Mastery and Coping Moderate the Negative Effect of Acute and Chronic Stressors on Mental Health-Related Quality of Life in HIV

Abstract

Acute and chronic life stressors have a detrimental effect on the health of people living with HIV. Psychosocial resources such as mastery, coping, and social support may play a critical role in moderating the negative effects of stressors on health-related quality of life. A total of 758 participants provided baseline enrolment data on demographics (age, gender, ethnicity, sexual orientation, education, employment, income), clinical variables (CD4 counts, viral load, AIDS-defining condition, time since HIV diagnosis), psychosocial resources (mastery, coping, social support), life stressors (National Population Health Survey [NPHS] Stress Questionnaire), and health-related quality of life (SF-36). We performed hierarchical multivariate regression analyses to evaluate the potential moderating effects of psychosocial resources on the relationship between stressors and health-related quality of life. The top three stressors reported by participants were trying to take on too many things at once (51%), not having enough money to buy the things they needed (51%), and having something happen during childhood that scared them so much that they thought about it years later (42%). Life stressors were significantly and inversely associated with both physical and mental health-related quality of life. Mastery and maladaptive coping had significant moderating effects on mental health but not on physical health. These results suggest that developing interventions that improve mastery and reduce maladaptive coping may minimize the negative impact of life stressors on the mental health of people with HIV. They also highlight that it is important for clinicians to be mindful of the impact of life stressors on the health of patients living with HIV.

Introduction

HIV increases people's vulnerability to psychological adjustment difficulties.¹ People with HIV experience stress derived from living with the disease, but also from other stressors that are disproportionately experienced in this population, such as abuse, trauma, and social rejection.² These challenges have been associated with a greater likelihood of psychological distress, decreased quality of life, and a higher prevalence of comorbid psychiatric disorders.^3
–5

Generalized stress has previously been associated with HIV disease progression and has been linked with other negative health outcomes.⁶ Previous research on the impact of stressors on the health of people with HIV has mainly focused on biological markers of HIV disease with little attention to health-related quality of life.^2,7 There is a need to better understand the relationship between life stressors and health outcomes that also incorporates psychosocial factors in the context of HIV disease.

Some psychosocial factors that have been shown to act as moderators of the relationship between life stressors and health include mastery, social support and coping. Mastery is defined as the extent to which people feel in control over forces that significantly affect their lives, and is thought to be a psychological resource related to personal control and self-efficacy.⁸ Although mastery has not been properly evaluated in the context of HIV, it has been shown to moderate the negative effects of such stressors as economic hardship⁹ and caregiving stress.¹⁰

Social support has been associated with better health-related quality of life among people with HIV.^11
–13 Social support may prevent people from experiencing stress in the first place, but it may also help moderate the negative effect on depressive mood for those who are already experiencing stress. The negative impact of stress on mood was attenuated for individuals with high levels of social support compared to those with lower levels of social support, suggesting that this psychosocial resource may moderate the effect of stress on mental health among people with HIV.^14
–16

The use of adaptive coping strategies has also been associated with greater health-related quality of life and less reliance on avoidant-oriented coping strategies has been associated with lower psychological distress.^17,18 Using denial as a coping strategy has been associated with poorer health-related quality of life^17,19 and using maladaptive coping strategies to deal with stress has been associated with poorer physical, social, and role functioning in people with HIV.²⁰ Sustained depression, denial coping strategies, and negative expectations have all been associated with more rapid disease progression.²¹ Conditions that limit individuals' ability to actively engage with relatives and close friends may decrease the stress moderating effect of social support.²⁰

This study focuses on the relationship between life stressors and health-related quality of life in people living with HIV. The major aims of this study were (a) to examine the relationship between stressors and health-related quality of life among people with HIV; and (b) to determine whether selected psychosocial resources (mastery, coping, and social support) moderate the relationship between stressors and health-related quality of life. A conceptual model for this study is presented in Fig. 1. We hypothesized that a greater number of stressors would be negatively associated with both physical and mental health-related quality of life, and that psychosocial resources would each reduce the negative impact of stressors on health-related quality of life.

FIG. 1.

A conceptual model of the relationship between stressors, health-related quality of life, and psychosocial resources.

Methods

Sample and procedure

The OHTN Cohort Study (OCS) is an ongoing observational cohort study that collects data on the clinical profile and social determinants of health of people living with HIV/AIDS across the province of Ontario, Canada.²² Clinical data are collected through electronic medical records, chart abstraction, and linkages with the Ontario Public Health Laboratories. Data on the social determinants of health and psychosocial resources are collected through interviewer-administered questionnaires. This article analyzes data collected through a questionnaire that takes approximately 90–120 minutes to complete at three clinical sites in the Greater Toronto Area (St. Michael's Hospital, Toronto General Hospital, and Sunnybrook Hospital).

All study participants are screened for eligibility and informed consent is obtained prior to questionnaire administration and medical chart abstraction. To be eligible for participation in the OCS, individuals must be 18+ years of age with evidence of HIV infection (either a positive HIV antibody test or laboratory evidence of HIV infection), residents of Ontario, and able to provide informed consent. OCS research studies are developed by investigators through Working Group and Scientific Steering Committee discussions and approved by the OCS Governance Committee, which is comprised of a majority of people living with HIV.

This cross-sectional study presents baseline enrolment data from 758 people living with HIV in Ontario who were enrolled in the OCS. The original sample consisted of 913 participants who completed the OCS questionnaire at baseline between December 2006 and March 2009. From this sample, we excluded 155 participants with missing data on the clinical and outcome variables examined in this study. Therefore, 83% of the 913 OCS participants comprised the final sample for this study. There were no significant differences between those who were included and those who were excluded from the analyses. This study was approved by the Research Ethics Board of the University of Toronto and the use of data for this study was approved by the OCS Governance Committee.

Measures

A structured interview was used to collect data on age, gender, ethnicity, sexual orientation, education, employment, personal income, and time since HIV diagnosis. Clinical data included most recent CD4 cell count, recent viral load, and history of AIDS-defining conditions. We dichotomized ethnicity into Caucasian versus non-Caucasian; sexual orientation into heterosexual versus gay, lesbian, or bisexual; education into high school and above versus less than high school; employment into employed (either full time or part time) versus unemployed; and personal income into CAN$30,000 or more per year versus less than CAN$30,000 per year based on the median value. CD4 cell count was categorized into 500 or more cells per milliliter versus less than 500 cells per milliliter, and viral load was categorized into detectable (≥50 copies per milliliter) versus undetectable (<50 copies per milliliter).

Life stressors

An inventory of stressors was administered using the National Population Health Survey (NPHS) Stress Questionnaire, which is routinely administered to the general Canadian population by Statistics Canada. The questionnaire focuses on three main types of stressors. Recent life events include acute changes that are detrimental to well-being and require a significant degree of adjustment within a short period of time. They comprise 10 negative events (e.g., physical attack, financial crisis, job loss or demotion) that the respondent or someone close to the respondent may have experienced. Participants were asked whether or not they have experienced these events in the past 12 months. Higher number of responses indicates greater number of stressors. Chronic or ongoing stressors refer to life stressors that develop gradually over time and can be related to social roles or circumstances. These include 17 stressors such as activity overload, financial difficulties, relationship or marital problems, child problems, and family health. Participants were asked to indicate whether or not they are currently experiencing any of the stressors. Childhood adversity measures the number of traumatic events that participants have been exposed to during their childhood or adolescence. The 7 events include parental divorce, prolonged parental unemployment, frequent parental alcohol or drug use, a lengthy hospital stay (2 weeks or more), and physical abuse. Of the 34 items included in the NPHS stress questionnaire, 10 items were not applicable to most participants (e.g., stressors related to children) and were therefore excluded. Total number of stressors was computed by summing up responses to the remaining 24 questions. The internal consistency of the 24 items in our sample was acceptable (α=0.74)

Mastery

The Pearlin Mastery Scale consists of seven items that evaluate sense of personal control over important life forces or outcomes.²³ Items were developed to address whether individuals believe they have control over what happens to them and control over the decisions they make, as well as whether they are able to deal effectively with problems. Each item is measured using a Likert scale with four response categories, and item scores are added together to generate a summary score. Possible scores range from 7 to 28, with higher scores representing a higher degree of mastery. The Pearlin Mastery Scale has demonstrated construct validity and good reliability.^24,25 In this sample, the internal consistency reliability was fairly high (α=0.80).

Social support

Social support was measured using the Medical Outcomes Study Social Support Survey (MOS-SSS).²⁶ This questionnaire includes 19 items that measure four different dimensions of social support, including emotional/informational support, tangible support, positive social interaction and affectionate support. Each item asks about the availability of a specific type of support, and is measured using a subscale with five response categories ranging from “none of the time” to “all of the time.” In this study, all four dimensions scores were added together to create a total social support score, which ranged from 19 to 95, with higher scores indicating stronger social support. This index has been shown to be reliable, with good convergent and discriminant validity.²⁶ In this sample, the internal consistency reliability was excellent (α=0.97).

Coping

The Brief COPE is a 28-item questionnaire that evaluates coping styles consisting of 14 two-item scales: active coping, planning, positive reframing, acceptance, humor, religion, emotional support, instrumental support, self-distraction, denial, venting, substance use, behavioral disengagement, and self-blame.²⁷ Participants respond to each item using a four-point scale to indicate how much they have been engaging in that coping behavior, ranging from “I have not been doing this at all” to “I have been doing this a lot.” Internal reliability of the 14 two-item scales has been shown to be adequate with good construct validity.²⁷ Adaptive coping can be calculated as the sum of scores for the first eight scales listed above and maladaptive coping for the remaining six scales.²⁸ Cronbach α values for this sample were 0.82 for adaptive coping and 0.76 for maladaptive coping.

Health-related quality of life

The Medical Outcomes Study SF-36 Health Survey consists of 36 items that combine to create 8 domains of health: physical functioning, role limitations due to physical health, bodily pain, general health perceptions, vitality, social functioning, role limitations due to emotional problems, and mental health. Scores range from 0 to 100 with higher scores reflecting higher health-related quality of life within the domain. We computed the Physical Health and the Mental Health Summary Scores using the developers' instructions.²⁹ We transformed both summary scores to have a mean of 50 and a standard deviation of 10. This means that scores below (or above) 50 indicate worse (or better) health-related quality of life compared to the reference population (i.e., the 1998 general U.S. population).²⁹ Several studies support the reliability and validity of the SF-36 in HIV populations.^30,31

Statistical analyses

To determine whether excluded individuals were significantly different from those included in the analysis, we compared the characteristics of the two groups on all variables using t tests and Wilcoxon rank-sum tests for continuous variables, and Pearson's χ² tests for categorical variables. Cronbach α values were computed for mastery, social support, adaptive, and maladaptive coping measures to assess internal consistency reliability. To test the primary hypotheses of this study, we used hierarchical linear multiple regression and assessed moderating effects using methods outlined by Frazier and colleagues.³² We entered variables into the multivariate regression model in blocks in the following order: (a) demographic variables: age, gender, ethnicity, sexual orientation, education, employment, and personal income; (b) biomedical variables: recent CD4 cell counts, recent viral load, AIDS-defining condition, time since HIV diagnosis; (c) total stressors; (d) psychosocial variables: mastery, social support, adaptive coping, maladaptive coping; and (e) interaction terms. Marital status was initially included in the model, but later excluded because of its multicollinearity with social support. The variable total stressors was entered into the regression model before psychosocial resources in order to determine its independent effect on physical and mental health-related quality of life.

We used mean substitution for mastery and social support variables to include a study sample as diverse and representative as possible. We mean centered predictor variables that are continuous (i.e., age, time since HIV diagnosis, stressors, adaptive coping, maladaptive coping, mastery, and social support) before they were entered into the model to aid with interpretation and to avoid multicollinearity.^32
–34 To preserve power, variables that were included in the interaction terms were kept continuous, and interaction terms were entered into the model simultaneously.³² The coefficients of determination (R ²) values were calculated to provide an indication of the goodness of fit of the regression model at each stage of the analysis. All reported p values are two-tailed. All analyses were performed using SPSS 16.0 (SPSS Inc., Chicago, IL).

We visually inspected potential moderating effects by plotting the relationship between stressors and health-related quality of life for low, medium, and high levels of the psychosocial resources (mastery, social support, adaptive and maladaptive coping) using the mean and standard deviations of this sample to categorize their scores into low (i.e., 1 standard deviation [SD] below the mean), medium (i.e., the mean), and high (i.e., 1 SD above the mean).

Results

Participants

Most participants in the sample were middle aged (mean age, 47), male (82%), Caucasian (64%), and identified as being gay, lesbian, or bisexual (67%). Although most of the participants in the sample had at least a high school education (87%), more than half indicated that they were unemployed (56%) and earned a personal income of less than $30,000 per year (55%). More than half (58%) had a recent CD4 cell count of less than 500 cells per milliliter, but only approximately one quarter (26%) of participants had a detectable viral load. Approximately one third (32%) of the participants had a history of at least one AIDS-defining condition, and the mean time since HIV diagnosis was 11 years (Table 1).

Table 1.

Demographic, Biomedical, and Psychosocial Characteristics of Participants (n=758)

Characteristic	n (%)	Mean (SD)
Demographic
Age (years)		47 (10)
Gender (female)	134 (18%)
Ethnicity (non-Caucasian)	271 (36%)
Sexual orientation (heterosexual)	252 (33%)
Education (<high school)	100 (13%)
Employment (unemployed)	426 (56%)
Personal Income (<$30,000/year)	420 (55%)
Biomedical
Recent CD4 cell count (<500)	441 (58%)
Viral load (detectable)	193 (26%)
AIDS-defining condition	241 (32%)
Time since HIV diagnosis (years)		11 (7)
Psychosocial
Adaptive coping		44.6 (8.8)
Maladaptive coping		21.9 (5.9)
Mastery		19.7 (3.6)
Social support		64.3 (25.6)
Quality of Life
Physical HRQOL		48.6 (8.8)
Mental HRQOL		45.2 (13.2)
Stressors
Childhood adversity		1.6 (1.6)
Recent life events		0.6 (0.9)
Ongoing stressors		3.2 (2.5)
Total number of stressors		5.4 (3.7)

SD, standard deviation; HRQOL, health-related quality of life.

The mean adaptive coping score was 44.6 (range, 16–64), while the mean maladaptive coping score was 21.9 (range, 12–48), indicating that participants utilized a variety of coping strategies. The mean mastery score was 19.7 (range, 7–28) and the mean social support score was 64.3 (range, 0–100). Mean physical and mental health-related quality of life scores were relatively low at 48.6 and 45.2 respectively, showing lower health-related quality of life than the reference population (i.e., the 1998 U.S. general population).²⁹

Nearly all participants (93%) indicated that they had experienced at least one stressful event during childhood, during the 12-month period prior to being interviewed, or during the time that the interview was conducted. The mean number of stressful events during childhood was 1.6, the mean number of stressful recent life events was 0.6, and the mean number of ongoing stressors was 3.2. The top five stressors reported by participants (Table 2) were trying to take on too many things at once (51%), not having enough money to buy the things they needed (51%), having something happen during childhood that scared them so much they thought about it years later (42%), having too much expected of them by others (39%), and wanting to move but not being able to move (33%).

Table 2.

Life Stressors Most Frequently Reported by the Study Participants (n=758) ^a

Stressors	n	%
Recent live events (past 12 months)
Did you or someone in your family have a major financial crisis?	183	24%
Did you have increased arguments with your partner?	75	10%
Did you or your partner experience a change of job for a worse one?	59	8%
Were you demoted at work or did take a cut in pay?	54	7%
Was any one of you beaten up or physically attacked?	54	7%
Did you go on Welfare?	52	7%
Ongoing (chronic) stressors
You are trying to take on too many things at once	387	51%
You don't have enough money to buy the things you need	385	51%
Too much is expected of you by others	293	39%
You would like to move, but you cannot	251	33%
There is too much pressure on you to be like other people	198	26%
Someone in your family has a drug or alcohol problem	177	23%
People are too critical of you or what you do	171	23%
Your neighborhood or community is too noisy or too polluted	169	22%
You have a parent, a child, or a partner who is in very bad health and may die	129	17%
Your work around the home is not appreciated	116	15%
Your friends are a bad influence	82	11%
Childhood adversity
Did something happen that scared you so much you thought about it for years after?	315	42%
Were you ever physically abused by someone close to you?	204	27%
Did either of your parents drink or use drugs so often that it caused problems for the family?	192	25%
Did you spend 2 weeks or more in a hospital?	181	24%
Did your parents get a divorce?	157	21%
Did your father or mother not have a job for a long time when they wanted to be working?	114	15%
Were you sent away from home because of something you did wrong?	71	9%

This table includes items endorsed by at least 5% of the study sample.

Association of demographic, biomedical, and psychosocial factors with physical health-related quality of life

Univariate regressions (Table 3) showed that being older, having less than a high school education, being unemployed, earning low income, having low recent CD4 cell counts, having lived with HIV for a longer period of time, and experiencing high numbers of life stressors were negatively associated with physical health-related quality of life. Being non-Caucasian and having high mastery and social support were positively associated with physical health-related quality of life.

Table 3.

Univariate Regressions of Demographic, Biomedical, and Psychosocial Characteristics on Physical and Mental Health-Related Quality of Life (n=758)

	Physical HRQOL			Mental HRQOL
Variable	β	(95% CI)	p Value	β	(95% CI)	p Value
Demographic
Age (years)	−0.14	(−0.20, −0.08)	<0.01	0.22	(0.13, 0.32)	<0.01
Gender (female)	−0.71	(−2.40, 0.91)	0.40	−0.93	(−3.39, 1.54)	0.46
Ethnicity (non-Caucasian)	1.59	(0.29, 2.90)	<0.01	−0.76	(−2.72, 1.20)	0.45
Sexual orientation (heterosexual)	−0.29	(−1.63, 1.04)	0.80	−0.66	(−2.66, 1.34)	0.52
Education (<high school)	−2.71	(−4.56, −0.86)	<0.01	−4.34	(−7.10, −1.57)	<0.01
Employment (unemployed)	−6.02	(−7.22, −4.83)	<0.01	−4.36	(−6.23, −2.48)	<0.01
Personal income (<$30,000/year)	−3.74	(−4.98, −2.50)	<0.01	−4.48	(−6.35, −2.61)	<0.01
Biomedical
Recent CD4 counts (<500 cell/mL)	−1.29	(−2.56, −0.01)	0.05	−0.18	(−2.09, 1.73)	0.85
Viral load (detectable)	−0.67	(−2.12, 0.74)	0.34	−1.69	(−3.85, 0.47)	0.12
AIDS-defining condition (yes)	−1.21	(−2.56, 0.14)	0.08	−0.10	(−2.12, 1.92)	0.92
Time since HIV diagnosis (years)	−0.26	(−0.35, −0.17)	<0.01	0.20	(0.06, 0.34)	<0.01
Psychosocial
Adaptive coping	0.06	(−0.01, 0.13)	0.08	0.22	(0.11, 0.32)	<0.01
Maladaptive coping	−0.06	(−0.16, 0.05)	0.26	−1.06	(−1.20, −0.92)	<0.01
Mastery	0.32	(0.14, 0.50)	<0.01	1.90	(1.68, 2.13)	<0.01
Social support	0.03	(0.004, 0.05)	0.02	0.22	(0.19, 0.25)	<0.01
Stressors
Total stressors	−0.30	(−0.47, −0.13)	<0.01	−1.62	(−1.85, −1.39)	<0.01

HRQOL, health-related quality of life; CI, confidence interval.

Results for the multivariate regression analysis for physical health-related quality of life are provided in Table 4. When the first block of demographic variables was entered into the regression model, age, ethnicity, employment status, and personal income were uniquely associated with physical health-related quality of life. When the second block of biomedical variables was entered, the variance in physical health-related quality of life explained by the model increased significantly; having low CD4 cell counts, a detectable viral load, and having lived with HIV for a longer time were each negatively associated with physical health-related quality of life. When total number of stressors was added to the model in the third step, it showed a significant negative relationship with physical health-related quality of life and the amount of variance explained by the model again increased significantly.

Table 4.

Hierarchical Multiple Linear Regression on Physical Health-Related Quality of Life (n=758)

	Step I	Step II	Step III	Step IV	Step V
Variable	β	β	β	β	β
Constant	51.83^a	53.21^a	52.85^a	52.79^a	52.53^a
Age (years)	−0.09^a	−0.06	−0.09^b	−0.08^b	−0.08^b
Gender (female)	−1.65	−1.54	−1.53	−1.48	−1.48
Ethnicity (non-Caucasian)	1.87^a	1.60^b	1.74^b	1.73^b	1.69^b
Sexual orientation (heterosexual)	0.08	−0.18	−0.30	−0.26	−0.29
Education (<high school)	−0.95	−0.99	−0.73	−0.62	−0.70
Employment (unemployed)	−4.81^a	−4.69^a	−4.53^a	−4.48^a	−4.52^a
Personal income (<$30,000/year)	−1.48^b	−1.21	−0.90	−0.87	−0.78
Recent CD4 counts (<500)		−1.40^b	−1.31^b	−1.33^b	−1.34^b
Viral load (detectable)		−1.50^b	−1.45^b	−1.50^b	−1.45^b
AIDS-defining condition		−0.73	−0.79	−0.73	−0.75
Time since HIV diagnosis (years)		−0.18^a	−0.18^a	−0.18^a	−0.18^a
Total stressors			−0.28^a	−0.26^b	−0.29^a
Adaptive coping				0.02	0.02
Maladaptive coping				0.00	−0.01
Mastery				0.10	0.11
Social support				−0.01	−0.01
Stressors×mastery					−0.05^b
R ²	0.140	0.166	0.177	0.179	0.185
R ² Change	0.140	0.026	0.011	0.002	0.006
F Change	17.47^a	5.79^a	10.24^a	0.37	5.11^b

p≤0.01.

p≤0.05.

When psychosocial variables (i.e., coping, mastery, and social support) were added to the model, the variance explained by the model did not significantly improve, and none of psychosocial variables were significantly associated with physical health-related quality of life. In the final step, we tested the two-way interactions between stress and psychosocial variables one at a time. The interaction term between total stressors and mastery was significant, but not clinically meaningful (interaction graph not shown).

Associations of demographic, biomedical, and psychosocial factors with mental health-related quality of life

In the univariate analyses (Table 3), having low levels of education, being unemployed, earning low income, using maladaptive coping strategies and experiencing greater numbers of stressors were negatively associated with mental health-related quality of life. Being older, time since HIV diagnosis, and using adaptive coping strategies, high mastery, and high social support were positively associated with mental health-related quality of life.

The multivariate regression results for mental health-related quality of life are presented in Table 5. When the demographic variables were entered as a block, age was positively associated with mental health-related quality of life while having less than a high school education and unemployment were negatively associated with mental health-related quality of life. When the second block of biomedical variables was entered, the amount of variance explained by the model did not improve significantly, and none of the variables added in this step were uniquely associated with mental health-related quality of life. When total number of stressors was added to the model in the third step, the variance in mental health-related quality of life increased by 15.9% above that which was explained by demographic and biomedical variables alone. Age, unemployment, time since HIV diagnosis, and stressors showed significant negative relationships with mental health-related quality of life. Adding psychosocial variables in the fourth step increased the variance in mental health-related quality of life explained by the model by an additional 21%. Mastery and social support were positively associated with mental health-related quality of life, whereas maladaptive coping had a negative relationship with mental health.

Table 5.

Hierarchical Multiple Linear Regression on Mental Health-Related Quality of Life (n=758)

	Step I	Step II	Step III	Step IV	Step V	Step VI
Variable	β	β	β	β	β	β
Constant	48.31^a	48.29^a	46.28^a	44.35^a	44.43^a	44.46^a
Age (years)	0.28^a	0.25^a	0.12^b	0.11^b	0.10^b	0.11^a
Gender (female)	0.35	0.25	0.29	−0.75	−0.63	−0.44
Ethnicity (non-Caucasian)	0.75	0.94	1.70	3.29^a	3.25^a	3.50^a
Sexual orientation (heterosexual)	0.56	0.81	0.14	1.13	1.17	1.02
Education (<high school)	−3.16^b	−3.06^b	−1.57	0.65	0.79	0.81
Employment (unemployed)	−4.38^a	−4.61^a	−3.74^a	−2.29^b	−2.22^b	−2.20^b
Personal income (<$30,000/year)	−1.48	−1.43	0.33	0.44	0.24	0.20
Recent CD4 counts (<500)		0.29	0.83	0.76	0.72	0.83
Viral load (detectable)		0.23	0.52	0.13	0.04	0.13
AIDS-defining condition		−0.79	−1.13	−0.50	−0.48	−0.32
Time since HIV diagnosis (years)		0.13	0.14	0.13^b	0.12	0.11
Total stressors			−1.59^a	−0.64^a	−0.62^a	−0.67^a
Adaptive coping				0.05	0.05	0.04
Maladaptive coping				−0.61^a	−0.64^a	−0.70^a
Mastery				1.00^a	0.99^a	1.13^a
Social support				0.09^a	0.09^a	0.09^a
Stressors×Maladaptive Coping					0.06^a	0.05^b
Stressors×Mastery					0.09^a	0.11^a
Stressors×Social Support					0.00	0.00
Mastery×Maladaptive Coping					0.01	0.02
Stressors×Mastery×Maladaptive
Coping						−0.01^a
R ²	0.081	0.085	0.243	0.453	0.465	0.474
R ² Change	0.081	0.004	0.159	0.210	0.012	0.009
F Change	9.41^a	0.78	156.35^a	71.17^a	4.12^a	12.34^a

p≤0.01.

p≤0.05.

We then added the two-way interaction terms between stressors and maladaptive coping, mastery, and social support. Two of these three interaction terms were significantly associated with mental health-related quality of life and the amount of variance explained by the model increased from 45.3% to 46.5%. The three-way interaction between stressors, mastery and maladaptive coping added in the last step was also significant, suggesting the interaction between stressors and maladaptive coping is further moderated by the level of mastery. The total variance explained by the final model was 47.4%, which was much higher than that the variance explained by the model for physical health (18.5%). The 3-way interaction is graphically displayed in Figs. 2A, B, and C. The figures suggest that stressors and maladaptive coping interact to predict low mental health-related quality of life, but only at low and medium levels of mastery. At a high level of mastery, a higher number of stressors was associated with lower mental health-related quality of life, with higher maladaptive coping only predicting the intercept of the regression line.

FIG. 2.

Three way interaction: Stressors and maladaptive coping interact to predict low mental health-related quality of life at (A) low, (B) medium, and (C) high levels of mastery among people with HIV (n=758). (Color image can be found at www.liebertonline.com/apc).

Discussion

This study found that a high proportion of the participants reported experiencing stressors in their lives and that having higher numbers of stressors was associated with lower physical and mental health-related quality of life. This is consistent with prior studies that have shown that stress has a negative association with physical and mental health.^2
–4,6

We found a positive direct relationship between mastery and mental health-related quality of life (but not with physical health), which suggests that people who feel capable of managing lives' challenges and retain a sense of personal control enjoy better mental health.³⁵ We also found that mastery had an indirect association with mental health-related quality of life, indicating that mastery plays a role in attenuating the negative effects of life stressors on mental health.³⁶ This protective effect of mastery suggests that people with HIV who have a high degree of personal control are less vulnerable to the damaging effects of life stressors. It might be that people with HIV with high levels of mastery seem better positioned to reframe life challenges and remain hopeful when facing difficult life circumstances. This may also suggest that people with high levels of mastery have better problem-solving skills that allow them to effectively intervene on the stressors and minimize their negative effects. We are not aware of interventions that have specifically targeted mastery to improve mental health outcomes in HIV, but many of the traditional psychotherapies and stress management therapies directly or indirectly address aspects of mastery and self-efficacy such as improving problem-solving abilities and coping skills.^37

–40

Our study also showed that a greater use of maladaptive coping mechanisms was associated with lower mental health-related quality of life (but not physical health) and that maladaptive coping acted as an antagonistic moderator of the relationship between stressors and mental health. That is, both stressors and maladaptive coping operate on the outcome in the same direction (both have a negative relationship with mental health-related quality of life) but the interaction term results in an opposite sign indicating a positive relationship. This might seem counterintuitive as it suggests that two negative influences make for a good outcome, but it simply indicates that while maladaptive coping had a negative association with mental health with fewer stressors, this negative effect was blunted as the number of stressors increased. We also hypothesize that the antagonistic interaction between stressors and maladaptive coping may be due to the fact that those facing short-term and uncontrollable stressors may gain short-term benefit from avoidance-based coping mechanisms, including self-distraction and venting.⁴¹

The significant high-order (three-way) interaction between stressors, maladaptive coping mechanisms, and sense of mastery suggests that the stress-intensifying effect of maladaptive coping on mental health was moderated by the level of mastery. In other words, the negative effects of maladaptive coping were amplified among those with a lower sense of mastery but diminished significantly among those who had higher levels of mastery. Among people living with HIV, low sense of mastery is associated with greater use of avoidant coping behavior, which may in turn preempt more effective coping efforts, involve damaging behaviors (e.g., substance use), and induce intrusion of stress-related thoughts.⁴²

Taken as a whole, these findings lend support to the idea that providing people with HIV with the skills to deal with stressors in an effective manner may improve their mental health and well-being.²⁰ This suggests that counseling programs could have a significant positive effect on health by reducing the reliance on maladaptive coping strategies while promoting the use of adaptive coping skills. Further research in this area could inform treatment and help customize specific stress management programs for HIV.

As shown in prior studies, social support exhibited a protective effect on mental health-related quality of life.^11,13 Individuals with a strong or extensive social support network may receive advice that could help them resolve a threatening situation before it has a negative impact on their mental health. In this study, however, we only identified a direct relationship between social support and mental health, but we were unable to document a moderating effect on the negative effects of stressors.

Even though psychosocial resources played a moderating role in the relationship between life stressors and mental health, they were not associated with better physical health. We showed significant univariate relationships between mastery and physical health, and between social support and physical health, but they ceased to be significant when biomedical factors and other demographic variables were controlled for in the final model. This suggests that biomedical factors are more powerful predictors of physical health and they may have overshadowed the association between psychosocial factors and physical health. It might also be the case that it takes a longer time for psychosocial resources to have a positive effect on physical health, and the cross-sectional nature of these analyses was inadequate to show the potential time-lag in this relationship. The lack of relationship between psychosocial resources and physical health is an area that warrants further investigation.

In terms of the other correlates of physical and mental health-related quality of life, as expected, biomedical variables were related to physical health and psychosocial variables were more strongly associated with mental health. While age was negatively associated with physical health, it was positively associated with mental health. Prior research has shown that with aging, people with HIV are at increased risk of various neurocognitive, hormonal, and metabolic disorders and experience poorer physical health than younger people.^43,44 This may also explain why time since HIV diagnosis was negatively associated with physical health. On the other hand, mental health may improve with age for several reasons. Older individuals may have had more time to adjust to the challenges of managing HIV. They may have a different perception of the illness because they are at a different stage in life or may have had more time to establish support networks or seek out programs of care, all of which can help improve mental health. This finding suggests that interventions that focus on strategies to deal with life stressors would be best to be implemented early on or close to the HIV diagnosis.

This study also found that ethnicity (being non-Caucasian) had a significant positive relationship with both physical and mental health-related quality of life. A study of quality of life among cancer patients found that African American individuals had better emotional well-being than Caucasian individuals, and hypothesized that this was due to greater access to social support in the African American population.⁴⁵ Another study that examined ethnic identity, race-related stress and quality of life found that ethnic identity was a significant predictor of quality of life, and that African Americans had higher psychological well-being scores than other ethnic groups.⁴⁶ The investigators also hypothesized that this may be partially attributable to social support networks.

The main findings of this study suggest that there may be multiple complementary approaches to developing interventions intended to have a positive effect on mental health.³⁵ Life stressors could be targeted directly by minimizing their scope or intensity. As health care providers may not have the time, skills or resources to directly influence some of the life stressors that their patients may be experiencing, it is important that they be aware of their patients' abilities to manage stressful situations and make referrals to counseling and therapeutic services when appropriate. Clinicians can play a central role by being mindful of the various types of stressors patients living with HIV are faced with, helping them recognize and acknowledge these stressors when they arise, and working with them to minimize health-care related stressors, such as the negative effect of stressful life events on adherence to antiretroviral medications.⁴⁷ On the other hand, clinicians and other health care professionals can also collaborate to develop mental health interventions that focus on strengthening peoples' own psychological resources. For example, people could be taught techniques to remain optimistic while coping with competing demands on their time and balance performance expectations. This approach would concentrate on enhancing a sense of mastery and coping skills instead of targeting the stressors directly. Previous studies have reported effectiveness of coping effectiveness training (CET), cognitive-behavioral stress management (CBSM), and time-limited dynamic psychotherapy (TLDP) in reducing perceived stress, burnout, anxiety, mood disturbance, and depressive symptoms among people living with HIV.^48

–51

This study has some limitations. First, the cross-sectional nature of the study does not allow us to determine the direction of the relationships and precludes us from making causality claims. While this study suggests that mastery moderates the negative effects of life stressors on mental health, we cannot rule out whether life stressors may instead moderate the relationship between mastery and mental health. Therefore, additional research should clarify whether more emphasis should be placed in developing a mastery-related intervention for individuals dealing with a high number of stressors or in developing a program for individuals with low levels of mastery to help them better deal with life stressors. Second, there may be residual confounding due to additional factors that could influence the relationship between stressors and health-related quality of life, but were not measured in this study. Third, the enumeration of stressful life events may not adequately represent the level of stress and the wide range of issues that somebody may be faced with in an individual's psychosocial environment. Finally, self-reported questionnaires may be associated with significant measurement error. In future studies, empirically supported interview measures with good criterion validity may be an option to reduce this measurement error.

Developing programs to maintain or improve quality of life has become increasingly important in recent years¹⁷ and there is a growing interest in studies of stress management among people with HIV.^38,52 Research that evaluates the impact of different types of stressors or examines each life stressor independently is a potential area of future study. The negative impact of stressors on health-related quality of life and disease progression warrants further exploration as this knowledge can be used to develop support services and programs that can help people with HIV effectively manage stress and minimize its impact on health-related quality of life.

Footnotes

Acknowledgments

The OHTN Cohort Study (Principal Investigator, Dr. Sean B. Rourke) is supported by the AIDS Bureau—Ontario Ministry of Health and Long-Term Care. Data collection sites include: Toronto General Hospital (Drs. Irving Salit and Janet Raboud), St. Michael's Hospital (Dr. Ahmed Bayoumi), Maple Leaf Medical Clinic (Drs. Mona Loutfy and Fred Crouzat), Sunnybrook Health Sciences Centre (Drs. Anita Rachlis and Nicole Mittmann), Kingston General Hospital (Dr. Wendy Wobeser), Ottawa General Hospital (Dr. Curtis Cooper), St. Joseph's Hospital, Hamilton (Dr. Marek Smieja), St. Clair Medical Associates (Dr. Ken Logue), University of Ottawa Health Services (Dr. Don Kilby), St. Joseph's Health Care, London (Dr. Edward Ralph), Sudbury Regional Hospital (Dr. Roger Sandre), and Windsor Regional Hospital (Dr. Jeffrey Cohen).

We gratefully acknowledge all of the people living with HIV who volunteered to participate in the OHTN Cohort Study and the work and support of the inaugural OCS Governance Committee: Darien Taylor (Chair), Dr. Evan Collins, Dr. Greg Robinson, Shari Margolese, Patrick Cupido, Tony Di Pede, Rick Kennedy, Michael Hamilton, Ken King, Brian Finch, Lori Stoltz, Dr. Ahmed Bayoumi, Dr. Clemon George, and Dr. Curtis Cooper. We thank all the interviewers, data collectors, research associates and coordinators, nurses, and physicians who provide support for data collection and extraction. The authors wish to thank the OHTN staff and their teams for data management and IT support (Mark Fisher, Director, Data Systems) and OCS management and coordination (Virginia Waring, Project Manager, OCS). The viral load data in the OCS was supplemented through a linkage with the viral load database of the Ontario Agency for Health Protection and Promotion.

Author Disclosure Statement

No competing financial interests exist.

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