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. Author manuscript; available in PMC: 2015 Sep 1.
Published in final edited form as: Curr HIV/AIDS Rep. 2014 Sep;11(3):291–307. doi: 10.1007/s11904-014-0220-1

Depression and adherence to antiretroviral therapy in low-, middle- and high-income countries: a systematic review and meta-analysis

Olalekan A Uthman 1,2, Jessica F Magidson 3,4, Steven A Safren 3, Jean B Nachega 5,6,7
PMCID: PMC4359613  NIHMSID: NIHMS666990  PMID: 25038748

Abstract

We investigated the associations between depressive symptoms and adherence to antiretroviral therapy (ART) among people living with HIV (PLHIV). We searched the PubMed, EMBASE and Cochrane CENTRAL databases for studies that studies that reported an association between depression and adherence to ART as a primary or secondary outcome. We used a random-effect model to pool the risk estimates from the individual studies. The odds ratio (OR) with their 95% CIs were used as summary estimates. Of 2,861 citations, 111 studies that recruited 42,366 PLHIV met our inclusion criteria. When reported, the rate of PLHIV with depressive symptoms ranged from 12.8% to 78% and the proportion of PLHIV who achieved good adherence (≥ 80%) ranged from 20% to 98%. There were no significant differences in rate of depressive symptoms in PLHIV by country income group; however, the proportion of PLHIV who achieved good adherence was significantly higher in lower-income countries (as defined in the 2012 World Bank Country Income Groups) (pooled rate = 86%) compared to higher-income countries (pooled rate = 67.5%; p<.05). We found that the likelihood of achieving good ART adherence was 42% lower among those with depressive symptoms compared to those without (pooled OR = 0.58, 95% CI 0.55 to 0.62). The relationship between depressive symptoms and adherence to ART was consistent across the country’s income group, study design, and adherence rates. We found that the magnitude of the association significantly decreases with more recent publications and increasing study sample size. The higher the prevalence of depressive symptoms of PLHIV recruited in the studies, the lower the likelihood of achieving good adherence to ART. In conclusion, the likelihood of achieving good adherence was lower among those with depressive symptoms compared to those without.

Keywords: HIV, ART, Depression, Adherence

INTRODUCTION

There is a significant global burden of psychiatric disorders among people living with HIV (PLHIV) across low-, middle-, and high-resource settings, and the presence of such disorders interferes with optimal adherence to antiretroviral therapy (ART), most notably with major depressive disorder [1, 2]. Depression is the most common psychiatric disorder among PLHIV [1], with HIV-positive individuals being twice as likely to be diagnosed with depression compared to HIV-negative individuals [3]. Even at subclinical levels, depressive symptoms have been shown to disrupt adherence to antiretroviral therapy (ART) [4, 2], which increases the likelihood of ART treatment failure, development of ART resistance, and continued HIV transmission [5–7]. Particularly in resource-limited settings, poor ART adherence may lead to failure of the only available first- and second-line ART regimens [8]. Further, depression also is associated with accelerated HIV disease progression and mortality among PLHIV [9, 10].

Although extensive research has documented the relationship between depression and nonadherence across diverse global settings, there have been few efforts to aggregate existing research using meta-analytic techniques. DiMatteo et al.[11] conducted a meta-analysis of the relationship between depression and adherence across numerous chronic conditions, although this meta-analysis did not include HIV/AIDS. More recently, there have been two other meta-analytic studies that examined ART adherence and depression with individuals with HIV. Gonzalez et al. (2011) [4] conducted a meta-analysis of the relationship between depression and ART adherence among PLHIV specifically, examining whether this relationship differed based upon key study characteristics, including types of assessments, study design, and setting. Nakimuli-Mpungu et al. (2012) [12] pooled studies conducted in sub-Saharan Africa that examined the prevalence of clinical depression and depressive symptoms, and examined associations with ART adherence when adherence was assessed. Each of these reviews pointed to a lower likelihood of adherence among individuals with depression compared to individuals without depression, and that this relationship is consistent across diverse settings, samples, and clinical thresholds.

Further efforts are needed to ensure up-to-date analysis of existing data on the relationship between depression and ART adherence and to expand upon prior meta-analyses to understand the extent to which depression influences ART adherence and the specific circumstances in which depression is most likely to affect ART adherence. This will ultimately improve our efforts in developing treatment approaches to simultaneously address depression and adherence and will inform how and when to implement depression and adherence interventions for PLHIV. This is crucial for planning of mental health programming in the context of HIV care worldwide, mobilizing resources in resource-limited global settings to address the behavioural health needs of PLHIV, and for promoting advocacy efforts to address mental health and adherence concerns among PLHIV worldwide.

This systematic review and meta-analysis builds upon prior reviews and meta-analyses in this area [11, 4, 12] by incorporating studies on depression and ART adherence published through April 2014 using additional databases, as well as examining novel subgroup analyses to test whether the association between depression and adherence differs based upon: 1) rates of depressive symptoms and ART adherence in study samples; 2) publication date; 3) country income group (low, middle, high); and 4) study characteristics (sample size, design). Further, we sought to identify whether there was a difference in prevalence rate of depressive symptoms and ART adherence by country income group.

MATERIAL AND METHODS

Information sources and Search strategy

We conducted searches on the PUBMED, EMBASE and Cochrane CENTRAL databases from inception in April 2014. We used key words related to: with the terms “depressive disorder” OR “depressive” AND “disorder”, “Depression” AND “HIV” OR “HIV” AND adherence AND antiretroviral AND “therapy” OR “therapy” OR “therapeutics” OR “therapeutics”. In addition, we manually checked the reference list of the identified studies.

Selection criteria

We evaluated each identified study against the following predetermined selection criteria:

  • Study population: people living with HIV on any ART, regardless of location.

  • Study design: all studies [cross-sectional studies, cohort studies, randomized controlled trials (RCTs)].

  • Outcomes: studies that reported an association between depression and adherence to ART as a primary or secondary outcome.

Data abstraction

For each study identified that met the selection criteria, we extracted the following data from each publication: the first author’s last name, the year of publication, the country where the study was performed, study design, years of data collection, sample size, measure of exposure (indicators of adherence and depression), mean age, percentage female, depression rate and adherence to ART rate and the risk estimate with corresponding 95% CIs. The information on the country where the study was performed was then classified both according to geographical area and country’s income level (high- and middle-income countries).

Study selection

Two of the authors (OU and JM) evaluated the eligibility of studies obtained from the literature search. In cases of discrepancy the third author (JBN) reviewed the studies until agreement was reached by consensus. One reviewer (OU) extracted data and others checked the extracted data.

Data synthesis

We used odds ratio (OR) to quantify the association between depression and adherence to ART. We used 80% or more as the threshold for good adherence as used by studies included in this meta-analysis as well as in our previous published work [13, 14]. If risk estimates were not reported, we calculated them from the raw data presented in the article, we followed Lipsey and Wilson [15] and Borenstein et al [16] procedures for computing odds ratios from effect sizes, mean, standard deviations, t tests, contingency table data, or exact p-values. Study specific natural logarithm of odds ratio were weighted by the inverse of the variance (presented or calculated from the confidence limits) to compute summary natural logarithm of odds ratio with 95% CIs. The meta-analyses were performed using a random-effects model of DerSimonian and Laird [17], which incorporates both within- and between-study variability, since we anticipated between-study heterogeneity. The pooled estimates were then converted back to odds ratios and 95% CIs for presentation.

Following the overall analyses, a number of subgroup analyses were performed with respect to publication year, study design, country’s income group, sample size, adherence measure methods, rate of depression and adherence rate. To evaluate the stability of the results and to test whether one study had an excessive influence on the meta-analysis, leave-one-study-out sensitivity analysis was performed [18]. The scope of this analysis was to evaluate the influence of individual studies, by estimating pooled estimate in the absence of each study. We assessed heterogeneity amongst trials by inspecting the forest plots and using the chi-squared test for heterogeneity with a 10% level of statistical significance, and using the I2 statistic with a value of 50% representing moderate heterogeneity [19, 20]. Random-effect meta-regression was performed to investigate the source of heterogeneity. The independent variable was natural logarithm of odds ratio and explanatory factors were study-level variables listed above. All tests were two tailed. For all tests, a probability level less than 0.05 was considered significant. This review was reported according to the PRISMA recommendations for meta-analyses [21]. Stata 12.1 (Stata Corporation, College Station, TX) software was used for statistical analyses.

RESULTS

Study characteristics

Figure 1 shows the process of study identification and selection. The literature search yielded 2861citations. After review of the title and abstract 134 full text articles were selected for critical reading. A total of 111 studies [22–77, 10, 78–98, 6, 99–130] that recruited 42,366 PLHIV met the inclusion criteria. Table 1 shows the characteristics of the included studies. These studies were published between 1993 and 2013. Most of the studies were conducted in the USA (n=74, 67.0%), followed by France (n=4, 4.5%), Ethiopia (n=4, 3.6%) and South Africa (n=4, 3.6%). Most of the studies were cross-sectional (n=86, 77.5%). Twenty-four studies had a cohort design (21.4%). Only one was a case-control study. Most of the studies used self-reported questionnaires to measure adherence (n=92, 82.8%), followed by pharmacy refills (n=8, 7.1%), MEMS caps (n=7, 6.2%) and pill count (n=4, 3.6%).

Figure 1.

Figure 1

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Study selection diagram

Table 1.

characteristics of included studies.

First author (year) Study design Country Income group Female (%) Adherence measure Depression measure Sample size
Nannis (1993)[92] Cross-sectional USA High 5 self-reported Self report 101
Singh (1996)[112] Cohort USA High 0 Pharm refill Self report 46
Demas (1998)[47] Cross-sectional USA High 40 self-reported Self report 49
Gordillo (1999)[57] Cohort Spain High 24 pill count BDI 362
Catz (2000)[41] Cross-sectional USA High 13 self-reported CES-D 72
Gifford (2000)[55] Cross-sectional USA High 14 self-reported BDI 104
Patterson (2000)[6] Cross-sectional USA High MEMS Caps Medical Records 81
Avants (2001)[30] Cross-sectional USA High 31 self-reported BDI 42
Catz (2001)[40] Cross-sectional USA High 20 self-reported Self report 113
Duran (2001)[51] Cross-sectional France High 20 self-reported Self report 336
Kalichman (2001)[69] Cross-sectional USA High 100 self-reported Self report 72
Murphy (2001)[87] Cross-sectional USA High 73 self-reported Self report 145
Pratt (2001)[101] Cross-sectional England High 13 self-reported Interview 222
Safren (2001)[105] Cross-sectional USA High 9 self-reported Self report 84
Stone (2001)[115] Cross-sectional USA High 100 self-reported Self report 289
Wagner (2001)[124] Cross-sectional USA High 1 self-reported Self report 595
Wilson (2001)[127] Cross-sectional USA High 100 self-reported Self report as interview 247
Molassiotis (2002)[83] Cross-sectional Hong Kong High 8 self-reported Self report 136
Simoni (2002)[110] Cross-sectional USA High 61 self-reported Self report 50
Spire (2002)[114] Cohort France High 22 self-reported Self report 445
Van Servellen (2002)[120] Cross-sectional USA High 30 self-reported Self report 182
Wagner (2003)[123] Cross-sectional USA High 18 self-reported Interview 180
Carrieri (2003)[39] Cohort France High 31 self-reported Self report 96
Delgado (2003)[46] Cohort Canada High 14 Pharm refill Self report 316
Johnson (2003)[65] Cross-sectional USA High 24 self-reported Self report 2765
Kalichman (2003)[68] Cross-sectional USA High 30 self-reported Self report 255
Palmer (2003)[98] Cross-sectional USA High 53 self-reported SCID-II 74
Tucker (2003)[118] Cross-sectional USA High 22 self-reported Interview 1910
Turner (2003)[119] Cross-sectional USA High 0 Pharm refill Medical Records 3246
Turner (2003)[119] Cross-sectional USA High 100 Pharm refill Medical Records 1857
Ammassari (2004)[26] Cross-sectional Italy High 46 self-reported Interview 135
Berg (2004)[33] Cohort USA High 43 MEMS Caps CES-D 113
Cook (2004)[43] Cross-sectional USA High 100 self-reported Self report 684
Gonzalez (2004)[56] Cross-sectional USA High 32 self-reported Self report 90
Ingersoll (2004)[63] Cross-sectional USA High 38.3 self-reported CIDI-SF 120
Kennedy (2004)[72] Cross-sectional USA High 14 self-reported Self report 201
Kleenberger (2004)[75] Cohort USA High 0 self-reported CES-D 486
Mohammed (2004)[82] Cross-sectional USA High 29.3 self-reported Self report 215
Moss (2004)[84] Cohort USA High 57 self-reported Self report 148
Murphy (2004)[86] Cross-sectional USA High 9 self-reported CES-D 114
Palepu (2004)[97] Cross-sectional USA High 21 self-reported CES-D 194
Samet (2004)[106] Cohort USA High 19 self-reported CES-D 204
Wilson (2004)[128] Cohort Australia High 10 self-reported Self report 182
Ahdieh-Grant (2005)[23] Cross-sectional USA High 100 self-reported Self report 903
Anastos (2005)[27] Cohort USA High 100 self-reported CES-D 204
Barfod (2005)[31] Cross-sectional Denmark High 21 self-reported Self report 887
Bottonari (2005)[36] Cross-sectional USA High 4 self-reported Self report 24
Hileriol (2005)[59] Cross-sectional Puerto Rico High 100 self-reported Self report 15
Ironson (2005)[64] Cross-sectional USA High 30 self-reported Self report 160
Li (2005)[77] Cohort USA High 0 self-reported Self report 623
Phillips (2005)[100] Cross-sectional USA High 100 self-reported Self report 125
Sledjeski (2005)[113] Cross-sectional USA High 16 self-reported Self report 53
Yun (2005)[130] Cross-sectional USA High 12 Pharm refill Medical Records 818
Boarts (2006)[35] Cohort USA High 18 self-reported Self report 57
Liu (2006)[79] Cross-sectional USA High 100 self-reported Self report 114
Markos (2006)[80] Cross-sectional Ethiopia Low 48 self-reported Self report 291
Mugavero (2006)[85] Cross-sectional USA High 29 self-reported Self report as interview 474
Nilsson-Schonnesson (2006)[94] Cohort Sweden High 22 self-reported Self report 141
O’Cleirigh (2006)[96] Cross-sectional USA High 17 self-reported Self report 152
Tadios (2006)[116] Cross-sectional Ethiopia Low 49 self-reported Self report 431
Waldrop-Valverde (2006)[125] Cross-sectional USA High 23 self-reported Self report 57
Amico (2007)[25] Cross-sectional USA High 44 self-reported CES-D 151
Arnsten (2007)[29] Cross-sectional USA High 35 self-reported BSI 636
Berger-Greenstein (2007)[34] Cross-sectional USA High 35.3 self-reported BDI-II 61
Cruess (2007)[44] Cross-sectional USA High 37 self-reported Self report 117
Gibbie (2007)[54] Cross-sectional Australia High 3 self-reported Self report 80
Holmes (2007)[60] Cohort USA High 19 MEMS Caps CES-D 116
Horne (2007)[62] Cross-sectional United Kingdom High self-reported Self report 117
Kim (2007)[73] Cross-sectional USA High 23 self-reported CES-D 266
Lima (2007)[10] Cohort Canada High 9 Pharm refill Self report 563
O’Cleirigh (2007)[95] Cross-sectional USA High 33 self-reported Self report 91
Ramadhani (2007)[102] Cross-sectional Tanzania Low 63 self-reported Hopkins Symptom Checklist (25 items) 150
Villes (2007)[121] Cross-sectional France High 22 self-reported Self report 841
Amberbir (2008)[24] Cross-sectional Ethiopia Low 60 self-reported CES-D 400
Cardarelli (2008)[38] Cross-sectional USA High 26 self-reported Self report 101
Cha (2008)[42] Cross-sectional USA High 33 self-reported BDI-II 215
Hermann (2008)[58] Cross-sectional Australia High 14 self-reported Self report 145
Hornberg (2008)[61] Cross-sectional USA High 11 Pharm refill Medical Records 3359
Johnson (2008)[66] Cross-sectional USA High 0 self-reported Self report 328
Leserman (2008)[76] Cross-sectional USA High 39 self-reported Self report 105
Littlewood (2008)[78] Cross-sectional USA High 44 self-reported CES-D 221
Roux (2008)[103] Cohort France High 28 self-reported CES-D 276
Sarna (2008)[107] Cross-sectional India Middle 13 self-reported BDI-II 309
Shin (2008)[108] Cross-sectional Peru Middle 2 self-reported Hopkins’s symptoms checklist 43
Shuter (2008)[109] Cross-sectional USA High 44 MEMS Caps Self report as interview 29
Thrasher (2008)[117] Cross-sectional USA High 22 self-reported Self report 1886
Vraneacu (2008)[122] Cohort USA High 24 MEMS Caps Self report 156
Applebaum (2009)[28] Cross-sectional USA High 33 self-reported Interview 67
Berg (2009)[32] Cross-sectional USA High 54 self-reported Self report via ACASI 70
Byakika-Tusime (2009)[37] Cross-sectional Uganda Low 70.1 pill count Becks Depression Inventory (BDI) (21 items) 177
Diliorio (2009)[48] Cross-sectional USA High 32 self-reported Self report 236
Kapetanovic (2009)[70] Cross-sectional USA High 100 self-reported Medical Records 328
Meade (2009)[81] Cross-sectional USA High 50 self-reported Self report via ACASI 180
Nakimuli-Mpungu (2009)[91] Cross-sectional Uganda Low 78 self-reported Self-Reporting Questionnaire (20 items) 122
Royal (2009)[104] Cross-sectional USA High 28 self-reported CES-D 350
Webb (2009)[126] Cohort USA High 46 self-reported Self report 168
Wood (2009)[129] Cohort USA High 15 MEMS Caps Self report 79
Adewuya (2010)[22] Cross-sectional Nigeria Middle 58 self-reported General Health Questionnaire (12 items) 182
Etienne (2010)[52] Cross-sectional Kenya, Uganda, Zambia, Nigeria, and Rwand 65.4 self-reported Center for Epidemiological studies Depression scale (20 items) 921
Farley (2010)[53] Cross-sectional Nigeria Middle 70 Pharm refill 399
Kacanek (2010)[67] Cohort USA High 23 self-reported Self report as interview 225
Peltzer (2010)[99] Cross-sectional South Africa Middle 73.4 self-reported Center for Epidemiological studies Depression scale (10 items) 519
Kekwaletswe (2011)[71] Cross-sectional South Africa Middle 68 self-reported Center for Epidemiological studies Depression scale (20 items) 304
Nachega (2011)[88] Cohort South Africa Middle 60 pill count BSI 274
De (2012)[45] Cross-sectional India Middle 100 self-reported beck’s depression inventory 94
King (2012)[74] Cohort USA High 27.9 self-reported CES-D 326
Nel (2012)[93] Cross-sectional South Africa Middle 82.2 self-reported Beck Depression Inventory II 101
Simoni (2012)[111] Cross-sectional USA High 33 MEMS Caps BDI II 1809
Do (2013)[49] Cross-sectional Viet Nma Middle 34 self-reported CES-D 615
Nakimuli-Mpungu (2013)[89] Uganda Low 66 pill count Mini neuropsychiatric interview (MINI) 400
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Overall summary of the meta-analyses

When reported, the rate of people living with HIV (PLHIV) with depressive symptoms ranged from 12.8% to as much as 78% (Figure 2); and proportion of PLHIV who achieved good (≥ 80%) adherence ranged from as low as 20% to as much as 98% (Figure 3). There was no significant difference in the rate of depressive symptoms in PLHIV across low-income (pooled rate = 31.8%, 95% CI 17.0% to 51.5%), middle-income (pooled rate = 47.4%, 95% CI 31.3 to 64.1%) and high-income countries (pooled rate = 37.1%, 95% CI 30.6 to 44.2%)(p-value for interaction = 0.313). The overall pooled rate was 39.1% (95%CI: 33.2% to 45.2% (Figure 2). However, the proportion of PLHIV who achieved good adherence was significantly higher in lower income countries (pooled rate = 86%, 95% CI 62.2% to 95.8%) than high-income countries (pooled rate = 67.5%, 95% CI 61.6% to 72.9%) (p-value for interaction = 0.017) (Figure 3). The proportion of PLHIV who achieved good adherence tended to be higher in lower income countries (pooled rate = 86%, 95% CI 62.2% to 95.8%) than middle income countries (pooled rate = 74.4%, 95% CI 62.7% to 83.4%), but this difference did not reach statistical significant level (p-value for interaction = 0.145). The random-effect meta-analysis yielded a pooled OR of 0.58 (95% CI 0.55 to 0.62, n=112), such that the likelihood of achieving good adherence was 42% lower among those with depressive symptoms compared to those without (Figure 4). There was evidence of substantial statistical heterogeneity between the study results (χ2 = 849; df = 111; p=0.000) with the degree of heterogeneity quantified by the I2 as 86.6%.

Figure 2.

Figure 2

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Prevalence of depression among people living with HIV

Figure 3.

Figure 3

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Proportion of people living with HIV with optimal (≥ 75%) adherence to antiretroviral therapy

Figure 4.

Figure 4

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Forest plot showing the association between depression and adherence to ART

Association by different subgroups

The results of subgroup analyses are shown in Table 2. There was not a significant difference in the likelihood of achieving good adherence among PLHIV with depression symptoms in studies conducted in middle-income countries (pooled OR = 0.39, 95% CI 0.25 to 0.63) compared with those from both low- (pooled OR = 0.52, 95% CI 0.40 to 0.68) and high-income countries (pooled OR = 0.59, 95% CI 0.56 to 0.63)(p-value for interaction = 0.338). Similarly, we found no evidence of statistically significant differentials in the association between depression and adherence to ART across study design, adherence rate and adherence measures.

Table 2.

Pooled estimates for the association between depression adherence to antiretroviral therapy and series of subgroup analyses

Subgroup n Odds ratio (95% CI) I2 (%)
Overall 111 0.58 (0.54 to 0.62) 86.9
Country’s income group
 High-income 92 0.59 (0.55 to 0.63) 87.8
 Middle-income 10 0.39 (0.25 to 0.63) 75.6
 Low-income 8 0.52 (0.40 to 0.68) 28.2
Publication year
 1993 to 2000 7 0.33 (0.13 to 0.83) 93.4
 2001 to 2005 46 0.58 (0.52 to 0.64) 85.6
 2006 to 2010 50 0.58 (0.52 to 0.64) 73.4
 2011 to 2013 8 0.61 (0.46 to 0.82) 79.3
Study design
 Cross-sectional 86 0.53 (0.48 to 0.58) 85.5
 Cohort 24 0.77 (0.71 to 0.83) 77.1
Sample size
 Less than 100 25 0.41 (0.27 to 0.62) 81.9
 100 to 500 67 0.61 (0.57 to 0.65) 82.8
 500 or more 19 0.70 (0.62 to 0.79) 73.4
Depression rate
 Less than 20% 5 0.63 (0.49 to 0.98) 83.4
 20 to 50 % 18 0.66 (0.55 to 0.78) 70.4
 50% plus 12 0.48 (0.41 to 0.57) 0.0
Adherence rate
 Less than 50% 6 0.52 (0.43 to 0.62) 0.0
 50 to 75% 27 0.75 (0.69 to 0.82) 73.9
 75% plus 23 0.62 (0.54 to 0.72) 80.3
Adherence measure
 Self-reported 92 0.61 (0.57 to 0.64) 83.9
 MEMS cap 7 0.48 (0.18 to 1.31) 96.5
 Pharmacy refills 8 0.62 (0.52 to 0.76) 29.1
 Pill count 4 0.56 (0.42 to 0.74) 0.0
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However, we found evidence that the magnitude of the association between depression and adherence to ART decreases with increasing year of publication, such that the likelihood of achieving good adherence among PLHIV with depressive symptoms were higher in the most recent studies (2011 to 2013) compared with those reported between 1993 and 2000 (pooled OR = 0.63 versus 0.33, p-value for interaction = 0.007) (Figure 5A). Similarly, we found evidence of small study bias, i.e. the association was more pronounced among studies with small sample sizes (Figure 5B). The result of subgroup analysis also showed statistically significant differentials in the reported association across depression rates, such that the likelihood of achieving good adherence among PLHIV with depression was lower in studies with the highest depression rates (50% or more) compared with studies with moderate (20 to 50%) and low (<20%) depression rates (pooled OR = 0.48 vs. 0.66 vs 0.63, p-value for interaction = 0.038).

Figure 5.

Figure 5

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Relation between natural logarithm of the odds ratio of depression and adherence to ART and publication year, sample size and depression rate.

The area of each circle is proportional to the precision of the odds ratio (inverse of its variance).

Factors modifying the association between depression and adherence to ART as identified by meta-regression analyses

Factors modifying the association between depression and adherence and proportion of explained between-study variability as identified by meta-regression analyses are shown in Table 3. In the multivariable model, only publication year, sample size and depression rate were statistically significantly associated with pooled estimates. Differences in depression rates, sample sizes and publication year explained 30.1%, 23.3%, and 17.4% in between study variability in measure of association between depression and adherence respectively.

Table 3.

Meta-regression analyses

Factor Ratio of odds ratio (95% CI) P-value Explained variance (%)
Country’s income group 0.0
 High-income 1.10 (0.75 to 1.61) 0.632
 Middle-income 0.83 (0.49 to 1.40) 0.489
 Low-income 1 (reference)
Publication year 17.4
 1993 to 2000 1 (reference)
 2001 to 2005 1.97 (1.33 to 2.92) 0.001
 2006 to 2010 1.97 (1.33 to 2.92) 0.001
 2011 to 2013 2.07 (1.26 to 3.39) 0.004
Study design 1.0
 Cross-sectional 0.87 (0.70 to 1.08) 0.208
 Cohort 1 (reference)
Sample size 23.3
 Less than 100 1 (reference)
 100 to 500 1.48 (1.15 to 1.91) 0.002
 500 or more 1.85 (1.39 to 2.47) 0.000
Depression rate 30.1
 Less than 20% 1 (reference)
 20 to 50 % 0.96 (0.68 to 1.35) 0.815
 50% plus 0.68 (0.47 to 0.99) 0.044
Adherence rate 5.0
 Less than 50% 1 (reference)
 50 to 75% 1.34 (0.94 to 1.94) 0.102
 75% plus 1.28 (0.89 to 1.84) 0.182
Adherence measure 0.0
 Self-reported 1 (reference)
 Pharmacy refills 1.09 (0.77 to 1.54) 0.628
 Pill count 0.98 (0.57 to 1.68) 0.948
 MEMS cap 0.82 (0.56 to 1.20) 0.299
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DISCUSSION

This study builds upon prior reviews and meta-analyses of the relationship between depression and ART nonadherence in low-, middle-, and high-income countries and examined whether the association between depression and ART adherence differs based on study characteristics, rates of adherence and/or depression, publication year, and country income level. Based upon 111 eligible studies with over 42,000 PLHIV across low-, middle-, and high-income countries, rates of depressive symptoms ranged from approximately 13% to 78%, and the proportion of PLHIV who achieved optimal adherence (≥ 80%) ranged from 20% to 98%. The wide range of rates of depressive symptoms reported across studies may reflect the variability in measurement tools to assess depressive symptoms among PLHIV and variability in the clinical cut-offs used across studies [131]. The overall pooled rate of depressive symptoms in our analysis (39.5%) is in line with prior pooled rates of depressive symptoms (as opposed to clinical levels of depression) (31.2%[12]; 29.5%[131]). The higher rate found in our analysis may reflect that we did not distinguish between clinical levels of depression and depressive symptoms, which may have inflated estimates in the current analysis. Pooled estimates from the combined studies indicated that the likelihood of achieving optimal adherence was 42% lower among those with depressive symptoms compared to those without. This is in line with a prior meta-analysis that included studies conducted in sub-Saharan Africa, which found that individuals with depressive symptoms had a 55% lower likelihood of achieving optimal adherence compared to individuals without depressive symptoms [12]. Prior meta-analyses not specific to low-resource settings have found the relationship between depression and ART non-adherence to have a small to moderate effect size (i.e., r = .19–.21) [11, 4]; with studies consistently demonstrating a lower likelihood of optimal ART adherence in the context of depression.

This study hypothesized that variations in the strength of the association between depression and ART non-adherence may be affected by a range of factors, including those related to study characteristics (study design, assessments, sample size, publication year, setting), rates of adherence and/or depression. Subgroup analyses showed that the relationship between depression and ART adherence did not significantly vary by country income group (low, middle, high), study design (cross-sectional vs. cohort), adherence measure used (MEMS, pill count, or pharmacy refills compared to self-report) or adherence rates (50–75% and >75% vs. <50%). A prior meta-analysis of the relationship between depression and ART adherence [4] found significantly greater associations between depression and ART adherence when adherence was assessed via interview vs. self-report, and via continuous vs. dichotomous measurement; however, in line with our findings, their analysis showed that the association did not differ when comparing MEMS and pharmacy refill to self-report. This further supports their conclusion that the relationship between depression and self-reported ART non-adherence cannot be explained exclusively by biased recall evident in the context of depression [4].

Our findings that the association between depression and ART adherence did not differ based upon study design or country income level was also consistent with the prior meta-analysis by Gonzalez et al [4]. Although the data available in our review was similarly skewed towards higher-income countries (n=93), there were more studies conducted in middle (n=10) and low-income counties (n=8) included in comparison to the previous meta-analysis, which compared only low (n=5) to high (n=90) resource settings. Findings demonstrate no significant differences by income group, with only a non-significant trend pointing to the lowest likelihood of depressed PLHIV achieving optimal adherence in middle-income countries compared to PLHIV in low- or high-income countries in our analysis.

When examining differences in rates of depression and ART adherence by country income group, there were no differences in rates of depression in PLHIV across low-, middle-, and high-income countries, further supporting prior evidence that depression is also a significant public health burden in developing countries. Although in this meta-analysis we did not examine whether the included studies used a culturally-validated measure of depression, a recently published meta-analysis [131] identified that assessments used to screen for depression among PLHIV in low-resource settings (i.e., Sub-Saharan Africa) largely demonstrated good internal consistency (alphas ranging from .63 to .95).. Our findings, along with those from prior meta-analyses [12, 131], suggest that PLHIV in developing countries are self-reporting symptoms that are in line with DSM-defined models of depression.

When comparing rates of ART adherence by income group, there were significant differences in rates of ART adherence across country income groups, such that PLHIV in lower-income countries were more likely to adhere than individuals in higher- and middle-income countries. These results continue to support the notion that optimal levels of ART adherence can be achieved in lower-resource settings [132], even in the context of psychiatric symptoms such as depression.

The lack of differences in the association between depression and ART adherence based upon cross-sectional vs. longitudinal design may suggest that depression is as strongly associated to adherence over time as it is when assessed simultaneously. However, more studies that incorporate longitudinal designs are necessary to replicate this finding, as well as to better understand the directionality of the association. Additionally, given that weaker associations were detected when study sample sizes were larger, future studies should also incorporate larger sample sizes to ensure stability of estimates and replication of existing findings.

There was a significantly weaker association between depression and ART adherence in more recent studies, such that the likelihood of PLHIV with depression achieving optimal adherence was higher in the most recent studies (i.e., since 2011 when the prior meta-analyses were published) compared to earlier studies published between 1993 and 2000. Although not formally tested, this may be due to design characteristics of the eight studies published between 2011 and 2013, or may potentially reflect the increased attention to and efforts to integrate depression and mental health interventions into HIV care for PLHIV in recent years [133–135] and possibly the impact of simplified ART regimens (lower pill burden and once-daily dosing frequency) which has been shown to improve adherence [136].

Studies that recruited samples with higher depression prevalence rates (i.e., >50%) had lower likelihood of ART adherence; given the higher rates of depression in these samples, these studies may have had targeted inclusion criteria based upon depression severity and may have in turn recruited a more impaired population (i.e., with greater psychiatric or medical co-morbidity), which may have also contributed to worse ART adherence. There also may have been greater power to detect a relationship between depression and ART nonadherence in a sample with higher rates of depression. Although we did not examine depression severity and its impact on the relationship between depression and ART adherence in the current meta-analysis, prior research has pointed to subclinical levels of depression also being disruptive to ART adherence [4]. However, to inform screening and targeted intervention efforts, continued research is needed to understand the clinical threshold in which depressive symptoms are most likely to interfere with ART adherence.

While informative, the results of this meta-analysis should be interpreted with caution. The observational nature of the data limits the ability to draw causal inferences. We found statistically significant heterogeneity across the studies, thus suggesting that the percentage of the variability in effect estimates that is due to heterogeneity rather than sampling error (chance) is important. The heterogeneity may be due to differences in year of publication, study sample sizes, geographical location, population recruited, reporting of adherence measures and variability in handling confounding and mediating factors. Despite the limitations noted, the findings of this systematic review and meta-analysis have important implications for guiding future research on the association between depression and ART nonadherence, including the need for more studies conducted in low- and middle-income countries, studies that incorporate a longitudinal design and larger sample sizes to further ascertain the strength and directionality of this relationship.

In conclusion, we found that the likelihood of achieving good adherence was lower among those with depressive symptoms compared to those without, and this association did not differ by country income level, although there were significantly higher rates of adherence in low- vs. higher income countries. The factors that were most relevant to the strength of the association between depression and ART adherence included the year of study, sample size, and depression prevalence rate in sample. Despite some remaining unanswered questions regarding the relationship between depression and ART adherence, research continues to point to the lower rates of adherence among individuals with depression, further suggesting the need to screen and treat depression in the context of HIV care [133–135] and to further develop parsimonious, cost-effective interventions to simultaneously address symptoms of depression and ART adherence among PLHIV [137, 138].

Acknowledgments

We thank Ms. Joyce Snyder, Pittsburgh University, Department of Epidemiology, Pittsburgh, PA, USA; and Ms. Debbie Harrison, Stellenbosch University, Department of Medicine and Center for Infectious Diseases, Cape Town, South Africa, for administrative support

FINANCIAL SUPPORT: There was no specific funding source for this study.

RESEARCH SUPPORT: The US National Institutes for Allergy and Infectious Disease-National Institutes of Health (NIAID-NIH), AIDS Clinical Trial Group (ACTG), Stellenbosch University (SU)-Clinical Trial Unit (CTU) Award: 2UM1AI069521-08 (J. B. N.); the US NIH-Fogarty International Center (FIC)/Health Resources and Services Administration (HRSA)/US President Emergency Plan for AIDS Relief (PEPFAR) Grant Award, T84HA21652-01-00 for Medical Education Partnership Initiative (MEPI) (J. B. N.); the European Developing Countries Clinical Trial Partnership (EDCTP) Senior Fellowship Award: TA-08-40200-021 (J. B. N.); the Wellcome Trust Southern Africa Consortium for Research Excellence (SACORE): WT087537MA (J. B. N.); FAS Marie Curie International Post Doc: 2012-0064 (O. A. U.); The MGH Global Psychiatric Clinical Research Training Program: NIH T32MH093310 (J. F. M); K24MH094214 (S. A. S.).

Footnotes

ROLE OF SPONSORS: The agencies had no role in the conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript. The conclusions and opinions expressed in this article are those of the authors and do not necessarily reflect those of the NIH, the US Department of Health and Human Services, PEPFAR, HRSA, and the Wellcome Trust.

POTENTIAL CONFLICT OF INTERESTS: None to declare

AUTHORS’S CONTRIBUTIONS: O.A.U. and J.M. contributed equally to this work and fulfilled the criteria of joint first authorship. Study concept and design: O. A. U., J.M.., and J.B.N. Acquisition of data: O. A. U., J.M. and J. B.N Analysis and interpretation of data: All. Drafting or writing of the manuscript: O. A. U., J. M., J.B.N., S.A. S. Critical revision of the manuscript for important intellectual content: All. Statistical analysis: O. A. U and J.B.N. Administrative, technical, or material support: J. B. N. and O. A. U. Study supervision: J. B. N., S.S. Access to study data: J. B. N. and O.A.U. had full access to all data in the study and had final responsibility for the decision to submit this manuscript for publication.

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