Quality of Life Research

, Volume 21, Issue 6, pp 993–1003 | Cite as

The associations among coping, nadir CD4+ T-cell count, and non-HIV-related variables with health-related quality of life among an ambulatory HIV-positive patient population

Article

Abstract

Purpose

We investigated HRQoL among HIV-positive outpatients from October, 2006-December, 2007, incorporating medical chart review, and a survey of coping styles.

Methods

Consented HIV-positive patients receiving medical care at University of Colorado Denver, with HAART as first antiretroviral regimen, completed the MOS-HIV and Brief COPE survey instruments. Linear regression identified a priori factors hypothesized to be associated with the MOS-HIV composite mental and physical health scores (MHS, PHS). Brief COPE survey maladaptive and adaptive coping components were added to the models and retained if significant.

Results

Among the 157 patient cohort, parsimonious multivariable linear regression models (P < 0.05) indicated higher nadir CD4+ T-cell counts and adaptive coping were associated with a higher MHS; public/no insurance, mental illness, current number of non-HIV medications, and maladaptive coping were inversely associated with MHS. Nadir CD4+ T-cell count and efavirenz use were associated with a higher PHS; mental illness, current number of non-HIV mediations, and maladaptive coping were inversely associated with PHS.

Conclusions

Factors independently associated with lower MHS and lower PHS include lower nadir CD4+ T-cell counts, and use of maladaptive coping. Efforts to reduce use of maladaptive coping strategies and earlier identification and treatment of HIV may improve HRQoL in HIV-positive patients.

Keywords

HIV Nadir CD4+ T-cell count Coping Quality of life 

Abbreviations

AIDS

Acquired immunodeficiency syndrome

ART

Antiretroviral therapy

ARV

Antiretroviral

HAART

Highly active antiretroviral therapy

HIV

Human immunodeficiency virus

HRQoL

Health-related quality of life

IDGP

Infectious disease group practice

IDU

Intravenous drug user

IQR

Inter-quartile range

MHS

Mental health score

MOS

Medical outcomes study

MSM

Men who have sex with men

NRTI

Nucleoside analog reverse transcriptase inhibitor

NNRTI

Non-nucleoside analog reverse transcriptase inhibitor

PHS

Physical health score

PI

Protease inhibitor

SD

Standard deviation

UCHSC

University of Colorado Health Sciences Center

Introduction

Human immunodeficiency virus (HIV) evolved from Simian immunodeficiency virus in a subspecies of chimpanzee native to west equatorial Africa. The virus entered the United States in the 1970s, and AIDS first appeared in the United States in 1981 among men who have sex with men (MSM), intravenous drug users (IDU), and persons of Haitian descent. The virus was identified in 1983 [1]. During the course of untreated HIV disease, there is gradual deterioration of immune function as CD4+ T-lymphocyte-cells (CD4+ T-cells) are depleted. When the CD4+ T-cell count falls below 200 cells/mm3, opportunistic infections are more likely to occur. A CD4+ T-cell count of 200 cells/mm3 or less in an HIV-infected person is one of the defining elements of an AIDS diagnosis. Left untreated, AIDS complications are fatal [2].

Prior to the introduction of the protease inhibitor (PI) class of drugs in 1996, treatment for HIV/AIDS was limited to use of one or two nucleoside analog reverse trascriptase inhibitor (NRTI) medications. With the addition of a PI to the treatment regimen, morbidity and mortality were significantly reduced [3]. Combination treatment for HIV/AIDS using two or more drug classes and at least three antiretroviral (ARV) drugs is considered to be “highly active antiretroviral therapy” or HAART. With newer drugs and drug classes, a greater number of such combinations are possible.

Prior to December 2007, the Department of Health and Human Services Panel on Antiretroviral Guidelines for Adults and Adolescents recommended deferring therapy in patients with CD4+ T-cell counts of 350 cells/mm3 or greater, in part, to avoid toxicities attributed to antiretroviral agents [4]. Based on these earlier guidelines, which were in effect from 2006 to November 2008, the “appropriate” CD4+ T-cell count to begin HAART was <200 cells/mm3, with consideration for the treatment at CD4+ T-cell counts between 200 and 350 cells/mm3 based on symptomatic disease and patient willingness to begin treatment. Current guidelines state that “Antiretroviral therapy (ART) should be initiated in all patients with… a CD4 count <350 cells/mm3… ART is also recommended for patients with CD4 counts between 350 and 500 cells/mm3… Patients initiating ART should be willing and able to commit to lifelong treatment and should understand the benefits and risks of therapy and importance of adherence” [5].

Generally, the “nadir” CD4+ T-cell count occurs before starting therapy, unless the patient decides to stop the course of treatment. Higher health-related quality of life (HRQoL) in HIV-positive patients is associated with higher nadir CD4+ T-cell counts, lower viral loads [6, 7], fewer self-reported symptoms [8], and having private health insurance [9]. How antiretroviral therapy, types of coping, demographic factors as well as co-morbidities and nadir CD4+ T-cell counts influence HRQoL in multivariable linear models is currently unknown. We hypothesize that parsimonious multivariable linear regression models for the mental and physical health components of HRQoL will include types of coping, nadir CD4+ T-cell counts, ARV treatment, number of co-morbidities, and demographic factors.

The purpose of this observational cross-sectional cohort study was to investigate the association of clinical, immunologic, virologic, antiretroviral, demographic, and coping styles with HRQoL utilizing the outcomes of mental and physical health scores (MHS and PHS) of the Medical Outcomes Survey for HIV (MOS-HIV), while adjusting for other significant effects. Additionally, the association between adaptive and maladaptive coping on the composite mental and physical HRQoL scores was investigated using the Brief COPE survey instrument.

Methods

Study population

We recruited consented HIV-positive patients whose first HIV treatment regimen was a HAART regimen from the Infectious Disease Group Practice (IDGP) Clinic of the University of Colorado Health Sciences Center (UCHSC) between October 2, 2006, and December 12, 2007. During a regularly scheduled clinic appointment (not for acute symptoms), potentially qualified patients were asked by their physician if they were interested in consenting for and participating in the study. The study protocol was approved by the Colorado Multiple Institutional Review Board.

The study population was compared with the 2005 IDGP patient population to determine whether significant demographic differences exist. We assessed health challenges (including depression) and quality of life by means of the MOS-HIV. Participants also completed the Brief COPE Inventory. Medical chart review was also performed. The surveys were completed using a personal computer located at the Health Sciences Center. Participants received a $20 check by mail for their completion of the surveys.

Analysis measures

The MHS and PHS of the MOS-HIV were the outcome variables for this study. Factors assessed for their association with the outcome variables are shown in Table 1. Mental illness was defined as any of the following diagnoses: depression, bipolar disorder, anxiety, personality disorder, attention deficit disorder, post-traumatic stress disorder, or mood disorder. Recreational drug use was defined as any use of the following: alcohol, heroin, cocaine, crack cocaine, methamphetamine, or inhalants.
Table 1

Study population characteristics

Characteristic

Study population [n = number data available out of 159]

Age in years: median (IQR)

43.0 (36–49) [159]

White race: n (%)

109 (68.6) [159]

Black race: n (%)

26 (16.4) [159]

Hispanic ethnicity (%)

21 (13.2) [159]

Asian/Pacific Island race/ethnicity: n (%)

3 (1.9) [159]

Male sex: n (%)

144 (90.6) [159]

Public/no insurance: n (%)

115 (72.3) [159]

IDU as 1st or 2nd risk factor: n (%)

30 (18.9) [159]

MSM as 1st or 2nd risk factor: n (%)

99 (62.3) [159]

Heterosexual as only risk factor: n (%)

27 (17.0) [159]

Transfusion only risk factor: n (%)

1 (0.7) [159]

Unknown risk factor: n (%)

2 (1.4) [159]

AIDS diagnosis: n (%)

95 (62.1) [153]

Years since HIV diagnosis: median (IQR)

7.0 (4.0–10.0) [159]

Taking HAART: n (%)

147 (92.5) [159]

Taking HAART with NNRTI: n (%)

73 (45.9) [159]

Taking HAART with PI: n (%)

73 (45.9) [159]

Taking HAART with 3 NRTI: n (%)

3 (1.9) [159]

Not taking HAART: n (%)

12 (7.5) [159]

Taking NRTI’s: n (%)

147 (92.5) [159]

 Zidovudine: n (%)

24 (15.1) [159]

 Stavudine: n (%)

12 (7.6) [159]

 Epivir: n (%)

54 (34.0) [159]

 Tenofovir: n (%)

104 (65.4) [159]

 Emtricitabine: n (%)

79 (49.7) [159]

 Abacavir: n (%)

17 (10.7) [159]

 Didanosine: n (%)

4 (2.5) [159]

Taking NNRTI’s: n (%)

73 (45.9) [159]

 Nevirapine: n (%)

16 (10.1) [159]

 Efavirenz: n (%)

57 (35.9) [159]

Taking PI’s: n (%)

73 (45.9) [159]

 Kaletra or Lopinavir/low dose ritonavir: n (%)

27 (17.0) [159]

 Atazanavir: n (%)

37 (23.3) [159]

 Nelfinavir: n (%)

5 (3.1) [159]

 Darunavir: n (%)

1 (0.6) [159]

 Fosamprenavir: n (%)

1 (0.6) [159]

 Saquinavir: n (%)

1 (0.6) [159]

 Ritonavir or low dose ritonavir: n (%)

38 (23.9) [159]

Characteristic

Study population [n = number data available out of 159]

Current log10 viral load: median (IQR)

1.4 (1.0–2.0) [159]

Viral load ≥400 copies/ml: n (%)

25 (15.7) [159]

Hepatitis C diagnosis: n (%)

11 (6.9) [159]

Pre-HAART CD4+ T-cell count: median (IQR)

185 (37–350) [126]

Pre-HAART CD4+ T-cell count > median: n (%)

63 (50.0) [126]

Pre-HAART CD4+ T-cell count <350 cells/mm3: n (%)

94 (74.6) [126]

Nadir CD4+ T-cell count: median (IQR)

180 (36–314) [157]

Nadir CD4+ T-cell count > median: n (%)

75 (47.8) [157]

Nadir CD4+ T-cell count < 350 cells/mm3: n (%)

124 (79.0) [157]

Years since nadir CD4+ T-cell count: median (IQR)

3.3 (1.2–7.0) [156]

Current CD4+ T-cell count: median (IQR)

455 (275–735) [154]

Current CD4+ T-cell percent: median (IQR)

25% (18–33) [154]

Mental illness diagnosis: n (%)**

54 (34.0) [159]

Current pneumocystis pneumonia prophylaxis

35 (22.3) [157]

Current/prior smoker

92 (63.9) [144]

Recreational drug use: n (%)

73 (54.5) [134]

0–1 Co-morbidities: n (%)

65 (40.9) [159]

2–3 Co-morbidities: n (%)

66 (41.5) [159]

4–8 Co-morbidities: n (%)

28 (17.6) [159]

1–4 psychiatric medications: n (%)

36 (22.4) [159]

0 Non-HIV medications: n (%)

64 (40.3) [159]

1–2 Non-HIV medications: n (%)

54 (34.0) [159]

3 or more Non-HIV medications: n (%)

41 (25.8) [159]

IQR inter-quartile range, MSM men who have sex with men, IDU intravenous drug user, AIDS acquired immunodeficiency syndrome, HIV human immunodeficiency virus, HAART highly active antiretroviral therapy, NNRTI non-nucleoside analog reverse transcriptase inhibitor, PI protease inhibitor, NRTI nucleoside analog reverse transcriptase inhibitor

** Diagnoses include depression, bipolar disorder, anxiety, personality disorder, attention deficit disorder, post-traumatic stress disorder, mood disorder

Includes alcohol, heroin, cocaine, crack cocaine, methamphetamine, inhalants

The three categories selected based upon literature review to represent maladaptive coping were as follows: self-distraction, behavioral disengagement, and self-blame [10, 11, 12]. The three categories selected to represent adaptive coping were as follows: active coping, acceptance, and positive reframing.

We used the Cronbach’s alpha test to calculate the internal consistency of each of the eight multi-item measures of the MOS-HIV, the six subscales of the Brief COPE used for the study as well as for the composite adaptive and maladaptive scores obtained from using selected coping categories from the Brief COPE. The MHS and PHS crude values were standardized using methodology specified by the MOS-HIV author [13].

In order to create the best predictive model, we used a minimal number of significant variables to minimize the variance of the regression model. Since our sample size was relatively small, we started by eliminating variables that did not have a significant slope, P > 0.05, in univariate regression.

Analysis methods

Summaries of descriptive data, univariate analyses, Cronbach’s alpha test, and multivariable linear regression analyses were performed with SAS version 9.2 (SAS Institute, Cary, NC). We compared patient characteristics for the study population and the 2005 clinic population using the Yates-corrected chi-square test or Fisher’s exact test for categorical variables and using the Kruskal–Wallis test of medians for continuous variables. Univariate, multivariable, and parsimonious multivariable linear regression analyzes using stepwise selection were performed for this study. Parsimonious multivariable linear regression models were created by initially including factors with P values ≤ 0.05 in the non-parsimonious multivariable linear models. Composite adaptive and maladaptive coping scores from the Brief COPE were added to the parsimonious multivariable models and retained, if significant.

Results

Study population and comparison to clinic population

Of the 188 patients consented for the study, three participants did not complete the computerized surveys, and one participant indicated after completing the surveys that he had prior zidovudine mono-therapy experience and was therefore withdrawn from the study. Upon completing the chart review, it was determined that an additional 25 participants (13.6%) had prior mono- or dual-therapy experience before starting HAART, thus making their data ineligible for the analysis. A description of the remaining 159 eligible participants is shown in Table 1.

In 2005, the IDGP of the UCHSC treated 1,253 HIV-positive patients. Of these patients, 84% were men, 64% had public or no insurance, 59% had a prior AIDS diagnosis, 73% were on HAART, 69% were MSM, 21% were heterosexual, and 18% had IDU as 1st or 2nd HIV risk factor, 14% had a chronic hepatitis C diagnosis and median age was 42.8 years (Table 2).
Table 2

Comparison of study population to 2005 clinic population

 

2005 Clinic population [n = number data available out of 1,253]

Study population [n = number data available out of 159]

P value

Age in years median (IQR)

42.8 (37.4–49.4) [1,139]

43.0 (36–49) [159]

0.68

Race/ethnicity

 White (%)

66.5 [1,088]

68.6 [159]

0.57

 Black (%)

16.4 [1,088]

16.4 [159]

0.91

 Hispanic (%)

14.9 [1,088]

13.2 [159]

0.66

Male sex (%)

83.8 [1,139]

90.6 [159]

0.035

Public/no insurance (%)

64.3 [1,139]

72.3 [159]

0.06

MSM as 1st or 2nd risk factor (%)

69.4 [1,051]

73.8 [157]

0.10

Heterosexual as 1st or 2nd risk factor (%)

20.6 [1,051]

21.7 [157]

0.91

IDU as 1st or 2nd risk factor (%)

18.2 [1,051]

18.5 [157]

0.93

AIDS diagnosis (%)

58.7 [1,068]

62.1 [153]

0.48

Years since HIV diagnosis, median (IQR)

8.5 (4.5–13.6) [1,253]

7.0 (4.0–10.0) [159]

0.026

Taking HAART (%)

73.2 [1,253]

92.5 [159]

<0.001

Hepatitis C diagnosis (%)

14.1 [1,253]

6.9 [159]

0.017

Mental illness diagnosis (%)**

41.4 [1,253]

34.0 [159]

0.09

IQR inter-quartile range, MSM men who have sex with men, IDU intravenous drug user, AIDS acquired immunodeficiency syndrome, HIV human immunodeficiency virus, HAART highly active antiretroviral therapy

** Diagnoses include depression, bipolar disorder, anxiety, personality disorder, attention deficit disorder, post-traumatic stress disorder, or mood disorder

Yates-corrected chi-square test or Kruskal–Wallis test of medians

The participant study population does not differ significantly from the 2005 clinic population with regard to age in years, race/ethnicity, HIV infection route, or history of an AIDS diagnosis (Table 2). However, there was a trend for the study population to have a greater proportion of participants with public/no insurance (P = 0.06) and to have a lower proportion of participants with a mental illness diagnosis (P = 0.09). Study participants were more likely to be men (P = 0.035), less likely to have a hepatitis C diagnosis (P = 0.017), have had fewer years since receiving an HIV diagnosis (P = 0.026), and were more likely to be taking a HAART regimen (P < 0.001). A requirement of this study was that participants had started ARV treatment with a HAART regimen.

Cronbach’s alpha and linear regression analyzes

The mean, standard deviation, and Cronbach’s alpha scores for the eight multi-item measures of the MOS-HIV, the six subscales of the Brief COPE utilized for this study, and the composite maladaptive coping and adaptive coping scores are shown in Table 3. The Cronbach’s alpha test results for all multi-item MOS-HIV measures and the composite adaptive and maladaptive coping scores were all >0.70, indicating very good internal consistency.
Table 3

Cronbach’s alpha test results of multi-item scales of the MOS-HIV, Brief COPE categories used in the analysis, and composite maladaptive and adaptive coping scores (n = 159)

Category

Mean ± SD

Cronbach’s alpha

MOS-HIV

 Overall health

17.2 ± 6.31

0.89

 Physical functioning

14.5 ± 3.52

0.90

 Role functioning

2.87 ± 0.90

0.79

 Mental health

21.8 ± 5.87

0.91

 Energy/fatigue

15.0 ± 4.74

0.88

 Cognitive functioning

19.3 ± 4.83

0.92

 Pain

8.15 ± 2.55

0.88

 Distress

18.1 ± 5.40

0.92

Brief COPE

 Active coping

3.81 ± 1.72

0.62

 Acceptance

5.13 ± 1.37

0.68

 Positive reframing

3.74 ± 1.91

0.73

 Self-distraction

2.87 ± 1.95

0.61

 Behavioral disengagement

0.67 ± 1.25

0.72

 Self-blame

2.13 ± 2.01

0.77

Composite coping scores

 Adaptive coping (active coping, acceptance, and positive reframing)

12.7 ± 3.86

0.75

 Maladaptive coping (self-distraction, behavioral disengagement, and self-blame)

5.67 ± 3.89

0.73

Univariate linear regression analyses for MHS and PHS score outcomes are shown in Table 4. Significant associations with the MHS are the following: nadir CD4+ T-cell count per 100 cells/mm3, a mental illness diagnosis, and number of other medications being taken. The model therefore predicted the following: the MHS increased by 20% over the nadir CD4+ T-cell range observed in the study, decreased by 16% due to the presence of mental illness, and decreased by up to 30% by the number of other medications being taken by study participants.
Table 4

Univariate linear regression analyses for MHS and PHS outcomes in the study (n = 159)

Effect (n)

Slope (95% confidence limits): MHS

P value

Slope (95% confidence limits): PHS

P value

Pre-HAART CD4+ T-cell count per 100 cells/mm3

0.5 (−0.4, 1.4)

0.27

0.5 (−0.0, 1.1)

0.07

Square-root transformed pre-HAART CD4+ T-cell count

0.1 (−0.2, 0.4)

0.42

0.2 (−0.0, 0.3)

0.07

Pre-HAART CD4+ T-cell count <350 cells/mm3

−0.7 (−5.7, 4.2)

0.77

−1.0 (−4.1, 2.0)

0.51

Nadir CD4+ T-cell count per 100 cells/mm3

1.1 (0.1, 2.2)

0.031

0.9 (0.2, 1.5)

0.008

Square-root transformed nadir CD4+ T-cell count

0.2 (−0.1, 0.5)

0.10

0.2 (0.0, 0.4)

0.018

Nadir CD4+ T-cell count <350 cells/mm3

−3.5 (−8.2, 1.2)

0.15

−2.8 (−5.8, 0.1)

0.06

Years since nadir CD4+ T-cell count

0.2 (−0.4, 0.7)

0.58

0.0 (−0.3, 0.4)

0.96

Log10 viral load

−0.8 (−2.3, 0.8)

0.35

−0.8 (−1.8, 0.2)

0.12

Public/no insurance (n = 115)

−8.5 (−12.6, −4.4)

<0.001

−4.1 (−6.9, −1.4)

0.003

Age in years

0.1 (−0.1, 0.3)

0.34

−0.1 (−0.2, 0.1)

0.22

Mental illness (n = 54)

−8.2 (−12, −4.3)

<0.001

−3.4 (−6.0, −0.8)

0.011

HAART daily dose frequency

−0.8 (−3.8, 2.3)

0.63

0.4 (−1.6, 2.4)

0.68

HAART daily pill burden

−0.2 (−1.2, 0.9)

0.76

0.2 (−0.5, 0.9)

0.62

White race (n = 109)

−0.4 (−4.6, 3.7)

0.84

−1.1 (−3.8, 1.6)

0.43

Black race (n = 26)

0.4 (−4.8, 5.6)

0.87

0.4 (−3.0, 3.8)

0.81

Hispanic ethnicity (n = 21)

−0.1 (−5.8, 5.6)

0.97

1.2 (−2.5, 4.8)

0.54

MSM HIV risk (n = 97)

4.6 (0.7, 8.5)

0.021

2.0 (−0.5, 4.6)

0.11

Heterosexual HIV risk (n = 29)

−4.9 (−9.8, 0.0)

0.05

−2.1 (−5.3, 1.2)

0.21

IDU HIV risk (n = 30)

−2.2 (−7.2, 2.7)

0.37

−1.5 (−4.7, 1.7)

0.35

No current HAART regimen (n = 12)

2.5 (−4.8, 9.8)

0.51

−1.3 (−6.0, 3.4)

0.58

HAART with PI (n = 74)

−1.7 (−5.5, 2.2)

0.39

−1.5 (−4.0, 1.0)

0.24

HAART with NNRTI (n = 73)

1.8 (−2.1, 5.6)

0.36

1.8 (−0.7, 4.3)

0.16

Use of efavirenz (n = 57)

3.9 (−0.1, 7.8)

0.06

3.1 (0.6, 5.7)

0.016

Use of tenofovir (n = 104)

−4.6 (−8.6, −0.6)

0.025

−2.3 (−4.9,−0.3)

0.08

Use of emtricitabine (n = 79)

−3.7 (−7.5, 0.1)

0.06

−1.7 (−4.2, −0.8)

0.18

Use of zidovudine (n = 24)

4.4 (−0.9, 9.8)

0.10

3.1 (−0.3, 6.6)

0.08

Use of nevirapine (n = 16)

−5.5 (−11.8, −0.9)

0.09

−3.7 (−7.8, 0.5)

0.08

Use of Kaletra or lopinavir/low dose ritonavir (n = 27)

−2.1 (−7.2, 3.0)

0.41

−2.4 (−5.7, 0.9)

0.15

Years since HIV diagnosis

0.1 (−0.3, 0.4)

0.77

−0.07 (−0.3, 0.2)

0.53

Hepatitis C diagnosis (n = 11)

−1.3 (−8.9, 6.3)

0.74

−4.3 (−9.2, 0.5)

0.08

Current or prior smoker (n = 92)

−2.6 (−6.8, 1.6)

0.23

−2.9 (−5.5, −0.2)

0.034

Recreational drug use (n = 73)

−0.1 (−4.3, 4.1)

0.95

1.1 (−1.6, 3.8)

0.42

Trimethoprin/sulfamethoxazole, dapsone, or aerosolized pentamidine use (n = 35)

−2.9 (−7.6, 1.8)

0.22

−1.8 (−4.8, 1.3)

0.25

AIDS diagnosis (n = 95)

−1.9 (−6.0, 2.1)

0.35

−2.5 (−5.1, 0.1)

0.06

Number of other co-morbidities

−1.7 (−2.9, −0.6)

0.002

−1.5 (−2.2, −0.8)

<0.001

Number of other medications (not including medications to treat mental illness)

−1.4 (−2.2, −0.5)

0.002

−1.5 (−2.0, −0.9)

<0.001

Recent CD4+ T-cell count per 100 cells/mm3

0.5 (−0.1, 1.0)

0.12

0.3 (−0.1, 0.7)

0.15

Recent CD4+ T-cell %

14.8 (−3.5, 33.0)

0.11

12.2 (0.4, 23.9)

0.043

Significant associations with the PHS are as follows: nadir CD4+ T-cell count per 100 cells/mm3, use of efavirenz, and the number of other medications being taken. The PHS increased by 16% over the nadir CD4+ T-cell range observed in the study, increased by 7% due to the use of efavirenz, and decreased by up to 33% by the number of other medications being taken by study participants.

Due to a greater number of participants having nadir CD4+ T-cell counts available compared to pre-HAART CD4+ T-cell counts, only nadir CD4+ T-cell count was entered into the multivariable linear regression. Since there may be correlation between recent CD4+ T-cell count and recent CD4+ T-cell percentage, only recent CD4+ T-cell percentage was entered into the multivariable linear regression.

The results of the parsimonious multivariable linear regression models are shown in Tables 5 and 6. Factors that remained in the multivariable model after stepwise selection that were associated with the MHS were nadir CD4+ T-cell count, public/no insurance, presence of mental illness, and number of other medications being taken (not including medications to treat mental illness). Multivariable models included only the 157 out of 159 patients for whom nadir CD4+ T-cell count was available. MSM as HIV risk factor, use of tenofovir, and number of other co-morbidities were removed from the multivariable model during stepwise selection due to non-significance.
Table 5

Multivariable and parsimonious multivariable linear regression analyses for MHS score outcome with independent variables having P values ≤ 0.05 in univariate analyses in the study (n = 157)

Effect (n)

MHS multivariable

MHS parsimonious multivariable

Slope (95% confidence limits): MHS

P value

Slope (95% confidence limits): MHS

P value

Intercept

53.7 (47.9, 59.6)

<0.001

55.0 (50.6, 59.4)

<0.001

Nadir CD4+ T-cell count per 100 cells/mm3

1.1 (0.1, 2.1)

0.032

1.1 (0.1, 2.1)

0.027

Public/no insurance (n = 115)

−3.9 (−8.2, 0.4)

0.07

−5.3 (−9.4, −1.2)

0.011

Mental illness (n = 54)

−7.9 (−12.2,−3.7)

<0.001

−7.8 (−11.6, −3.9)

<0.001

MSM HIV risk (n = 97)

3.1 (−0.6, 6.8)

0.10

–*

Use of tenofovir (n = 104)

−2.9 (−6.6, 0.9)

0.13

Number of other co-morbidities

0.2 (−1.2,1.5)

0.82

Number of other medications (not including medications to treat mental illness)

−1.1 (−2.1, −0.1)

0.026

−1.2 (−2.0, −0.3)

0.008

r square value for model

 

0.22

* Indicates that effect had P value >0.05

Table 6

Multivariable and parsimonious multivariable linear regression analyses for PHS score outcome with independent variables having P values ≤ 0.05 in univariate analyses in the study (n = 157)

Effect (n)

PHS multivariable

PHS parsimonious multivariable

Slope (95% confidence limits): PHS

P value

Slope (95% confidence limits): PHS

P value

Intercept

48.3 (44.2, 52.5)

<0.001

46.8 (44.8, 48.8)

<0.001

Nadir CD4+ T-cell count per 100 cells/mm3

0.8 (0.1, 1.6)

0.048

0.9 (0.3, 1.5)

0.004

Public/no insurance (n = 115)

−1.3 (−4.1, 1.6)

0.37

*

Mental illness (n = 54)

−2.3 (−5.2, 0.5)

0.11

−4.3 (−6.6, −2.0)

<0.001

Use of efavirenz (n = 58)

3.5 (1.0, 5.9)

0.006

3.7 (1.5, 5.9)

0.001

Current or prior smoker

−1.3 (−3.8, 1.2)

0.29

Number of other co-morbidities

−0.4 (−1.3, 0.5)

0.38

Number of other medications (not including medications to treat mental illness)

−1.0 (−1.7, −0.3)

0.004

−1.2 (−1.7, −0.7)

<0.001

Recent CD4+ T-cell %

1.5 (−12.7, 15.8)

0.83

r square value for model

 

0.26

* Indicates that effect had P value >0.05

Factors that were associated with the PHS in the multivariable model after stepwise selection were nadir CD4+ T-cell count, presence of mental illness, use of efavirenz, and number of other medications being taken (not including medications to treat mental illness). Public/no insurance, current or prior smoker, number of other co-morbidities, and recent CD4% were removed from the model during stepwise selection due to non-significance.

When composite adaptive and maladaptive coping scores were added to the prior multivariable models as shown in Table 7, a significantly inverse association between the outcome of MHS was found for the following factors: public/no insurance, a mental illness diagnosis, the number of non-HIV medications being taken (not including medications to treat mental illness), and maladaptive coping (consisting of behavioral disengagement, self-blame, and self-distraction). Significant positive associations between the MHS and the following factors were found: nadir CD4+ T-cell count and adaptive coping (consisting of active coping, acceptance, and positive reframing). Interpretations of the model predict that the MHS decreases for public/no insurance by 6%, mental illness by 7%, maladaptive coping by 5%, and number of non-HIV medications being taken by up to 18%, and the MHS increases for nadir CD4+ T-cell count over the range of values observed in the study by 13% and for adaptive coping by 1%. The r square value for the model was 0.57.
Table 7

Composite adaptive and maladaptive coping scores added to parsimonious multivariable linear regression models, and final parsimonious multivariable linear regression models with the outcome being MHS scores in the study (n = 157)

Effect (n)

MHS multivariable

MHS final parsimonious multivariable

Slope (95% confidence limits): MHS

P value

Slope (95% confidence limits): MHS

P value

Correlation coefficient type II*

Intercept

55.9 (50.2, 61.5)

<0.001

55.9 (50.2, 61.5)

<0.001

 

Nadir CD4+ T-cell count per 100 cells/mm3

0.9 (0.1, 1.6)

0.023

0.9 (0.1, 1.6)

0.023

0.18

Public/no insurance (n = 115)

−3.5 (−6.5, −0.4)

0.027

−3.5 (−6.5, −0.4)

0.027

0.18

Mental illness (n = 54)

−4.0 (−7.0, −1.0)

0.009

−4.0 (−7.0, −1.0)

0.009

0.21

Number of other medications (not including medications to treat mental illness)

−0.9 (−1.6, −0.3)

0.005

−0.9 (−1.6, −0.3)

0.005

0.23

Adaptive coping

0.6 (0.3, 1.0)

<0.001

0.6 (0.3, 1.0)

<0.001

0.27

Maladaptive coping

−2.0 (−2.3, −1.6)

<0.001

−2.0 (−2.3, −1.6)

<0.001

0.67

r square value for model

0.57

0.57

* Absolute value

As shown in Table 8, a significantly inverse association between the PHS was found for the following factors: a mental illness diagnosis, the number of non-HIV medications being taken (not including medications to treat mental illness), and maladaptive coping (consisting of behavioral disengagement, self-blame, and self-distraction). Significant associations between the PHS and the following were found: nadir CD4+ T-cell count and the use of efavirenz. The model predicts that the PHS decreases for the presence of mental illness by 7%, and maladaptive coping by 1%, and the PHS increases for nadir CD4+ T-cell count over the range of values observed in the study by 13% and for the use of efavirenz by 7%. The r square value for the model was 0.30.
Table 8

Composite adaptive and maladaptive coping scores added to parsimonious multivariable linear regression models, and final parsimonious multivariable linear regression models with the outcome being PHS scores in the study (n = 157)

Effect (n)

PHS multivariable

PHS final parsimonious multivariable

Slope (95% confidence limits): PHS

P value

Slope (95% confidence limits): PHS

P value

Correlation coefficient type II*

Intercept

46.4 (42.1, 50.7)

<0.001

49.1 (46.6, 51.6)

<0.001

 

Nadir CD4+ T-cell count per 100 cells/mm3

0.8 (0.3, 1.4)

0.005

0.8 (0.2, 1.3)

0.009

0.21

Mental illness (n = 54)

−3.2 (−5.5, −0.9)

0.007

−3.6 (−5.8, −1.3)

0.002

0.25

HAART with efavirenz (n = 58)

3.3 (1.2, 5.5)

0.003

3.5 (1.3, 5.6)

0.002

0.25

Number of other medications (not including medications to treat mental illness)

−1.1 (−1.6, −0.6)

<0.001

−1.1 (−1.6, −0.6)

<0.001

0.34

Adaptive coping

0.2 (−0.1, 0.5)

0.13

–**

 

Maladaptive coping

−0.4 (−0.7, −0.2)

0.002

−0.4 (−0.7, −0.1)

0.004

0.23

r square value for model

0.31

0.30

* Absolute value

** Indicates that effect had P value >0.05

Discussion

In this study, we found that having public/no insurance, which is often used as a surrogate for lower socio-economic status, having a mental illness diagnosis, and the number of non-HIV medications taken (not including those to treat mental illness) were inversely associated with higher MHS, and it is noteworthy that the maladaptive coping behaviors of behavioral disengagement, self-blame, and self-distraction were independently and inversely associated with higher MHS while increasing nadir CD4+ T-cell count per 100 cells/mm3, and the adaptive coping behaviors of active coping, acceptance, and positive reframing were associated with higher MHS as well. We also found an independent and inverse association with a higher PHS with maladaptive coping behaviors of behavioral disengagement, self-blame, and self-distraction.

Although the study population differed from the overall clinic population by sex, years since HIV diagnosis, percentage taking HAART, current viral load, and percentage with a hepatitis C diagnosis, the results obtained from this study population may have generalizability to clinics serving similar HIV-positive populations whose first ART regimen was considered to be HAART.

Others have found similar associations between nadir CD4+ T-cell count and PHS scores [7], and lower PHS among patients who started HAART with CD4+ T-cell counts <350 cells/mm [14]. The association that we found between lower MHS and diagnosed mental illness is similar to an association found between lower MHS and depressive symptoms [15]. The association between lower MHS and public/no insurance has been previously reported [9]. The associations we found between the number of other medications being taken (other than those to treat mental illness) and their inverse relationship to the outcomes of MHS and PHS is similar to the associations found between the number of self-reported symptoms and lower MHS [16], PHS and lower overall HRQoL [8, 17, 18].

The inverse association we found between our composite maladaptive coping score and both MHS and PHS is similar to the findings that the behavioral disengagement was associated with lower MHS and PHS, and self-distraction was associated with lower MHS [19]. Our finding of the association between our composite adaptive coping score and MHS is similar to that of others who found that the lower levels of acceptance coping were associated with lower MHS [20, 21].

A novel finding of this study is the association between efavirenz use and higher PHS. It should be noted that the efavirenz potentially has central nervous system effects that are uniquely attributed to this ARV [22]. One possible explanation for our study finding is that the protease inhibitor use, although not significant, had an inverse relationship with PHS (slope = −1.5, P = 0.24) in univariate linear regression analyzes. Nevirapine, a commonly used NNRTI, also had an inverse relationship with PHS (slope = −3.7, P = 0.08) in univariate logistic regression analysis. Of note is the fact that nevirapine is not recommended for patients with higher CD4+ T-cell counts due to the risk of developing hepatotoxicity [4, 5], and its use was therefore reserved for patients with more severe disease. Of the 57 patients taking efavirenz at the date that the surveys were completed, only one patient was concurrently taking a protease inhibitor.

An additional novel finding of our study is that nadir CD4+ T-cell count was associated with a higher MHS. A possible explanation for this association is that lower MHS was found among patients with neurological impairment [23], and lower CD4+ T-cell nadirs were found to be associated with the development of neuro-cognitive impairment [24]. Our study reflects the practice at the time that the US Department of Health and Human Services Treatment Guidelines were in effect during the study enrollment period with deferral of treatment until there was further degradation of the CD4+ T-cell count. Such a deferral may promote the development of co-morbidities that include neuro-cognitive impairment, greater probability of ART-related adverse events [25], as well as increasing the risk of major cardiovascular events [26, 27].

Our study has several limitations. Pre-HAART CD4+ T-cell count was missing for 33 patients, so we were not able to adequately analyze it as well as nadir CD4+ T-cell count was analyzed. Similarly, recreational drug use information was missing for 25 patients, and smoking history was missing for 15 patients. Item 11D was unintentionally not entered into the computerized version of the survey. As per the MOS-HIV instructions, item 11D was one of five questions measuring overall health status and was assigned the value of the mean of the other four questions of this measure. The impact of the absence of this question is reduced only because the overall health status measure has the second highest number of questions in the survey, in contrast to the quality of life measure that relies on only one question.

An additional limitation of this study is that it is cross-sectional. However, it does indicate that maladaptive coping is significantly and inversely associated with HRQoL. A prospective study can include an educational intervention and measure changes in adaptive and maladaptive coping and the outcome of HRQoL, or other outcomes over the course of the study.

Our results indicate that the adaptive and maladaptive coping have significant associations with mental health quality of life, and maladaptive coping is significantly associated with physical health quality of life. In the same manner that susceptibility testing is part of the current standard of care to determine whether a patient’s strain of HIV is resistant to antiviral agent or class before the first HAART regimen is started, a baseline assessment of the patient’s coping skills may indicate that an educational intervention is warranted in order to maximize the health outcome of the patient.

An educational program for people with diabetes lists as part of self-care behavior, healthy coping, which includes motivation to change behavior and setting achievable behavioral goals [28]. Educational programs that have been shown to improve outcomes for patients with other chronic conditions, if altered to specifically address the challenges of living with HIV, may provide an opportunity that would improve outcomes, including HRQoL, for patients with HIV infection [29]. The Centers for Disease Control and Prevention have identified 69 evidence-based HIV behavioral interventions as of June 2009. One such intervention, the Healthy Living Project, has improving health care practices and quality of life as a goal, and is one of six best-evidence individual- and group-level interventions [30]. Other best-evidence interventions have specific target populations. Focus on the Future targets young African-American heterosexual men newly diagnosed with HIV; Living in the Face of Trauma targets HIV-positive adults with childhood sexual abuse histories [29].

Although we found in our study that maladaptive coping is significantly associated with a lower HRQoL among patients with HIV infection, replacing maladaptive coping with adaptive coping behaviors should have a positive effect on HRQoL. In order for HIV infection to remain a chronic, rather than a fatal condition, “treatment” can include patient participation in evidence-based educational interventions designed to teach adaptive coping strategies. For the patient, having the opportunity to learn new skills for living healthy with HIV may also be beneficial for other life challenges that the patient may face. In addition, initiation of ART earlier in the course of disease, rehabilitations of users of illicit drugs, and more access to mental health care may also improve HRQoL.

Notes

Acknowledgments

University of Colorado Health Sciences Center Adult General Clinical Research Center National Institutes of Health grant MO1 #RR00051 provided funding for patient participation honoraria.

Conflicts of interest

Dr. Armon has no conflicts of interest. Dr. Lichtenstein receives research support from Merck, Pfizer, and TaiMed. He serves on advisory boards for Abbott, Merck, Bristol-Myers Squibb, Gilead, and Tibotec.

References

  1. 1.
    National Institutes of Health. (1999). NIAID-Supported scientists discover origin of HIV-1. http://www3.niaid.nih.gov/news/newsreleases/1999/hivorigin.htm.
  2. 2.
    National Institutes of Health. (2004). How HIV causes AIDS. http://www.niaid.nih.gov/factsheets/howhiv.htm.
  3. 3.
    Palella, F. J., Jr., Delaney, K. M., Moorman, A. C., et al. (1998). Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV outpatient study investigators. New England Journal of Medicine, 338(13), 853–860.PubMedCrossRefGoogle Scholar
  4. 4.
    US Public Health Service. (2006). Guidelines for the use of antiretroviral agents in HIV-infected adults and adolescents. www.aidsinfo.nih.gov/guidelines/default_db2.asp?id=50.
  5. 5.
    US Public Health Service. (2011). Guidelines for the use of antiretroviral agents in HIV-infected adults and adolescents. www.aidsinfo.nih.gov.
  6. 6.
    Burgoyne, R. W., Rourke, S. B., Behrens, D. M., & Salit, I. E. (2004). Long-term quality-of-life outcomes among adults living with HIV in the HAART era: The interplay of changes in clinical factors and symptom profile. AIDS and Behavior, 8(2), 151–163.PubMedCrossRefGoogle Scholar
  7. 7.
    Vidrine, D. J., Amick, B. C., I. I. I., Gritz, E. R., & Arduino, R. C. (2003). Functional status and overall quality of life in a multiethnic HIV-positive population. AIDS Patient Care & STDs, 17(4), 187–197.CrossRefGoogle Scholar
  8. 8.
    Preau, M., Vincent, E., Spire, B., et al. (2005). Health-related quality of life and health locus of control beliefs among HIV-infected treated patients. Journal of Psychosomatic Research, 59, 407–413.PubMedCrossRefGoogle Scholar
  9. 9.
    Campsmith, M. L., Nakashima, A. K., & Davidson, A. J. (2003). Self-reported health-related quality of life in persons with HIV infection: results from a multi-site interview project. Health & Quality of Life Outcomes, 1, 12.CrossRefGoogle Scholar
  10. 10.
    Dougall, A. L., Smith, A. W., Somers, T. J., Posluszny, D. M., Rubinstein, W. S., & Baum, A. (2009). Coping with genetic testing for breast cancer susceptibility. Psychosomatic Medicine, 71(1), 98–105.PubMedCrossRefGoogle Scholar
  11. 11.
    Fogel, J. (2004). Internet breast health information use and coping among women with breast cancer. Cyberpsychology & Behavior, 7(1), 59–63.CrossRefGoogle Scholar
  12. 12.
    Olley, B. O., Seedat, S., & Stein, D. J. (2006). Persistence of psychiatric disorders in a cohort of HIV/AIDS patients in South Africa: A 6-month follow-up study. Journal of Psychosomatic Research, 61(4), 479–484.PubMedCrossRefGoogle Scholar
  13. 13.
  14. 14.
    Mauskopf, J., Kitahata, M., Kauf, T., Richter, A., & Tolson, J. (2005). HIV antiretroviral treatment: Early versus later. Journal of Acquired Immune Deficiency Syndromes: JAIDS, 39(5), 562–569.PubMedGoogle Scholar
  15. 15.
    Jia, H., Uphold, C. R., Wu, S., Chen, G. J., & Duncan, P. W. (2005). Predictors of changes in health-related quality of life among men with HIV infection in the HAART era. AIDS Patient Care & STDs, 19(6), 395–405.CrossRefGoogle Scholar
  16. 16.
    Carrieri, P., Spire, B., Duran, S., et al. (2003). Health-related quality of life after 1 year of highly active antiretroviral therapy. Journal of Acquired Immune Deficiency Syndromes: JAIDS, 32(1), 38–47.PubMedCrossRefGoogle Scholar
  17. 17.
    Preau, M., Leport, C., Salmon-Ceron, D., et al. (2004). Health-related quality of life and patient-provider relationships in HIV-infected patients during the first 3 years after starting PI-containing antiretroviral treatment. AIDS Care, 16(5), 649–661.PubMedCrossRefGoogle Scholar
  18. 18.
    Preau, M., Protopopescu, C., Spire, B., et al. (2007). Health related quality of life among both current and former injection drug users who are HIV-infected. Drug and Alcohol Dependence, 86(2–3), 175–182.PubMedCrossRefGoogle Scholar
  19. 19.
    Vosvick, M., Koopman, C., Gore-Felton, C., Thoresen, C., Krumboltz, J., & Spiegel, D. (2003). Relationship of functional quality of life to strategies for coping with the stress of living with HIV/AIDS. Psychosomatics, 44(1), 51–58.PubMedCrossRefGoogle Scholar
  20. 20.
    Jia, H., Uphold, C. R., Zheng, Y., et al. (2007). A further investigation of health-related quality of life over time among men with HIV infection in the HAART era. Quality of Life Research, 16, 961–968.PubMedCrossRefGoogle Scholar
  21. 21.
    Perez, J. E., Chartier, M., Koopman, C., Vosvick, M., Gore-Felton, C., & Spiegel, D. (2009). Spiritual striving, acceptance coping, and depressive symptoms among Adults living with HIV/AIDS. Journal of Health Psychology, 14(1), 88–97.PubMedCrossRefGoogle Scholar
  22. 22.
    Maggiolo, F. (2009). Efavirenz: A decade of clinical experience in the treatment of HIV. Journal of Antimicrobial Chemotherapy, 64(5), 910–928.PubMedCrossRefGoogle Scholar
  23. 23.
    Tozzi, V., Balestra, P., Galgani, S., et al. (2003). Neurocognitive performance and quality of life in patients with HIV infection. AIDS Research and Human Retroviruses, 19(8), 643–652.PubMedCrossRefGoogle Scholar
  24. 24.
    Robertson, K. R., Smurzynski, M., Parsons, T. D., et al. (2007). The prevalence and incidence of neurocognitive impairment in the HAART era. AIDS, 21(14), 1915–1921.PubMedCrossRefGoogle Scholar
  25. 25.
    Lichtenstein, K. A., Armon, C., Buchacz, K., et al. (2008). Initiation of antiretroviral therapy at CD4 cell counts >/=350 cells/mm3 does not increase incidence or risk of peripheral neuropathy, anemia, or renal insufficiency. Journal of Acquired Immune Deficiency Syndromes: JAIDS, 47(1), 27–35.PubMedCrossRefGoogle Scholar
  26. 26.
    D:A:D Study Group. (2008). Use of nucleoside reverse transcriptase inhibitors and risk of myocardial infarction in HIV-infected patients enrolled in the D:A:D study: A multi-cohort collaboration. Lancet, April, 1417–1426.Google Scholar
  27. 27.
    Lichtenstein, K. A., Armon, C., Buchacz, K., et al. (2010). Low CD4+ T cell count is a risk factor for cardiovascular disease events in the HIV outpatient study. Clinical Infectious Diseases, 51(4), 435–447.CrossRefGoogle Scholar
  28. 28.
    American Association of Diabetes Educators. (2009). Self-care behaviors. http://www.diabeteseducator.org/ProfessionalResources/AADE7/.
  29. 29.
    Centers for Disease Control and Prevention. (2009). Compendium of Evidence-Based HIV Prevention Interventions. http://www.cdc.gov/hiv/topics/research/prs/evidence-based-interventions.htm.
  30. 30.
    Centers for Disease Control and Prevention. (2009). Healthy living project. http://www.cdc.gov/hiv/topics/research/prs/resources/factsheets/healthy-living.htm.

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Children’s Hospital ColoradoAuroraUSA
  2. 2.National Jewish HealthDenverUSA

Personalised recommendations