Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 RESEARCH ARTICLE Prevalence and factors associated with mild depressive and anxiety symptoms in older adults living with HIV from the Kenyan coast Patrick N. Mwangala1,2,3,§ , Carophine Nasambu1, Ryan G. Wagner4, Charles R. Newton1,3,5,6 and Amina Abubakar1,3,5,6 §Corresponding author: Patrick N. Mwangala, Centre for Geographic Medicine Research Coast, Kenya Medical Research Institute (KEMRI), KEMRI Wellcome Trust Research Programme, Kilifi, PO BOX 230–80108, Kenya. (pmwangala@kemri-wellcome.org) Abstract Introduction: Empirical research on the burden and determinants of common mental disorders (CMDs), especially depression and anxiety, among older adults living with HIV (OALWH) in sub-Saharan Africa is inadequate. To bridge the gap in Kenya we: (1) determined the prevalence of CMDs among OALWH on routine HIV care compared to HIV-negative peers; (2) investi- gated HIV status as an independent predictor of CMDs in older adults; and (3) investigated CMD determinants. Methods: In a cross-sectional study conducted between 2020 and 2021, the prevalence of CMDs and associated determi- nants were investigated at the Kenyan coast among 440 adults aged ≥50 years (257 OALWH). The Patient Health Question- naire and Generalized Anxiety Disorder scale were administered alongside measures capturing biopsychosocial information. Logistic regression was used to examine the correlates of CMDs. Results: No significant differences were found in the prevalence of mild depressive symptoms, 23.8% versus 18.2% (p = 0.16) and mild anxiety symptoms, 11.7% versus 7.2% (p = 0.12) among OALWH compared to HIV-negative peers, respectively. HIV status was not independently predictive of CMDs. Among OALWH, higher perceived HIV-related stigma, ageism, increasing household HIV burden, loneliness, increasing functional disability, sleeping difficulties, chronic fatigue and advanced age (>70 years) were associated with elevated CMDs. Among HIV-negative older adults, loneliness, increased medication burden and sleeping difficulties were associated with elevated depressive symptoms. Easier access to HIV care was the only factor asso- ciated with lower CMDs among OALWH. Conclusions: On the Kenyan coast, the burden of moderate and severe CMDs among older adults is low; however, both OALWH and their HIV-negative peers have a similar relatively high burden of mild depressive and anxiety symptoms. Our results also suggest that determinants of CMDs among OALWH in this setting are predominantly psychosocial factors. These results highlight the need for psychosocial interventions (at the family, community and clinical levels) to mitigate the risks of mild CMDs as they are known to be potentially debilitating. Keywords: common mental disorders; HIV infection; older adults; prevalence; correlates; Kenya Additional information may be found under the Supporting Information tab of this article. Received 26 January 2022; Accepted 31 July 2022 Copyright © 2022 The Authors. Journal of the International AIDS Society published by John Wiley & Sons Ltd on behalf of the International AIDS Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 1 INTRODUCT ION Common mental disorders (CMDs), especially depression and anxiety, are among the leading causes of disability worldwide [1, 2] and cost the global economy about US$1.2 trillion a year [3]. Notably, the global burden of both disorders has not reduced since 1990, despite compelling evidence of cost- effective interventions [4]. Efforts to address this substantial burden of CMDs should be directed at the most vulnerable in society, including older adults living with HIV (OALWH) resid- ing in sub-Saharan Africa (SSA). In Kenya, the increasing popu- lation of OALWH [5] is experiencing an elevated incidence of chronic age-related conditions [6], all potentially resulting in an increase of CMDs. The prevalence of depression among OALWH in SSA ranges from 6% to 59% [7] compared to 26% among their HIV- negative peers [8]. Outside SSA, estimates of depression among OALWH range from 19% to 45% in Asia [9, 10], 39% 57 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 https://orcid.org/0000-0001-9046-1465 mailto:pmwangala@kemri-wellcome.org http://creativecommons.org/licenses/by/4.0/ Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 to 90% in the United States [11, 12], 16% to 35% in Latin America and the Caribbean [13], and 28% in Europe [14]. Anxiety, on the other hand, ranges from 3% to 21% among OALWH in SSA [7] and 35% to 56% in high-income countries (HICs) [14, 15]. Understanding the determinants of CMDs among older adults is critical in designing and implementing contextually relevant mental health interventions. A recent review indi- cated that frequently reported determinants of depression among OALWH were mainly socio-demographic in nature [7]. Among psychosocial factors, HIV-related stigma [16], HIV sta- tus disclosure [16] and increasing disability scores have been associated with higher odds of depressive symptoms, while social support [17], resilience [16] and spirituality [17] have been shown to be protective factors. Biomedical and lifestyle factors, including alcohol use [17], current/former tobacco smoking [18] and back pain [19], have been associated with elevated levels of depression among OALWH. Among HIV- negative older adults in SSA, rural residence [20, 21], poor social network [20], living alone [22], being female [8, 20] and a lifetime of unskilled occupation [21] have been associated with elevated odds of depression. No study in SSA has exam- ined the determinants of anxiety among OALWH. Several reasons call for investigating depression and anxi- ety among older adults. Firstly, known risk factors for CMD, such as poverty, are more prevalent in older ages [23, 24]. Secondly, CMDs in late life are severely under-researched and underdiagnosed in primary care [7, 25]. Besides, the progno- sis of CMDs among old adults appears to be worse than for young people [26]. Late-life CMDs may also elevate the risk of developing dementia [27]. Among people living with HIV (PLWH), depression and anxiety have been associated with non-adherence to combination antiretroviral therapy (cART), risky sexual behaviours, reduced quality of life and higher sui- cide rates [28–30]. Given that CMDs are rarely detected but can have serious health impacts on older people, it is increas- ingly important to assess for mild, moderate and severe levels to determine the scale of the problem [31]. To bridge the gap in Kenya, our study seeks to: (1) deter- mine the prevalence of depressive and anxiety symptoms among OALWH compared to their HIV-negative peers; (2) investigate HIV status as an independent predictor of depres- sive and anxiety symptoms in the older adults; and (3) inves- tigate the determinants of CMDs among older adults at the coast of Kenya. 2 METHODS 2.1 Study design and setting This cross-sectional study was conducted at the Kenyan coast in Kilifi and Mombasa Counties between February 2020 and October 2021. The majority of Kilifi residents are rural dwellers whose main form of livelihood is subsistence farm- ing and small-scale trading [32]. Kilifi has an adult HIV preva- lence of 4.5% [33]. Mombasa County borders Kilifi County to the north and is considered urban. The common sources of income in Mombasa county include tourism, wholesale and retail trade. The Mombasa adult HIV prevalence is 7.5% [33]. 2.2 Study participants and recruitment 2.2.1 Older adults living with HIV OALWH were recruited from two public HIV specialized clin- ics in Kilifi and Mombasa Counties. As inclusion criteria, par- ticipants had to be aged ≥50 years of age, with a confirmed HIV seropositivity status, and on cART. In both facilities, we were assisted by community health volunteers or healthcare providers in reviewing existing records to identify all potential participants. We used system- atic sampling to identify potential clients for the study. 2.2.2 Older adults without HIV All HIV-negative older adults were recruited in Kilifi County. The Kilifi Health and Demographic Surveillance System was used to identify families with eligible older adults. Poten- tial participants aged ≥50 were randomly identified from the existing database and followed up at their homes using GPS coordinates. To be included in the study, individuals had to be ≥50 years, and residents of Kilifi county, and provide consent for participation, including willingness to be tested for HIV using a rapid HIV testing kit (OraQuick) for a confirmation of HIV-negative status. We chose HIV-negative adults aged ≥50 years as our comparison group to the OALWH based on pre- vious research [34, 35]. 2.3 Sample size calculations Our sample size was calculated using previous studies from Uganda [36] and South Africa [35]. An overall sample size of at least 372 individuals was needed to detect a difference in CMDs between OALWH and HIV-negative community con- trols at 80% power and a 5% level of statistical significance. A sample of 450 participants was considered sufficient, allow- ing for missing data. 2.4 Measures The research instruments were programmed on android tablets using the Research Electronic Data Capture (RED- Cap) platform [37] for face-to-face interviewer administration. All research assistants were trained for 2 weeks by the first author prior to data collection. 2.4.1 Socio-demographic and asset index form Socio-demographic information was captured in REDCap. We also collected information on individual and family ownership of disposable assets for asset index computation. Participants were also asked to provide information on their access to social support, food security in the past week, the number of PLWH within the household and whether they were caring for a sick family member. 2.4.2 General health form We captured the participant’s anthropometric details. Other information included hours spent on sedentary activities in a day, sexual activity, household HIV burden, number of medica- tions one used and common somatic complaints. 58 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 Among OALWH, HIV-specific information questions were asked relating to the disclosure of HIV status and access to HIV care. Information regarding their current cART regimen and overall duration on cART was extracted from medical records. Blood samples were also collected from OALWH for viral load testing. 2.4.3 Psychosocial measures The brief 12-item HIV stigma scale [38] was utilized to assess participants’ perceived HIV-related stigma. Higher scores indi- cate a greater level of perceived stigma. In the current study, this scale yielded good internal consistency alpha, 0.78 (95% CI 0.73, 0.83). The 12-item World Health Organization Disability Assessment Schedule 2 (WHODAS 2) [39] was used to assess for functional disability among participants. Higher scores indicate a greater level of disability. In this study, the tool demonstrated good internal consistency alpha, 0.77 (95% CI 0.70, 0.84). The UCLA 8-item loneliness scale [40] was used to assess participants’ perceived loneliness. Higher scores equate to a greater level of loneliness. In the present study, this scale had acceptable internal consistency alpha, 0.61 (95% CI 0.56, 0.64). The 20-item Ageism survey [41] was used to assess partici- pants’ experiences of ageism. Higher scores indicate more fre- quent experiences of ageism. In the current study, the internal reliability (Cronbach’s alpha) was 0.89 (95% CI 0.87, 0.92). 2.4.4 Measures of common mental disorders The 7-item Generalized Anxiety Disorder scale (GAD-7) [42] and the 9-item Patient Health Questionnaire (PHQ-9) [43] were utilized to measure anxiety and depressive symptoms in the previous 2-week period, respectively. The total scores range from 0 to 21 for GAD-7 and 0 to 27 for PHQ-9. For GAD-7, total scores of 5–9, 10–14 and 15–21 repre- sent mild, moderate and severe anxiety symptoms, respec- tively [44]. Total scores of 5–9, 10–14 and 15–27 indicate mild, moderate and severe depressive symptoms [45], respec- tively. A cut-off score of ≥5 for both PHQ-9 and GAD-7 was used to define a positive screen for depressive and anx- iety symptoms in the current study, similar to previous stud- ies in Ethiopia [46] and Tanzania [47]. In the present study, the internal consistency alphas for PHQ-9 and GAD-7 were good, 0.75 (95% CI 0.70, 0.79) and 0.74 (95% CI 0.70, 0.79), respectively. 2.4.5 Translation of new study measures All study questionnaires not previously adapted to the local language of Swahili underwent the recommended adaptation procedure in line with international guidelines [48]. 2.5 Statistical analysis All analyses were carried out in STATA version 15.0 (Stata- Corp LP, College Station, TX, USA). Independent Student’s t- test and Chi-square test were used to compare differences in continuous and categorical independent variables, respec- tively. We used proportions as percentages to estimate the prevalence of CMDs among OALWH and their HIV-negative counterparts. The Chi-square test was utilized to compare group differences on binary outcome variables. To investigate HIV status as an independent predictor of CMDs, we uti- lized logistic regression analyses adjusting for exposure vari- ables that accounted for differences in CMDs. To examine the correlates of CMDs, we used logistic regression models to explore univariate relationships between the binary out- come variables and the different exposure variables. Expo- sure variables with a p-value < 0.15 in the univariate analy- sis were subsequently entered into the multivariable models using forward selection (data for OALWH and HIV-negative older adults were analysed separately for this set of analy- ses). In all models, collinearity was checked and for all tests of the hypothesis, a two-tailed p-value < 0.05 was regarded as statistically significant. The overall fit of the final models was examined by Hosmer and Lemeshow (HL) goodness of fit test where a p-value of > 0.05 was considered a good fit. The HL test results were cross-checked using McFadden’s pseudo- R squared statistic. 2.6 Ethics approval and consent to participate The study was approved by the Kenya Medical Research Institute Scientific and Ethics Review Unit (Ref: KEMRI/SERU/CGMR-C/152/3804). Permission to conduct the study was granted by the research office Kilifi (Ref: HP/KCHS/VOL.X/171) and Mombasa (Ref: COH/Msa/RSC/04). All respondents provided written informed consent for their participation. 3 RESULTS 3.1 Sample characteristics A total of 440 participants were included in this study, with a mean age of 60.1 (SD = 6.9) years. The participant response rate was 90%. This included 72 (16%) OALHIV in Mombasa and 368 (84%) in Kilifi. Most participants had formal educa- tion (63.2%), were unemployed (65.5%), lived in multigenera- tional households (81.6%) and cared for a sick family member (66.4%). OALWH were likely to be younger, unmarried, more educated, have lower monthly household income, live alone, with a smaller number of dependents and more food insecure (Table 1). 3.2 HIV-related characteristics of OALWH The majority of the OALWH had disclosed their HIV status (95.3%) and were on first-line cART treatment (90%). The mean (SD) duration of HIV treatment was 11.4 (4.3) years. Nearly, all (98.1%) of the OALWH had a viral load of ≤1000 copies/ml (Table 2). 3.3 Prevalence estimates for CMDs The prevalence of mild depressive symptoms was 23.8% among OALWH compared to 18.2% in the comparison group (p = 0.16). The prevalence of mild anxiety symptoms was 11.7% among OALWH compared to 7.2% in the comparison group (p = 0.12). The prevalence of comorbid mild depressive 59 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 Table 1. Characteristics of the study population by HIV status, N = 440 HIV status Characteristic Total sample N = 440 Older adults without HIV, n = 183 Older adults living with HIV, n = 257 p-value Socio-demographic Age (years) 50–59 227 (51.6) 84 (45.9) 143 (55.6) 0.02 60–69 171 (38.9) 74 (40.4) 97 (37.7) ≥70 42 (9.5) 25 (13.7) 17 (6.6) Sex Female 258 (58.6) 98 (53.6) 160 (62.3) 0.07 Male 182 (41.4) 85 (46.4) 97 (37.7) Marital status Never married 12 (2.8) 4 (2.2) 8 (3.1) <0.001† Separated/Divorced/Widowed 181 (41.1) 45 (24.6) 136 (52.9) Married/cohabiting 247 (56.1) 134 (73.2) 113 (44.0) Education level None 162 (36.8) 90 (49.2) 72 (28.0) <0.001† Primary 182 (41.4) 65 (35.5) 117 (45.5) Secondary 73 (16.6) 22 (12.0) 51 (19.9) Tertiary 23 (5.2) 6 (3.3) 17 (6.6) Employment Unemployed 288 (65.5) 126 (68.9) 162 (63.0) 0.1 Employed 116 (26.3) 39 (21.3) 77 (30.0) Retired 36 (8.2) 18 (9.8) 18 (7.0) Monthly household income (Ksh) ≤10,000 279 (63.4) 69 (37.7) 210 (81.7) <0.001 Above 10,000 161 (36.6) 114 (62.3) 47 (18.3) Living arrangements Multiple generational families 359 (81.6) 169 (92.3) 190 (73.9) <0.001† Single generational families 41 (9.3) 6 (3.3) 35 (13.6) Alone 40 (9.1) 8 (4.4) 32 (12.5) Number of dependents, mean (SD) 3.2 (2.6) 3.6 (2.5) 2.9 (2.7) 0.01 Caring for a sick family member, OM = 2 Yes 291 (66.4) 104 (57.1) 187 (73.1) 0.001 No 147 (33.6) 78 (42.9) 69 (26.9) Access to instrumental/social support None 199 (45.2) 93 (50.8) 106 (41.2) 0.07 Sometimes 215 (48.9) 83 (45.4) 132 (51.4) Most of the time 26 (5.9) 7 (3.8) 19 (7.4) Food insecurity (lack of food in the past week), OM = 3 Never 293 (67.1) 134 (73.6) 159 (62.4) 0.002† Sometimes 119 (27.2) 45 (24.7) 74 (29.0) Most of the times/always 25 (5.7) 3 (1.7) 22 (8.6) Asset index scorea—mean (SD) 2.3 (1.5) 1.9 (1.2) 2.5 (1.6) <0.001 Body mass index—mean (SD), OM = 11 24.9 (6.0) 24.7 (6.1) 25.0 (5.9) 0.7 Loneliness scoreb—mean (SD), OM = 3 13.9 (3.7) 13.0 (3.4) 14.6 (3.7) <0.001 Functional disability scorec—mean (SD), OM = 2 2.5 (4.3) 1.5 (3.0) 3.1 (4.9) <0.001 Ageism scored—mean (SD) 4.2 (5.9) 3.0 (4.4) 5.0 (6.6) <0.001 Hours spent in sedentary activities in a day, mean (SD), OM = 12 4.5 (2.6) 4.3 (2.1) 4.6 (2.9) 0.3 (Continued) 60 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 Table 1. (Continued) HIV status Characteristic Total sample N = 440 Older adults without HIV, n = 183 Older adults living with HIV, n = 257 p-value Sexually active, OM = 4 Yes 206 (47.3) 105 (57.4) 101 (39.9) <0.001 No 230 (52.7) 78 (42.6) 152 (60.1) Sleeping difficulties in the past month, OM = 4 None 276 (63.3) 125 (68.3) 151 (59.7) 0.01† Sometimes 131 (30.1) 53 (29.0) 78 (30.8) Most of the times/always 29 (6.6) 5 (2.7) 24 (9.5) Chronic fatigue Yes 56 (12.7) 21 (11.5) 35 (13.6) 0.5 No 384 (87.3) 162 (88.5) 222 (86.4) Number of medications participants are currently using, mean (SD), OM = 8 1.6 (1.6) 0.4 (1.2) 2.4 (1.2) <0.001 Note: All numbers are reported as frequencies with percentages unless otherwise stated. p-values are for the difference between OALWH and their HIV-negative peers by sample characteristic. p-values have been derived from Chi-square test (or Fisher’s exact test) and independent Student’s t-test for categorical and continuous independent variables, respectively. Abbreviations: Ksh, Kenya shillings; OM, observation with missing value; SD, standard deviation. aScore range = 0–8, higher scores indicate better socio-economic status. bScore range = 8–27, higher scores indicate greater loneliness. cScore range = 0–33, higher scores indicate increasing disability. dScore range = 0–34, higher scores indicate increasing ageism. †Based on Fisher’s exact test. and anxiety symptoms among OALWH was 10.1% compared to 4.4% in the comparison group (p = 0.03) (Table 3). 3.4 Association between HIV status and CMDs In univariate and multivariate logistic regression analyses, HIV seropositivity was not significantly associated with depressive or anxiety symptoms (Table 4). However, HIV seropositivity was significantly associated with higher odds of a positive screen for depressive and anxiety symptoms co-occurrence in univariate but not in the multivariate model. 3.5 Determinants of CMDs in OALWH Table 5 presents results from logistic regression analyses exploring the determinants of CMDs among OALWH. 3.5.1 Depressive symptoms In the multivariable logistic regression model, factors signif- icantly associated with higher odds of depressive symptoms among OALWH were functional disability, ageism, sleeping dif- ficulties, chronic fatigue, increasing household HIV burden and perceived HIV-related stigma. Easier access to HIV care was significantly associated with lower odds of depressive symp- toms. 3.5.2 Anxiety symptoms In the multivariable analyses, age ≥70, perceived loneliness, functional disability, ageism and sleeping difficulties were sig- nificantly associated with higher odds of anxiety symptoms among OALWH. 3.5.3 Comorbid depressive and anxiety symptoms In the multivariable analyses, perceived functional disability and ageism were significantly associated with higher odds of comorbid depressive and anxiety symptoms. Easier access to HIV care was significantly associated with lower odds of comorbid depressive and anxiety symptoms. 3.6 Determinants of CMDs in HIV-negative older adults The current work largely focused on OALWH. As such, we only provide a summary of the factors associated with CMDs among the HIV-negative older adults using the available data, particularly focusing on mild depressive symptoms whose prevalence was relatively high in the current sample, that is >10%. In multivariable analyses (Table S1), sleeping diffi- culties, perceived loneliness and increasing medication bur- den were significantly associated with higher odds of a pos- itive screen for depressive symptoms among HIV-negative older adults. Greater sedentary behaviour and higher monthly household income were significantly associated with lower odds of depressive symptoms. 61 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 Table 2. HIV-related clinical and psychosocial characteristics of OALWH, n = 257 Characteristic Mean (SD) or frequency (%) HIV status disclosure Yes 245 (95.3%) No 12 (4.7%) Household HIV burden, mean (SD); OM = 5 1.4 (1.6) cART regimen First line 233 (90.7%) Second line 23 (8.9%) Third line 1 (0.4%) cART regimen change/interruption since HIV diagnosis Yes 110 (42.8%) No 147 (57.2%) Duration on cART (years), mean (SD), OM = 10 11.4 (4.3) Viral suppression, OM = 45 Yes 208 (98.1%) No 4 (1.9%) Access to HIV care, OM = 4 Easily accessible 169 (66.8%) Not easily accessible 84 (33.2%) Perceived HIV-stigma score, OM = 1 Personalized stigmaa—mean (SD) 5.0 (1.9) Disclosure concernsb—mean (SD) 8.6 (2.0) Concerns about public attitudesc—mean (SD) 7.6 (2.2) Negative self-imaged—mean (SD) 6.4 (2.1) Overall stigmae—mean (SD) 27.5 (5.4) Abbreviations: cART, combination antiretroviral therapy; OM, obser- vation with missing value; OALWH, older adults living with HIV. aScore range = 3–12, higher scores indicate greater stigma. bScore range = 3–12, higher scores indicate greater stigma. cScore range = 3–12, higher scores indicate greater stigma. dScore range = 3–12, higher scores indicate greater stigma. eScore range = 12–44, higher scores indicate greater stigma. 4 D ISCUSS ION To our knowledge, this is the first study in Kenya and among the first reports from SSA investigating CMDs among OALWH compared to their HIV-negative peers. Our study found a relatively high burden of mild depressive and anx- iety symptoms; however, HIV status was not independently associated with these symptoms. The correlates of CMDs in our study were predominantly psychosocial factors, many of which are potentially modifiable, thus highlighting the need to address the psychosocial needs of these adults alongside their biomedical needs. Our finding of no significant differences in the prevalence of CMDs between OALWH and their HIV-negative peers is dissimilar to other emerging reports in SSA. In rural Uganda, OALWH on cART had a significantly lower prevalence of probable depression than their HIV-negative peers [36], sim- ilar to what was reported in rural South Africa [35]. Our finding also offers an interesting contrast to the predominant findings in HICs showing that OALWH present with worse CMDs than their HIV-negative peers [24]. The observed vari- ation could reflect contextual differences across settings, for example healthcare systems, informal support systems and mental health resources that are likely to alter the risk pro- file of these adults. HIV status was not found to be an inde- pendent predictor of CMDs in our study. This observation is consistent with previous findings from South Africa [49] and Uganda [19] but contrasts with common research find- ings in HICs [24]. Our study may be part of an emerging body of evidence in SSA showing that the psychological health of OALWH is not worse than among those without HIV. HIV-related stigma was significantly associated with higher odds of depressive symptoms among OALWH in our study. These findings are consistent with previous findings in the literature [12, 16, 50] and provide additional evidence of the critical role of addressing intersecting stigma in improv- ing the mental wellbeing of OALWH. Relatedly, ageism was also significantly associated with higher odds of depressive symptoms, anxiety symptoms and their co-occurrence among OALWH. This finding is consistent with previous research conducted outside SSA [51]. Ageism could have an adverse impact on the mental health of OALWH through psychologi- cal, behavioural and physiological pathways [52]. Interventions addressing both ageist and HIV-stigmatizing attitudes at the community level will potentially improve the mental health of OALWH. Increasing household HIV burden was significantly asso- ciated with higher odds of depressive symptoms among OALWH in our study. This may be related to the high care- giving burden often experienced by OALWH caring for HIV- positive children. This observation concurs with previous find- ings in the study setting [53]. Loneliness is common among older adults in general [54]. In this study, higher perceived loneliness was significantly associ- ated with elevated odds of anxiety symptoms among OALWH and depressive symptoms among HIV-negative older adults. Our finding is consistent with previous studies from HICs [12, 55]. Theoretical models suggest that loneliness has cog- nitive, biological and social consequences that could poten- tially heighten the risk of subsequent CMDs [56]. Higher perceived functional disability was also strongly associated with higher odds of depressive symptoms, anxiety symptoms and their co-occurrence among OALWH in the current study, consistent with previous findings [19, 57, 58]. Since func- tional disability occurs frequently among OALWH, there is a need for early identification to help preserve functional independence. Sleep disturbance is a prominent symptom in people with CMDs, especially depression, and was formerly regarded as a main secondary indicator of depression [59]. Nonetheless, multiple prospective studies have identified insomnia as an independent risk indicator for emerging or recurrent depres- sion, suggesting that sleep problems are not necessarily sec- ondary effects of CMDs but a predictive prodromal symptom [60, 61]. In this study, persistent sleep problems were signif- icantly associated with higher odds of depressive symptoms, anxiety symptoms and their co-occurrence among OALWH and higher odds of depressive symptoms among HIV-negative 62 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 Table 3. Prevalence of common mental disorders in OALWH versus their HIV-negative peers Older adults without HIV, n = 181 Older adults living with HIV, n = 256 Number Prevalence (95% CI) Number Prevalence (95% CI) p-value Severity of depressive symptoms Mild 28 15.5 (10.5, 21.6) 49 19.1 (14.5, 24.5) 0.3† Moderate 5 2.8 (0.9, 6.3) 8 3.1 (1.4, 6.1) Severe – – 4 1.6 (0.4, 4.0) Positive depressive symptoms screen (cut-off ≥5) Yes 33 18.2 (12.9, 24.6) 61 23.8 (18.7, 29.5) 0.2 Severity of anxiety symptoms Mild 13 7.2 (3.9, 12.0) 26 10.2 (6.7, 14.5) 0.1† Moderate – – 4 1.6 (0.4, 4.0) Positive anxiety symptoms screen (cut-off ≥5) Yes 13 7.2 (3.9, 12.0) 30 11.7 (8.0, 16.3) 0.1 Positive screen for comorbid depressive and anxiety symptoms Yes 8 4.4 (1.9, 8.4) 26 10.1 (6.7, 14.5) 0.03 Abbreviation: 95% CI, 95% confidence interval; OALWH, older adults living with HIV. †Based on Fisher’s exact test. older adults. This finding is consistent with previous find- ings [59, 62, 63]. A combination of pharmacological and non-pharmacological interventions for sleep disturbances may effectively reduce and possibly prevent CMDs [64]. Chronic fatigue was also significantly associated with increased odds of depressive symptoms among OALWH in our study, similar to what has been reported elsewhere [65, 66]. Fatigue is a vital indicator of ageing-related declines in health and functioning [67]. Fatigue management strategies, such as adequate rest and sleep, are likely to improve the mental health of OALWH. Among HIV-negative older adults, an increased medication burden was also significantly asso- ciated with higher odds of depressive symptoms, consistent with previous findings [68]. While the exact mechanism for this association is unknown, we know that the use of medica- tion increases as the number of medical conditions rises. Mul- tiple medications, which are easily detected by clinicians, can provide an important clue to healthcare providers to further investigate depression in their clients. Among socio-demographic factors, old age (≥70 years) was significantly associated with higher odds of anxiety symptoms in OALWH, while higher monthly household income was sig- nificantly associated with lower depressive symptoms among HIV-negative older adults. Mixed findings have been reported on these factors previously [69]. Easier access to HIV care was the only protective indicator for CMDs in OALWH in the current study. Given that many OALWH in Kenya face unique challenges with seeking HIV care services [70], programmes aimed at strengthening HIV care access or financial support have the potential to improve OALWHs’ mental wellbeing. Further decentralizing HIV care into the community possibly utilizing community health work- ers may also be beneficial. Among HIV-negative older adults, an increasing number of hours on sedentary activities was significantly associated with lower odds of depressive symptoms. More studies are needed to better understand the mechanism involved. Emerging data suggest that passive sedentary behaviours, for example televi- sion watching, increase the risk of depression, while mentally active sedentary behaviours, for example reading, may be pro- tective against depression [71]. Most of our data (about 84%) were collected after the onset of the COVID-19 pandemic. Some studies have reported elevated levels of loneliness, depression, anxiety and insomnia among older adults following the outbreak of COVID-19 [72–74], while others have reported no changes before and during the pandemic despite increased loneliness during the pandemic [75]. Other studies have shown that younger populations have had higher rates of CMDs com- pared to older adults [76–79]. While it is possible that the emergence of the pandemic may have created an environ- ment where the determinants of poor mental health could have been exacerbated, our study found low prevalences of CMDs, similar to previous research, suggesting higher resilience to the mental health effects of COVID-19 [80]. The long-term impacts of the pandemic remain unclear, espe- cially in SSA where data on older adults’ mental health are very scarce. More studies are needed to elucidate these findings. The strengths of the current study include the focus on a neglected but rapidly growing population of OALWH, the use of a comparison group and sufficient sample size. Nonethe- less, the cross-sectional nature of the study precludes any conclusion on causality. We recruited our OALWH from public HIV clinics, as such, our findings may not be readily generaliz- able to OALWH who may be out of care or attending private or urban HIV clinics or recruited from the community. We also utilized self-report screening measures which could be subject to reporting bias. Relatedly, the mental health screen- ing measures do not give a clinical diagnosis of the studied CMDs, hence, we only report the symptomatology of these conditions. 63 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 Table 4. Association between HIV status and common mental disorders across the whole sample of older adults Positive screen for depressive symptoms Positive screen for anxiety symptoms Comorbid depressive and anxiety symptoms Crude analysis Adjusted analysis Crude analysis Adjusted analysis Crude analysis Adjusted analysis Covariate OR (95% CI) aOR (95% CI) OR (95% CI) aOR (95% CI) OR (95% CI) aOR (95% CI) HIV status Seronegative Ref Ref Ref Ref Ref Ref Seropositive 1.40 (0.87, 2.25) 0.54 (0.27, 1.07) 1.72 (0.87, 3.39) 0.46 (0.18, 1.19) 2.46* (1.09, 5.57) 0.74 (0.25, 2.22) Sex Male Ref Ref Ref Female 1.68 (0.84, 3.38) 2.49 (0.95, 6.54) 1.93 (0.67, 5.56) Age (years) 50–59 Ref Ref Ref 60–69 0.89 (0.46, 1.70) 1.78 (0.71, 4.47) 1.72 (0.59, 4.98) Above 70 0.56 (0.19, 1.69) 3.99* (1.08, 14.71) 3.94 (0.92, 16.94) Marital status Never married Ref Ref Ref Separated/Divorced/ Widowed 0.14* (0.03, 0.69) 0.40 (0.06, 2.75) 0.83 (0.06, 11.27) Married/cohabiting 0.12* (0.02, 0.62) 0.48 (0.07, 3.53) 1.26 (0.09, 18.17) Asset Index score 0.77* (0.60, 0.98) 1.06 (0.77, 1.46) 0.91 (0.63, 1.32) Sexually active No Ref Ref Ref Yes 2.61* (1.24, 5.47) 0.96 (0.38, 2.43) 0.81 (0.28, 2.29) Functional disability score 1.18** (1.09, 1.28) 1.14* (1.05, 1.24) 1.14* (1.04, 1.24) Loneliness score 1.12* (1.03, 1.22) 1.16* (1.04, 1.30) 1.12 (0.99, 1.27) Ageism score 1.10** (1.04, 1.16) 1.16** (1.08, 1.24) 1.16** (1.08, 1.25) Caring for a sick family member 2.08* (1.08, 4.02) 3.50* (1.32, 9.28) 3.07* (1.03, 9.20) Chronic fatigue 3.14** (1.46, 6.72) 1.80 (0.69, 4.67) 1.86 (0.66, 5.24) Sleeping difficulties for the past month None Ref Ref Ref Sometimes 4.62** (2.38, 8.98) 3.64* (1.47, 9.03) 4.69* (1.59, 13.88) Most of the time/always 10.89** (3.57, 33.24) 4.89* (1.35, 17.63) 7.30* (1.76, 30.27) Number of the final model 431 431 431 Hosmer–Lemeshow test X2 = 433.90; p = 0.25 X2 = 307.20; p = 0.99 X2 = 338.67; p = 0.99 Variance explained 35.0% 36.4% 38.8% Abbreviations: aOR, adjusted odds ratio; CMD, common mental disorder; GAD, generalized anxiety disorder; OR, odds ratio; Ref, reference group. *p-value < 0.05, **p-value < 0.001. Despite the outlined limitations, this study has important implications for the care of older adults in our setting. We observed substantial levels of mild depressive and anxiety symptoms in both OALWH and their HIV-negative peers, highlighting the need for culturally appropriate mental health interventions in these older adults, regardless of their HIV status. Routine screening for CMDs should be strength- ened to identify those at risk. Risk indicators for depressive symptoms, anxiety symptoms and their co-occurrence in this study were predominantly psychosocial factors. Unfor- tunately, there is a paucity of research on psychosocial interventions among OALWH [81] and those in the general population [82], especially in SSA. Our findings highlight the need to strengthen the evidence base for interventions for CMDs among older adults in low-resource settings like Kenya. 64 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 Table 5. Univariate and multivariable analysis of correlates of common mental disorders among OALWH Positive screen for depressive symptoms Positive screen for anxiety symptoms Comorbid depressive and anxiety symptoms Covariate Univariate analysis OR (95% CI) Multivariable analysis aOR (95% CI) Univariate analysis OR (95% CI) Multivariable analysis aOR (95% CI) Univariate analysis OR (95% CI) Multivariable analysis aOR (95% CI) Age (years) 50–59 Ref Ref Ref Ref Ref Ref 60–69 0.77 (0.41, 1.44) 0.55 (0.23, 1.32) 1.18 (0.51, 2.72) 2.03 (0.66, 6.20) 1.12 (0.45, 2.76) 0.73 (0.19, 2.84) ≥70 1.35 (0.44, 4.15) 1.00 (0.19, 5.24) 4.19** (1.27, 13.80) 7.43** (1.25, 44.36) 4.55** (1.37, 15.09) 4.76 (0.70, 32.55) Sex Male Ref Ref Ref Ref Ref Ref Female 1.34 (0.73, 2.45) 1.36 (0.58, 3.19) 1.79 (0.76, 4.19) 2.17 (0.58, 8.08) 1.73 (0.70, 4.29) 2.59 (0.64, 10.45) Marital status Never married Ref – – – – – Separated/Divorced/ Widowed 0.19** (0.04, 0.82) – – – – – Married/cohabiting 0.16** (0.04, 0.73) – – – – – Education level None Ref – Ref – Ref – Primary 0.80 (0.41, 1.57) – 0.51* (0.22, 1.19) – 0.52* (0.22, 1.25) – Secondary 0.78 (0.34, 1.80) – 0.38* (0.11, 1.24) – 0.20** (0.04, 0.96) – Tertiary 0.16* (0.02, 1.28) – 0.28 (0.03, 2.30) – 0.31 (0.04, 2.59) – Monthly household income (Ksh) ≤10,000 Ref – Ref – Above 10,000 0.33*** (0.12, 0.86) – 0.13* (0.02, 1.02) – 0.16* (0.02, 1.22) – Living arrangements Multiple generational families – – – – Ref – Single generational families – – – – 2.11* (0.77, 5.78) – Alone – – – – 1.05 (0.29, 3.82) – Number of dependents, mean (SD) – – 0.87* (0.74, 1.04) – – – Caring for a sick family member No Ref – – – – – Yes 0.63* (0.33, 1.16) – – – – – Food insecurity (lack of food in the past week) Never Ref Ref – Ref – Sometimes 2.92*** (1.55, 5.49) – 4.21*** (1.75, 10.14) – 5.95*** (2.18, 16.20) – (Continued) 65 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 Table 5. (Continued) Positive screen for depressive symptoms Positive screen for anxiety symptoms Comorbid depressive and anxiety symptoms Covariate Univariate analysis OR (95% CI) Multivariable analysis aOR (95% CI) Univariate analysis OR (95% CI) Multivariable analysis aOR (95% CI) Univariate analysis OR (95% CI) Multivariable analysis aOR (95% CI) Most of the times/always 2.9** (1.11, 7.62) – 6.21** (1.96, 19.70) – 9.56*** (2.76, 33.15) – Loneliness score, mean (SD) 1.21*** (1.12, 1.32) 1.29*** (1.16, 1.43) 1.16** (1.01, 1.34) 1.28*** (1.15, 1.42) Functional disability score, mean (SD) 1.25*** (1.15, 1.35) 1.15** (1.04, 1.28) 1.19*** (1.10, 1.28) 1.10** (1.01, 1.20) 1.18*** (1.10, 1.27) 1.12** (1.03, 1.22) Ageism score, mean (SD) 1.14*** (1.08, 1.19) 1.10** (1.04, 1.17) 1.17*** (1.10, 1.24) 1.14*** (1.06, 1.23) 1.17*** (1.11, 1.24) 1.23*** (1.13, 1.33) Hours spent in sedentary behaviours in a day, mean (SD) – – – – 1.13* (1.00, 1.28) – Sexually active No – – Ref – Ref – Yes – – 0.44* (0.18, 1.06) – 0.44* (0.17, 1.15) – Sleeping difficulties in the past month None Ref Ref Ref Ref Ref – Sometimes 6.59*** (3.18, 13.66) 3.51** (1.38, 8.89) 10.17*** (3.29, 31.47) 6.41** (1.73, 23.83) 11.75*** (3.28, 42.0) – Most of the times/always 25.60*** (9.00, 72.99) 8.82*** (2.33, 33.37) 18.25*** (4.94, 67.40) 6.18** (1.26, 30.22) 20.31*** (4.80, 85.96) – Chronic fatigue No Ref Ref Ref – Ref – Yes 7.90*** (3.66, 17.02) 5.41** (1.78, 16.38) 2.68** (1.09, 6.62) – 3.36** (1.33, 8.46) – Number of medications participants are currently using, mean (SD) – – – – 1.24* (0.94, 1.64) – HIV status disclosure Yes Ref – – – – – No 2.40* (0.73, 7.85) – – – – – Household HIV burden, mean (SD) 1.30** (1.09, 1.55) 1.37** (1.08, 1.74) 1.20* (0.98, 1.47) – 1.21* (0.98, 1.49) – cART regimen change/interruption since HIV diagnosis No – – Ref – Ref – Yes – – 2.13* (0.97, 4.68) – 1.88* (0.82, 4.34) – Access to HIV care Not easily accessible Ref Ref Ref – Ref Ref Easily accessible 0.47** (0.26, 0.85) 0.35** (0.15, 0.83) 0.36** (0.16, 0.78) – 0.29** (0.12, 0.68) 0.16** (0.04, 0.60) (Continued) 66 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 Table 5. (Continued) Positive screen for depressive symptoms Positive screen for anxiety symptoms Comorbid depressive and anxiety symptoms Covariate Univariate analysis OR (95% CI) Multivariable analysis aOR (95% CI) Univariate analysis OR (95% CI) Multivariable analysis aOR (95% CI) Univariate analysis OR (95% CI) Multivariable analysis aOR (95% CI) Perceived HIV-stigma score, mean (SD) Personalized stigma 1.21** (1.04, 1.41) 1.27** (1.02, 1.59) 1.16* (0.96, 1.42) – 1.25** (1.02, 1.54) – Disclosure concerns – – – – – – Concerns about public attitudes 1.23** (1.06, 1.42) – 1.29** (1.06, 1.57) – 1.23** (1.00, 1.51) – Negative self-image 1.22** (1.06, 1.40) – 1.20** (1.00, 1.44) – 1.36** (1.12, 1.66) – Overall stigma 1.11*** (1.05, 1.18) – 1.10** (1.02, 1.20) – 1.13** (1.04, 1.23) – n for the final model 251 252 245 Variance explained 41.7% 41.0% 42.0% Hosmer–Lemeshow test X2 = 241.96; p-value = 0.35 X2 = 210.85; p-value = 0.79 X2 = 176.09; p-value = 0.99 cvMean AUC (95% CI) 0.91 (0.87, 0.96) 0.88 (0.82, 0.95) 0.92 (0.89, 0.96) Note: Only a priori variables (age and sex), as well as those with p-value < 0.15 in the univariate analysis or multivariable p < 0.05, are pre- sented here. Ten independent variables were fitted for the multivariable model on depressive symptoms, seven variables for both the anxiety symptoms and CMD comorbidity. Abbreviations: aOR, adjusted odds ratio; cvMean AUC, cross-validated mean area under the curve for the final multivariable model; OR, odds ratio; Ref, reference group; OALWH, older adults living with HIV. *p value < 0.15, **p value < 0.05, ***p value < 0.01. 5 CONCLUS IONS Ambulatory, out-patient OALWH and their HIV-negative peers from the community have similar levels of mild depressive and anxiety symptoms. Additionally, living with HIV is not predictive of CMDs in this setting. Our study provides an initial understanding of the determinants of CMDs from a low-resource setting. Modifiable risk factors, such as ageism, HIV-related stigma, loneliness, functional disability and sleep- ing difficulties, represent a target for preventive interventions through psychosocial interventions at the family, community and clinical levels. AUTHORS ’ AFF I L IAT IONS 1Centre for Geographic Medicine Research Coast, Kenya Medical Research Insti- tute (KEMRI), Kilifi, Kenya; 2School of Public Health, University of the Witwater- srand, Parktown, South Africa; 3Institute for Human Development, Aga Khan Uni- versity, Nairobi, Kenya; 4MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Faculty of Health Sciences, University of the Witwa- tersrand, Johannesburg, South Africa; 5Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK; 6Department of Public Health, Pwani University, Kilifi, Kenya COMPET ING INTERESTS The authors have no competing interests to disclose. AUTHORS ’ CONTR IBUT IONS PNM, CRN and AA conceptualized the study. PNM, CRN, RGW and AA designed the study. PNM and CN programmed the study questions on tablets and managed project data for the entire study period. PNM analysed the data. PNM, CN, RGW, CRN and AA contributed to the interpretation of the data. PNM wrote the first draft of the manuscript and all the authors reviewed the subsequent versions and approved the final draft for submission. ACKNOWLEDGEMENTS We would like to thank all the participants who voluntarily took part in this study. We are grateful to the community health volunteers and healthcare providers at the HIV clinics for their overwhelming support during the study. We also acknowl- edge Sadaka Charo, Richard Karisa, Irene Kasichana, Maureen Nyadzua, Haprity Mwangata, Linda Moranga, Khamis Katana, Beatrice Kabunda, Katana Ngombo, AlfredNgombo, Collins Kipkoech andMarthaKombe for their immense role in data collection. Thiswork is publishedwith the permission of the director of KenyaMed- ical Research Institute. FUNDING This work was funded by the Wellcome Trust International Master’s Fellowship to PNM (grant number 208283/Z/17/Z). Further funding supporting this work was from (1) the Medical Research Council (grant number MR/M025454/1) to AA. This award is jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under MRC/DFID concordant agreement and is also part of the EDCTP2 programme supported by the European Union; (2) DELTAS Africa Initiative [DEL-15-003]. The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS) ’s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa’s Development Planning and 67 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust [107769/Z/10/Z] and the UK government. The funders did not have a role in the design and conduct of the study or interpretation of study findings. DISCLA IMER The views expressed in this publication are those of the author(s) and not necessar- ily those of AAS, NEPAD Agency, Wellcome Trust or the UK government. For the purpose of Open Access, the author has applied a CC-BY public copyright license to any accepted manuscript version arising from this submission. DATA AVAILAB I L ITY STATEMENT Application for data access can be made through the Data Governance Com- mittee of the KEMRI Wellcome Trust Research Programme who will review the application and advise as appropriate ensuring that uses are compatible with the consent obtained from participants for data collection. Requests can be sent to the coordinator of the Data Governance Committee using the following email dgc@kemri-wellcome.org. REFERENCES 1. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, AbbasifardM, et al. Global bur- den of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet North Am Ed. 2020;396(10258):1204–22. 2. GBD Mental Disorders Collaborators. Global, regional, and national burden of mental disorders in 204 countries and territories, 1990–2019: a system- atic analysis from the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137–50. 3. Chisholm D, Sweeny K, Sheehan P, Rasmussen B, Smit F, Cuijpers P, et al. Scaling-up treatment of depression and anxiety: a global return on investment anal- ysis. Lancet Psychiatry. 2016;3(5):415–24. 4. Patel V, ChisholmD, Parikh R, Charlson FJ, Degenhardt L, Dua T, et al. Address- ing the burden of mental, neurological, and substance use disorders: key messages fromDiseaseControl Priorities. LancetNorth AmEd. 2016;387(10028):1672–85. 5. National AIDS and STI Control Programme. Preliminary KENPHIA 2018 Report. Nairobi: NASCOP; 2020. 6. Ministry ofHealth. Kenya STEPwise survey for non-communicable diseases risk factors 2015 report. Nairobi; 2015. 7. Mwangala PN,Mabrouk A,Wagner R, Newton CR, Abubakar AA.Mental health and well-being of older adults living with HIV in sub-Saharan Africa: a systematic review. BMJ Open. 2021;11(9):e052810. 8. Bedaso A, Mekonnen N, Duko B. Estimate of the prevalence of depression among older people in Africa: a systematic review and meta-analysis. Aging Ment Health. 2022;26(6):1095–1105. 9. Liu H, He X, Levy JA, Xu Y, Zang C, Lin X. Psychological impacts among older and younger people living with HIV/AIDS in Nanning, China. J Aging Res. 2014;2014:576592. 10. Luo S, Yang X,Wang Z, Qin P, Jiang H, Chen X, et al. Negative attitudes toward aging mediated the association between HIV status and depression among older people in Mainland China. J Affect Disord. 2020;277:1005–12. 11. De Oliveira GC, Cianelli R, Villegas N, Solorzano Martinez A, Hires K, Muheriwa SR. Social determinants of depression among older black women living with HIV. J Am Psychiatr Nurs Assoc. 2020;26(6):576–85. 12. Grov C, Golub SA, Parsons JT, Brennan M, Karpiak SE. Loneliness and HIV- related stigma explain depression among older HIV-positive adults. AIDS Care. 2010;22(5):630–9. 13. Cabrera DM, Diaz MM, Grimshaw A, Salvatierra J, Garcia PJ, Hsieh E. Aging with HIV in Latin America and the Caribbean: a systematic review. Curr HIV/AIDS Rep. 2021;18(1):1–47. 14. Ronel J, Dinkel A, Wolf E, Marten-Mittag B, Mueck B, Mayr C, et al. Anxiety, depression, and health-related quality of life in aging people living with HIV com- pared to diabetes patients and patients withminor health conditions: a longitudinal study. Psychol Health Med. 2018;23(7):823–30. 15. Soliman S, Seal D, Bruce O, Dalton M, Palmer A, Pardini M, et al. Associations of depression and anxiety with substance use and social health among older adults living with HIV. Health Behav Res. 2020;3(1):2. 16. Kalomo EN, Jun JS, Lee K, Kaddu MN. HIV stigma, resilience and depressive symptoms among older adults living with HIV in rural Namibia. Afr J AIDS Res. 2020;19(3):198–205. 17. Kalomo EN, Jun JS, Lee KH, Kaddu MN. Depressive symptoms among older adults with HIV in Namibia: the role of social support and spirituality. Afr J AIDS Res. 2021;20(1):25–31. 18. Bernard C, Font H, Diallo Z, Ahonon R, Tine JM, N’guessan Abouo F, et al. Prevalence and factors associated with severe depressive symptoms in older west African people living with HIV. BMC Psychiatry. 2020;20(1):1–11. 19. Kinyanda E, Kuteesa M, Scholten F, Mugisha J, Baisley K, Seeley J. Risk of major depressive disorder among older persons living in HIV–endemic central and southwestern Uganda. AIDS Care. 2016;28(12):1516–21. 20. Gureje O, Oladeji B, Abiona T. Incidence and risk factors for late-life depression in the Ibadan Study of Ageing. Psychol Med. 2011;41(9):1897– 906. 21. Ojagbemi A, Bello T, Gureje O. Gender differential in social and economic pre- dictors of incident major depressive disorder in the Ibadan Study of Ageing. Soc Psychiatry Psychiatr Epidemiol. 2018;53(4):351–61. 22. McKinnon B, Harper S, Moore S. The relationship of living arrangements and depressive symptoms among older adults in sub-Saharan Africa. BMC Public Health. 2013;13(1):1–9. 23. Weyerer S, Eifflaender-Gorfer S, Wiese B, Luppa M, Pentzek M, Bickel H, et al. Incidence and predictors of depression in non-demented primary care atten- ders aged 75 years and older: results from a 3-year follow-up study. Age Ageing. 2013;42(2):173–80. 24. Brennan-Ing M. Emerging issues in HIV and aging. Sage; 2020. 25. Mitchell AJ, Rao S, Vaze A. Do primary care physicians have particular diffi- culty identifying late-life depression? A meta-analysis stratified by age. Psychother Psychosom. 2010;79(5):285–94. 26. Mitchell AJ, Subramaniam H. Prognosis of depression in old age compared to middle age: a systematic review of comparative studies. Am J Psychiatry. 2005;162(9):1588–601. 27. da Silva J, Gonçalves-Pereira M, Xavier M, Mukaetova-Ladinska EB. Affec- tive disorders and risk of developing dementia: systematic review. Br J Psychiatry. 2013;202(3):177–86. 28. Hoare J, Sevenoaks T, Mtukushe B,Williams T, Heany S, Phillips N. Global sys- tematic review of common mental health disorders in adults living with HIV. Curr HIV/AIDS Rep. 2021:18(6):569–80. 29. Bernard C, Dabis F, de Rekeneire N. Prevalence and factors associated with depression in people living with HIV in sub-Saharan Africa: a systematic review and meta-analysis. PLoS One. 2017;12(8):e0181960. 30. Brandt C, Zvolensky MJ, Woods SP, Gonzalez A, Safren SA, O’Cleirigh CM. Anxiety symptoms and disorders among adults living with HIV and AIDS: a criti- cal review and integrative synthesis of the empirical literature. Clin Psychol Rev. 2017;51:164–84. 31. Pietrzak RH, Maruff P, Woodward M, Fredrickson J, Fredrickson A, Krystal JH, et al. Mild worry symptoms predict decline in learning and memory in healthy older adults: a 2-year prospective cohort study. Am J Geriatr Psychiatry. 2012;20(3):266–75. 32. Kenya National Bureau of Statistics. The 2019 Kenya Population and Housing Census: Population by County and Sub-county. Kenya National Bureau of Statis- tics; 2019. 33. National AIDS Control Council (NACC). Kenya HIV County Profiles. 2016. 34. Kowal P, Dowd JE. Definition of an older person. Proposed working definition of an older person in Africa for the MDS Project. Geneva: World Health Organiza- tion; 2001. 35. Nyirenda M, Chatterji S, Rochat T, Mutevedzi P, Newell M-L. Prevalence and correlates of depression among HIV-infected and -affected older people in rural South Africa. J Affect Disord. 2013;151(1):31–8. 36. Manne-Goehler J, Kakuhikire B, Abaasabyoona S, Bärnighausen TW, Okello S, Tsai AC, et al. Depressive symptoms before and after antiretroviral therapy initia- tion among older-aged individuals in rural Uganda. AIDS Behav. 2019;23(3):564– 71. 37. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research elec- tronic data capture (REDCap)—ametadata-drivenmethodology andworkflow pro- cess for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81. 38. ReiniusM,Wettergren L,WiklanderM, SvedhemV, EkströmAM, Eriksson LE. Development of a 12-item short version of the HIV stigma scale. Health Qual Life Outcomes. 2017;15(1):1–9. 39. Üstün TB, Chatterji S, Kostanjsek N, Rehm J, Kennedy C, Epping-Jordan J, et al. Developing the World Health Organization disability assessment schedule 2.0. Bull World Health Organ. 2010;88:815–23. 40. Hays RD, DiMatteo MR. A short-form measure of loneliness. J Pers Assess. 1987;51(1):69–81. 68 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977 mailto:dgc@kemri-wellcome.org Mwangala PN et al. Journal of the International AIDS Society 2022, 25(S4):e25977 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full | https://doi.org/10.1002/jia2.25977 41. Palmore E. The ageism survey: first findings. Gerontologist. 2001;41(5):572– 5. 42. Spitzer RL, Kroenke K,Williams JB, LöweB. A brief measure for assessing gen- eralized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7. 43. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. 44. Nyongesa MK, Mwangi P, Koot HM, Cuijpers P, Newton CR, Abubakar A. The reliability, validity and factorial structure of the Swahili version of the 7-item gen- eralized anxiety disorder scale (GAD-7) among adults living with HIV from Kilifi, Kenya. Ann Gen Psychiatry. 2020;19(1):1–10. 45. Mwangi P, Nyongesa MK, Koot HM, Cuijpers P, Newton CR, Abubakar A. Val- idation of a Swahili version of the 9-item Patient Health Questionnaire (PHQ-9) among adults living with HIV compared to a community sample from Kilifi, Kenya. J Affect Disord Rep. 2020;1:100013. 46. Abadiga M. Depression and its associated factors among HIV/AIDS patients attending ART clinics at Gimbi General hospital, West Ethiopia, 2018. BMC Res Notes. 2019;12(1):1–8. 47. Belenky NM, Cole SR, Pence BW, Itemba D, Maro V, Whetten K. Depressive symptoms, HIV medication adherence, and HIV clinical outcomes in Tanzania: a prospective, observational study. PLoS One. 2014;9(5):e95469. 48. Abubakar A, Van De Vijver FJ. How to adapt tests for sub-Saharan Africa. In: Abubakar A, Van De Vijver FJ. (eds) Handbook of applied developmental science in sub-Saharan Africa. Springer; 2017. p. 197–212. 49. Geldsetzer P, Vaikath M, Wagner R, Rohr JK, Montana L, Gómez-Olivé FX, et al. Depressive symptoms and their relation to age and chronic diseases among middle-aged and older adults in rural South Africa. J Gerontol Ser A. 2019;74(6):957–63. 50. Brown MJ, Adeagbo O. HIV and aging: double stigma. Health (N. Y). 2021;10(13):14. 51. Chang E-S, Kannoth S, Levy S, Wang S-Y, Lee JE, Levy BR. Global reach of ageism on older persons’ health: a systematic review. PLoS One. 2020;15(1):e0220857. 52. Levy B. Stereotype embodiment: a psychosocial approach to aging. Curr Dir Psychol Sci. 2009;18(6):332–6. 53. Mwangala PN, Ssewanyana D, Mwangi P, Chongwo E, Nasambu C, Kagonya VA, et al. Correlates of health-related quality of life in primary caregivers of HIV infected and HIV exposed uninfected adolescents at the Kenyan Coast. Health Qual Life Outcomes. 2022;20(1):11. 54. Chawla K, Kunonga TP, Stow D, Barker R, Craig D, Hanratty B. Prevalence of loneliness amongst older people in high-income countries: a systematic review and meta-analysis. PLoS One. 2021;16(7):e0255088. 55. Lee SL, Pearce E, Ajnakina O, Johnson S, Lewis G, Mann F, et al. The associa- tion between loneliness and depressive symptoms among adults aged 50 years and older: a 12-year population-based cohort study. Lancet Psychiatry. 2021;8(1):48– 57. 56. Hawkley LC, Cacioppo JT. Loneliness matters: a theoretical and empirical review of consequences and mechanisms. Ann Behav Med. 2010;40(2):218–27. 57. Avila-Funes JA, Zamudio-Rodríguez A, Muñoz-Nevárez LA, Belaunzarán- Zamudio PF, Díaz-Ramos JA, Alcala-Zermeno JL, et al. Correlates of depres- sive symptoms among older adults living with HIV. Int J Geriatr Psychiatry. 2018;33(9):1260–4. 58. Ware D, Rueda S, PlankeyM, Surkan P, Okafor CN, Teplin L, et al. The longitu- dinal impact of employment, retirement and disability status on depressive symp- toms among men living with HIV in theMulticenter AIDS Cohort Study. PLoS One. 2020;15(10):e0239291. 59. Fang H, Tu S, Sheng J, Shao A. Depression in sleep disturbance: a review on a bidirectional relationship, mechanisms and treatment. J Cell Mol Med. 2019;23(4):2324–32. 60. Morin CM, LeBlanc M, Daley M, Gregoire J, Merette C. Epidemiology of insomnia: prevalence, self-help treatments, consultations, and determinants of help-seeking behaviors. Sleep Med. 2006;7(2):123–30. 61. Jaussent I, Bouyer J, Ancelin M-L, Akbaraly T, Peres K, Ritchie K, et al. Insom- nia and daytime sleepiness are risk factors for depressive symptoms in the elderly. Sleep. 2011;34(8):1103–10. 62. Gallo JJ, Hwang S, Truong C, Reynolds III CF, Spira AP. Role of persistent and worsening sleep disturbance in depression remission and suicidal ideation among older primary care patients: the PROSPECT study. Sleep. 2020;43(10):zsaa063. 63. Liu H, Zhao M, Ren J, Qi X, Sun H, Qu L, et al. Identifying factors associated with depression among men living with HIV/AIDS and undergoing antiretroviral therapy: a cross-sectional study inHeilongjiang, China. HealthQual LifeOutcomes. 2018;16(1):1–10. 64. Franzen PL, Buysse DJ. Sleep disturbances and depression: risk relationships for subsequent depression and therapeutic implications. Dialog Clin Neurosci. 2008;10(4):473. 65. Jong E, Oudhoff LA, Epskamp C, Wagener MN, van Duijn M, Fischer S, et al. Predictors and treatment strategies of HIV-related fatigue in the combined antiretroviral therapy era. AIDS. 2010;24(10):1387–405. 66. Gebreyesus T, Belay A, Berhe G, Haile G. Burden of fatigue among adults liv- ing with HIV/AIDS attending antiretroviral therapy in Ethiopia. BMC Infect Dis. 2020;20(1):1–10. 67. TorossianM, Jacelon CS. Chronic illness and fatigue in older individuals: a sys- tematic review. Rehabil Nurs. 2021;46(3):125. 68. Palapinyo S, Methaneethorn J, Leelakanok N. Association between polyphar- macy and depression: a systematic review and meta-analysis. J Pharm Pract Res. 2021;51(4):280–99. 69. Maier A, Riedel-Heller SG, Pabst A, Luppa M. Risk factors and protec- tive factors of depression in older people 65+. A systematic review. PLoS One. 2021;16(5):e0251326. 70. Kiplagat J, Mwangi A, Chasela C, Huschke S. Challenges with seeking HIV care services: perspectives of older adults infected with HIV in western Kenya. BMC Public Health. 2019;19(1):1–12. 71. Huang Y, Li L, Gan Y, Wang C, Jiang H, Cao S, et al. Sedentary behaviors and risk of depression: a meta-analysis of prospective studies. Transl Psychiatry. 2020;10(1):1–10. 72. Krendl AC, Perry BL. The impact of sheltering in place during the COVID- 19 pandemic on older adults’ social and mental well-being. J Gerontol Ser B. 2021;76(2):e53–8. 73. Wong SYS, ZhangD, Sit RWS, Yip BHK, Chung RY-N,WongCKM, et al. Impact of COVID-19 on loneliness, mental health, and health service utilisation: a prospec- tive cohort study of older adults with multimorbidity in primary care. Br J Gen Pract. 2020;70(700):e817–24. 74. Webb LM, Chen CY. The COVID-19 pandemic’s impact on older adults’ men- tal health: contributing factors, coping strategies, and opportunities for improve- ment. Int J Geriatr Psychiatry. 2022;37(1):1–7. 75. Van Tilburg TG, Steinmetz S, Stolte E, Van der Roest H, de Vries DH. Lone- liness and mental health during the COVID-19 pandemic: a study among Dutch older adults. J Gerontol Ser B. 2021;76(7):e249–55. 76. Santomauro DF, Herrera AMM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet North Am Ed. 2021;398(10312):1700–12. 77. Czeisler MÉ, Lane RI, Petrosky E, Wiley JF, Christensen A, Njai R, et al. Men- tal health, substance use, and suicidal ideation during the COVID-19 pandemic— United States, June 24–30, 2020. Morb Mortal Wkly Rep. 2020;69(32): 1049. 78. González-Sanguino C, Ausín B, Castellanos MÁ, Saiz J, López-Gómez A, Ugidos C, et al. Mental health consequences during the initial stage of the 2020 Coronavirus pandemic (COVID-19) in Spain. Brain Behav Immun. 2020;87: 172–6. 79. Klaiber P, Wen JH, DeLongis A, Sin NL. The ups and downs of daily life during COVID-19: age differences in affect, stress, and positive events. J Gerontol Ser B. 2021;76(2):e30–7. 80. Vahia IV, Jeste DV, Reynolds CF. Older adults and the mental health effects of COVID-19. JAMA. 2020;324(22):2253–4. 81. Bhochhibhoya A, Harrison S, Yonce S, Friedman DB, Ghimire PS, Li X. A sys- tematic review of psychosocial interventions for older adults living with HIV. AIDS Care. 2021;33(8):971–82. 82. Forsman AK, Nordmyr J, Wahlbeck K. Psychosocial interventions for the pro- motion of mental health and the prevention of depression among older adults. Health Promot Int. 2011;26(suppl_1):i85–107. SUPPORT ING INFORMAT ION Additional information may be found under the Supporting Information tab for this article: Table S1. Univariate and multivariable analysis of the cor- relates of depressive symptoms among HIV-negative older adults. 69 http://onlinelibrary.wiley.com/doi/10.1002/jia2.25977/full https://doi.org/10.1002/jia2.25977