.1017/S0033291706007501 [DOI] [PubMed] [Google Scholar]

[zoi230263r49] 49.Huang CM, Fan YT, Lee SH, et al. Cognitive reserve-mediated neural modulation of emotional control and regulation in people with late-life depression. Soc Cogn Affect Neurosci. 2019;14(8):849-860. doi: 10.1093/scan/nsz054 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r50] 50.Ponsoni A, Damiani Branco L, Cotrena C, Milman Shansis F, Fonseca RP. The effects of cognitive reserve and depressive symptoms on cognitive performance in major depression and bipolar disorder. J Affect Disord. 2020;274:813-818. doi: 10.1016/j.jad.2020.05.143 [DOI] [PubMed] [Google Scholar]

[zoi230263r51] 51.Capistrant BD, Berkman LF, Glymour MM. Does duration of spousal caregiving affect risk of depression onset? evidence from the Health and Retirement Study. Am J Geriatr Psychiatry. 2014;22(8):766-770. doi: 10.1016/j.jagp.2013.01.073 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r52] 52.Holahan CJ, Moos RH, Moerkbak ML, Cronkite RC, Holahan CK, Kenney BA. Spousal similarity in coping and depressive symptoms over 10 years. J Fam Psychol. 2007;21(4):551-559. doi: 10.1037/0893-3200.21.4.551 [DOI] [PubMed] [Google Scholar]

[zoi230263r53] 53.Van Orden KA, Braithwaite S, Anestis M, et al. An exploratory investigation of marital functioning and order of spousal onset in couples concordant for psychopathology. J Marital Fam Ther. 2012;38(suppl 1):308-319. doi: 10.1111/j.1752-0606.2011.00277.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230263r54] 54.Pradeep N, Sutin AR. Spouses and depressive symptoms in older adulthood. Sci Rep. 2015;5:8594. doi: 10.1038/srep08594 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Shared Risk Factors for Depressive Disorder Among Older Adult Couples in Korea

Ji Won Han, MD, PhD

Hee Won Yang, MD

Jong Bin Bae, MD

Dae Jong Oh, MD

Dong Gyu Moon, MD

Eunji Lim, MD

Jin Shin, MD

Bong Jo Kim, MD, PhD

Dong Woo Lee, MD, PhD

Jeong Lan Kim, MD, PhD

Jin Hyeong Jhoo, MD, PhD

Joon Hyuk Park, MD, PhD

Jung Jae Lee, MD, PhD

Kyung Phil Kwak, MD, PhD

Seok Bum Lee, MD, PhD

Seok Woo Moon, MD, PhD

Seung-Ho Ryu, MD, PhD

Shin Gyeom Kim, MD, PhD

Ki Woong Kim, MD, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objectives

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Design and Participants

Assessment of Covariates

Assessment of Depression

Statistical Analysis

Results

Table 1. Demographic and Clinical Characteristics of the Participantsa.

Table 2. Concordance of Demographic and Clinical Characteristics Within Couples.

Table 3. Association of Depressive Disorders of Index Participants on the Risk of Depressive Disorders of Their Spouses.

Figure. Mediating Role of the Factors Shared Within Couples in the Shared Risk of Depressive Disorders.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 1. Demographic and Clinical Characteristics of the Participants^a.