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Intersecting Relationships of Psychosocial and Structural Syndemic Problems Among People with HIV in South Africa: Using Network Analysis to Identify Influential Problems

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Abstract

In South Africa, little is known about interrelationships between syndemic problems among people with HIV (PWH). A better understanding of syndemic problems may yield important information regarding factors amenable to mitigation. We surveyed 194 PWH in Khayelitsha, outside of Cape Town, South Africa. We used network analysis to examine the frequency of 10 syndemic problems and their interrelationships. Syndemic problems among PWH in South Africa were common; 159 (82.8%) participants reported at least 2 co-occurring syndemic problems and 90 (46.9%) endorsed 4 or more. Network analysis revealed seven statistically significant associations. The most central problems were depression, substance use, and food insecurity. Three clusters of syndemics were identified: mood and violence; structural factors; and behavioral factors. Depression, substance use, and food insecurity commonly co-occur among PWH in sub-Saharan Africa and interfere with HIV outcomes. Network analysis can identify intervention targets to potentially improve HIV treatment outcomes.

Resumen

En Sudáfrica, poco se sabe sobre interrelaciones entre problemas sindémicos entre personas con VIH (PCV). Un major entendimiento de los problemas sindémicos puede arrojar información importante sobre los factores susceptibles de mitigación. Utilizamos el análisis de redes para examinar la frecuencia de 10 problemas sindémicos y sus interrelaciones. Problemas sindémicos entre PCV en Sudáfrica eran communes; 159 (82.8%) participantes presentaron al menos 2 problemas sindémicos concurrentes y 90 (46.9%) presentaron 4 o más. El análisis de red reveló siete asociaciones estadísticamente significativas. Los problemas más centrales fueron la depresión, el uso de sustancias y la inseguridad alimentaria. Se indetificaron tres grupos de sindemias: estado de ánimo y violencia; factores estructurales; y factores de comportamiento. La depresión, el uso de sustancias y la inseguridad alimentaria comúnmente ocurren simultáneamente entre las PCV en el África subsahariana e interfieren con los resultados del VIH. El análisis de redes puede identificar objetivos de intervención para potencialmente mejorar los resultados del tratamiento del VIH.

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References

  1. UNAIDS. South Africa [Internet]. 2018 [cited 2018 Nov 6]. Available from: http://www.unaids.org/en/regionscountries/countries/southafrica

  2. Cichowitz C, Maraba N, Hamilton R, Charalambous S, Hoffmann CJ. Depression and alcohol use disorder at antiretroviral therapy initiation led to disengagement from care in South Africa. PLoS ONE. 2017;12(12):e0189820.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kagee A, Remien RH, Berkman A, Hoffman S, Campos L, Swartz L. Structural barriers to ART adherence in Southern Africa: challenges and potential ways forward. Glob Public Health. 2011;6(1):83–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Myer L, Smit J, Roux LL, Parker S, Stein DJ, Seedat S. Common mental disorders among HIV-infected individuals in South Africa: prevalence, predictors, and validation of brief psychiatric rating scales. AIDS Patient Care STDs. 2008;22(2):147–58.

    Article  PubMed  Google Scholar 

  5. Dahab M, Charalambous S, Hamilton R, Fielding K, Kielmann K, Churchyard GJ, et al. “That is why I stopped the ART”: patients’ & providers’ perspectives on barriers to and enablers of HIV treatment adherence in a South African workplace programme. BMC Public Health. 2008;8(1):63.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kader R, Seedat S, Govender R, Koch JR, Parry CD. Hazardous and harmful use of alcohol and/or other drugs and health status among south African patients attending HIV clinics. AIDS Behav. 2014;18(3):525–34.

    Article  CAS  PubMed  Google Scholar 

  7. Magidson JF, Saal W, Nel A, Remmert JE, Kagee A. Relationship between depressive symptoms, alcohol use, and antiretroviral therapy adherence among HIV-infected, clinic-attending patients in South Africa. J Health Psychol. 2017;22(11):1426–33.

    Article  PubMed  Google Scholar 

  8. Mills EJ, Nachega JB, Bangsberg DR, Singh S, Rachlis B, Wu P, et al. Adherence to HAART: a systematic review of developed and developing nation patient-reported barriers and facilitators. PLoS Med. 2006;3(11): e438.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Naidoo P, Peltzer K, Louw J, Matseke G, Mchunu G, Tutshana B. Predictors of tuberculosis (TB) and antiretroviral (ARV) medication non-adherence in public primary care patients in South Africa: a cross sectional study. BMC Public Health. 2013;13(1):396.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Nel A, Kagee A. Common mental health problems and antiretroviral therapy adherence. AIDS Care. 2011;23(11):1360–5.

    Article  PubMed  Google Scholar 

  11. Nel A, Kagee A. The relationship between depression, anxiety and medication adherence among patients receiving antiretroviral treatment in South Africa. AIDS Care. 2013;25(8):948–55.

    Article  PubMed  Google Scholar 

  12. Rose AL, Belus JM, Ma T, Lee JS, Wan C, De Los RA, et al. The relationship between harmful alcohol use and antiretroviral non-adherence in people accessing HIV treatment in Cape Town, South Africa: an event-level analysis. AIDS Behav. 2022;26(6):2055–66.

    Article  PubMed  Google Scholar 

  13. Sikkema KJ, Mulawa MI, Robertson C, Watt MH, Ciya N, Stein DJ, et al. Improving AIDS Care After Trauma (ImpACT): pilot outcomes of a coping intervention among HIV-infected women with sexual trauma in South Africa. AIDS Behav. 2018;22(3):1039–52.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Biadgilign S, Deribew A, Amberbir A, Deribe K. Barriers and facilitators to antiretroviral medication adherence among HIV-infected paediatric patients in Ethiopia: a qualitative study. SAHARA-J J Soc Asp HIVAIDS. 2009;6(4). Available from: https://www.ajol.info/index.php/saharaj/article/view/50639

  15. Himmelgreen DA, Romero-Daza N, Turkon D, Watson S, Okello-Uma I, Sellen D. Addressing the HIV/AIDS—food insecurity syndemic in sub-Saharan Africa. Afr J AIDS Res. 2009;8(4):401–12.

    Article  PubMed  Google Scholar 

  16. Pienaar M, van Rooyen FC, Walsh CM. Household food security and HIV status in rural and urban communities in the Free State province, South Africa. SAHARA J J Soc Asp HIVAIDS Res Alliance. 2017;14(1):118–31.

    Google Scholar 

  17. Senkomago V, Guwatudde D, Breda M, Khoshnood K. Barriers to antiretroviral adherence in HIV-positive patients receiving free medication in Kayunga, Uganda. AIDS Care. 2011;23(10):1246–53.

    Article  PubMed  Google Scholar 

  18. Singer M. Toward a critical biosocial model of ecohealth in Southern Africa: the HIV/AIDS and nutrition insecurity syndemic. Ann Anthropol Pract. 2011;35(1):8–27.

    Article  Google Scholar 

  19. Drain PK, Losina E, Parker G, Giddy J, Ross D, Katz JN, et al. Risk factors for late-stage HIV disease presentation at initial HIV diagnosis in Durban, South Africa. PLoS ONE. 2013;8(1): e55305.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kagee A, Steel H, Coetzee B. The relationship between structural barriers to adherence to antiretroviral therapy, psychological distress, and health-related quality of life. S Afr J Psychol. 2014;44(2):170–9.

    Article  Google Scholar 

  21. Mepham S, Zondi Z, Mbuyazi A, Mkhwanazi N, Newell ML. Challenges in PMTCT antiretroviral adherence in northern KwaZulu-Natal, South Africa. AIDS Care. 2011;23(6):741–7.

    Article  CAS  PubMed  Google Scholar 

  22. Coetzee B, Kagee A, Vermeulen N. Structural barriers to adherence to antiretroviral therapy in a resource-constrained setting: the perspectives of health care providers. AIDS Care. 2011;23(2):146–51.

    Article  PubMed  Google Scholar 

  23. Goudge J, Ngoma B. Exploring antiretroviral treatment adherence in an urban setting in South Africa. J Public Health Policy. 2011;32(1):S52-64.

    Article  PubMed  Google Scholar 

  24. Pitpitan EV, Kalichman SC, Eaton LA, Cain D, Sikkema KJ, Watt MH, et al. Co-occurring psychosocial problems and HIV risk among women attending drinking venues in a South African township: a syndemic approach. Ann Behav Med. 2013;45(2):153–62.

    Article  PubMed  Google Scholar 

  25. Singer M. A dose of drugs, a touch of violence, a case of AIDS: conceptualizing the Sava Syndemic. Free Inq Creat Sociol. 1996;24(2):99–110.

    Google Scholar 

  26. Stall R, Friedman M, Catania JA. Interacting epidemics and gay men’s health: a theory of syndemic production among urban gay men. In: Wolitski RJ, Stall R, Valdiserri RO, editors. Unequal opportunity: health disparities affecting gay and bisexual men in the United States. New York: Oxford University Press; 2008. p. 251–74.

    Google Scholar 

  27. Fried EI, Cramer AOJ. Moving forward: challenges and directions for psychopathological network theory and methodology. Perspect Psychol Sci. 2017;12(6):999–1020.

    Article  PubMed  Google Scholar 

  28. Biello KB, Oldenburg CE, Safren SA, Rosenberger JG, Novak DS, Mayer KH, et al. Multiple syndemic psychosocial factors are associated with reduced engagement in HIV care among a multinational, online sample of HIV-infected MSM in Latin America. AIDS Care. 2016;28(sup1):84–91.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Blashill AJ, Bedoya CA, Mayer KH, O’Cleirigh C, Pinkston MM, Remmert JE, et al. Psychosocial syndemics are additively associated with worse ART adherence in HIV-infected individuals. AIDS Behav. 2015;19(6):981–6.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Friedman MR, Stall R, Silvestre AJ, Wei C, Shoptaw S, Herrick A, et al. Effects of syndemics on HIV viral load and medication adherence in the multicentre AIDS cohort study. AIDS. 2015;29(9):1087–96.

    Article  PubMed  Google Scholar 

  31. Glynn TR, Safren SA, Carrico AW, Mendez NA, Duthely LM, Dale SK, et al. High levels of syndemics and their association with adherence, viral non-suppression, and biobehavioral transmission risk in Miami, a US City with an HIV/AIDS epidemic. AIDS Behav. 2019;23(11):2956–65.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Harkness A, Bainter SA, O’Cleirigh C, Mendez NA, Mayer KH, Safren SA. Longitudinal effects of syndemics on ART non-adherence among sexual minority men. AIDS Behav. 2018;22(8):2564–74.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Harkness A, Bainter SA, O’Cleirigh C, Albright C, Mayer KH, Safren SA. Longitudinal effects of syndemics on HIV-positive sexual minority men’s sexual health behaviors. Arch Sex Behav. 2019;48(4):1159–70.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kuhns LM, Hotton AL, Garofalo R, Muldoon AL, Jaffe K, Bouris A, et al. An index of multiple psychosocial, syndemic conditions is associated with antiretroviral medication adherence among HIV-positive youth. AIDS Patient Care STDs. 2016;30(4):185–92.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Mizuno Y, Purcell DW, Knowlton AR, Wilkinson JD, Gourevitch MN, Knight KR. Syndemic vulnerability, sexual and injection risk behaviors, and HIV continuum of care outcomes in HIV-positive injection drug users. AIDS Behav. 2015;19(4):684–93.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Pantalone DW, Valentine SE, Woodward EN, O’Cleirigh C. Syndemic indicators predict poor medication adherence and increased healthcare utilization for urban HIV-positive men who have sex with men. J Gay Lesbian Ment Health. 2018;22(1):71–87.

    Article  PubMed  Google Scholar 

  37. Sullivan KA, Messer LC, Quinlivan EB. Substance abuse, violence, and HIV/AIDS (SAVA) syndemic effects on viral suppression among HIV positive women of color. AIDS Patient Care STDs. 2015;29(S1):S42–8.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Walters SM, Braksmajer A, Coston B, Yoon I, Grov C, Downing MJ, et al. A syndemic model of exchange sex among HIV-positive men who have sex with men. Arch Sex Behav. 2020. https://doi.org/10.1007/s10508-020-01628-8.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wawrzyniak AJ, Rodríguez AE, Falcon AE, Chakrabarti A, Parra A, Park J, et al. Association of individual and systemic barriers to optimal medical care in people living with HIV/AIDS in Miami-Dade County. JAIDS J Acquir Immune Defic Syndr. 2015;1(69):S63.

    Article  Google Scholar 

  40. Choi KW, Smit JA, Coleman JN, Mosery N, Bangsberg DR, Safren SA, et al. Mapping a syndemic of psychosocial risks during pregnancy using network analysis. Int J Behav Med. 2019. http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2019-11144-001&site=ehost-live

  41. Lee JS, Safren SA, Bainter SA, Rodríguez-Díaz CE, Horvath KJ, Blashill AJ. Examining a syndemics network among Young Latino men who have sex with men. Int J Behav Med. 2020;27(1):39–51.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Lee JS, Bainter SA, Carrico AW, Glynn TR, Rogers BG, Albright C, et al. Connecting the dots: a comparison of network analysis and exploratory factor analysis to examine psychosocial syndemic indicators among HIV-negative sexual minority men. J Behav Med. 2020;43(6):1026–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Choi KW, Batchelder AW, Ehlinger PP, Safren SA, O’Cleirigh C. Applying network analysis to psychological comorbidity and health behavior: depression, PTSD, and sexual risk in sexual minority men with trauma histories. J Consult Clin Psychol. 2017;85(12):1158.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Cramer AOJ, Waldorp LJ, van der Maas HLJ, Borsboom D. Comorbidity: a network perspective. Behav Brain Sci. 2010;33:137–93.

    Article  PubMed  Google Scholar 

  45. Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23:617.

    Article  PubMed  Google Scholar 

  46. Rhemtulla M, Fried EI, Aggen SH, Tuerlinckx F, Kendler KS, Borsboom D. Network analysis of substance abuse and dependence symptoms. Drug Alcohol Depend. 2016;161:230–7.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Fried EI, Epskamp S, Nesse RM, Tuerlinckx F, Borsboom D. What are “good” depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. J Affect Disord. 2016;1(189):314–20.

    Article  Google Scholar 

  48. Armour C, Fried EI, Deserno MK, Tsai J, Pietrzak RH. A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in U.S. military veterans. J Anxiety Disord. 2017;45:49–59.

    Article  PubMed  Google Scholar 

  49. Spiller TR, Schick M, Schnyder U, Bryant RA, Nickerson A, Morina N. Symptoms of posttraumatic stress disorder in a clinical sample of refugees: a network analysis. Eur J Psychotraumatol. 2017;8(sup3):1318032.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.

    Article  PubMed  Google Scholar 

  51. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;1(95): 103208.

    Article  Google Scholar 

  52. Brislin RW. Back-translation for cross-cultural research. J Cross-Cult Psychol. 1970;1(3):185–216.

    Article  Google Scholar 

  53. Meltzer-Brody S, Churchill E, Davidson JRT. Derivation of the SPAN, a brief diagnostic screening test for post-traumatic stress disorder. Psychiatry Res. 1999;88(1):63–70.

    Article  CAS  PubMed  Google Scholar 

  54. Radloff LS. The CES-D scale a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385–401.

    Article  Google Scholar 

  55. Andersen LS, Magidson JF, O’Cleirigh C, Remmert JE, Kagee A, Leaver M, et al. A pilot study of a nurse-delivered cognitive behavioral therapy intervention (Ziphamandla) for adherence and depression in HIV in South Africa. J Health Psychol. 2018;23(6):776–87.

    Article  PubMed  Google Scholar 

  56. Connor KM, Kobak KA, Churchill LE, Katzelnick D, Davidson JR. Mini-SPIN: a brief screening assessment for generalized social anxiety disorder. Depress Anxiety. 2001;14(2):137–40.

    Article  CAS  PubMed  Google Scholar 

  57. WHO Assist Working Group. The alcohol, smoking and substance involvement screening test (ASSIST): development, reliability and feasibility. Addiction. 2002;97(9):1183–94.

    Article  Google Scholar 

  58. Humeniuk R, World Health Organization. The Alcohol, smoking and substance involvement screening test (ASSIST): manual for use in primary care. Geneva: World Health Organization; 2010.

  59. Saunders JB, Aasland OG, Babor TF, De la Fuente JR, Grant M. Development of the alcohol use disorders identification test (AUDIT). WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addict-ABINGDON. 1993;88:791–791.

    Article  CAS  Google Scholar 

  60. Conigrave KM, Hall WD, Saunders JB. The AUDIT questionnaire: choosing a cut-off score. Alcohol use disorder identification test. Addict Abingdon Engl. 1995;90(10):1349–56.

    Article  CAS  Google Scholar 

  61. Coates J, Swindale A, Bilinsky P. Household Food Insecurity Access Scale (HFIAS) for Measurement of Food Access: Indicator Guide: Version 3. Washington, DC: Food and Nutrition Technical Assistance Project, Academy for Educational Development; 2007. https://doi.org/10.1037/e576842013-001

  62. Coetzee B, Kagee A. The development of an inventory to assess the structural barriers to clinic attendance and pill-taking amongst users of antiretroviral therapy. AIDS Behav. 2013;17(1):319–28.

    Article  PubMed  Google Scholar 

  63. R Development Core Team. R: a language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2016. Available from: http://www.R-project.org

  64. Mazumder R, Hastie T. The graphical lasso: new insights and alternatives. Electron J Stat. 2012;9(6):2125–49.

    Google Scholar 

  65. Borsboom D, Cramer AO. Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol. 2013;9:91–121.

    Article  PubMed  Google Scholar 

  66. Holgado-Tello FP, Chacón-Moscoso S, Barbero-García I, Vila-Abad E. Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables. Qual Quant. 2010;44(1):153.

    Article  Google Scholar 

  67. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU. Complex networks: structure and dynamics. Phys Rep. 2006;424(4):175–308.

    Article  Google Scholar 

  68. Csardi G, Nepsuz T. Igraph: network analysis and visualization. Int J. 2006. Available from: https://igraph.org

  69. Yang Z, Algesheimer R, Tessone CJ. A comparative analysis of community detection algorithms on artificial networks. Sci Rep. 2016;6(1):30750.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Epskamp S, Fried EI. bootnet: bootstrap methods for various network estimation routines. 2017. Available from: https://CRAN.R-project.org/package=bootnet

  71. Opsahl T, Agneessens F, Skvoretz J. Node centrality in weighted networks: generalizing degree and shortest paths. Soc Netw. 2010;32(3):245–51.

    Article  Google Scholar 

  72. Tsai AC, Mendenhall E, Trostle JA, Kawachi I. Co-occurring epidemics, syndemics, and population health. The Lancet. 2017;389(10072):978–82.

    Article  Google Scholar 

  73. Columb MO, Sagadai S. Multiple comparisons. Curr Anaesth Crit Care. 2006;17(3):233–6.

    Article  Google Scholar 

  74. Bringmann LF, Vissers N, Wichers M, Geschwind N, Kuppens P, Peeters F, et al. A network approach to psychopathology: new insights into clinical longitudinal data. PLoS ONE. 2013;8(4): e60188.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Elovainio M, Hakulinen C, Pulkki-Råback L, Juonala M, Raitakari OT. A network approach to the analysis of psychosocial risk factors and their association with health. J Health Psychol. 2020;25(10–11):1587–600.

    Article  PubMed  Google Scholar 

  76. Beck AT. Cognitive models of depression. Clin Adv Cogn Psychother Theory Appl. 2002;14(1):29–61.

    Google Scholar 

  77. Clark DA, Beck AT. Cognitive theory and therapy of anxiety and depression: convergence with neurobiological findings. Trends Cogn Sci. 2010;14(9):418–24.

    Article  PubMed  Google Scholar 

  78. Elwood LS, Hahn KS, Olatunji BO, Williams NL. Cognitive vulnerabilities to the development of PTSD: a review of four vulnerabilities and the proposal of an integrative vulnerability model. Clin Psychol Rev. 2009;29(1):87–100.

    Article  PubMed  Google Scholar 

  79. Foa EB, Steketee G, Rothbaum BO. Behavioral/cognitive conceptualizations of post-traumatic stress disorder. Behav Ther. 1989;20(2):155–76.

    Article  Google Scholar 

  80. Friedman MJ, Yehuda R. Post-traumatic stress disorder and comorbidity: psychobiological approaches to differential diagnosis. In: Neurobiological and clinical consequences of stress: From normal adaptation to post-traumatic stress disorder. Philadelphia, PA, US: Lippincott Williams & Wilkins Publishers; 1995. p. 429–45.

  81. Ingram RE, Price JM. Vulnerability to psychopathology: risk across the lifespan. New York: Guilford Press; 2010.

    Google Scholar 

  82. Tull MT, Gratz KL, Salters K, Roemer L. The role of experiential avoidance in posttraumatic stress symptoms and symptoms of depression, anxiety, and somatization. J Nerv Ment Dis. 2004;192(11):754–61.

    Article  PubMed  Google Scholar 

  83. Leung CW, Epel ES, Willett WC, Rimm EB, Laraia BA. Household food insecurity is positively associated with depression among low-income supplemental nutrition assistance program participants and income-eligible nonparticipants. J Nutr. 2015;145(3):622–7.

    Article  CAS  PubMed  Google Scholar 

  84. Boateng GO, Workman CL, Miller JD, Onono M, Neilands TB, Young SL. The syndemic effects of food insecurity, water insecurity, and HIV on depressive symptomatology among Kenyan women. Soc Sci Med. 2020;15: 113043.

    Google Scholar 

  85. Tsai AC, Bangsberg DR, Frongillo EA, Hunt PW, Muzoora C, Martin JN, 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(12):2012–9.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Tsai AC, Tomlinson M, Comulada WS, Rotheram-Borus MJ. Food insufficiency, depression, and the modifying role of social support: evidence from a population-based, prospective cohort of pregnant women in Peri-Urban South Africa. Soc Sci Med. 2016;1982(151):69–77.

    Article  Google Scholar 

  87. Alpert JE, Fava M. Nutrition and depression: the role of folate. Nutr Rev. 1997;55(5):145–9.

    Article  CAS  PubMed  Google Scholar 

  88. Friedman M. Analysis, nutrition, and health benefits of tryptophan. Int J Tryptophan Res. 2018;1(11):1178646918802282.

    Google Scholar 

  89. German L, Kahana C, Rosenfeld V, Zabrowsky I, Wiezer Z, Fraser D, et al. Depressive symptoms are associated with food insufficiency and nutritional deficiencies in poor community-dwelling elderly people. J Nutr. 2011;15(1):6.

    Google Scholar 

  90. Popa T, Ladea M. Nutrition and depression at the forefront of progress. J Med Life. 2012;5(4):414–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Racagni G, Popoli M. Cellular and molecular mechanisms in the long-term action of antidepressants. Dialogues Clin Neurosci. 2008;10(4):385–400.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Batchelder AW, Foley JD, Wirtz MR, Mayer K, O’Cleirigh C. Substance use stigma, avoidance coping, and missed HIV appointments among MSM who use substances. AIDS Behav. 2021;25(5):1454–63.

    Article  PubMed  PubMed Central  Google Scholar 

  93. Hammarlund R, Crapanzano K, Luce L, Mulligan L, Ward K. Review of the effects of self-stigma and perceived social stigma on the treatment-seeking decisions of individuals with drug- and alcohol-use disorders. Subst Abuse Rehabil. 2018;23(9):115–36.

    Article  Google Scholar 

  94. Peirce R, Frone M, Russell M, Cooper M, Mudar P. A longitudinal model of social contact, social support, depression, and alcohol use. Health Psychol. 2000;1(19):28–38.

    Article  Google Scholar 

  95. Psaros C, Smit JA, Mosery N, Bennett K, Coleman JN, Bangsberg DR, et al. PMTCT adherence in pregnant South African Women: the role of depression, social support, stigma, and structural barriers to care. Ann Behav Med. 2020;54(9):626–36.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Bangsberg DR, Deeks SG. Spending more to save more: interventions to promote adherence. Ann Intern Med. 2010;152(1):54–6.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Kehoe K, Boulle A, Tsondai PR, Euvrard J, Davies MA, Cornell M. Long-term virologic responses to antiretroviral therapy among HIV-positive patients entering adherence clubs in Khayelitsha, Cape Town, South Africa: a longitudinal analysis. J Int AIDS Soc. 2020;23(5): e25476.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Venter WF, Kaiser B, Pillay Y, Conradie F, Gomez GB, Clayden P, et al. Cutting the cost of South African antiretroviral therapy using newer, safer drugs. South Afr Med J Suid-Afr Tydskr Vir Geneeskd. 2016;107(1):28–30.

    CAS  Google Scholar 

  99. Magidson JF, Fatch R, Orrell C, Amanyire G, Haberer JE, Hahn JA, et al. Biomarker-measured unhealthy alcohol use in relation to CD4 count among individuals starting ART in Sub-Saharan Africa. AIDS Behav. 2018. https://doi.org/10.1007/s10461-018-2364-2.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Mitchell J, Wight M, Van Heerden A, Rochat TJ. Intimate partner violence, HIV, and mental health: a triple epidemic of global proportions. Int Rev Psychiatry. 2016;28(5):452–63.

    Article  PubMed  Google Scholar 

  101. Matseke G, Rodriguez VJ, Peltzer K, Jones D. Intimate partner violence among HIV positive pregnant women in South Africa. J Psychol Afr. 2016. https://doi.org/10.1080/14330237.2016.1185912.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Battersby J. Urban food insecurity in Cape Town, South Africa: an alternative approach to food access. Dev South Afr. 2011;28(4):545–61.

    Article  Google Scholar 

  103. Mendenhall E, Kohrt BA, Logie CH, Tsai AC. Syndemics and clinical science. Nat Med. 2022;28(7):1359–62.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Funding for this project came from the National Institute of Mental Health F31MH122279 (Lee) and R01MH103770 (Safren, O’Cleirigh). Some additional support was from P30MH116867 (Safren), K24DA040489 (Safren), and Dr. Lee’s time on this manuscript was supported by T32MH116140 (Henderson, Fricchione). Dr. Magidson’s time on this manuscript was supported by K23DA041901 (Magidson). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health, the National Institute of Drug Abuse, or the National Institutes of Health, or any of the other funders. Data were collected in public primary care clinics in Khayelitsha outside of Cape Town, South Africa. We would like to thank the City of Cape Town Health Department for allowing us access to their clinics and for their ongoing support. We would also like to thank the clinic staff and study participants for their time spent on the project, as well as the study team who worked tirelessly on this project. Special thanks to Nicolas Cardenas for translating the Abstract into Spanish.

Funding

Funding for this project came from the National Institute of Mental Health F31MH122279 (Lee) and R01MH103770 (Safren, O’Cleirigh). Some additional support was from P30MH116867 (Safren), K24DA040489 (Safren), and T32MH116140 (Henderson, Fricchione). Dr. Magidson’s time on this manuscript was supported by K23DA041901 (Magidson). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health, the National Institute of Drug Abuse, or the National Institutes of Health, or any of the other funders.

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Contributions

JSL, SAS, JAJ, and COC formulated the research questions, SAS, CO, and JAJ designed the study, LSA, JSL, AMS, AK, JFM, JAJ, COC, and SAS carried out the study, JSL analyzed the data, SAB provided statistical consultation, ACT provided conceptual consultation, and all authors contributed to the writing of the manuscript.

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Correspondence to Jasper S. Lee.

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Ethical approval and consent to participate

All study procedures were approved by the University of Miami institutional review board and the University of Cape Town ethics committee. All participants completed the informed consent process prior to participation.

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Not applicable.

Competing interests

Dr. Safren receives royalties for books published by Oxford University Press, Springer/Humana Press, and Guilford Publications.

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Lee, J.S., Bainter, S.A., Tsai, A.C. et al. Intersecting Relationships of Psychosocial and Structural Syndemic Problems Among People with HIV in South Africa: Using Network Analysis to Identify Influential Problems. AIDS Behav 27, 1741–1756 (2023). https://doi.org/10.1007/s10461-022-03906-z

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