JAIDS Journal of Acquired Immune Deficiency Syndromes:
Reliability and Validity of Depression Assessment Among Persons With HIV in Sub-Saharan Africa: Systematic Review and Meta-analysis
Tsai, Alexander C. MD*,†,‡,§
*Chester M. Pierce, MD Division of Global Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA;
†Massachusetts General Hospital Center for Global Health, Boston, MA;
‡Harvard Medical School, Boston, MA;
§Mbarara University of Science and Technology, Mbarara, Uganda.
Correspondence to: Alexander C. Tsai, MD, Massachusetts General Hospital Center for Global Health, Room 1529-E3, Massachusetts General Hospital, 100 Cambridge Street, 15th Floor, Boston, MA 02114 (e-mail: email@example.com).
No specific funding was awarded for the conduct of this study. The author receives salary support from US National Institutes of Health K23MH096620 and the Robert Wood Johnson Health and Society Scholars Program.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.jaids.com).
Received January 08, 2014
Accepted April 25, 2014
Objective: The aim of this study was to systematically review the reliability and validity of instruments used to screen for major depressive disorder or assess depression symptom severity among persons with HIV in sub-Saharan Africa.
Design: Systematic review and meta-analysis.
Methods: A systematic evidence search protocol was applied to 7 bibliographic databases. Studies examining the reliability and/or validity of depression assessment tools were selected for inclusion if they were based on the data collected from HIV-positive adults in any African member state of the United Nations. A random-effects meta-analysis was used to calculate pooled estimates of depression prevalence. In a subgroup of studies of criterion-related validity, the bivariate random-effects model was used to calculate pooled estimates of sensitivity and specificity.
Results: Of 1117 records initially identified, I included 13 studies of 5373 persons with HIV in 7 sub-Saharan African countries. Reported estimates of Cronbach alpha ranged from 0.63 to 0.95, and analyses of internal structure generally confirmed the existence of a depression-like construct accounting for a substantial portion of variance. The pooled prevalence of probable depression was 29.5% [95% confidence interval (CI): 20.5 to 39.4], whereas the pooled prevalence of major depressive disorder was 13.9% (95% CI: 9.7 to 18.6). The Center for Epidemiologic Studies Depression scale was the most frequently studied instrument, with a pooled sensitivity of 0.82 (95% CI: 0.73 to 0.87) for detecting major depressive disorder.
Conclusions: Depression-screening instruments yielded relatively high false positive rates. Overall, few studies described the reliability and/or validity of depression instruments in sub-Saharan Africa.
HIV and major depressive disorder are frequently comorbid1 and are both highly prevalent in sub-Saharan Africa. Although treatment scale-up has led to dramatic gains in life expectancy,2 HIV remains one of the leading causes of disease burden throughout sub-Saharan Africa.3 Depressive disorders are also a major contributor to the burden of disease throughout the region.4,5 The 2 conditions are mutually reinforcing: symptoms of depression may increase the probability of HIV acquisition,6 whereas biological factors7 and psychosocial aspects of living with HIV (eg, stigma,8,9 poverty,10,11 and food insecurity8,12) may exacerbate psychological distress and increase the probability of developing depressive disorders. The public health significance of identifying and adequately treating depressive disorders among persons with HIV is further magnified by their important adverse HIV-related impacts, including delays in treatment initiation,13 treatment nonadherence,14,15 and disease progression.16,17
Specific diagnostic criteria exist to guide the identification of persons with the syndrome of depression, but these are described explicitly as “guidelines to be informed by clinical judgment” that are “not meant to be used in a cookbook fashion”18 (p.xxiii). Making the diagnosis of major depressive disorder additionally requires the exercise of clinical judgment, assessment of symptom severity and functional impairment, and the exclusion of alternative medical or psychiatric explanations for the symptoms. Where relying on such clinical expertise is often infeasible, those managing large-scale studies or programs may instead use structured instruments. These are frequently used to generate clinician ratings of patient symptoms but can often be self-administered.19 The rating scores are then used either to define cases of a depressive syndrome or measure the severity of a depressive syndrome.20 Some instruments, such as the Patient Health Questionnaire,21 are used for both. Importantly, however, there is little evidence to suggest that routine depression screening improves health outcomes.22–26
Although prior reviews focused on sub-Saharan Africa have examined depression assessment in general population samples27 and during pregnancy and postpartum,28 the potentially overlapping symptoms of HIV and depression pose important challenges. Namely, somatic symptoms of HIV can masquerade as somatic symptoms of depression, particularly among persons with advanced HIV disease.29–31 In 1 national study of persons with HIV in the US, depression was underdiagnosed by a factor of 2.32 Several studies have also found that depression is undertreated among persons who screen positive for mood or anxiety disorders.33–36 Comparable studies are yet to be conducted in sub-Saharan Africa, but it is likely that the same patterns of underdiagnosis and undertreatment prevail, given the pervasive disparities in resources allocated to mental health systems throughout the region.37,38 At the same time, somatic symptoms represent a common class of presenting symptoms of common mental disorders in many sub-Saharan African countries.27,39–46 The purpose of my study, therefore, was to systematically review the reliability and validity of instruments used to screen for major depressive disorder or assess depression symptom severity among persons with HIV in African settings.
Systematic Search Protocol
All study procedures were reviewed by the Partners Human Research Committee and deemed exempt from full review because the study was based on anonymous, public-use data with no identifiable information on participants. The systematic evidence search was conducted in January–May 2012. Seven bibliographic databases were used: African Journals Online, the African Journal Archive, the Cumulative Index to Nursing and Allied Health Literature, Embase, the Medical Literature Analysis and Retrieval System Online (MEDLINE), PsycINFO, and the World Health Organization African Index Medicus (see Table S1, Supplemental Digital Content, http://links.lww.com/QAI/A532). The MEDLINE search was updated in December 2013 to identify articles published in the interim period. After all citations were imported into EndNote reference management software (version X5, Thomson Reuters, New York, NY), the “Find Duplicates” algorithm was used to exclude duplicate references. The titles and abstracts, and then the full texts of the articles, were sequentially screened to select articles for inclusion. To identify other potentially relevant studies, I searched the reference lists of selected articles and queried colleagues in departments of psychiatry and psychology at 2 African academic institutions.
Selected articles had to have met each of the following 3 criteria: (a) the study was based on data collected from HIV-positive adults in any African member state of the United Nations; (b) an instrument was administered to assess for depressed mood, such as a diagnostic interview schedule, screening measure, or symptom rating scale; and (c) the article presented evidence of the reliability and/or validity of the instrument. Pregnant women were excluded given that they were the focus of a recently published review.28 There were no language restrictions. A wide range of reliability and validity evidence was considered acceptable for this review, including evaluations of linguistic, conceptual, or metric equivalence47; analyses of measurement reliability, such as test–retest reliability or internal consistency; or studies that confirmed hypothesized relationships between the instrument and other variables of interest,48 such as a reference criterion standard [eg, diagnosis of major depressive disorder consistent with the Diagnostic and Statistical Manual of Mental Disorders (DSM)] or variables conceptually thought to be related to depression (eg, HIV stigma49 and social support9). Because virtually any study estimating the association between depression and another variable of interest could potentially be considered as presenting evidence of construct validity, and because Cronbach alpha coefficients are near-universally reported, studies in which these were the sole form of evidence presented were excluded from consideration.
For each selected article, data were extracted regarding the study population, sampling strategy, sample size, inclusion criteria, depression instrument, and type of reliability and/or validity evidence provided. The number of cases of probable depression (study participants whose scores on a screening instrument or symptom rating scale exceeded a specified threshold) and of major depressive disorder (study participants who met diagnostic criteria according to the DSM) were also extracted. To calculate pooled estimates of probable depression and major depressive disorder, the variances of the raw proportions were first stabilized using a Freeman Tukey–type double arcsine transformation,50,51 and then the proportions were pooled using random-effects meta-analysis.52 Between-study heterogeneity was assessed with the I2 statistic.53 Because HIV treatment is known to have important beneficial effects on depression7 and other psychosocial outcomes,10,54 random-effects meta-regression models55 were fit to the data to explore the extent to which differences in HIV treatment status could explain heterogeneity in prevalence across studies. Treatment status was categorized as currently on HIV antiretroviral therapy [ART] vs. other (ie, not on ART, treatment-naive patients newly initiating ART who were assessed at pretreatment baseline, or mixed samples in general HIV care). Small sample size–related bias was investigated by visually inspecting graphical plots of the transformed prevalence estimates against the standard error of the transformed prevalence estimates, and also by using the Begg and Mazumdar56 rank correlation test and the Egger et al57 linear regression test.
For studies that provided evidence of criterion-related validity, I assessed quality according to the Quality Assessment of Diagnostic Accuracy Studies tool.58 For the subsample of studies that provided evidence of criterion-related validity for the Center for Epidemiologic Studies-Depression (CES-D) scale, data were extracted on the numbers of participants classified as true positives, true negatives, false positives, and false negatives according to the threshold values specified by the authors. These numbers were then used to construct 2 × 2 tables and compute the estimated sensitivity and specificity values. Pooled estimates of sensitivity and specificity, and their associated 95% confidence intervals (CIs), were calculated using the bivariate random-effects model.59,60 The summary receiver-operating characteristic (ROC) curves were then constructed to produce a 95% confidence ellipse within the ROC curve space.61 Between-study heterogeneity was assessed with the I2 statistic for the pooled diagnostic odds ratio.53 Small sample size–related bias was investigated by plotting the logarithm of the diagnostic odds ratios against the inverse square root of the effective sample size, and by fitting the accompanying regression model of the logarithm of the diagnostic odds ratios against the inverse square root of the effective sample size, weighting by the effective sample size.62 All statistical analyses were implemented by using the Stata software package (version 12.1, StataCorp LP, College Station, TX).
Of the 1117 records returned from the electronic database search, 110 duplicates were excluded, along with 880 records that did not seem to meet inclusion criteria based on the titles and/or abstracts alone (Fig. 1). Full text appraisal was completed for 127 records. Of these, 112 did not meet inclusion criteria and were excluded. Colleagues at African academic institutions suggested 1 additional journal article. Three studies reported findings across multiple publications; to avoid double counting, these findings were aggregated and then assigned to the first publication by calendar year. The final sample included 15 journal articles and 1 PhD dissertation (that was matched to a subsequently published journal article), representing 13 unique studies.
Summary statistics for the selected studies are provided in Table 1. The 13 studies enrolled 5373 persons with HIV in 7 different sub-Saharan African countries. Study sites were primarily located in southern or eastern Africa, with South Africa and Uganda accounting for more than one-half of the studies. Although I did not exclude studies from northern or western Africa, none were identified, and only 1 study was based on data from central Africa (Cameroon). The median sample size was 368 (interquartile range, 200–610). Most studies were based on data collected from outpatients currently on ART or newly initiating ART, or from mixed samples of outpatients in general HIV care (eg, either on ART or pre-ART).
Altogether, the reliability and validity of 9 different depression instruments were assessed (Table 2; see Table S2, Supplemental Digital Content, http://links.lww.com/QAI/A532). Although the CES-D and the Patient Health Questionnaire were the most frequently studied, none of the instruments was the subject of study in >3 different countries. The prevalence of probable depression (ie, as determined on the basis of screening instruments) among 4461 participants enrolled in 11 studies varied from 6.5% to 75%, with a pooled prevalence of 30.2% (95% CI: 19.7 to 41.8; I2 = 98.4) (Fig. 2). Publication bias did not seem to be present (P values ranged from 0.88 to 0.97). Meta-regression suggested that HIV treatment status was an effect modifier (P = 0.048): in studies of participants on HIV treatment, the pooled prevalence was 17.5% (95% CI: 6.5 to 32.3), whereas in studies of treatment-naive persons or mixed (treated/untreated) samples, the pooled prevalence was 37.8% (95% CI: 27.7 to 48.5).
The most frequently described types of reliability and/or validity evidence supplied in these studies were of scale reliability [9 (69%)], criterion-related validity [8 (62%)], and factor structure [8 (62%)]. The reported Cronbach alpha coefficients ranged from 0.63 to 0.95, with only 1 study reporting an estimate below the conventional threshold for “good” internal consistency. Across contexts, depression instruments had statistically significant associations with related constructs, including HIV stigma, social support, and health status. Analyses of internal structure generally confirmed the existence of a depression-like construct accounting for a substantial portion of variance. Of the 5 studies in which a multifactor structure was thought to best fit the data, 4 identified a factor related to somatic symptoms. Only 2 studies, both using qualitative methods, assessed aspects of linguistic or technical equivalence.
Among the 7 studies that used a criterion standard to determine a DSM-consistent diagnosis of major depressive disorder, the prevalence of major depressive disorder varied from 2.7% to 18.1%, with a pooled prevalence of 14.5% (95% CI: 8.9 to 21.2; I2 = 93.4). Publication bias did not seem to be present (P values ranged from 0.13 to 0.19). HIV treatment status was not a statistically significant effect modifier (P = 0.16); in treated samples, the pooled prevalence was 6.7% (95% CI: 0.6 to 18.5), whereas in naive/mixed samples, the pooled prevalence was 17.9% (95% CI: 11.8 to 24.9).
Too few studies assessed the performance of the same screening instrument (irrespective of setting) to permit pooled estimates of sensitivity and specificity, with the exception of 4 studies that examined the criterion-related validity of the CES-D. When summarized within ROC curve space, the data suggested a pooled sensitivity of 0.82 (95% CI: 0.73 to 0.87) and a pooled specificity of 0.73 (95% CI: 0.63 to 0.80) at threshold values ranging from 16 to 22 (Fig. 3). There was a substantial between-study heterogeneity underlying these estimates, as suggested by I2 values of 50.2 and 92.4, respectively, but there was limited ability to adequately investigate this heterogeneity given the small number of studies. Examination of the log-diagnostic odds ratios plotted against the inverse square root of effective sample size, and the accompanying linear regression test (P = 0.63), did not suggest small sample size–related bias.
The quality assessment demonstrated several areas in which the studies of criterion-related validity tended to have methodological shortcomings (see Table S3, Supplemental Digital Content, http://links.lww.com/QAI/A532). Specifically, most of these studies were assessed to be at risk of bias due to the use of a screening threshold that was not prespecified (ie, the reported threshold was selected to optimize sensitivity and/or specificity). Otherwise, most studies were at a low risk of bias on the other domains. In general, few concerns were noted about applicability.
In this systematic review and meta-analysis, there were several important findings. First, I identified only 13 unique studies of 9 different instruments used to assess depression among persons with HIV in sub-Saharan Africa. Second, screening instruments were generally found to reliably measure depression-like constructs and to correlate with related constructs in the expected fashion. Third, depression was highly prevalent, particularly in studies of treatment-naive persons or of untreated/mixed samples. However, the prevalence of probable depression (as determined by screening) exceeded the prevalence of DSM-consistent diagnoses of major depressive disorder by a factor of 2. These findings have important research and programmatic implications for persons with HIV in sub-Saharan Africa.
Before each of these findings are discussed in detail, it is important to note that the data identified in this systematic search do not permit conclusions about the reliability or validity of depression assessment in general population samples in African countries. For example, investigators have used qualitative methods to elicit local concepts of distress and then have validated newly developed scales in Rwanda,80–83 Uganda,83–86 and Zimbabwe.42,87–89 Although these studies make important contributions to understanding cultural concepts of distress in African settings, particularly aspects of linguistic and conceptual equivalence, they would not have been included in this review, as they were not based on samples of persons with HIV. Because the primary incremental contribution of validation studies conducted with samples of persons with HIV relates primarily to our understanding of construct and criterion-related validity, it is likely that excluding studies conducted in the general population would result in a sample of studies less focused on aspects of equivalence.
Keeping this limitation in mind, I identified relatively few studies describing the reliability or validity of instruments used to screen for major depressive disorder or assess depression symptom severity among persons with HIV in African settings. Although the evidence search was not limited to sub-Saharan Africa, no studies from northern Africa were identified, and Cameroon was the only study site in western or central Africa. In settings where data were identified, depression instruments were found to correlate with conceptually related constructs in the expected fashion, providing some support for the idea that these Western-derived instruments are measuring the same construct in different cultures.90 Among multifactor instruments, a majority identified a somatic factor, confirming the importance of careful symptom assessment among persons with HIV so that false positives can be minimized.29,30
Among the participants in the studies reviewed, depression screening was associated with relatively high false positive rates: the pooled prevalence of probable depression by screening was 30.2%, whereas the pooled prevalence of major depressive disorder was 14.5%. This 2-fold difference is notable given that screening studies are frequently but inappropriately cited in support of impact statements describing the high prevalence of depressive disorders in the context of the HIV epidemic in sub-Saharan Africa. Yet, although screening instruments may generate overestimates, the 14.5% pooled prevalence rate of major depressive disorder estimated in my study still exceeds the 4%–6% age-standardized prevalence in the region.5 Thus, despite the relatively high rate of false positives identified through screening, the burden of depression among persons with HIV in sub-Saharan Africa should still be considered relatively high.
HIV treatment status seemed to explain some of the variation in outcomes, as the prevalence of depression was greater in treatment-naive samples. This finding is consistent with prior work describing declines in depression symptom severity among persons with HIV undergoing antiretroviral treatment.7,8,91–96 The mechanisms underlying these observed effects are unclear but may be related to biological,7 economic,10,11 or psychosocial54 changes associated with treatment. If confirmed, the “antidepressant” effects of HIV treatment could lend further support to treatment initiation as an HIV prevention strategy.97,98
Interpretation of my findings is subject to 3 primary limitations, in addition to those mentioned previously. First, as with all systematic reviews, my evidence search may have missed some relevant studies. A comparison of my sample with that of a related review by Sweetland et al,99 however, suggests this possibility is unlikely. They searched 2 bibliographic databases for validity studies conducted among adults in sub-Saharan Africa and published before 2012. They identified 4 studies of persons with HIV (compared with 6 studies included in my review that were published before 2012) and 11 studies of pregnant or postpartum women (compared with 25 studies included in a recently published review of perinatal depression28). A second limitation, which applies to interpretation of the pooled prevalence estimates, is that this review was focused specifically on studies of reliability and/or validity. Conventional studies of depression prevalence among persons with HIV that did not also provide evidence of reliability and/or validity, such as those by Nakasujja et al94 (53.9% with probable depression) and Kinyanda et al100 (8.1% with major depressive disorder) in Uganda, would have been excluded. Therefore, it is likely that the studies in my review do not represent the universe of prevalence studies. At the same time, however, this is unlikely to have biased my pooled prevalence estimates in either direction, given that reliability and validity studies are unlikely to systematically overestimate (or underestimate) the prevalence of either probable depression or major depressive disorder. A third limitation is that too few studies of the same instrument were identified to permit comparisons between instruments. Although the CES-D was the most frequently studied instrument, few studies of other instruments were found.
The most important policy and programmatic implications of this systematic review and meta-analysis relate to the possibility of integrating depression assessment and treatment into HIV programs in sub-Saharan Africa. The estimates presented here further underscore the potential public health impacts of effective depression treatment as recognized in the International Association of Physicians in AIDS Care guidelines on improving care for persons with HIV.101 Although effective depression treatment may carry important spillover benefits for persons with HIV,102,103 global disparities in HIV3 are paralleled by disparities in mental health systems and human resources for mental health.38,104 Shifting responsibility for mental health screening and/or treatment monitoring to nonspecialist lay health workers is a priority area of research105 that has been proposed as 1 potential mechanism for extending limited human resources.106 Such tasks will require brief, locally validated screening instruments107,108 whose use can be integrated cleanly into the lay health workers' overall workflow without causing undue burden.26,109 In many sub-Saharan African countries, mental health system resource constraints introduce additional concerns. First, in the absence of appropriate depression care support systems,110 the value of depression screening strategies remains unclear. Second, although screening instruments are generally expected to yield relatively high false positive rates, an excess of false positives could easily overwhelm the capacity for outpatient mental health care delivery.111 The findings presented in this review, although based on a paucity of evidence overall, suggest that more work needs to be done to identify valid and discriminating instruments that can be used for screening and treatment monitoring in these contexts.
The author thanks Jennifer Scott and Jennifer Zhu, for their assistance with data collection; and Dickens Akena, Landon Myer, Enbal Shacham, and Soraya Seedat, for their correspondence in response to his requests for additional information related to their studies.
1. Ciesla JA, Roberts JE. Meta-analysis of the relationship between HIV infection and risk for depressive disorders. Am J Psychiatry. 2001;158:725–730.
2. Bor J, Herbst AJ, Newell ML, et al.. Increases in adult life expectancy in rural South Africa: valuing the scale-up of HIV treatment. Science. 2013;339:961–965.
3. Ortblad KF, Lozano R, Murray CJ. The burden of HIV: insights from the Global Burden of Disease Study 2010. AIDS. 2013;27:2003–2017.
4. Whiteford HA, Degenhardt L, Rehm J, et al.. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet. 2013;382:1575–1586.
5. Ferrari AJ, Charlson FJ, Norman RE, et al.. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. 2013;10:e1001547.
6. Tsai AC, Mimiaga MJ, Dilley JW, et al.. Does effective depression treatment alone reduce secondary HIV transmission risk? Equivocal findings from a randomized controlled trial. AIDS Behav. 2013;17:2765–2772.
7. Martinez P, Tsai AC, Muzoora C, et al.. Reversal of the kynurenine pathway of tryptophan catabolism may improve depression in ART-treated HIV-infected Ugandans. J Acquir Immune Defic Syndr. 2014;65:456–462.
8. Tsai AC, Bangsberg DR, Frongillo EA, et al.. Food insecurity, depression and the modifying role of social support among people living with HIV/AIDS in rural Uganda. Soc Sci Med. 2012;74:2012–2019.
9. Takada S, Weiser SD, Kumbakumba E, et al.. The dynamic relationship between social support and HIV stigma in rural Uganda. Ann Behav Med. Epub February 06, 2014. doi:10.1007/s12160-013-9576-5.
10. Tsai AC, Bangsberg DR, Weiser SD. Harnessing poverty alleviation to reduce the stigma of HIV in sub-Saharan Africa. PLoS Med. 2013;10:e1001557.
11. Venkataramani AS, Haberer JE, Thirumurthy H, et al.. CD4+ count at antiretroviral therapy initiation and economic restoration in rural Uganda. AIDS. 2014;28(8):1221–1226.
12. Tsai AC, Bangsberg DR, Emenyonu N, et al.. The social context of food insecurity among persons living with HIV/AIDS in rural Uganda. Soc Sci Med. 2011;73:1717–1724.
13. Drain PK, Losina E, Parker G, et al.. Risk factors for late-stage HIV disease presentation at initial HIV diagnosis in Durban, South Africa. PLoS One. 2013;8:e55305.
14. Starace F, Ammassari A, Trotta MP, et al.. Depression is a risk factor for suboptimal adherence to highly active antiretroviral therapy. J Acquir Immune Defic Syndr. 2002;31 suppl 3:S136–S139.
15. Gonzalez JS, Batchelder AW, Psaros C, et al.. Depression and HIV/AIDS treatment nonadherence: a review and meta-analysis. J Acquir Immune Defic Syndr. 2011;58:181–187.
16. Burack JH, Barrett DC, Stall RD, et al.. Depressive symptoms and CD4 lymphocyte decline among HIV-infected men. JAMA. 1993;270:2568–2573.
17. Ickovics JR, Hamburger ME, Vlahov D, et al.. Mortality, CD4 cell count decline, and depressive symptoms among HIV-seropositive women: longitudinal analysis from the HIV Epidemiology Research Study. JAMA. 2001;285:1466–1474.
18. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th ed: DSM-IV. Washington, DC: American Psychiatric Association; 1994.
19. Carroll BJ, Fielding JM, Blashki TG. Depression rating scales. A critical review. Arch Gen Psychiatry. 1973;28:361–366.
20. Gibbons RD, Clark DC, Kupfer DJ. Exactly what does the Hamilton depression rating scale measure? J Psychiatr Res. 1993;27:259–273.
21. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary care evaluation of mental disorders. Patient Health Questionnaire. JAMA. 1999;282:1737–1744.
22. Gilbody S, Sheldon T, House A. Screening and case-finding instruments for depression: a meta-analysis. CMAJ. 2008;178:997–1003.
23. Thombs BD, de Jonge P, Coyne JC, et al.. Depression screening and patient outcomes in cardiovascular care: a systematic review. JAMA. 2008;300:2161–2171.
24. Meijer A, Roseman M, Delisle VC, et al.. Effects of screening for psychological distress on patient outcomes in cancer: a systematic review. J Psychosom Res. 2013;75:1–17.
25. Thombs BD, Arthurs E, Coronado-Montoya S, et al.. Depression screening and patient outcomes in pregnancy or postpartum: a systematic review. J Psychosom Res. 2014;76:433–446.
26. Hung KJ, Tomlinson M, le Roux IM, et al.. Community-based prenatal screening for postpartum depression in a South African township. Int J Gynecol Obstet. 2014;126:74–77.
27. Tomlinson M, Swartz L, Kruger LM, et al.. Manifestations of affective disturbance in sub-Saharan Africa: key themes. J Affect Disord. 2007;102:191–198.
28. Tsai AC, Scott JA, Hung KJ, et al.. Reliability and validity of instruments for assessing perinatal depression in African settings: systematic review and meta-analysis. PLoS One. 2013;8:e82521.
29. Kalichman SC, Sikkema KJ, Somlai A. Assessing persons with human immunodeficiency virus (HIV) infection using the Beck Depression Inventory: disease processes and other potential confounds. J Pers Assess. 1995;64:86–100.
30. Kalichman SC, Rompa D, Cage M. Distinguishing between overlapping somatic symptoms of depression and HIV disease in people living with HIV-AIDS. J Nerv Ment Dis. 2000;188:662–670.
31. Lyketsos CG, Hoover DR, Guccione M, et al.. Changes in depressive symptoms as AIDS develops. The Multicenter AIDS Cohort Study. Am J Psychiatry. 1996;153:1430–1437.
32. Asch SM, Kilbourne AM, Gifford AL, et al.. Underdiagnosis of depression in HIV: who are we missing? J Gen Intern Med. 2003;18:450–460.
33. Burnam MA, Bing EG, Morton SC, et al.. Use of mental health and substance abuse treatment services among adults with HIV in the United States. Arch Gen Psychiatry. 2001;58:729–736.
34. Pence BW, O'Donnell JK, Gaynes BN. Falling through the cracks: the gaps between depression prevalence, diagnosis, treatment, and response in HIV care. AIDS. 2012;26:656–658.
35. Israelski DM, Prentiss DE, Lubega S, et al.. Psychiatric co-morbidity in vulnerable populations receiving primary care for HIV/AIDS. AIDS Care. 2007;19:220–225.
36. Weaver MR, Conover CJ, Proescholdbell RJ, et al.. Utilization of mental health and substance abuse care for people living with HIV/AIDS, chronic mental illness, and substance abuse disorders. J Acquir Immune Defic Syndr. 2008;47:449–458.
37. Jacob KS, Sharan P, Mirza I, et al.. Mental health systems in countries: where are we now? Lancet. 2007;370:1061–1077.
38. Saxena S, Thornicroft G, Knapp M, et al.. Resources for mental health: scarcity, inequity, and inefficiency. Lancet. 2007;370:878–889.
39. Ebigbo PO. Development of a culture specific (Nigeria) screening scale of somatic complaints indicating psychiatric disturbance. Cult Med Psychiatry. 1982;6:29–43.
40. Staugard F. Traditional Medicine in Botswana: Traditional Healers. Gaborone: Ipelegeng Publishers; 1985.
41. Patel V. Explanatory models of mental illness in sub-Saharan Africa. Soc Sci Med. 1995;40:1291–1298.
42. Patel V, Gwanzura F, Simunyu E, et al.. The phenomenology and explanatory models of common mental disorder: a study in primary care in Harare, Zimbabwe. Psychol Med. 1995;25:1191–1199.
43. Patel V, Todd C, Winston M, et al.. Common mental disorders in primary care in Harare, Zimbabwe: associations and risk factors. Br J Psychiatry. 1997;171:60–64.
44. Okulate GT, Olayinka MO, Jones OB. Somatic symptoms in depression: evaluation of their diagnostic weight in an African setting. Br J Psychiatry. 2004;184:422–427.
45. Okello ES, Neema S. Explanatory models and help-seeking behavior: pathways to psychiatric care among patients admitted for depression in Mulago hospital, Kampala, Uganda. Qual Health Res. 2007;17:14–25.
46. Okello ES, Ngo VK, Ryan G, et al.. Qualitative study of the influence of antidepressants on the psychological health of patients on antiretroviral therapy in Uganda. Afr J AIDS Res. 2012;11:37–44.
47. Brislin RW. Understanding Culture's Influence on Behavior, 1st ed. Fort Worth, TX: Harcourt Brace Jovanovich; 1993.
48. American Educational Research Association, APA, and National Council on Measurement in Education. Standards for Educational and Psychological Testing. Washington, DC: American Psychological Association; 1999.
49. Schmitz MF, Crystal S. Social relations, coping, and psychological distress among persons with HIV/AIDS. J Appl Soc Psychol. 2000;30:665–685.
50. Freeman MF, Tukey JW. Transformations related to the angular and the square root. Ann Math Stat. 1950;21:607–611.
51. Stuart A, Ord JK. Kendall's Advanced Theory of Statistics, 6th ed. Baltimore, MD: Edward Arnold Publishers; 1994.
52. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–188.
53. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–1558.
54. Tsai AC, Bangsberg DR, Bwana M, et al.. How does antiretroviral treatment attenuate the stigma of HIV? Evidence from a cohort study in rural Uganda. AIDS Behav. 2013;17:2725–2731.
55. Berkey CS, Hoaglin DC, Mosteller F, et al.. A random-effects regression model for meta-analysis. Stat Med. 1995;14:395–411.
56. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088–1101.
57. Egger M, Davey Smith G, Schneider M, et al.. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–634.
58. Whiting PF, Rutjes AW, Westwood ME, et al.. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529–536.
59. Reitsma JB, Glas AS, Rutjes AW, et al.. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58:982–990.
60. Arends LR, Hamza TH, van Houwelingen JC, et al.. Bivariate random effects meta-analysis of ROC curves. Med Decis Making. 2008;28:621–638.
61. Walter SD. Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data. Stat Med. 2002;21:1237–1256.
62. Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol. 2005;58:882–893.
63. Kagee A, Nel A, Saal W. Factor structure of the Beck Depression Inventory-II among South Africans receiving antiretroviral therapy. AIDS Care. 2014;26:257–262.
64. Shacham E. Self-Reported Psychological Distress Symptoms of Individuals Self-Seeking HIV-Related Psychosocial Support in Western Kenya [PhD Dissertation]. Indianapolis: Indiana University; 2006.
65. Akena D, Joska J, Obuku EA, et al.. Sensitivity and specificity of clinician administered screening instruments in detecting depression among HIV-positive individuals in Uganda. AIDS Care. 2013;25:1245–1252.
66. Chishinga N, Kinyanda E, Weiss HA, et al.. Validation of brief screening tools for depressive and alcohol use disorders among TB and HIV patients in primary care in Zambia. BMC Psychiatry. 2011;11:75.
67. Kaharuza FM, Bunnell R, Moss S, et al.. Depression and CD4 cell count among persons with HIV infection in Uganda. AIDS Behav. 2006;10:S105–S111.
68. Myer L, Smit J, Roux LL, et al.. Common mental disorders among HIV-infected individuals in South Africa: prevalence, predictors, and validation of brief psychiatric rating scales. AIDS Pat Care STDs. 2008;22:147–158.
69. Singh D, Sunpath H, John S, et al.. The utility of a rapid screening tool for depression and HIV dementia amongst patients with low CD4 counts—a preliminary report. Afr J Psychiatry (Johannesbg). 2008;11:282–286.
70. Reda AA. Reliability and validity of the Ethiopian version of the hospital anxiety and depression scale (HADS) in HIV infected patients. PLoS One. 2011;6:e16049.
71. Wouters E, Booysen FL, Ponnet K, et al.. Wording effects and the factor structure of the hospital anxiety & depression scale in HIV/AIDS patients on antiretroviral treatment in South Africa. PLoS One. 2012;7:e34881.
72. Epino HM, Rich ML, Kaigamba F, et al.. Reliability and construct validity of three health-related self-report scales in HIV-positive adults in rural Rwanda. AIDS Care. 2012;24:1576–1583.
73. Spies G, Kader K, Kidd M, et al.. Validity of the K-10 in detecting DSM-IV-defined depression and anxiety disorders among HIV-infected individuals. AIDS Care. 2009;21:1163–1168.
74. Monahan PO, Shacham E, Reece M, et al.. Validity/reliability of PHQ-9 and PHQ-2 depression scales among adults living with HIV/AIDS in western Kenya. J Gen Intern Med. 2009;24:189–197.
75. Pence BW, Gaynes BN, Atashili J, et al.. Validity of an interviewer-administered patient health questionnaire-9 to screen for depression in HIV-infected patients in Cameroon. J Affect Disord. 2012;143:208–213.
76. Nakimuli-Mpungu E, Mojtabai R, Alexandre PK, et al.. Cross-cultural adaptation and validation of the self-reporting questionnaire among HIV+ individuals in a rural ART program in southern Uganda. HIV AIDS (Auckl). 2012;4:51–60.
77. Akena D, Musisi S, Joska J, et al.. The association between AIDs related stigma and major depressive disorder among HIV-positive individuals in Uganda. PLoS One. 2012;7:e48671.
78. Pappin M, Wouters E, Booysen FL. Anxiety and depression amongst patients enrolled in a public sector antiretroviral treatment programme in South Africa: a cross-sectional study. BMC Public Health. 2012;12:244.
79. Shacham E, Reece M, Monahan PO, et al.. Measuring psychological distress symptoms in individuals living with HIV in western Kenya. J Ment Health. 2008;17:424–434.
80. Bolton P. Cross-cultural validity and reliability testing of a standard psychiatric assessment instrument without a gold standard. J Nerv Ment Dis. 2001;189:238–242.
81. Bolton P. Local perceptions of the mental health effects of the Rwandan genocide. J Nerv Ment Dis. 2001;189:243–248.
82. Bolton P, Neugebauer R, Ndogoni L. Prevalence of depression in rural Rwanda based on symptom and functional criteria. J Nerv Ment Dis. 2002;190:631–637.
83. Bolton P, Tang AM. An alternative approach to cross-cultural function assessment. Soc Psychiatry Psychiatr Epidemiol. 2002;37:537–543.
84. Wilk CM, Bolton P. Local perceptions of the mental health effects of the Uganda acquired immunodeficiency syndrome epidemic. J Nerv Ment Dis. 2002;190:394–397.
85. Bolton P, Wilk CM, Ndogoni L. Assessment of depression prevalence in rural Uganda using symptom and function criteria. Soc Psychiatry Psychiatr Epidemiol. 2004;39:442–447.
86. Bolton P, Wilk CM. How do Africans view the impact of HIV? A report from a Ugandan community. AIDS Care. 2004;16:123–128.
87. Patel V, Simunyu E, Gwanzura F. Kufungisisa (thinking too much): a Shona idiom for non-psychotic mental illness. Cent Afr J Med. 1995;41:209–215.
88. Patel V. Spiritual distress: an indigenous model of nonpsychotic mental illness in primary care in Harare, Zimbabwe. Acta Psychiatr Scand. 1995;92:103–107.
89. Patel V, Simunyu E, Gwanzura F, et al.. The Shona Symptom Questionnaire: the development of an indigenous measure of common mental disorders in Harare. Acta Psychiatr Scand. 1997;95:469–475.
90. Geisinger KF. Testing and assessment in cross-cultural psychology. In: Graham JR, Naglieri JA, Weiner IB, eds. Handbook of Psychology. Volume 10: Assessment Psychology. Hoboken, NJ: John Wiley & Sons, Inc.; 2003.
91. Rabkin JG, Ferrando SJ, Lin SH, et al.. Psychological effects of HAART: a 2-year study. Psychosom Med. 2000;62:413–422.
92. Judd FK, Cockram AM, Komiti A, et al.. Depressive symptoms reduced in individuals with HIV/AIDS treated with highly active antiretroviral therapy: a longitudinal study. Aust N Z J Psychiatry. 2000;34:1015–1021.
93. Low-Beer S, Chan K, Yip B, et al.. Depressive symptoms decline among persons on HIV protease inhibitors. J Acquir Immune Defic Syndr. 2000;23:295–301.
94. Nakasujja N, Skolasky RL, Musisi S, et al.. Depression symptoms and cognitive function among individuals with advanced HIV infection initiating HAART in Uganda. BMC Psychiatry. 2010;10:44.
95. Wagner GJ, Ghosh-Dastidar B, Garnett J, et al.. Impact of HIV antiretroviral therapy on depression and mental health among clients with HIV in Uganda. Psychosom Med. 2012;74:883–890.
96. Okeke EN, Wagner GJ. AIDS treatment and mental health: evidence from Uganda. Soc Sci Med. 2013;92:27–34.
97. Sikkema KJ, Watt MH, Drabkin AS, et al.. Mental health treatment to reduce HIV transmission risk behavior: a positive prevention model. AIDS Behav. 2010;14:252–262.
98. Cohen MS, Chen YQ, McCauley M, et al.. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med. 2011;365:493–505.
99. Sweetland AC, Belkin GS, Verdeli H. Measuring depression and anxiety in sub-Saharan Africa. Depress Anxiety. 2014;31:223–232.
100. Kinyanda E, Hoskins S, Nakku J, et al.. Prevalence and risk factors of major depressive disorder in HIV/AIDS as seen in semi-urban Entebbe district, Uganda. BMC Psychiatry. 2011;11:205.
101. Thompson MA, Mugavero MJ, Amico KR, et al.. Guidelines for improving entry into and retention in care and antiretroviral adherence for persons with HIV: evidence-based recommendations from an International Association of Physicians in AIDS Care panel. Ann Intern Med. 2012;156:817–833.
102. Tsai AC, Weiser SD, Petersen ML, et al.. A marginal structural model to estimate the causal effect of antidepressant medication treatment on viral suppression among homeless and marginally housed persons with HIV. Arch Gen Psychiatry 2010;67:1282–1290.
103. Tsai AC, Karasic DH, Hammer GP, et al.. Directly observed antidepressant medication treatment and HIV outcomes among homeless and marginally housed HIV-positive adults: a randomized controlled trial. Am J Public Health. 2013;103:308–315.
104. Tomlinson M, Lund C. Why does mental health not get the attention it deserves? an application of the Shiffman and Smith framework. PLoS Med. 2012;9:e1001178.
105. Tomlinson M, Rudan I, Saxena S, et al.. Setting priorities for global mental health research. Bull World Health Organ. 2009;87:438–446.
106. Eaton J, McCay L, Semrau M, et al.. Scale up of services for mental health in low-income and middle-income countries. Lancet. 2011;378:1592–1603.
107. Beaglehole R, Epping-Jordan J, Patel V, et al.. Improving the prevention and management of chronic disease in low-income and middle-income countries: a priority for primary health care. Lancet. 2008;372:940–949.
108. Ssebunnya J, Kigozi F, Kizza D, et al.. Integration of mental health into primary health care in a rural district in Uganda. Afr J Psychiatry (Johannesbg). 2010;13:128–131.
109. Tsai AC, Tomlinson M, Dewing S, et al.. Antenatal depression case-finding by community health workers in South Africa: feasibility of a mobile phone application. Arch Womens Ment Health. Epub Mar 30, 2014. doi:10.1007/s00737-014-0426-7.
110. O'Connor EA, Whitlock EP, Beil TL, et al.. Screening for depression in adult patients in primary care settings: a systematic evidence review. Ann Intern Med. 2009;151:793–803.
111. Kagee A, Tsai AC, Lund C, et al.. Screening for common mental disorders: reasons for caution and a way forward. Int Health. 2013;5:11–14.
depression; HIV; Africa, south of the Sahara; sensitivity and specificity; reproducibility of results
Supplemental Digital Content
© 2014 by Lippincott Williams & Wilkins
What does "Remember me" mean?
By checking this box, you'll stay logged in until you logout. You'll get easier access to your articles, collections,
media, and all your other content, even if you close your browser or shut down your
To protect your most sensitive data and activities (like changing your password),
we'll ask you to re-enter your password when you access these services.
What if I'm on a computer that I share with others?
If you're using a public computer or you share this computer with others, we recommend
that you uncheck the "Remember me" box.
Highlight selected keywords in the article text.
Data is temporarily unavailable. Please try again soon.
Readers Of this Article Also Read