Sociodemographic Factors Associated with Emotional Distress, Transactional Sex and Psychoactive Substance Use during the First Wave of the COVID-19 Pandemic
Abstract
:1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Stoebenau, K.; Heise, L.; Wamoyi, J.; Bobrova, N. Revisiting the understanding of “transactional sex” in sub-Saharan Africa: A review and synthesis of the literature. Soc. Sci. Med. 2016, 168, 186–197. [Google Scholar] [CrossRef]
- Higgins, J.A.; Hoffman, S.; Dworkin, S.L. Rethinking gender, heterosexual men, and women’s vulnerability to HIV/AIDS. Am. J. Public Health 2010, 100, 435–445. [Google Scholar] [CrossRef]
- Walls, N.E.; Bell, F. Correlates of Engaging in Survival Sex among Homeless Youth and Young Adults. J. Sex Res. 2010, 48, 423–436. [Google Scholar] [CrossRef]
- Formson, C.; Hilhorst, D. Researching Livelihoods and Services Affected by Conflict the Many Faces of Transactional Sex: Women’s Agency, Livelihoods and Risk Factors in Humanitarian Contexts: A Literature Review. The Secure Livelihoods Research Consortium. 2016. Available online: http://www.securelivelihoods.org/publications_details.aspx?resourceid=393 (accessed on 27 January 2023).
- Lamontagne, E.; Folayan, M.O.; Arije, O.; Enemo, A.; Sunday, A.; Muhammad, A.; Nyako, H.Y.; Abdullah, R.M.; Okiwu, H.; Undelikwo, V.A.; et al. The effects of COVID-19 on food insecurity, financial vulnerability and housing insecurity among women and girls living with or at risk of HIV in Nigeria. Afr. J. AIDS Res. 2022, 21, 297–305. [Google Scholar] [CrossRef]
- Folayan, M.; Ibigbami, O.; El Tantawi, M.; Brown, B.; Aly, N.; Ezechi, O.; Abeldaño, G.; Ara, E.; Ayanore, M.; Ellakany, P.; et al. Factors Associated with Financial Security, Food Security and Quality of Daily Lives of Residents in Nigeria during the First Wave of the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2021, 18, 7925. [Google Scholar] [CrossRef]
- Onyango, M.A.; Resnick, K.; Davis, A.; Shah, R.R. Gender-based violence among adolescent girls and young women: A neglected con-sequence of the West African Ebola outbreak. In Pregnant in the Time of Ebola; Schwartz, D.A., Anoko, J.N., Abramowitz, S.A., Eds.; Springer: Cham, Switzerland, 2019; pp. 121–132. [Google Scholar]
- Jacobson, L.; Regan, A.; Heidari, S.; Onyango, M.A. Transactional sex in the wake of COVID-19: Sexual and reproductive health and rights of the forcibly displaced. Sex. Reprod. Health Matters 2020, 28, 1822493. [Google Scholar] [CrossRef]
- Martínez-Vélez, N.A.; Tiburcio, M.; Rey, G.N.; Velázquez, J.A.V.; Arroyo-Belmonte, M.; Sánchez-Hernández, G.Y.; Fernández-Torres, M. Psychoactive Substance Use and Its Relationship to Stress, Emotional State, Depressive Symptomatology, and Perceived Threat During the COVID-19 Pandemic in Mexico. Front. Public Health 2021, 9, 709410. [Google Scholar] [CrossRef]
- Charmandari, E.; Tsigos, C.; Chrousos, G. Endocrinology of the stress response. Annu. Rev. Physiol. 2005, 67, 259–284. [Google Scholar] [CrossRef]
- McEwen, B.S. Physiology and Neurobiology of Stress and Adaptation: Central Role of the Brain. Physiol. Rev. 2007, 87, 873–904. [Google Scholar] [CrossRef]
- Folayan, M.O.; Ibigbami, O.; Brown, B.; El Tantawi, M.; Aly, N.M.; Zuñiga, R.A.A.; Abeldaño, G.F.; Ara, E.; Ellakany, P.; Gaffar, B.; et al. Fear of contagion, emotional stress and coping strategies used by adults during the first wave of the COVID-19 pandemic in Nigeria. BMC Psychiatry 2022, 22, 732. [Google Scholar] [CrossRef]
- Wang, Q.Q.; Kaelber, D.C.; Xu, R.; Volkow, N.D. COVID-19 risk and outcomes in patients with substance use disorders: Analyses from electronic health records in the United States. Mol. Psychiatry 2021, 26, 30–39. [Google Scholar] [CrossRef]
- Dubey, M.J.; Ghosh, R.; Chatterjee, S.; Biswas, P.; Chatterjee, S.; Dubey, S. COVID-19 and addiction. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 817–823. [Google Scholar] [CrossRef]
- Schulte, M.T.; Hser, Y.-I. Substance Use and Associated Health Conditions throughout the Lifespan. Public Health Rev. 2014, 35, 3. [Google Scholar] [CrossRef]
- Höflich, A.; Michenthaler, P.; Kasper, S.; Lanzenberger, R. Circuit mechanisms of reward, anhedonia, and depression. Int. J. Neuropsychopharmacol. 2019, 22, 105–118. [Google Scholar] [CrossRef]
- Linnemann, P.; Wellmann, J.; Berger, K.; Teismann, H. Effects of age on trait resilience in a population-based cohort and two patient cohorts. J. Psychosom. Res. 2020, 136, 110170. [Google Scholar] [CrossRef]
- Kocalevent, R.-D.; Zenger, M.; Heinen, I.; Dwinger, S.; Decker, O.; Brähler, E. Resilience in the General Population: Standardization of the Resilience Scale (RS-11). PLoS ONE 2015, 10, e0140322. [Google Scholar] [CrossRef]
- Weitzel, E.C.; Glaesmer, H.; Hinz, A.; Zeynalova, S.; Henger, S.; Engel, C.; Löffler, M.; Reyes, N.; Wirkner, K.; Witte, A.V.; et al. What Builds Resilience? Sociodemographic and Social Correlates in the Population-Based LIFE-Adult-Study. Int. J. Environ. Res. Public Health 2022, 19, 9601. [Google Scholar] [CrossRef]
- Sanchez-Gomez, M.; Giorgi, G.; Finstad, G.L.; Urbini, F.; Foti, G.; Mucci, N.; Zaffina, S.; León-Perez, J.M. COVID-19 Pandemic as a Traumatic Event and Its Associations with Fear and Mental Health: A Cognitive-Activation Approach. Int. J. Environ. Res. Public Health 2021, 18, 7422. [Google Scholar] [CrossRef]
- Riskind, J.H.; Alloy, L.B. Cognitive Vulnerability to Psychological Disorders: Overview of Theory, Design, And Methods. J. Soc. Clin. Psychol. 2006, 25, 705–725. [Google Scholar] [CrossRef]
- Young, N.A.; Minton, A.R.; Mikels, J.A. The appraisal approach to aging and emotion: An integrative theoretical framework. Dev. Rev. 2021, 59, 100947. [Google Scholar] [CrossRef]
- Perchtold, C.M.; Papousek, I.; Fink, A.; Weber, H.; Rominger, C.; Weiss, E.M. Gender Differences in Generating Cognitive Reappraisals for Threatening Situations: Reappraisal Capacity Shields Against Depressive Symptoms in Men, but Not Women. Front. Psychol. 2019, 10, 553. [Google Scholar] [CrossRef]
- Johnson, K.C.; Leblanc, A.J.; Deardorff, J.; Bockting, W.O. Invalidation Experiences Among Non-Binary Adolescents. J. Sex Res. 2019, 57, 222–233. [Google Scholar] [CrossRef]
- Alley, D.; Suthers, K.; Crimmins, E. Education and Cognitive Decline in Older Americans. Res. Aging 2007, 29, 73–94. [Google Scholar] [CrossRef]
- Madhavan, A.; Bajaj, G.; Bajaj, P.D.; D’Souza, D.F. Cognitive abilities among employed and unemployed middle-aged women—A systematic review. Clin. Epidemiol. Glob. Health 2022, 15, 101042. [Google Scholar] [CrossRef]
- Gotlib, I.H.; Joormann, J. Cognition and Depression: Current Status and Future Directions. Annu. Rev. Clin. Psychol. 2010, 6, 285–312. [Google Scholar] [CrossRef]
- McArthur, J.C.; Steiner, J.; Sacktor, N.; Nath, A. Human immunodeficiency virus-associated neurocognitive disorders: Mind the gap. Ann. Neurol. 2010, 67, 699–714. [Google Scholar]
- Okruszek, L.; Piejka, A.; Krawczyk, M.; Schudy, A.; Wiśniewska, M.; Żurek, K.; Pinkham, A. Owner of a lonely mind? Social cognitive capacity is associated with objective, but not perceived social isolation in healthy individuals. J. Res. Pers. 2021, 93, 104103. [Google Scholar] [CrossRef]
- Arranz, A.B.; Oliva, A.; de Miguel, M.S.; Olabarrieta, F.; Richards, M. Quality of family context and cognitive development: A cross sectional and longitudinal study. J. Fam. Stud. 2010, 16, 130–142. [Google Scholar] [CrossRef]
- Epting, S. Vulnerable groups, virtual cities, and social isolation. Technol. Soc. 2021, 67, 101711. [Google Scholar] [CrossRef]
- Health Care Resource Centre. How Drug Addiction Affects Relationships. Available online: https://www.hcrcenters.com/blog/how-drug-addiction-affects-relationships/ (accessed on 8 October 2019).
- El Tantawi, M.; Folayan, M.O.; Nguyen, A.L.; Aly, N.M.; Ezechi, O.; Uzochukwu, B.S.C.; Alaba, O.A.; Brown, B. Validation of a COVID-19 mental health and wellness survey questionnaire. BMC Public Health 2022, 22, 1509. [Google Scholar] [CrossRef] [PubMed]
- Rutherford, G.S.W.; Hair, J.F.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis with Readings. The Statistician 1988, 37, 484. [Google Scholar] [CrossRef]
- Everitt, B.S. Multivariate Analysis: The Need for Data, and other Problems. Br. J. Psychiatry 1975, 126, 237–240. [Google Scholar] [CrossRef]
- Van Selm, M.; Jankowski, N.W. Conducting online surveys. Qual. Quant. 2006, 4, 435–456. [Google Scholar] [CrossRef]
- King, D.B.; O’Rourke, N.; DeLongis, A. Social media recruitment and online data collection: A beginner’s guide and best practices for accessing low-prevalence and hard-to-reach populations. Can. Psychol. Can. 2014, 55, 240–249. [Google Scholar] [CrossRef]
- Ellakany, P.; Zuñiga, R.A.A.; El Tantawi, M.; Brown, B.; Aly, N.M.; Ezechi, O.; Uzochukwu, B.; Abeldaño, G.F.; Ara, E.; Ayanore, M.A.; et al. Impact of the COVID-19 pandemic on student’ sleep patterns, sexual activity, screen use, and food intake: A global survey. PLoS ONE 2022, 17, e0262617. [Google Scholar] [CrossRef] [PubMed]
- Folayan, M.O.; Ibigbami, O.; Brown, B.; El Tantawi, M.; Uzochukwu, B.; Ezechi, O.C.; Aly, N.M.; Abeldaño, G.F.; Ara, E.; Ayanore, M.A.; et al. Differences in COVID-19 Preventive Behavior and Food Insecurity by HIV Status in Nigeria. AIDS Behav. 2022, 26, 739–751. [Google Scholar] [CrossRef] [PubMed]
- Harkness, A. The Pandemic Stress Index; University of Miami: Miami, FL, USA, 2020. [Google Scholar]
- World Bank. World Bank Country and Lending Groups. 2020. Available online: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (accessed on 27 January 2023).
- Kohler, U. Survey research methods during the COVID-19 crisis. Surv. Res. Methods 2020, 14, 93–94. [Google Scholar]
- Cao, W.; Fang, Z.; Hou, G.; Han, M.; Xu, X.; Dong, J.; Zheng, J. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Res. 2020, 287, 112934. [Google Scholar] [CrossRef]
- Turon, H.; Carey, M.; Boyes, A.; Hobden, B.; Dilworth, S.; Sanson-Fisher, R. Agreement between a single-item measure of anxiety and depression and the Hospital Anxiety and Depression Scale: A cross-sectional study. PLoS ONE 2019, 14, e0210111. [Google Scholar] [CrossRef]
- Lloyd-Williams, M.; Dennis, M.; Taylor, F.; Baker, I. Is asking patients in palliative care, “Are you depressed?” appropriate? Prospective study. BMJ 2003, 327, 372–373. [Google Scholar] [CrossRef]
- Kawase, E.; Karasawa, K.; Shimotsu, S.; Imasato, S.; Ito, K.; Matsuki, H.; Sakano, Y.; Horikawa, N. Evaluation of a one-question interview for depression in a radiation oncology department in Japan. Gen. Hosp. Psychiatry 2006, 28, 321–322. [Google Scholar] [CrossRef] [PubMed]
- Chen, I.-H.; Pakpour, A.H.; Leung, H.; Potenza, M.N.; Su, J.-A.; Lin, C.-Y.; Griffiths, M.D. Comparing generalized and specific problematic smartphone/internet use: Longitudinal relationships between smartphone application-based addiction and social media addiction and psychological distress. J. Behav. Addict. 2020, 9, 410–419. [Google Scholar] [CrossRef]
- Yu, L.; Shek, D.T.L. Testing Longitudinal Relationships between Internet Addiction and Well-Being in Hong Kong Adolescents: Cross-Lagged Analyses Based on three Waves of Data. Child Indic. Res. 2018, 11, 1545–1562. [Google Scholar] [CrossRef]
- Chen, I.-H.; Chen, C.-Y.; Pakpour, A.H.; Griffiths, M.D.; Lin, C.-Y.; Li, X.-D.; Tsang, H.W.H. Problematic internet-related behaviors mediate the associations between levels of internet engagement and distress among schoolchildren during COVID-19 lockdown: A longitudinal structural equation modeling study. J. Behav. Addict. 2021, 10, 135–148. [Google Scholar] [CrossRef] [PubMed]
- Medina, N.; Alastruey-Izquierdo, A.; Bonilla, O.; Ortíz, B.; Gamboa, O.; Salazar, L.R.; Mercado, D.; Pérez, J.C.; Denning, D.W.; Arathoon, E.; et al. Impact of the COVID-19 pandemic on HIV care in Guatemala. Int. J. Infect. Dis. 2021, 108, 422–427. [Google Scholar] [CrossRef]
- HSRC. HSRC Responds to the COVID-19 Outbreak. 2020. Available online: http://www.hsrc.ac.za/en/news/media-and-covid19/hsrc-study-on-covid19 (accessed on 28 December 2022).
- Dema, E.; Sonnenberg, P.; Gibbs, J.; Conolly, A.; Willis, M.; Riddell, J.; Pérez, R.B.; Copas, A.J.; Tanton, C.; Bonell, C.; et al. How did the COVID-19 pandemic affect access to condoms, chlamydia and HIV testing, and cervical cancer screening at a population level in Britain? (Natsal-COVID). Sex. Transm. Infect. 2022. [Google Scholar] [CrossRef]
- Matheson, C.; Bon, L.; Bowman, L.; Hannah, A.; MacLeod, K. Vulnerability, Risk and Harm for People Who Use Drugs and Are Engaged in Transactional Sex: Learning for Service Delivery. Int. J. Environ. Res. Public Health 2022, 19, 1840. [Google Scholar] [CrossRef]
- Wamoyi, J.; Stobeanau, K.; Bobrova, N.; Abramsky, T.; Watts, C. Transactional sex and risk for HIV infection in sub-Saharan Africa: A systematic review and meta-analysis. J. Int. AIDS Soc. 2016, 19, 20992. [Google Scholar] [CrossRef]
- Roe, L.; Proudfoot, J.; Teck, J.T.W.; Irvine, R.D.G.; Frankland, S.; Baldacchino, A.M. Isolation, Solitude and Social Distancing for People Who Use Drugs: An Ethnographic Perspective. Front. Psychiatry 2021, 11, 623032. [Google Scholar] [CrossRef]
- Moxon, D.; Waters, J. Sourcing illegal drugs as a hidden older user: The ideal of ‘social supply’. Drugs Educ. Prev. Policy 2018, 26, 412–421. [Google Scholar] [CrossRef]
- Bardwell, G.; Kerr, T.; McNeil, R. The Opioid Overdose Epidemic and the Urgent Need for Effective Public Health Interventions That Address Men Who Use Drugs Alone. Am. J. Men’s Health 2019, 13. [Google Scholar] [CrossRef]
- Malins, P.; Fitzgerald, J.L.; Threadgold, T. Spatial ‘Folds’: The entwining of bodies, risks and city spaces for women injecting drug users in Melbourne’s Central Business District. Gend. Place Cult. 2006, 13, 509–527. [Google Scholar] [CrossRef]
- Rhodes, T. The ‘risk environment’: A framework for understanding and reducing drug-related harm. Int. J. Drug Policy 2002, 13, 85–94. [Google Scholar] [CrossRef]
- Gomez, R.; Thompson, S.J.; Barczyk, A.N. Factors Associated with Substance Use Among Homeless Young Adults. Subst. Abus. 2010, 31, 24–34. [Google Scholar] [CrossRef]
- Mc Carty, D.; Argeriou, M.; Huebner, R.B.; Lubran, B. Alcoholism, drug abuse, and the homeless. Am. Psychol. 1991, 46, 1139–1148. [Google Scholar] [CrossRef]
Variables | Total | Emotional Distress (n%) | AOR; 95% CI; p Value | |
---|---|---|---|---|
N = 426 | Yes | No | ||
n (%) | 231 (54.2) | 195 (45.8) | ||
Economic region | ||||
LIC | 5 (1.2) | 3 (60.0) | 2 (40.0) | 1.599; 0.235–10.896; p = 0.632 |
LMIC | 233 (54.7) | 125 (53.6) | 108 (46.4) | 0.834; 0.512–1.358; p = 465 |
UMIC | 47 (11.0) | 31 (66.0) | 16 (34.0) | 1.539; 0.730–3.243; p = 0.257 |
HIC | 141 (33.1) | 72 (51.1) | 69 (48.9) | 1.000 |
Age | 32.1 (9.5) | 31.7 (9.4) | 32.6 (9.6) | 1.000; 0.977–1.023; p = 0.983 |
Sex at birth | ||||
Male | 247 (58.0) | 127 (51.4) | 120 (48.6) | 1.000 |
Female | 179 (42.0) | 104 (58.1) | 75 (41.9) | 1.219; 0.799–1.861; p = 0.359 |
Level of education | ||||
No formal education | 15 (3.5) | 7 (46.7) | 8 (53.3) | 0.721; 0.234–2.222; p = 0.569 |
Primary | 24 (5.6) | 17 (70.8) | 7 (29.2) | 2.242; 0.845–5.951; p = 0.105 |
Secondary | 98 (23.0) | 56 (57.1) | 42 (42.9) | 0.447; 0.035–5.647; p = 0.533 |
Tertiary | 289 (67.8) | 151 (52.2) | 138 (47.8) | 1.000 |
Employment status | ||||
Retired | 3 (0.7) | 1 (33.3) | 2 (66.7) | 0.447; 0.035–5.647; p = 0.533 |
Student | 48 (11.3) | 36 (75.0) | 12 (25.0) | 2.800; 1.278–6.138; p = 0.010 |
Employed | 305 (71.6) | 151 (49.5) | 154 (50.5) | 1.000 |
Unemployed | 70 (16.4) | 43 (61.4) | 27 (38.6) | 1.356; 0.736–2.501; p = 0.329 |
Sexual identity | ||||
Heterosexuals | 303 (71.1) | 158 (52.1) | 145 (47.9) | 0.946; 0.594–1.506; p = 0.816 |
Sexual minority individuals | 123 (28.9) | 73 (59.3) | 50 (40.7) | 1.000 |
Living with HIV | ||||
Yes | 102 (23.9) | 71 (69.6) | 31 (30.4) | 2.582; 1.491–4.472; p = 0.001 |
No | 324 (76.1) | 160 (49.4) | 164 (50.6) | 1.000 |
Socially isolated | ||||
Yes | 294 (69.0) | 167 (56.8) | 127 (43.2) | 1.393; 0.901–2.153; p = 0.136 |
No | 132 (31.0) | 64 (48.5) | 68 (51.5) | 1.000 |
Quality of relationship with family | ||||
Same/Better | 272 (63.8) | 161 (59.2) | 111 (40.8) | 1.829; 1.186–2.821; p = 0.006 |
Worse | 154 (36.2) | 70 (45.5) | 84 (54.5) | 1.000 |
Variables | Total | Emotional Distress n (%) | AOR; 95% CI; p Value | |
N = 680 | Yes | No | ||
n (%) | 452 (66.5) | 228 (33.5) | ||
Economic region | ||||
LIC | 10 (1.5) | 3 (30.0) | 7 (70.0) | 0.243; 0.056–1.050; p = 0.058 |
LMIC | 348 (51.2) | 219 (62.9) | 129 (37.1) | 0.717; 0.477–1.077; p = 0.109 |
UMIC | 131 (19.3) | 100 (76.3) | 31 (23.7) | 1.184; 0.680–2.063; p = 0.550 |
HIC | 191 (28.1) | 130 (68.1) | 61 (31.9) | 1.000 |
Age | 35.7 (13.6) | 35.3 (14.3) | 36.6 (12.1) | 0.991; 0.976–1.006; p = 0.236 |
Sex at birth | ||||
Male | 331 (48.7) | 206 (62.2) | 125 (37.8) | 1.000 |
Female | 349 (51.3) | 246 (70.5) | 103 (29.5) | 1.085; 0.767–1.534; p = 0.648 |
Level of education | ||||
No formal education | 4 (0.6) | 2 (50.0) | 2 (50.0) | 0.590; 0.074–4.691; p = 0.618 |
Primary | 34 (5.0) | 20 (58.8) | 14 (41.2) | 0.671; 0.305–1.478; p = 0.322 |
Secondary | 144 (21.2) | 118 (81.9) | 26 (18.1) | 1.979; 1.190–3.289; p = 0.008 |
College/university | 498 (73.2) | 312 (62.7) | 186 (37.3) | 1.000 |
Employment status | ||||
Retired | 38 (5.6) | 31 (81.6) | 7 (18.4) | 2.772; 1.052–7.302; p = 0.039 |
Student | 107 (15.7) | 88 (82.2) | 19 (17.8) | 2.270; 1.223–4.212; p = 0.009 |
Employed | 455 (66.9) | 271 (59.6) | 184 (40.4) | 1.000 |
Unemployed | 80 (11.8) | 62 (77.5) | 18 (22.5) | 2.263; 1.258–4.071; p = 0.006 |
Sexual identity | ||||
Heterosexuals | 508 (74.7) | 330 (65.0) | 178 (35.0) | 0.816; 0.529–1.259; p = 0.359 |
Sexual minority individuals | 172 (25.3) | 122 (70.9) | 50 (29.1) | 1.000 |
Living with HIV | ||||
Yes | 62 (9.1) | 48 (77.4) | 14 (22.6) | 1.865; 0.937–3.713; p = 0.076 |
No | 618 (90.9) | 404 (65.4) | 214 (34.6) | 1.000 |
Socially isolated | ||||
Yes | 451 (66.3) | 324 (71.8) | 127 (28.2) | 2.069; 1.450–2.953; p < 0.001 |
No | 229 (33.7) | 128 (55.9) | 101 (44.1) | 1.000 |
Quality of relationship with family | ||||
Same/Better | 489 (71.9) | 333 (68.1) | 156 (31.9) | 1.423; 0.973–2.081; p = 0.069 |
Worse | 191 (28.1) | 119 (62.3) | 72 (37.7) | 1.000 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Folayan, M.O.; Zuñiga, R.A.A.; Ezechi, O.C.; Aly, N.M.; Lusher, J.; Nguyen, A.L.; El Tantawi, M. Sociodemographic Factors Associated with Emotional Distress, Transactional Sex and Psychoactive Substance Use during the First Wave of the COVID-19 Pandemic. BioMed 2023, 3, 113-123. https://doi.org/10.3390/biomed3010010
Folayan MO, Zuñiga RAA, Ezechi OC, Aly NM, Lusher J, Nguyen AL, El Tantawi M. Sociodemographic Factors Associated with Emotional Distress, Transactional Sex and Psychoactive Substance Use during the First Wave of the COVID-19 Pandemic. BioMed. 2023; 3(1):113-123. https://doi.org/10.3390/biomed3010010
Chicago/Turabian StyleFolayan, Morenike Oluwatoyin, Roberto Ariel Abeldaño Zuñiga, Oliver C. Ezechi, Nourhan M. Aly, Joanne Lusher, Annie L. Nguyen, and Maha El Tantawi. 2023. "Sociodemographic Factors Associated with Emotional Distress, Transactional Sex and Psychoactive Substance Use during the First Wave of the COVID-19 Pandemic" BioMed 3, no. 1: 113-123. https://doi.org/10.3390/biomed3010010