Sage Journals: Discover world-class research

Abstract

Adolescents and young women (AGYW) represent 15.1% of the global population and face a heightened risk of depression, especially in low and middle-income countries. We investigated the prevalence and determinants of Depression among AGYW at risk of HIV in urban slums of Kampala. A quantitative cross-sectional design was used to assess depression in 394 AGYW (14-24 years) using the Patient Health Questionnaire from January to May 2023. Prevalence was analyzed using proportions and 95% confidence intervals. Statistical tests, including chi-square, Fisher’s exact, ANOVA, and Mann-Whitney U, explored associations. Logistic regression models assessed risk, and the Hosmer-Lemeshow test evaluated the model fit. The prevalence of depression was 7.9% (95% CI = 5.6%-11.0%). Marital status (P = .024), having a high number of multiple sexual partners in the last 3 months (P = .029), and having a high number of multiple paying sexual partners in the last 3 months (P = .005) were significantly associated with depression. Thus, advocacy is crucial for improving depression screening and treatment for AGYW.

Keywords

depression adolescent girls young women HIV infection risk urban slums

Introduction

Globally, adolescent girls and young women (AGYW) aged 15 to 24 years account for 15.1% (594 million) of the world’s population^1,2 and they are at a greater risk for mental health conditions such as depression.³ Depression is closely related to suicide, which is the fourth most common cause of death among 15 to 29-year-olds.³ Depression is more common in women compared to men, with a likelihood of onset during puberty.^4
-6 Adolescence is a vital phase of development characterized by rapid physical, emotional, and social changes, and urban slum environments can exacerbate the challenges faced by young individuals, including mental health issues such as depression. According to recent data, 34% of adolescents worldwide reported having heightened self-reported depressive symptoms between 2001 and 2020.⁷ In the USA, the frequency of major depressive episodes among teenagers grew dramatically from 8.1% in 2009 to 15.8% in 2019, according to the National Survey on Drug Use and Health. In Africa, studies conducted among adolescents and youth have reported rates of depression ranging from 12% to 36%.^8
-10 Studies conducted in Uganda that have mainly focused on adolescents (10-19-year-olds) have reported rates of depression ranging from 18.2% to 34%.^11
-15 Furthermore, in Uganda, depression has been reported more among AGYW compared to their male counterparts.⁶ Several risk factors, such as hormonal fluctuations, societal expectations, academic pressures, interpersonal dynamics, experiencing bullying, having a family history of depression, education, and income, have been reported as predisposing to depression among AGYW.^16
-18 AGYW living in urban slums of Kampala, where sex work, substance use, and intimate partner violence are more common, may be more prone to depression than AGYW in different environments.

Kampala, the capital city of Uganda, is characterized by a diverse socio-economic landscape, with urban slums emerging as focal points. Yet, the inhabitants face challenges such as food security, money, theft, access to medication, energy access, and sanitation,¹⁹ which predispose them to unique health disparities. Research has consistently demonstrated the adverse effects of living in urban slums on mental health.²⁰ Overcrowded living conditions, limited access to basic amenities, and high levels of environmental stressors contribute to increased vulnerability to mental health disorders.²¹ AGYW residing in urban slums often confront a combination of socio-economic, environmental, and psychosocial stressors that may predispose them to mental health challenges, including depression.²² Several factors contribute to the vulnerability of AGYW in urban slums to depression, including limited access to quality education, economic opportunities, and healthcare services, societal expectations, gender-based violence, poor quality of home life, and the latent violence factor.^6,9,23,24

A direct correlation between AGYW with depression, developing new HIV infections, has been documented.²⁵ In the sub-Saharan Africa region, approximately 160 000 new HIV infections occurred among AGYW aged 15 to 24 in the year 2022, while in Uganda, 15 000 new HIV infections were recorded.²⁶ Interventions such as pre-exposure prophylaxis (PrEP) for HIV prevention are effective^27
-29 yet it is greatly affected by depression symptoms that cause low uptake and non-adherence.^30,31 Data on depression among AGYW living in urban slums and at risk of HIV infection are still limited. This study aimed to determine the prevalence and determinants of Depression among AGYW at risk of HIV in urban slums of Kampala. This will inform targeted interventions, policy recommendations, and community-based support systems designed to improve mental health outcomes and enhance the overall quality of life for this vulnerable population.

Methodology

Study Design and Setting

This was a cross-sectional study (January-May 2023) nested within a randomized controlled trial (RCT) that aimed to determine the effect of a peer support intervention on oral PrEP uptake and adherence. AGYW were included in the RCT if they were HIV and Hepatitis B negative, not pregnant, not taking oral PrEP, and were willing to attend study visits. The study site was the AIDS Information Center (AIC) - Kampala branch. The AIC, established in 1990, is a non-governmental organization providing HIV prevention, treatment, and care services to individuals and communities in Uganda living with and affected by HIV through 8 branches countrywide.

Study Population

AGYW (15-24 years) were mobilized from urban slums characterized by sex-work venues, alcohol, and drug consumption.

Inclusion and Exclusion Criteria

All AGYW enrolled in the RCT were included in this cross-sectional analysis.

Data Collection Procedures

Data collection was conducted by both the study staff and the participants themselves. The study staff collected data on socio-demographics, patient health questionnaire 9 (PHQ-9), and alcohol use. While the participants self-entered their data electronically in a REDCap database that had both the read and audio versions mounted on a tablet. This data included their sexual behavior, IPV, drug use, and contraceptive use. The use of the 2 data collection methods helped improve the privacy, understanding, and honesty, especially for sensitive topics such as sexual behavior or substance use. These tools were available in either English or Luganda, with the Luganda version offered in both read-only and audio formats to enable illiterate participants to self-administer the questionnaires. All study participants were trained on the use of tablets during their first visit before the commencement of data collection.

Dependent Variable

The main outcome was depression, which was assessed using an interviewer-administered patient health questionnaire (PHQ-9) on 394 AGYW (that was not on the tablet). The PHQ-9 has 9 items with a total score of 0 to 27 and has been validated for use in the Ugandan setting.³² A cutoff point of ≥10 was considered in this study^32,33 with those scoring below 10 being regarded as non-depressed, while those with a score of ≥10 were regarded as depressed. A total of 394 AGYW were assessed for depressive scores using the PHQ-9.

Independent Variables

These variables included; demographics (age, tribe, country of origin, highest level of education, marital status, and weekly income), vulnerability factors (nature of job, sexual activity, use of modern contraception, multiple sexual partners, sex in exchange for money, having unprotected sex, and use of drugs of addiction), and stress factors (number of children, physical abuse in the past 3 months, and psychological/emotional abuse in the past 3 months). The drugs of addiction were assessed by asking the participants if they had ever used any drugs of addiction in their lifetime and in the past 3 months. The drugs asked about included: marijuana, sniffing petrol/solvent, heroin, cocaine, khat, tobacco, and Kuber.

Statistical Analysis Methods

This analysis aimed to determine the prevalence of depression and identify its determinants among adolescent girls and young women (AGYW) at risk of HIV in urban slums of Kampala. To achieve this, data were analyzed using STATA version 18 at a 5% level of statistical significance. Depression, the primary outcome, was defined as a PHQ-9 score of 10 or higher.

Descriptive statistics were used to summarize participants’ socio-demographic, vulnerability, and stress-related characteristics. Categorical variables were presented as frequencies and percentages, while continuous variables were summarized using means with standard deviations or medians with interquartile ranges, depending on their distribution. The prevalence of depression was estimated with corresponding 95% confidence intervals.

To assess associations between depression and explanatory variables, bivariate analyses were performed using chi-square or Fisher’s exact tests for categorical variables, and ANOVA or Mann-Whitney U tests for continuous variables as appropriate. Variables that were statistically significant at this stage were considered for multivariable analysis.

A multivariable logistic regression model was then fitted to identify independent factors associated with depression, adjusting for age and other relevant covariates identified a priori. Adjusted odds ratios (aOR) with 95% confidence intervals were reported. Model adequacy was assessed using the Hosmer–Lemeshow goodness-of-fit test, and potential interactions between key predictors were explored to better understand their combined effect.

Conceptual Framework

Figure 1 above shows how the socio-demographic, vulnerability and stress factors lead to the occurrence of depression in an individual.

Figure 1.

Conceptual framework of risk factors for depression.

Ethical Considerations

We obtained ethical clearance from the Uganda Virus Research Institute ethics committee (ref: GC/127/918), the Uganda National Council for Science and Technology (ref: HS2490ES).

Consenting Procedures

All study participants provided written informed consent before engaging in study procedures. This included participants aged 15 to 17 years who are regarded as emancipated/mature minors in this study, as per the prevailing national guidelines, and were allowed to provide their written informed consent independently.³⁴

Results

Socio-demographic factors: a total of 394 study participants were interviewed in this study, with a mean age of 19.4 years (SD ± 2.3). Almost two-thirds of the participants, 258 (65.5%), were Baganda by tribe, 388 (98.5%) were Ugandans, 310 (78.7%) had completed a primary level of education, 57 (14.5%) were separated/widowed, and the majority 254 (64.5%) earned an income between 2.6 and 13 US dollars (Table 1).

Table 1.

Socio-Demographic and Vulnerability Factors.

Socio-demographic factors
Factor	Level	Frequency (%) n = 394
Age	Mean (SD)	19.4 (2.3)
Ethnicity	Muganda	258 (65.5)
Ethnicity	Other	136 (34.5%)
Country of origin	Uganda	388 (98.5%)
Country of origin	Other	6 (1.5%)
Education level	Never been to school	18 (4.6%)
	Primary	310 (78.7%)
	Secondary	62 (15.7%)
	University/tertiary	4 (1.0%)
Marital status	Married	8 (2.0%)
	Separated/widowed	57 (14.5%)
	Single	329 (83.5%)
Income	None	31 (7.9%)
	Between 10 000 and 50 000 (2.6-13 USD)	254 (64.5%)
	Above 50 000 (13 USD)	109 (27.7)
Vulnerability factors
Factor	Level	Frequency (%)
What is your main job?	No Job	22 (5.6%)
	Sex work	311 (78.9%)
	Other	61 (15.5%)
Are you currently using any methods to delay or avoid getting pregnant?	Yes	340 (86.3%
	No	54 (13.7%)
What main methods are you currently using to avoid or delay pregnancy	Oral pills	39 (11.5%)
	Injectable	82 (24.1%)
	IUCD	5 (1.5%)
	Implant	31 (9.1%)
	Condoms	139 (40.9%)
	Other	44 (12.9%)
In the last 3 months, with how many different men did you have sexual intercourse?	0-9	203 (52.1%)
	10-49	162 (41.5%)
	50 & above	25 (6.4%)
During the last 3 months, with how many different men did you have sex? (paying)	0-9	230 (59.4%)
	10-49	133 (34.4%)
	50 & above	24 (6.2%)
During the last 3 months, how often did you use a condom with men?	Never used condoms	27 (7.0%)
	Sometimes/most	201 (51.9%)
	Always use condoms	159 (41.1%)
During the last 3 months, how often did you use a condom with paying men?	Never used condoms	21 (5.7%)
	Sometimes/most	188 (50.7%)
	Always use condoms	162 (43.7%)
Have you ever used any drugs of addiction?	Yes	140 (35.5%)
Have you ever used any drugs of addiction?	No	254 (64.5%)
In the past 3 months, did you use any drugs of addiction?	Yes	121 (86.4%)
In the past 3 months, did you use any drugs of addiction?	No	19 (13.6%)
In the past 3 months, how frequent did you use drugs of addiction?	Daily	46 (38.0%)
	At least once a week	58 (47.9%)
	At least once/less a month	17 (14.1%)
In the last 3 months, did you use drugs of addiction daily for a period of at least 2 consecutive weeks?	Yes	37 (80.4%)
	No	9 (19.6%)
Vulnerability factors
Factor	Level	Frequency (%)
When did you usually/mostly use drugs of addiction?	During work	50 (41.3%)
	Out of work	40 (33.1%)
	During and out of work (anytime)	31 (25.6%)
What type of drugs of addiction did you use most of the time?	Marijuana	33 (27.3%)
	Sniffing petrol/solvent	6 (5.0%)
	Heroine	4 (3.3%)
	Cocaine	10 (8.3%)
	Khat	24 (19.8%)
	Tobacco	31 (25.6%)
	Kuber	13 (10.7%)
What is the main reason you use drugs of addiction?	To get courage to do my job	57 (47.1%)
	My friends use them	21 (17.4%)
	I feel good when I take drugs	6 (5.0%)
	To forget my problems/relieve stress	34 (28.1%)
	Other reasons	3 (2.5%)
Do you sometimes inject yourself with drugs of addiction?	Yes	12 (10.3%)
Do you sometimes inject yourself with drugs of addiction?	No	105 (89.7%)

For vulnerability factors, 311 (78.9%) were sex workers, 340 (86.3%) were currently using a contraceptive method, with the main method used being condoms (139 (40.9%)). Two hundred and three (52.1%) of the participants had a low number of multiple sexual partners within the last 3 months, while 230 (59.4%) had a low number of multiple sexual partners (paying) within the last 3 months. Only 159 (41.1%) of the participants used condoms consistently, while the rest 228 (58.9%) were inconsistent condom users. For those who had paying sexual partners, only 162 (43.7%) had consistent condom use. More than a third, 140 (35.5%), reported having ever used any drugs of addiction, and of these, 121 (86.4%) had used them in the past 3 months, with 46 (38.0%) reporting daily usage. Most of the participants, 50 (41.3%), used drugs during work, and the most common drug of use being was marijuana, 33 (27.3%), followed by tobacco, 31 (25.6%). The main reason for drug use was to get the courage to do their job, 57 (47.1%), followed by forgetting their problems/relieving stress, 34 (28.1%), and only 12 (10.3%) reported sometimes injecting themselves with drugs of addiction (Table 1).

Among the stress factors, 135 (34.3%) of the participants had 1 or more child/children, 128 (84.2%) had experienced physical violence, and 143 (84.6%) had experienced psychological/emotional violence.

Prevalence and the Determinants of Depression Among the Participants

The prevalence of depression was 7.9% (95% CI = 5.6%-11.0%) among the participants.

In an unadjusted analysis, marital status, having multiple men in the last 3 months, having multiple paying sex partners in the last 3 months, and having experienced physical violence were significantly associated with depression among AGYW (Table 2). After fitting in a multivariate logistic regression model; marital status (aOR = 2.62, 95% CI: 1.14-6.02), having a high number of multiple sexual partners in the last 3 months (aOR = 2.37, 95% CI: 1.09-5.13), and having a high number of multiple paying sexual partners in the last 3 months (aOR = 3.11, 95% CI: 1.42-6.81) were the only factors that had a significant association with depression among AGYW (Table 2).

Table 2.

Determinants of Depression Among AGYW At Risk of HIV Infection In Kampala, Uganda (January To May 2023).

Factor	Level	Crude estimates		Adjusted model
Factor	Level	Odds ratio (95% CI)	p-value	Odds ratio (95% CI)	p-value
Ethnicity	Muganda	Reference	.783		.637
Ethnicity	Other	0.90 (0.41; 1.96)	.783	0.83 (0.37; 1.83)	.637
Country of origin	Uganda	Reference	.434		.455
Country of origin	Other	2.39 (0.27; 21.09)	.434	2.32 (0.26; 21.06)	.455
Education level	Never been to school	-	.284	-	.307
	Primary	1.95 (0.57; 6.64)		1.90 (0.56; 6.49)
	Secondary	-		-
	University/tertiary	-		-
Marital status	Married	-	.024	-	.024
	Separated/widowed	2.62 (1.14; 6.02)		2.62 (1.14; 6.02)
	Single	-		-
Income	None	Reference	.075		.094
	<10 000-50 000 (2.6-13 USD)	2.02 (0.26; 15.76)		1.75 (0.22; 13.77)
	>50 000 (13 USD)	4.42 (0.56; 35.03)		3.79 (0.47; 30.40)
What is your main job?	No job	Reference	.166		.213
	Sex work	2.16 (0.28; 16.64)		1.74 (0.22; 13.57)
	Other	0.35 (0.02; 5.85)		0.30 (0.02; 5.12)
Are you currently using any methods to delay or avoid getting pregnant?	Yes	Reference	.141		.138
	No	1.96 (0.80; 4.80)	.141	1.99 (0.80; 4.91)	.138
In the last 3 months, with how many different men did you have sexual intercourse?	0-9	Reference	.022		.029
	10-49	2.46 (1.14; 5.29)		2.37 (1.09; 5.13)
	50 & above	-		-
During the last 3 months, with how many different men did you have sex? (paying)	0-9	Reference	.003		.005
	10-49	3.32 (1.53; 7.21)		3.11 (1.42; 6.81)
	50 & above	-		-
During the last 3 months, how often did you use a condom with men?	Never	Reference	.694		.659
	Sometimes/most	2.40 (0.31; 18.84)		2.55 (0.32; 20.30)
	Always	2.12 (0.26; 17.03)		2.20 (0.27; 17.92)
During the last 3 months, how often did you use a condom with paying men?	Never	-	.141	-	.101
	Sometimes/most	1.81 (0.82; 3.99)		1.95 (0.88; 4.34)
	Always	-		-
Have you ever used any drugs of addiction?	Yes	Reference	.439		.455
Have you ever used any drugs of addiction?	No	0.75 (0.35; 1.57)	.439	0.75 (0.35; 1.59)	.455
In the past 3 months, did you use any drugs of addiction?	Yes	Reference	.302		.223
In the past 3 months, did you use any drugs of addiction?	No	2.08 (0.52; 8.38)	.302	2.45 (0.58; 10.36)	.223
How many children do you have?	None	Reference	.350		.859
How many children do you have?	One/more	1.43 (0.68; 3.00)	.350	1.08 (0.47; 2.47)	.859
Physical violence	Yes	Reference	.016		.061
Physical violence	No	3.64 (1.27; 10.41)	.016	2.84 (0.95; 8.49)	.061
Psychological/emotional violence	Yes	Reference	.482		.500
Psychological/emotional violence	No	0.58 (0.13; 2.66)	.482	0.59 (0.13; 2.74)	.500

Bold indicates the significant results.

Discussion of Results

This study aimed to determine the prevalence and determinants of Depression among AGYW at risk of HIV in urban slums of Kampala. We found a low prevalence of depression of 7.9%. This is much lower than earlier studies conducted in Uganda, which showed prevalence rates ranging from 18.2% to 34%.^11,13,15 This difference may be due to the following reasons: one of these studies was conducted at a primary healthcare facility that lacked provision of mental health services¹⁵ while the other study was conducted among participants with a confirmed diagnosis of cancer, who were on treatment. Dealing with a diagnosis of an incurable illness and the side effects of treatment are stressful situations, putting them at high risk for depression.¹¹ The other study was conducted in Northern Uganda, where the participants were living in a humanitarian and fragile setting that had been affected by civil war for close to 2 decades.¹⁴ The prevalence from the current study is similar to a study by Kinyanda et al³⁵ where 7.6% of the participants had a major depressive disorder in 4 districts of north-eastern Uganda. This similarity may be because both studies did not focus on people seeking treatment at the health facility, as the latter study was conducted at a community level, while the current study recruited its participants from a community, despite being facility-based. Therefore, both studies give a true reflection of the burden of depression at a community level, which calls for community-based interventions while combating depression among AGYW.

The only sociodemographic factor linked to depression in this study was marital status, with the separated/widowed AGYW being at risk of developing depression 2.6 times compared to their married counterparts. This result is consistent with research conducted in Northern Uganda by Kabunga and Nambozo,¹⁴ which noted marriage as a protective factor against depression. Similarly, other researchers have noted that marriage protects against mental health problems^35
-38 for reasons that marriage protects against stress, a key factor in causing depression.³⁹

As regards vulnerability factors, having had sexual intercourse with a high number of multiple partners in the last 3 months was significantly associated with depression. This is in agreement with findings from a cross-sectional population-based study conducted in Uganda among participants aged 18 to 30 years, in which female participants with a greater number of lifetime sexual partners were at high risk for depression.⁴⁰ Researchers in other areas have documented similar associations,^41
-43 yet such a positive association wanes with age and only remains significant in young adulthood.⁴⁴ Also, more than half of the participants (58.9%) inconsistently used condoms, putting them at high risk for unintended pregnancies and sexually transmitted infections, including HIV infection, which predisposes them to mental health problems such as depression. On the contrary, Ramrakha et al⁴⁵ in their study noted that having multiple sexual partners was not linked to a higher incidence of depression.

Having had sex with a high number of multiple paying partners in the last 3 months was also significantly associated with depression. This may be because sex work is illegal in most parts of SSA, including Uganda,⁴⁶ which leads to broad criminalization by society as well as inadequate access to healthcare and support for this group⁴⁷ hence putting them at high risk for mental health problems, including depression.

Lastly, although not focused on in this study, a bidirectional relationship between depression and risky sexual behaviors has been documented.²⁵ With suggestions that it may predispose AGYW to risky sexual practices through mechanisms such as low self-esteem, impaired decision-making, and the search for emotional validation. This therefore calls for interventions that focus on breaking the intertwined relationship to reduce vulnerability to both depression and HIV risk among adolescent girls and young women.

Conclusion

Our study found a low prevalence of depression, with factors such as marital status, having had sexual intercourse with a high number of multiple partners in the last 3 months, and having had sexual intercourse with a high number of multiple paying partners in the last 3 months being significantly associated with depression. Therefore, there is a need for continued advocacy for mental health service provision among AGYW to increase the screening, early identification, and treatment in this population.

Limitations of the Study

This being a cross-sectional study, the causal direction of factors associated with depression could not be determined.

The sample size was calculated using the main RCT outcome (PrEP uptake), hence was likely underpowered for assessing factors associated with depression. The study, however, contributes information on an underlying prevalence of depression among AGYW in urban slums who would not be identified in the routine HIV prevention programs.

Supplemental Material

sj-doc-1-inq-10.1177_00469580251412738 – Supplemental material for Prevalence and Determinants of Depression Among Adolescent Girls and Young Women at Risk of HIV in Urban Slums of Kampala

Supplemental material, sj-doc-1-inq-10.1177_00469580251412738 for Prevalence and Determinants of Depression Among Adolescent Girls and Young Women at Risk of HIV in Urban Slums of Kampala by Philip Amanyire, Eugene Kinyanda, Leticia Kyohangirwe, Richard Stephen Mpango, Wilber Ssembajjwe and Yunia Mayanja in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Supplemental Material

sj-docx-2-inq-10.1177_00469580251412738 – Supplemental material for Prevalence and Determinants of Depression Among Adolescent Girls and Young Women at Risk of HIV in Urban Slums of Kampala

Supplemental material, sj-docx-2-inq-10.1177_00469580251412738 for Prevalence and Determinants of Depression Among Adolescent Girls and Young Women at Risk of HIV in Urban Slums of Kampala by Philip Amanyire, Eugene Kinyanda, Leticia Kyohangirwe, Richard Stephen Mpango, Wilber Ssembajjwe and Yunia Mayanja in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

We acknowledge the contribution of the AGYW from the urban slums of Kampala who constituted our study sample. We also recognize the efforts of the research assistants and the contributions of the staff of the AIDS Information Center (AIC) - Kampala branch.

ORCID iDs

Philip Amanyire

Eugene Kinyanda

Leticia Kyohangirwe

Richard Stephen Mpango

Wilber Ssembajjwe

Yunia Mayanja

Consent to Participate

Authors Contributions

PA, EK, LK, RSM, WS, and YM made significant contributions to the conception, design, acquisition of data, drafting of the manuscript, critical review, and coming up with the final version for publication. WS helped with the data analysis.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was part of the second European & Developing Countries Clinical Trials Partnership (EDCTP2) program supported by the European Union (CSA2020NoE-3102). The contents of this manuscript are the responsibility of the authors and do not necessarily state or reflect those of EDCTP.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Due to confidentiality requirements, the datasets created and analyzed for this work are not publicly available; however, upon reasonable request, the corresponding author can provide them in an aggregated and de-identified format.

Supplemental Material

Supplemental material for this article is available online.

References

World-Bank. Source: population (number). Data retrieved from World Bank Gender Data Portal. 2022. Accessed March 17, 2024. https://genderdata.worldbank.org/en/indicator/sp-pop?age=15-19

World-Bank. Source: population (number). Data retrieved from World Bank Gender Data Portal. 2022. Accessed March 17, 2024. https://genderdata.worldbank.org/en/indicator/sp-pop?age=20-24

WHO. World Health Organization. Adolescent mental health—key facts. 2023. Accessed February 1, 2024. https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health

American Psychiatric Association. DSM-5-TR. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision. 5th ed. American Psychiatric Association Publishing; 2022.

Mridha

Hossain

Khan

MSA

, et al. Prevalence and associated factors of depression among adolescent boys and girls in Bangladesh: findings from a nationwide survey. BMJ Open. 2021;11(1):e038954.

Logie

Okumu

Mwima

Hakiza

Chemutai

Kyambadde

Contextual factors associated with depression among urban refugee and displaced youth in Kampala, Uganda: findings from a cross-sectional study. Confl Health. 2020;14:45.

Shorey

Wong

CHJ

. Global prevalence of depression and elevated depressive symptoms among adolescents: a systematic review and meta-analysis. Br J Clin Psychol. 2022;61(2):287-305.

Larsen

Kinuthia

Lagat

, et al. Depression and HIV risk behaviors among adolescent girls and young women seeking family planning services in Western Kenya. Int J STD AIDS. 2020;31(7):652-664.

Osok

Kigamwa

Stoep

Huang

Kumar

Depression and its psychosocial risk factors in pregnant Kenyan adolescents: a cross-sectional study in a community health centre of Nairobi. BMC Psychiatry. 2018;18(1):10.

10.

Kuringe

Materu

Nyato

, et al. Prevalence and correlates of depression and anxiety symptoms among out-of-school adolescent girls and young women in Tanzania: a cross-sectional study. PLoS One. 2019;14(8):e0221053.

11.

Akimana

Abbo

Balagadde-Kambugu

Nakimuli-Mpungu

Prevalence and factors associated with major depressive disorder in children and adolescents at the Uganda cancer institute. BMC Cancer. 2019;19:466.

12.

Nalugya-Sserunjogi

Rukundo

Ovuga

Kiwuwa

Musisi

Nakimuli-Mpungu

Prevalence and factors associated with depression symptoms among school-going adolescents in Central Uganda. Child Adolesc Psychiatry Ment Health. 2016;10:39-48.

13.

Nabunya

Damulira

Byansi

, et al. Prevalence and correlates of depressive symptoms among high school adolescent girls in Southern Uganda. BMC Public Health. 2020;20:1792.

14.

Kabunga

Nambozo

Prevalence and factors associated with depression among out-of-school adolescents in Ayere village, Northern Uganda. 2021.

15.

Kyohangirwe

Okello

Namuli

Ndeezi

Kinyanda

Prevalence and factors associated with major depressive disorder among adolescents attending a primary care facility in Kampala, Uganda. Trop Doct. 2020;50(1):30-36. doi:10.1177/0049475519879586

16.

Rudolph

. Puberty as a developmental context of risk for psychopathology. In: Handbook of developmental psychopathology. Springer US; 2014:331-354

17.

Wahid

Ottman

Hudhud

, et al. Identifying risk factors and detection strategies for adolescent depression in diverse global settings: a Delphi consensus study. J Affect Disord. 2021;279:66-74.

18.

Rai

Zitko

Jones

Lynch

Araya

Country- and individual-level socioeconomic determinants of depression: multilevel cross-national comparison. Br J Psychiatry. 2013;202(3):195-203.

19.

Van Leeuwen

Sekeramayi

Martell

Feinberg

Bowersox-Daly

. A baseline analysis of the Katanga slums: informing urban public policy in Kampala, Uganda. Afr Popul Stud. 2017;31(2):3845-3854.

20.

Patel

Kleinman

. Poverty and common mental disorders in developing countries. Bull World Health Organ. 2003;81:609-615.

21.

Lund

Breen

Flisher

, et al. Poverty and common mental disorders in low and middle income countries: a systematic review. Soc Sci Med. 2010;71(3):517-528.

22.

Patel

Flisher

Hetrick

McGorry

Mental health of young people: a global public-health challenge. Lancet. 2007;369(9569):1302-1313.

23.

Henry

Bakeera-Kitaka

Lubega

Snyder

LaRussa

Pfeffer

Depressive symptoms, sexual activity, and substance use among adolescents in Kampala, Uganda. Afr Health Sci. 2019;19(2):1888-1896.

24.

Somrongthong

Wongchalee

Laosee

Depression among adolescents: a study in a Bangkok slum community. Scand J Caring Sci. 2013;27(2):327-334.

25.

Goin

Pearson

Craske

, et al. Depression and incident HIV in adolescent girls and young women in HIV prevention trials network 068: targets for prevention and mediating factors. Am J Epidemiol. 2020;189(5):422-432.

26.

HIV/AIDS JUNP on. The Path That Ends AIDS: UNAIDS Global AIDS Update. UNAIDS; 2023. Published online 2023.

27.

O Murchu

Marshall

Teljeur

, et al. Oral pre-exposure prophylaxis (PrEP) to prevent HIV: a systematic review and meta-analysis of clinical effectiveness, safety, adherence and risk compensation in all populations. BMJ Open. 2022;12(5):e048478.

28.

Donnell

Baeten

Bumpus

, et al. HIV protective efficacy and correlates of tenofovir blood concentrations in a clinical trial of PrEP for HIV prevention. JAIDS J Acquir Immune Defic Syndr. 2014;66(3):340-348.

29.

Spinner

Boesecke

Zink

, et al. HIV pre-exposure prophylaxis (PrEP): a review of current knowledge of oral systemic HIV PrEP in humans. Infection. 2016;44:151-158.

30.

Velloza

Hosek

Donnell

, et al. Assessing longitudinal patterns of depressive symptoms and the influence of symptom trajectories on HIV pre-exposure prophylaxis adherence among adolescent girls in the HPTN 082 randomized controlled trial. J Int AIDS Soc. 2021;24 Suppl 2:e25731.

31.

Velloza

Baeten

Haberer

, et al. Effect of depression on adherence to oral PrEP among men and women in East Africa. JAIDS J Acquir Immune Defic Syndr. 2018;79(3):330-338.

32.

Kaggwa

Najjuka

Ashaba

Mamun

MA.

Psychometrics of the Patient Health Questionnaire (PHQ-9) in Uganda: a systematic review. Front Psychiatry. 2022;13:781095.

33.

Kroenke

Spitzer

Williams

JBW

Löwe

The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. Gen Hosp Psychiatry. 2010;32(4):345-359.

34.

Uganda, Technology UNC for S&. National Guidelines for Research Involving Humans as Research Participants. Uganda: National Council for Science and Technology Kampala-Uganda; 2014.

35.

Kinyanda

Kizza

Abbo

Ndyanabangi

Levin

Prevalence and risk factors of depression in childhood and adolescence as seen in 4 districts of North-Eastern Uganda. BMC Int Health Hum Rights. 2013;13:19.

36.

Jace

Makridis

CA.

Does marriage protect mental health? evidence from the COVID-19 pandemic. Soc Sci Q. 2021;102(6):2499-2515.

37.

Scott

Wells

Angermeyer

, et al. Gender and the relationship between marital status and first onset of mood, anxiety and substance use disorders. Psychol Med. 2010;40(9):1495-1505.

38.

Grundström

Konttinen

Berg

Kiviruusu

Associations between relationship status and mental well-being in different life phases from young to middle adulthood. SSM Popul Health. 2021;14:100774.

39.

Kessler

Essex

Marital status and depression: the importance of coping resources. Soc Forces. 1982;61(2):484-507.

40.

Lundberg

Rukundo

Ashaba

, et al. Poor mental health and sexual risk behaviours in Uganda: a cross-sectional population-based study. BMC Public Health. 2011;11(1):10.

41.

Karle

Agardh

Larsson

Arunda

MO.

Risky sexual behavior and self-rated mental health among young adults in Skåne, Sweden - a cross-sectional study. BMC Public Health. 2023;23(1):9. doi:10.1186/s12889-022-14823-0

42.

Sun

Liao

, et al. Identifying causal associations between early sexual intercourse or number of sexual partners and major depressive disorders: a bidirectional two-sample Mendelian randomization analysis. J Affect Disord. 2023;333:121-129. doi:10.1016/j.jad.2023.04.079

43.

Kosunen

Kaltiala-Heino

Rimpelä

Laippala

Risk-taking sexual behaviour and self-reported depression in middle adolescence - a school-based survey. Child Care Health Dev. 2003;29(5):337-344. doi:10.1046/j.1365-2214.2003.00357.x

44.

Vasilenko

Lanza

ST.

Predictors of multiple sexual partners from adolescence through young adulthood. J Adolesc Health. 2014;55(4):491-497. doi:10.1016/j.jadohealth.2013.12.025

45.

Ramrakha

Paul

Bell

Dickson

Moffitt

Caspi

The relationship between multiple sex partners and anxiety, depression, and substance dependence disorders: a cohort study. Arch Sex Behav. 2013;42(5):863-872. doi:10.1007/s10508-012-0053-1

46.

Scorgie

Chersich

Ntaganira

Gerbase

Lule

YR.

Socio-demographic characteristics and behavioral risk factors of female sex workers in sub-Saharan Africa: a systematic review. AIDS Behav. 2012;16(4):920-933. doi:10.1007/s10461-011-9985-z

47.

Panchanadeswaran

Rahill

Joshi

, et al. Female sex workers’ experiences of violence and aggression from sexual partners. In: Martin

Preedy

Patel

, eds. Handbook of Anger, Aggression, and Violence. Springer International Publishing; 2022;1-19.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.04 MB

0.03 MB