Profiling the Plight of Adolescent Girls and Young Women: Risks and Pathways for Intervention in Rural Uganda

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Understanding their baseline risk profiles is essential for designing effective, targeted interventions. Methods This study utilized a retrospective, cross-sectional design to analyze secondary data from 124,258 AGYW across four districts in Uganda. K-means cluster analysis was employed to categorize participants into High-Risk and Low-Risk groups based on socio-economic, educational, and behavioral indicators. Bivariate analysis was used to validate the distinctiveness of these clusters, and logistic regression identified predictors of high-risk behaviors, including transactional sex, school dropout, and experiences of violence. Results The analysis revealed that 24.7% of AGYW were classified into the High-Risk group, with the majority (92%) being aged 20–25 years, compared to 9–14 years in the Low-Risk group. High-risk AGYW were significantly more likely to engage in transactional sex, drop out of school, and experience violence. Education emerged as a protective factor, with school enrollment increasing the likelihood of Low-Risk classification by 108 times (AOR = 108.154, p < 0.001). Economic desperation, particularly transactional sex, was a strong predictor of being classified as High-Risk (AOR = 1599.966, p < 0.001). Additionally, experiences of violence and substance use further compounded the vulnerabilities of this group. Conclusion Adolescent girls and young women in rural Uganda, particularly those aged 20–25, face significant vulnerabilities due to poverty, violence, and limited access to education. Integrated interventions focusing on education, economic empowerment, and violence prevention are critical for reducing these risks, fostering resilience, and promoting sustainable change for AGYW. Addressing these challenges through targeted strategies will improve their well-being and contribute to long-term development outcomes. Adolescent Girls and Young Women (AGYW) Risk Profiling Transactional Sex School Dropout Violence and Vulnerability 1.0 Introduction Adolescent girls and young women (AGYWs) in sub-Saharan Africa (SSA) face significant social, economic, and cultural vulnerabilities that place them at disproportionate risk for adverse outcomes, particularly HIV( 1 , 2 ). In SSA, AGYWs account for 87% of global HIV infections among women aged 15–24, a stark reminder of the systemic inequities fueling the epidemic in this region ( 3 , 4 ). In Uganda, AGYWs contribute to over two-thirds of new HIV infections, reflecting an urgent need to address the underlying drivers of vulnerability ( 5 ). Multiple factors contribute to the heightened HIV risk among AGYWs. Socio-economic deprivation, early marriage, and limited access to education compel many young women to engage in transactional sex, exposing them to older male partners who may wield greater power in relationships ( 6 ). These dynamics often restrict AGYWs’ ability to negotiate safe sexual practices, further exacerbating their susceptibility to HIV and other sexually transmitted infections (STIs). Additionally, pervasive gender inequality entrenched in patriarchal norms continues to marginalize AGYWs, leaving them with limited autonomy over their sexual and reproductive health ( 7 , 8 ). Socio-economic instability remains a key driver of AGYW vulnerabilities. Poverty often forces young women to adopt survival strategies such as transactional sex, which has been strongly associated with a higher risk of HIV acquisition ( 1 ). Studies show that AGYWs engaged in transactional sex frequently lack bargaining power, making them vulnerable to coercive relationships and unprotected sex ( 9 ). Compounding these risks is the limited availability of social safety nets, leaving AGYWs reliant on relationships with financially dominant partners to meet basic needs ( 10 , 11 ). Educational disengagement further exacerbates AGYWs’ vulnerabilities. Girls who drop out of school due to early pregnancies, caregiving responsibilities, or economic barriers face diminished opportunities for socio-economic advancement ( 12 , 13 ). Evidence indicates that out-of-school AGYWs are significantly more likely to engage in early sexual activity and risky behaviors compared to their peers who remain in school ( 6 ). Educational attainment has been consistently linked to delayed sexual debut and a lower likelihood of early marriage, demonstrating its critical role as a protective factor against HIV ( 14 ). Gender-based violence (GBV) is another pervasive issue that disproportionately affects AGYWs. Reports indicate that over 40% of AGYWs in SSA experience some form of GBV, whether physical, emotional, or sexual ( 15 – 17 ). GBV not only impacts their mental health but also undermines their ability to seek help or assert control over their sexual health. AGYWs subjected to violence are at greater risk of engaging in risky sexual behaviors and acquiring HIV due to coercion and fear of retribution ( 9 , 18 ). Substance use among AGYWs also poses a significant risk. Alcohol and illicit drug use are often associated with risky behaviors, including unprotected sex and multiple sexual partners ( 7 ). These behaviors exacerbate AGYWs’ susceptibility to HIV and other adverse health outcomes. Substance use further intersects with other vulnerabilities, creating a feedback loop that perpetuates cycles of risk ( 19 ). To mitigate these intersecting vulnerabilities, the DREAMS (Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe) initiative was launched under the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) in 2014. DREAMS adopts a holistic approach to HIV prevention, addressing both structural and behavioral drivers of risk. It integrates interventions across socio-economic empowerment, educational support, and health services to create an enabling environment for AGYWs to thrive ( 20 ). A key feature of DREAMS is its risk stratification process, which uses screening tools to identify high-risk AGYWs. This data-driven approach ensures efficient resource allocation by tailoring interventions to participants' specific needs. The program also emphasizes the importance of community engagement, leveraging social support networks to foster resilience among AGYWs ( 1 ). DREAMS has shown promise in reducing HIV incidence among AGYWs in SSA. However, existing studies have primarily focused on program outcomes, with limited attention to the baseline vulnerabilities of participants at enrollment. Understanding these vulnerabilities is crucial for designing interventions that address root causes and ensure long-term sustainability. Despite significant investments in interventions like DREAMS, there remains a critical gap in understanding the granular context-specific socio-economic, educational, and behavioral factors that continue to drive risk among AGYWs. Existing literature predominantly evaluates intervention outcomes, often overlooking the contextual vulnerabilities that shape AGYWs’ risk profiles. This lack of comprehensive baseline risk-profile data hinders efforts to tailor interventions and address the structural drivers of risk effectively ( 21 ). This study leverages data collected during the DREAMS enrollment process to fill this gap. By analyzing data covering 124,258 AGYW, the study provides insights that can inform targeted interventions and broader policy frameworks. The overarching goal of this study was to establish a comprehensive baseline risk profile of AGYWs, focusing on their socio-economic, educational, and behavioral vulnerabilities. Specific objectives include: To examine the socio-economic, educational, and behavioral vulnerabilities of AGYWs, including household headship, employment status, school attendance, reasons for dropout, early pregnancy, transactional sex, and experiences of violence, and to analyze their associations with other risk factors. To identify and characterize distinct risk profiles of AGYWs using cluster analysis, emphasizing the intersections of socio-economic, educational, and behavioral vulnerabilities for targeted intervention planning. Our study shifts focus from intervention outcomes to baseline characteristics of adolescent girls and young women (AGYW) by development interventions like DREAMS (Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe). Profiling AGYW vulnerabilities enhances targeting mechanisms, ensuring resources reach the most at-risk individuals while informing program design with evidence-based risk profiles to address poverty, educational disengagement, and gender-based violence. We believe that establishing robust baselines provides a solid evaluation framework, enabling the assessment of program effectiveness. Additionally, the findings offer policy implications with actionable recommendations for addressing AGYW vulnerabilities in resource-constrained settings. We used secondary data from Uganda, which is of global relevance. 2.0 Methodology 2.1 Study Design This study employed a retrospective, cross-sectional design to analyze secondary data collected during the enrollment phase of the DREAMS (Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe) intervention. The DREAMS program sought to reduce HIV risk among adolescent girls and young women by addressing socio-economic, educational, and behavioral vulnerabilities. The primary objective of this study was to leverage data collected during the DREAMS enrollment process to establish baseline risk profiles of AGYWs targeted by the intervention. This approach provided critical insights into the characteristics and vulnerabilities of this high-risk population, enabling the identification of key areas requiring intervention. 2.2 Study Setting and Participants The DREAMS intervention targeted AGYW across four Districts of Mubende, Mityana, Kassanda, and Luwero, focusing on communities characterized by high rates of poverty, educational disengagement, and adverse health and social outcomes. Participants were recruited based on predefined high-risk criteria, which included being out of school, experiencing violence, engaging in transactional sex, or living in socio-economically vulnerable conditions. The region has an HIV prevalence of 6.2% which is above the 5.8% national average ( 20 ). The dataset analyzed in this study was drawn from a subset of AGYWs screened during the enrollment phase. Inclusion criteria required participants to meet the DREAMS program’s risk thresholds and have complete screening data. Records with missing or duplicated entries were excluded. For this study, a total of 124,258 AGYWs were used to provide a comprehensive baseline assessment of vulnerabilities. 2.3 Data Collection The data analyzed in this study were collected using a standardized screening tool developed specifically for the DREAMS enrollment process. This tool was implemented during one-on-one interviews conducted by trained social workers in private settings to ensure confidentiality. The tool captured a wide range of socio-economic, educational, and behavioral indicators, including the following domains: Socio-Economic Indicators Information was collected on household headship (e.g., whether participants lived in child-headed households), work status (whether participants were engaged in income-generating activities), and living conditions, which reflected the overall socio-economic stability of the household. Educational Indicators : Data were collected on school attendance status (in school or out of school), the number of school days missed during the last term, reasons for absenteeism or dropout (such as lack of school fees or family responsibilities), and interest in returning to school for those who were out of school. Sexual and Reproductive Health Indicators : Participants provided information on their age at first pregnancy, history of sexual activity (including consensual, coerced, or forced experiences), engagement in transactional sex (exchanging sex for items or favors), and the number of sexual partners in the past 12 months. Violence Indicators : Participants were asked about their experiences with physical violence, emotional/psychological abuse, and sexual violence. These responses helped identify AGYWs who had been subjected to various forms of harm. Substance Use Indicators : Data were gathered on alcohol consumption and illicit drug use, as well as any consequences of substance use, such as social or health problems. All data were entered into a centralized database, ensuring standardization and accuracy. The structured tool was designed to comprehensively capture the vulnerabilities of AGYWs at the time of their enrollment into the DREAMS program. 2.4 Data Analysis The dataset was cleaned, coded, and analyzed using SPSS Version 27 to explore the vulnerabilities of adolescent girls and young women. Descriptive statistics provided an overview of key variables, with frequencies and percentages summarizing categorical data such as school attendance and household headship, while means and standard deviations were used for continuous variables like age. To classify participants into distinct risk groups, K-means clustering analysis was employed. This method effectively partitioned participants into High Risk and Low-Risk clusters based on variables such as household headship, work status, school attendance, transactional sex, experiences of violence, and age at first pregnancy, offering a nuanced understanding of the multidimensional vulnerabilities within the population. To validate the clusters, bivariate analysis was conducted, examining associations between cluster membership and key outcomes such as transactional sex, multiple sexual partners, and experiences of violence. Chi-square tests assessed these relationships, while odds ratios quantified the association's strength, confirming the clusters' distinctiveness. Logistic regression models were subsequently used to identify predictors of high-risk behaviors, providing deeper insights into the socio-economic and behavioral factors shaping AGYW’s risk profiles. This combined approach ensured the robustness and relevance of the clustering results, offering a solid foundation for targeted interventions. 2.5 Ethical Considerations This study adhered to the ethical principles outlined in the Declaration of Helsinki and was conducted under the ethical approvals granted by the Mildmay Uganda Research and Ethics Committee (MUREC) (REC REF 0804–2018) and the Uganda National Council of Science and Technology (SS639ES). Permission to use the database for this secondary analysis was obtained through administrative clearance from Mildmay Uganda, ensuring compliance with institutional guidelines for data access and usage. A waiver of assent and parental consent for the use of secondary data was granted by the Mildmay Research Ethics Committee (REC) due to the retrospective nature of the study. This waiver was deemed appropriate as the study utilized pre-existing, anonymized data, with no direct interaction with participants or collection of new data. All data extracted from the database were fully anonymized before analysis to safeguard participants’ privacy. Identifying information was removed, ensuring confidentiality in both the analysis and reporting stages of the study. 3.0 Results 3.1 Background characteristics of study participants The study included a total of 124,259 respondents, with a mean age of 14.57 years (SD = 4.369). The age distribution was as follows: 63.9% (79,374) were aged 9–14 years, 22.7% (28,200) were aged 20–25 years, and 13.4% (16,685) were aged 15–19 years. This reflects a diverse age composition across the groups, providing a comprehensive representation of the target population. 3.2 Cluster Analysis and Socio-Economic Risk Profiling The cluster analysis of adolescent girls and young women (AGYW) identified two distinct groups based on their behavioral risk profiles as shown in Table 1 : a high-risk group (24.7%, 30,663 participants) and a low-risk group (75.3%, 93,595 participants). The moderate separation between these groups, with a Euclidean distance of 2.689, supports the validity of the two-cluster solution. The high-risk group exhibited significantly higher incidences of transactional sexual relationships, sexual violence, and substance use, with the engagement in transactional relationships showing a notably higher F-statistic (F = 85,902.655, p < 0.001), indicating that economic hardship plays a crucial role in driving these behaviors. These findings highlight a disproportionate vulnerability within the high-risk group, emphasizing the need for targeted interventions to address the socio-economic factors contributing to these risky behaviors and improve the well-being of AGYW, particularly those facing financial instability and other forms of adversity. Transactional Sexual Relationships and Socio-Economic Vulnerabilities The high-risk group demonstrated a significantly greater likelihood of engaging in transactional sexual relationships, as indicated by the variable "Have you stayed in a relationship expecting gifts, favors, or financial help?" (F = 85,902.655, p < 0.001). This suggests that economic hardships and limited access to resources are driving factors behind these relationships. Additionally, the high-risk group reported higher levels of school dropout or non-attendance ("Are you currently in school?" F = 105,585.313, p < 0.001), emphasizing the role of socio-economic challenges such as poverty and household instability. These factors push AGYW toward risky survival strategies that expose them to further vulnerabilities, including sexual exploitation. Prevalence of Violence and Emotional Abuse Violence, both physical and sexual, was significantly more prevalent in the high-risk group. Participants were more likely to report experiencing sexual violence ("Have you experienced sexual violence?" F = 10,598.956, p < 0.001) and physical violence ("Have you experienced physical violence?" F = 2,875.226, p < 0.001). Emotional abuse and neglect were also significant issues, with "Have you experienced continuous ridicule, insults, or emotional neglect?" (F = 8,098.577, p < 0.001) showing marked differences between the groups. This compounded burden of physical and psychological abuse increases susceptibility to mental health issues such as depression, anxiety, and PTSD, impeding social and educational development. Substance Use as a Coping Mechanism Substance use was significantly more prevalent among the high-risk group. Variables such as "Have you ever consumed alcohol or used illicit drugs?" (F = 9,463.589, p < 0.001) and "Have you had more than 3 drinks at one time or used illicit drugs frequently?" (F = 1,367.420, p < 0.001) distinguish this group from the low-risk group. These behaviors may serve as coping mechanisms for socio-economic and emotional hardships. Substance use impairs decision-making and increases the likelihood of engaging in unsafe sexual behaviors, exacerbating vulnerability to exploitation, health risks like HIV, and unwanted pregnancies. In contrast, the low-risk group demonstrated minimal substance use, highlighting behavioral disparities. Household Headship and Family Dynamics Household headship emerged as a significant factor influencing the high-risk group. The household headship variable (F = 8.520, p = 0.004) revealed that AGYW in the high-risk group were more likely to live in households where the head was absent or under the age of 18. This points to a lack of stable family structures and adult supervision, contributing to socio-economic and behavioral challenges. The absence of a strong parental figure or household head can lead to greater exposure to risky environments, economic hardship, limited access to education, and increased susceptibility to exploitation, compounding the vulnerabilities faced by AGYW in the high-risk group. Table 1 ANOVA Results for Behavioral Risk Profiles of Adolescent Girls and Young Women (AGYW) in Rural Uganda Rik Question F P-value Stayed in a relationship for gifts or favors? 85,902.655 .000 Experienced forced or coerced sexual activity? 554,068.366 .000 Alcohol or drugs caused problems in the last three months? 6,492.030 .000 Currently sexually active? 500,465.289 .000 Engaged in transactional sex? 73,207.282 .000 Currently working? 10,313.093 .000 Experienced sexual violence? 10,598.956 .000 Experienced physical violence? 2,875.226 .000 Threatened with a knife, gun, or weapon in the last year? 871.961 .000 Told you were unloved or undeserving of love in the last year? 2,473.662 .000 Told you were unloved, wished dead, or made to feel worthless? 1,590.401 .000 Threatened with a knife, gun, or weapon in the past year? 1,402.222 .000 Punched, kicked, whipped, or beaten with an object in the past year? 5,371.876 .000 Used condoms regularly with the most recent partner? 10,253.498 .000 Choked, smothered, or burned intentionally in the past year? 714.399 .000 Experienced ridicule, insults, or emotional neglect? 8,098.577 .000 Forced to have sex through physical force, harassment, threats, or tricks? 1,734.619 .000 Inappropriately touched (e.g., fondling or touching sexual body parts)? 9,786.720 .000 Had more than three drinks or used drugs frequently? 1,367.420 .000 Consumed alcohol or used illicit drugs (e.g., marijuana, cocaine, shisha)? 9,463.589 .000 Are both parents living? 19,841.502 .000 Currently in school? 105,585.313 .000 Number of sexual partners in the last 12 months? 335,632.865 .000 Age group? 625,795.458 .000 Is the head of the household under the age of 18? 8.520 .004 Ever been pregnant? 5,739.747 .000 3.3 Profiling Risk Distribution Among Adolescent Girls and Young Women in Rural Uganda: A Cluster and Bivariate Analysis The study utilized a two-step approach to identify and characterize risk factors. A cluster analysis first segmented participants into High Risk and Low-Risk groups based on their behavioral profiles. This was followed by a bivariate analysis shown in Table 2 , which not only validated the clustering results but also provided a detailed characterization of the risk distribution across socio-economic, educational, and behavioral dimensions. The results reveal stark disparities between the two groups (p < .001 for all key variables), emphasizing the robustness of the clusters and offering critical insights into the vulnerabilities faced by adolescent girls and young women (AGYW). Cluster Composition and Validation The cluster analysis classified 24.7% (30,664) of participants into the High-Risk group and 75.3% (93,595) into the Low-Risk group. Age emerged as a key differentiator: 92.0% of High-Risk participants were aged 20–25 years, compared to 84.8% of Low-Risk participants who were aged 9–14 years (p < .001). The moderate Euclidean distance of 2.689 between the clusters confirms a clear separation, supporting the validity of the two-cluster solution. This segmentation highlights the life stage as a significant determinant of exposure to socio-economic and behavioral risks. Socio-Economic Vulnerabilities Parental status and employment status were critical socio-economic factors influencing risk profiles. Among High-Risk participants, 94.0% lacked both parents, compared to 52.4% in the Low-Risk group (p < .001). Employment status also differed significantly, with 9.9% of High-Risk participants engaged in employment, while employment was nearly nonexistent in the Low-Risk group (p < .001). These findings suggest that the High-Risk cluster is characterized by greater socio-economic instability, where the absence of parental support and engagement in precarious work amplify vulnerabilities. Educational Disparities Education emerged as a protective factor, predominantly defining the Low-Risk group. While 83.6% of Low-Risk participants were actively enrolled in school, 91.4% of High-Risk participants were not in school (p < .001). This disparity underscores the role of education in mitigating risky behaviors. School attendance provides AGYW with resources, networks, and knowledge, reducing their susceptibility to risky behaviors. Conversely, school dropouts among the High-Risk group exacerbate socio-economic challenges and limit their capacity to avoid exploitative situations. Behavioral and Substance Use The High-Risk group exhibited significantly higher engagement in risky behaviors, including sexual activity and transactional relationships. A notable 85.3% of High-Risk participants reported being sexually active, compared to only 0.3% in the Low-Risk group (p < .001). Furthermore, 43.9% of the High-Risk group engaged in transactional sex, highlighting economic desperation as a driving force behind these behaviors (p < .001). In contrast, transactional relationships were virtually absent in the Low-Risk group. These findings illustrate how behavioral risks are concentrated in the High-Risk group, further entrenching socio-economic vulnerabilities. Significant disparities in alcohol and drug use were observed between the clusters. Among High-Risk participants, 24.4% reported consuming alcohol or using illicit drugs, compared to only 5.7% in the Low-Risk group (p < .001). Frequent and heavy substance use, characterized by consuming more than three drinks at a time or using drugs regularly, was significantly higher in the High-Risk cluster (1.5%) compared to the Low-Risk cluster (0.0%) (p < .001). These findings suggest that substance use serves as a coping mechanism for socio-economic and emotional stressors within the High-Risk group, further exacerbating their vulnerabilities. Experience of Violence The High-Risk group faced a disproportionate burden of violence. All participants in this group reported experiencing physical violence, compared to 91.4% in the Low-Risk group (p < .001). Additionally, 24.6% of High-Risk participants reported sexual violence, compared to only 5.1% in the Low-Risk group (p < .001). Emotional abuse was also significantly higher among High-Risk participants. Exposure to violence not only increases immediate risks but also contributes to long-term psychological impacts, such as anxiety, depression, and PTSD. These patterns further highlight the compounded vulnerabilities of the High-Risk group. Table 2 Risk Distribution Among Adolescent Girls and Young Women in Rural Uganda: A Cluster and Bivariate Analysis Variable Category High Risk (N, %) Low Risk (N, %) p-value Age Group (years) 9–14 2 (0.0) 79,372 (84.8) < .001 15–19 2,462 (8.0) 14,223 (15.2) 20–25 28,200 (92.0) 0 (0.0) Alcohol/Drug Use No 23,178 (75.6) 88,293 (94.3) < .001 Yes 7,486 (24.4) 5,302 (5.7) Parental Status No 28,821 (94.0) 49,000 (52.4) < .001 Yes 1,843 (6.0) 44,595 (47.6) Household Head Under 18 No 30,664 (100.0) 93,569 (100.0) .004 Yes 0 (0.0) 26 (0.0) Education Status (Currently in School) No 28,020 (91.4) 15,378 (16.4) < .001 Yes 2,644 (8.6) 78,217 (83.6) Sexual Activity No 4,510 (14.7) 93,299 (99.7) < .001 Yes 26,154 (85.3) 296 (0.3) Condom Use No 27,583 (90.0) 93,561 (100.0) < .001 Yes 3,081 (10.0) 34 (0.0) Transactional Sex No 17,194 (56.1) 93,586 (100.0) < .001 Yes 13,470 (43.9) 9 (0.0) Threatened with Weapon No 30,663 (100.0) 89,502 (95.6) < .001 Yes 1 (0.0) 4,093 (4.4) Physical Violence No 30,663 (100.0) 85,571 (91.4) < .001 Yes 1 (0.0) 8,024 (8.6) Sexual Violence No 23,121 (75.4) 88,783 (94.9) < .001 Yes 7,543 (24.6) 4,812 (5.1) Employment Status No 27,619 (90.1) 93,594 (100.0) < .001 Yes 3,045 (9.9) 1 (0.0) Frequent Alcohol/Drug Use No 30,194 (98.5) 93,577 (100.0) < .001 Yes 470 (1.5) 18 (0.0) 3.4 Cluster and Bivariate Analysis of Risk Profiles Among AGYW in Rural Uganda Collinearity Diagnostics and Model Stability We conducted the collinearity diagnostics for the logistic regression model and revealed no significant multicollinearity concerns, as indicated by a Condition Index ranging from 1.000 to 4.836, well below the threshold of 10. The Eigenvalues ranged from 0.095 to 2.221, reflecting distinct contributions of predictors to the model. Variance proportions confirmed predictor independence, with "School Status" and "Sexual Activity" contributing predominantly to Dimension 4 (89% and 43%, respectively; Eigenvalue = 0.095), while "Transactional Sex" aligned with Dimension 3 (84%; Eigenvalue = 0.321). As shown in Table 3 , the findings demonstrate the stability and reliability of the model, ensuring that predictors independently explain risk classification among adolescent girls and young women. Table 3 Collinearity Diagnostics for Predictors of Risk Cluster Classification Model Dimension Eigenvalue Condition Index Variance Proportions 1 2.221 1.000 0.03 2 1.363 1.277 0.01 3 0.321 2.631 0.01 4 0.095 4.836 0.94 Logistic Regression Results Following Cluster and Bivariate Analysis of Risk Profiles Among AGYW in Rural Uganda Building on the cluster and bivariate analysis, a binary logistic regression was conducted to identify predictors of High Risk and Low-Risk classification among adolescent girls and young women as shown in Table 4 . The model demonstrated excellent explanatory power, with a -2 Log Likelihood of 23,765.883, a Cox & Snell R Square of 60.4%, and a Nagelkerke R Square of 89.8%. These results indicate that the model accounts for nearly 90% of the variance in risk classification, underscoring its robustness in identifying socio-economic and behavioral predictors. Educational engagement emerged as a critical protective factor. AGYW who were currently enrolled in school were 108 times more likely to be classified in the Low-Risk group compared to those not in school (AOR = 108.154, 95% CI: 93.844–124.646, p < .001). This finding highlights the protective role of education, which reduces exposure to socio-economic vulnerabilities and risky behaviors. In contrast, sexual activity was a significant predictor of High-Risk classification. Participants who reported being sexually active were almost exclusively in the High-Risk group (AOR = 0.000, 95% CI: 0.000–0.001, p < .001), indicating a strong association between sexual activity and increased exposure to risks. Economic vulnerability, reflected in transactional sex, further distinguished High-Risk participants. AGYW who reported having engaged in sex in exchange for items such as airtime, food, or school fees were 1600 times more likely to belong to the High-Risk group (AOR = 1599.966, 95% CI: 769.091–3328.465, p < .001). This underscores the role of economic hardship in driving risky behaviors, reflecting the need for interventions that address financial instability. The collective findings from the logistic regression, cluster analysis, and bivariate analysis provide a comprehensive understanding of risk profiles among AGYW, emphasizing the interplay of educational, behavioral, and economic factors in shaping vulnerabilities. Table 4 Adjusted Odds Ratios (AOR) and 95% Confidence Intervals for Predictors of Risk Classification Among AGYW Variable p-value AOR (Exp(B) 95% CI for AOR School Status < .001 108.154 93.844–124.646 sexually activity < .001 0.000 0.000–0.001 Transactional sex < .001 1599.966 769.091–3328.465 Constant < .001 5.370 4.0 Discussion Our findings revealed the intricate challenges adolescent girls and young women (AGYW) face in rural Uganda, where socio-economic, educational, and behavioral factors converge to shape their risk profiles. By clustering participants into High Risk and Low-Risk groups, we illuminated significant disparities: those in the High-Risk category were more likely to engage in transactional sex, experience violence, and drop out of school. These behaviors are not isolated but are deeply entrenched in broader issues of poverty, social inequality, and limited access to resources. These findings resonate with global evidence emphasizing the critical role of addressing socio-economic hardship to reduce risky behaviors among vulnerable populations ( 1 ). Interventions must target these systemic challenges to create pathways toward safer and more empowering futures for AGYW. The analysis showed that education emerged as a transformative protective factor for AGYW, significantly increasing their likelihood of belonging to the Low-Risk group. Girls enrolled in school were found to be 108 times more likely to be classified as low-risk, consistent with studies demonstrating the empowering effects of education ( 22 ). Education equips young women with knowledge, critical thinking skills, and confidence to make safer decisions, shielding them from exploitative behaviors. However, persistent barriers such as poverty, early pregnancies, and entrenched cultural norms continue to limit educational opportunities for many AGYW ( 21 , 23 ). Addressing these challenges requires practical measures such as scholarships, mentorship programs, and community-driven advocacy to ensure education remains accessible and inclusive, particularly in rural areas. We also noted that early and unprotected sexual activity significantly increased the vulnerabilities of AGYW, with sexually active participants overwhelmingly falling into the High-Risk category. These findings highlight the urgent need for comprehensive sexual and reproductive health (SRH) education that goes beyond biological facts to address the social and economic contexts driving risky sexual behaviors. Existing research supports the need for tailored interventions that include peer-led counseling, contraceptive access, and dismantling harmful cultural norms around sexuality ( 6 ). Programs like PEPFAR have demonstrated the potential of integrated SRH initiatives to empower AGYW, making such approaches critical in this context. Transactional sex emerged as a stark indicator of economic desperation among AGYW in the High-Risk group. Girls engaging in such relationships were found to be 1600 times more likely to belong to this category, illustrating the profound influence of poverty on decision-making. These findings align with other studies linking transactional sex to socio-economic hardships, including lack of parental support and limited income-generating opportunities ( 1 ). The transactional nature of these relationships often places AGYW in exploitative and unsafe situations, further entrenching their vulnerabilities. To counter this, economic empowerment programs tailored for AGYW, such as vocational training, financial literacy education, and access to microfinance, are essential. These interventions should aim to provide sustainable livelihood options, enabling AGYW to break free from cycles of dependency and exploitation. We observed violence as a pervasive issue among AGYW in the High-Risk group, with many experiencing physical, sexual, and emotional abuse. This exposure not only heightens immediate risks but also leaves lasting psychological and social scars, contributing to mental health challenges like anxiety, depression, and PTSD. Evidence from similar settings highlights how violence disrupts education, limits economic opportunities, and perpetuates social exclusion ( 6 , 21 ). Protecting AGYW from violence requires multi-sectoral approaches, including strengthening legal protections, community-level interventions to challenge harmful norms, and ensuring accessible support services for survivors. Survivor-centered programs, such as counseling and safe shelters, must be integrated into broader initiatives to ensure AGYW has the resources to recover and rebuild their lives. Substance use emerged as both a symptom and a driver of vulnerability among High-Risk AGYW. The significantly higher prevalence of substance use in this group underscores its role as a coping mechanism for socio-economic and emotional stressors. However, substance use exacerbates existing vulnerabilities, impairing judgment and increasing exposure to risky behaviors such as unprotected sex and violence. These findings align with studies that advocate for integrated prevention and support programs targeting AGYW struggling with substance use ( 24 ). Community-based interventions, such as peer support networks and accessible counseling, could play a pivotal role in reducing substance use and addressing the root causes of emotional and psychological distress. 5.0 Practical and Policy Implications This study highlights the urgent need to support adolescent girls and young women (AGYW) in rural Uganda by addressing the challenges they face in their daily lives. On a practical level, keeping AGYW in school is a powerful way to shield them from risks like early pregnancies, transactional sex, and violence. Efforts to reduce school dropouts could include scholarships for vulnerable families, community mentorship programs, and flexible re-entry policies for girls who leave school due to early motherhood. Empowering AGYW economically through vocational training, small business support, and savings programs can help them build independence and avoid exploitative relationships. Additionally, sexual and reproductive health education should not only be accessible but also relatable, addressing the specific needs and experiences of AGYW. Establishing safe spaces where girls can seek mental health support and assistance after experiencing violence or substance misuse is crucial for their recovery and resilience. On a broader scale, policies must reflect a commitment to the holistic well-being of AGYW. Education policies should ensure that every girl has the opportunity to complete her studies, regardless of socio-economic barriers. Enforcing laws to prevent child marriages and gender-based violence while providing accessible and confidential reporting mechanisms can create safer communities for girls. Policies promoting reproductive health services need to account for cultural sensitivities while making care widely available and free from stigma. Economic programs must address the underlying poverty that drives many AGYW into high-risk behaviors by creating pathways to sustainable livelihoods for them and their families. A united effort involving government, civil society, and local leaders is needed to ensure these policies and interventions reach the girls who need them most. Together, we can create a future where AGYW have the resources and support to lead safe, healthy, and empowered lives. 6.0 Conclusion This study sheds light on the immense challenges faced by adolescent girls and young women (AGYW) in rural Uganda, revealing the harsh realities of poverty, violence, and limited opportunities that shape their lives. A significant proportion of these young women are categorized as high-risk, grappling with circumstances that no one should endure being forced to leave school, engaging in transactional sex out of economic desperation, or suffering violence that leaves both visible and invisible scars. Behind the statistics are real lives, filled with dreams and aspirations, yet constrained by systemic barriers that perpetuate inequality and vulnerability. These findings serve as a powerful reminder of the urgent need for collective action. Families, communities, policymakers, and stakeholders must collaborate to dismantle the structural challenges that perpetuate these cycles of risk. Education emerged as a significant factor in this study, offering a pathway to empowerment and reduced vulnerabilities. Ensuring access to quality education for AGYW must remain a top priority, complemented by interventions that address economic hardships through skills development, vocational training, and financial support. Moreover, creating safe spaces where AGYW can thrive, free from violence and exploitation, is critical. Multi-sectoral approaches integrating legal protections, survivor-centered support, and community awareness are essential for fostering an environment where these young women can flourish. This research is a heartfelt call to action: with empathy, purpose, and investment, we can transform their stories of struggle into narratives of resilience, dignity, and hope. Together, we have the power to ensure brighter futures for AGYW. 7.0 Strengths, Limitations, and Directions for Future Research Our study offers several strengths that enhance its reliability and impact. Using a combination of cluster analysis, bivariate analysis, and logistic regression, it provides a detailed understanding of the socio-economic, educational, and behavioral factors shaping the risk profiles of AGYW in rural Uganda. A large sample size of over 120,000 participants enhances statistical power and generalizability. The focus on key risk indicators, such as education, sexual activity, and transactional sex, alongside broader determinants like violence and substance use, ensures the findings are both actionable and contextually relevant for designing targeted interventions. However, limitations exist. The cross-sectional design restricts causal inferences, necessitating longitudinal studies to track changes in risk factors over time. The reliance on self-reported data raises concerns about potential biases, particularly underreporting of sensitive issues like sexual activity and violence. Moreover, structural determinants, such as cultural norms or systemic barriers, are underexplored. Future research should include qualitative approaches to understand AGYW’s lived experiences and contextual dynamics. Additionally, studies focusing on marginalized sub-groups, such as AGYW with disabilities or those in conflict zones, would enhance inclusivity. Addressing these gaps can guide more comprehensive strategies to support AGYW and inform inclusive interventions in similar contexts. Declarations Ethical Approval This study was conducted in accordance with the Declaration of Helsinki and received ethical approval from the Mildmay Uganda Research and Ethics Committee (REC REF 0804–2018) and the Uganda National Council for Science and Technology (SS639ES). Disclosure The authors declare no conflicts of interest related to this study. Data Sharing Statement Data available from the corresponding author upon reasonable request, subject to ethical approvals and confidentiality. Consent for Publication Not applicable. This manuscript does not include any person’s data in any form (including individual details, images, or videos). Funding This project was funded by the US Centers for Disease Control and Prevention (CDC), Kampala Office, under Cooperative Agreement GH002046. The implementation of the Mubende Region HIV Project, project code 18033, was managed by Mildmay Uganda. The study received no additional funding beyond this support. Author Contribution Conceptualization (Dedrix Stephenson Bindeeba, Jane Senyondo Nakawesi), Methodology (Dedrix Stephenson Bindeeba, Andrew Mugisa), Principal Investigation (Dedrix Stephenson Bindeeba), Data Extraction (Andrew Mugisa, Ronald Mulebeke), Formal Analysis (Dedrix Stephenson Bindeeba, Jane Senyondo Nakawesi), Writing Original Draft (Dedrix Stephenson Bindeeba), Validation (Catherine Senyimba, Ronald Mulebeke, Yvonne Karamagi), Writing Review & Editing (All authors), and Approval of Final Manuscript (All authors). Acknowledgement We sincerely appreciate the leadership and communities of Mubende, Mityana, Kassanda, and Luwero Districts for their invaluable support and cooperation, which were instrumental in the successful implementation of this work. We also extend our deepest gratitude to the Centers for Disease Control and Prevention (CDC) Uganda office for funding this project under the DREAMS initiative. Data Availability Data Availability StatementThis study utilized secondary data on AGYW participants. Full datasets cannot be shared publicly due to the presence of potentially identifying information. The content and responses from participants, even after anonymization, could still pose risks to confidentiality. Access to the data may be granted upon reasonable request and in alignment with the ethical approval obtained for this study. Requests can be directed to the the corresponding Author. References Selin A, DeLong SM, Julien A, MacPhail C, Twine R, Hughes JP et al. Prevalence and Associations, by Age Group, of IPV Among AGYW in Rural South Africa. Sage Open. 2019;9(1). Chimbindi N, Birdthistle I, Shahmanesh M, Osindo J, Mushati P, Ondenge K et al. Translating DREAMS into practice: Early lessons from implementation in six settings. PLoS ONE. 2018;13(12). Donenberg G, Merrill KG, Atujuna M, Emerson E, Bray B, Bekker LG. Mental health outcomes of a pilot 2-arm randomized controlled trial of a HIV-prevention program for South African adolescent girls and young women and their female caregivers. BMC Public Health. 2021;21(1). Godfrey C, Nkengasong J. Prioritizing Mental Health in the HIV/AIDS Response in Africa. N Engl J Med. 2023;389(7):581–3. UNAIDS, Global HIV. & AIDS statistics 2021 fact sheet. Published 2021. Accessed November 30, 2024. https://www.unaids.org/en/resources/fact-sheet Singla DR, Waqas A, Hamdani SU, Suleman N, Zafar SW, Zill-e-Huma et al. Implementation and effectiveness of adolescent life skills programs in low- and middle-income countries: A critical review and meta-analysis. Behav Res Ther. 2020;130. Yankah E, Aggleton P. EffEcts and EffEctivEnEss of LifE skiLLs Education for Hiv prEvEntion in Young pEopLE. Volume 20. AIDS Education and Prevention; 2008. Mhlongo S, Mason-Jones AJ, Ford K. Sexual, reproductive and mental health among young men (10–24) in low-and-middle income countries: a scoping review. Frontiers in Reproductive Health. Volume 5. Frontiers Media SA; 2023. Winskell K, Miller KS, Allen KA, Obong’o CO. Guiding and supporting adolescents living with HIV in sub-Saharan Africa: The development of a curriculum for family and community members. Child Youth Serv Rev. 2016;61:253–60. Muhwezi WW, Abbo C, Okello ES, Akello G, Ovuga EBL. Assessment of the relationship between life events with psychosocial competence of students in selected secondary schools in northern and central Uganda. Afr Health Sci. 2020;20(3):1426–37. Luseno WK, Field SH, Iritani BJ, Odongo FS, Kwaro D, Amek NO, et al. Pathways to Depression and Poor Quality of Life Among Adolescents in Western Kenya: Role of Anticipated HIV Stigma, HIV Risk Perception, and Sexual Behaviors. AIDS Behav. 2021;25(5):1423–37. Kuringe E, Materu J, Nyato D, Majani E, Ngeni F, Shao A 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). Norbu L, Gurung N. Action Research Addressing the Implementation of Life Skills Education on Adolescent Reproductive Sexual Health (Teenage Pregnancy, Sexual Activities, STIs, HIV/AIDs) for the Positive Behavioural Outcome. Asian J Biology. 2021;7–27. UNAIDS, Global HIV. & AIDS statistics 2020 fact sheet. Published 2020. Accessed November 30, 2024. https://www.unaids.org/en/resources/fact-sheet Ngoma-Hazemba A, Chavula MP, Sichula N, Silumbwe A, Mweemba O, Mweemba M et al. Exploring the barriers, facilitators, and opportunities to enhance uptake of sexual and reproductive health, HIV and GBV services among adolescent girls and young women in Zambia: a qualitative study. BMC Public Health. 2024;24(1). Fang L, Chuang DM, Al-Raes M. Social support, mental health needs, and HIV risk behaviors: A gender-specific, correlation study. BMC Public Health. 2019;19(1). Wight D, Sekiwunga R, Namutebi C, Zalwango F, Siu GE. A Ugandan Parenting Programme to Prevent Gender-Based Violence: Description and Formative Evaluation. Res Soc Work Pract. 2022;32(4):448–64. Bulstra CA, Hontelez JAC, Otto M, Stepanova A, Lamontagne E, Yakusik A, et al. Integrating HIV services and other health services: A systematic review and metaanalysis. PLoS Medicine. Volume 18. Public Library of Science; 2021. Anyanwu MU. Psychological distress in adolescents: prevalence and its relation to high-risk behaviors among secondary school students in Mbarara Municipality, Uganda. BMC Psychol. 2023;11(1). PEPFAR. Annual progress report. Published 2023. Accessed November 30. 2024. https://www.state.gov/annual-reports-to-congress-on-the-presidents-emergency-plan-for-aids-relief/ Uganda Bureau of Statistics (UBOS). We Are Evidence-Based: Multidimensional Child Poverty in Uganda. Published 2024. Accessed November 30. 2024. https://www.ubos.org Bose DL, Hundal A, Seth K, Hadi S, ul, Singh S, Singh S et al. PROTOCOL: Social and behaviour change communication interventions for strengthening HIV prevention and research among adolescent girls and young women in low- and middle-income countries: An evidence and gap map. Campbell Syst Reviews. 2022;18(1). Ziraba A, Orindi B, Muuo S, Floyd S, Birdthistle IJ, Mumah J et al. Understanding HIV risks among adolescent girls and young women in informal settlements of Nairobi, Kenya: Lessons for DREAMS. PLoS ONE. 2018;13(5). Hokororo A, Kihunrwa AF, Kalluvya S, Changalucha J, Fitzgerald DW, Downs JA. Barriers to access reproductive health care for pregnant adolescent girls: A qualitative study in Tanzania. Acta Paediatr Int J Paediatrics. 2015;104(12):1291–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5570319","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":388241416,"identity":"26de8f27-c1bf-42bc-a170-15b46e728f01","order_by":0,"name":"Dedrix Stephenson Bindeeba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDCCAwwGDAkMB3gYgCQzA4MNUIix8QApWtJAWhoIawGRUC2HoYJ4AN/x5m0fHrbdkTFnTz74uKDmvN3a9sNAW2psonFpkTxzrHhGwplnPJY9z5KNZxy7nbztTCJQy7G03AYcWgxu5BgzJFQc5gEyzKR52G4nmx0AamFsOIxby/03QC0GIC3533/z/DuXbHb+IQEtN3jgtrAx87YdsDO7QcAWyTNpxQwgvxiceWYsPbMvOcHsBtCWBDx+4Tt+eDPjz7Y79gbHkx9+LvhmZ292Pv3hgw81Nji1YIBEsMoEYpWDgD0pikfBKBgFo2BkAAA/C2vyEdmiKAAAAABJRU5ErkJggg==","orcid":"","institution":"Mildmay Uganda","correspondingAuthor":true,"prefix":"","firstName":"Dedrix","middleName":"Stephenson","lastName":"Bindeeba","suffix":""},{"id":388241417,"identity":"6edb6d98-267b-48ee-a933-3a2de3daf21b","order_by":1,"name":"Jane Senyondo Nakawesi","email":"","orcid":"","institution":"Mildmay Uganda","correspondingAuthor":false,"prefix":"","firstName":"Jane","middleName":"Senyondo","lastName":"Nakawesi","suffix":""},{"id":388241418,"identity":"8bfde9c6-6b95-40e5-9806-72f02fe6ca74","order_by":2,"name":"Andrew Mugisa","email":"","orcid":"","institution":"Mildmay Uganda","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Mugisa","suffix":""},{"id":388241419,"identity":"0ecce75c-5432-4428-88ed-4c2ffbf243d6","order_by":3,"name":"Catherine Senyimba","email":"","orcid":"","institution":"Mildmay Uganda","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Senyimba","suffix":""},{"id":388241420,"identity":"114eafec-da30-4f3f-bfe8-3b77132a1023","order_by":4,"name":"Ronald Mulebeke","email":"","orcid":"","institution":"Mildmay Uganda","correspondingAuthor":false,"prefix":"","firstName":"Ronald","middleName":"","lastName":"Mulebeke","suffix":""},{"id":388241421,"identity":"03d36c3a-3f04-4e71-ac1c-79000d8b6af9","order_by":5,"name":"Yvonne Karamagi","email":"","orcid":"","institution":"Mildmay Uganda","correspondingAuthor":false,"prefix":"","firstName":"Yvonne","middleName":"","lastName":"Karamagi","suffix":""}],"badges":[],"createdAt":"2024-12-03 08:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5570319/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5570319/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71497021,"identity":"69195f31-85b5-4ca5-801e-75e121ed142a","added_by":"auto","created_at":"2024-12-16 08:24:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":859963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5570319/v1/f05264b9-8e7a-4529-9642-ee52c3bde428.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Profiling the Plight of Adolescent Girls and Young Women: Risks and Pathways for Intervention in Rural Uganda","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eAdolescent girls and young women (AGYWs) in sub-Saharan Africa (SSA) face significant social, economic, and cultural vulnerabilities that place them at disproportionate risk for adverse outcomes, particularly HIV(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In SSA, AGYWs account for 87% of global HIV infections among women aged 15\u0026ndash;24, a stark reminder of the systemic inequities fueling the epidemic in this region (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In Uganda, AGYWs contribute to over two-thirds of new HIV infections, reflecting an urgent need to address the underlying drivers of vulnerability (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultiple factors contribute to the heightened HIV risk among AGYWs. Socio-economic deprivation, early marriage, and limited access to education compel many young women to engage in transactional sex, exposing them to older male partners who may wield greater power in relationships (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These dynamics often restrict AGYWs\u0026rsquo; ability to negotiate safe sexual practices, further exacerbating their susceptibility to HIV and other sexually transmitted infections (STIs). Additionally, pervasive gender inequality entrenched in patriarchal norms continues to marginalize AGYWs, leaving them with limited autonomy over their sexual and reproductive health (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocio-economic instability remains a key driver of AGYW vulnerabilities. Poverty often forces young women to adopt survival strategies such as transactional sex, which has been strongly associated with a higher risk of HIV acquisition (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Studies show that AGYWs engaged in transactional sex frequently lack bargaining power, making them vulnerable to coercive relationships and unprotected sex (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Compounding these risks is the limited availability of social safety nets, leaving AGYWs reliant on relationships with financially dominant partners to meet basic needs (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEducational disengagement further exacerbates AGYWs\u0026rsquo; vulnerabilities. Girls who drop out of school due to early pregnancies, caregiving responsibilities, or economic barriers face diminished opportunities for socio-economic advancement (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Evidence indicates that out-of-school AGYWs are significantly more likely to engage in early sexual activity and risky behaviors compared to their peers who remain in school (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Educational attainment has been consistently linked to delayed sexual debut and a lower likelihood of early marriage, demonstrating its critical role as a protective factor against HIV (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGender-based violence (GBV) is another pervasive issue that disproportionately affects AGYWs. Reports indicate that over 40% of AGYWs in SSA experience some form of GBV, whether physical, emotional, or sexual (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). GBV not only impacts their mental health but also undermines their ability to seek help or assert control over their sexual health. AGYWs subjected to violence are at greater risk of engaging in risky sexual behaviors and acquiring HIV due to coercion and fear of retribution (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubstance use among AGYWs also poses a significant risk. Alcohol and illicit drug use are often associated with risky behaviors, including unprotected sex and multiple sexual partners (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These behaviors exacerbate AGYWs\u0026rsquo; susceptibility to HIV and other adverse health outcomes. Substance use further intersects with other vulnerabilities, creating a feedback loop that perpetuates cycles of risk (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo mitigate these intersecting vulnerabilities, the DREAMS (Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe) initiative was launched under the U.S. President\u0026rsquo;s Emergency Plan for AIDS Relief (PEPFAR) in 2014. DREAMS adopts a holistic approach to HIV prevention, addressing both structural and behavioral drivers of risk. It integrates interventions across socio-economic empowerment, educational support, and health services to create an enabling environment for AGYWs to thrive (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA key feature of DREAMS is its risk stratification process, which uses screening tools to identify high-risk AGYWs. This data-driven approach ensures efficient resource allocation by tailoring interventions to participants' specific needs. The program also emphasizes the importance of community engagement, leveraging social support networks to foster resilience among AGYWs (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). DREAMS has shown promise in reducing HIV incidence among AGYWs in SSA. However, existing studies have primarily focused on program outcomes, with limited attention to the baseline vulnerabilities of participants at enrollment. Understanding these vulnerabilities is crucial for designing interventions that address root causes and ensure long-term sustainability.\u003c/p\u003e \u003cp\u003eDespite significant investments in interventions like DREAMS, there remains a critical gap in understanding the granular context-specific socio-economic, educational, and behavioral factors that continue to drive risk among AGYWs. Existing literature predominantly evaluates intervention outcomes, often overlooking the contextual vulnerabilities that shape AGYWs\u0026rsquo; risk profiles. This lack of comprehensive baseline risk-profile data hinders efforts to tailor interventions and address the structural drivers of risk effectively (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study leverages data collected during the DREAMS enrollment process to fill this gap. By analyzing data covering 124,258 AGYW, the study provides insights that can inform targeted interventions and broader policy frameworks. The overarching goal of this study was to establish a comprehensive baseline risk profile of AGYWs, focusing on their socio-economic, educational, and behavioral vulnerabilities. Specific objectives include:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo examine the socio-economic, educational, and behavioral vulnerabilities of AGYWs, including household headship, employment status, school attendance, reasons for dropout, early pregnancy, transactional sex, and experiences of violence, and to analyze their associations with other risk factors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo identify and characterize distinct risk profiles of AGYWs using cluster analysis, emphasizing the intersections of socio-economic, educational, and behavioral vulnerabilities for targeted intervention planning.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eOur study shifts focus from intervention outcomes to baseline characteristics of adolescent girls and young women (AGYW) by development interventions like DREAMS (Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe). Profiling AGYW vulnerabilities enhances targeting mechanisms, ensuring resources reach the most at-risk individuals while informing program design with evidence-based risk profiles to address poverty, educational disengagement, and gender-based violence. We believe that establishing robust baselines provides a solid evaluation framework, enabling the assessment of program effectiveness. Additionally, the findings offer policy implications with actionable recommendations for addressing AGYW vulnerabilities in resource-constrained settings. We used secondary data from Uganda, which is of global relevance.\u003c/p\u003e"},{"header":"2.0 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThis study employed a retrospective, cross-sectional design to analyze secondary data collected during the enrollment phase of the DREAMS (Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe) intervention. The DREAMS program sought to reduce HIV risk among adolescent girls and young women by addressing socio-economic, educational, and behavioral vulnerabilities. The primary objective of this study was to leverage data collected during the DREAMS enrollment process to establish baseline risk profiles of AGYWs targeted by the intervention. This approach provided critical insights into the characteristics and vulnerabilities of this high-risk population, enabling the identification of key areas requiring intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Setting and Participants\u003c/h2\u003e \u003cp\u003eThe DREAMS intervention targeted AGYW across four Districts of Mubende, Mityana, Kassanda, and Luwero, focusing on communities characterized by high rates of poverty, educational disengagement, and adverse health and social outcomes. Participants were recruited based on predefined high-risk criteria, which included being out of school, experiencing violence, engaging in transactional sex, or living in socio-economically vulnerable conditions. The region has an HIV prevalence of 6.2% which is above the 5.8% national average (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dataset analyzed in this study was drawn from a subset of AGYWs screened during the enrollment phase. Inclusion criteria required participants to meet the DREAMS program\u0026rsquo;s risk thresholds and have complete screening data. Records with missing or duplicated entries were excluded. For this study, a total of 124,258 AGYWs were used to provide a comprehensive baseline assessment of vulnerabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection\u003c/h2\u003e \u003cp\u003eThe data analyzed in this study were collected using a standardized screening tool developed specifically for the DREAMS enrollment process. This tool was implemented during one-on-one interviews conducted by trained social workers in private settings to ensure confidentiality. The tool captured a wide range of socio-economic, educational, and behavioral indicators, including the following domains:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSocio-Economic Indicators\u003c/strong\u003e \u003cp\u003eInformation was collected on household headship (e.g., whether participants lived in child-headed households), work status (whether participants were engaged in income-generating activities), and living conditions, which reflected the overall socio-economic stability of the household.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEducational Indicators\u003c/b\u003e: Data were collected on school attendance status (in school or out of school), the number of school days missed during the last term, reasons for absenteeism or dropout (such as lack of school fees or family responsibilities), and interest in returning to school for those who were out of school. \u003cb\u003eSexual and Reproductive Health Indicators\u003c/b\u003e: Participants provided information on their age at first pregnancy, history of sexual activity (including consensual, coerced, or forced experiences), engagement in transactional sex (exchanging sex for items or favors), and the number of sexual partners in the past 12 months.\u003c/p\u003e \u003cp\u003e\u003cb\u003eViolence Indicators\u003c/b\u003e: Participants were asked about their experiences with physical violence, emotional/psychological abuse, and sexual violence. These responses helped identify AGYWs who had been subjected to various forms of harm. \u003cb\u003eSubstance Use Indicators\u003c/b\u003e: Data were gathered on alcohol consumption and illicit drug use, as well as any consequences of substance use, such as social or health problems. All data were entered into a centralized database, ensuring standardization and accuracy. The structured tool was designed to comprehensively capture the vulnerabilities of AGYWs at the time of their enrollment into the DREAMS program.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e \u003cp\u003eThe dataset was cleaned, coded, and analyzed using SPSS Version 27 to explore the vulnerabilities of adolescent girls and young women. Descriptive statistics provided an overview of key variables, with frequencies and percentages summarizing categorical data such as school attendance and household headship, while means and standard deviations were used for continuous variables like age. To classify participants into distinct risk groups, K-means clustering analysis was employed. This method effectively partitioned participants into High Risk and Low-Risk clusters based on variables such as household headship, work status, school attendance, transactional sex, experiences of violence, and age at first pregnancy, offering a nuanced understanding of the multidimensional vulnerabilities within the population.\u003c/p\u003e \u003cp\u003eTo validate the clusters, bivariate analysis was conducted, examining associations between cluster membership and key outcomes such as transactional sex, multiple sexual partners, and experiences of violence. Chi-square tests assessed these relationships, while odds ratios quantified the association's strength, confirming the clusters' distinctiveness. Logistic regression models were subsequently used to identify predictors of high-risk behaviors, providing deeper insights into the socio-economic and behavioral factors shaping AGYW\u0026rsquo;s risk profiles. This combined approach ensured the robustness and relevance of the clustering results, offering a solid foundation for targeted interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Ethical Considerations\u003c/h2\u003e \u003cp\u003e This study adhered to the ethical principles outlined in the Declaration of Helsinki and was conducted under the ethical approvals granted by the Mildmay Uganda Research and Ethics Committee (MUREC) (REC REF 0804\u0026ndash;2018) and the Uganda National Council of Science and Technology (SS639ES). Permission to use the database for this secondary analysis was obtained through administrative clearance from Mildmay Uganda, ensuring compliance with institutional guidelines for data access and usage.\u003c/p\u003e \u003cp\u003eA waiver of assent and parental consent for the use of secondary data was granted by the Mildmay Research Ethics Committee (REC) due to the retrospective nature of the study. This waiver was deemed appropriate as the study utilized pre-existing, anonymized data, with no direct interaction with participants or collection of new data. All data extracted from the database were fully anonymized before analysis to safeguard participants\u0026rsquo; privacy. Identifying information was removed, ensuring confidentiality in both the analysis and reporting stages of the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Background characteristics of study participants\u003c/h2\u003e \u003cp\u003eThe study included a total of 124,259 respondents, with a mean age of 14.57 years (SD\u0026thinsp;=\u0026thinsp;4.369). The age distribution was as follows: 63.9% (79,374) were aged 9\u0026ndash;14 years, 22.7% (28,200) were aged 20\u0026ndash;25 years, and 13.4% (16,685) were aged 15\u0026ndash;19 years. This reflects a diverse age composition across the groups, providing a comprehensive representation of the target population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Cluster Analysis and Socio-Economic Risk Profiling\u003c/h2\u003e \u003cp\u003eThe cluster analysis of adolescent girls and young women (AGYW) identified two distinct groups based on their behavioral risk profiles as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: a high-risk group (24.7%, 30,663 participants) and a low-risk group (75.3%, 93,595 participants). The moderate separation between these groups, with a Euclidean distance of 2.689, supports the validity of the two-cluster solution. The high-risk group exhibited significantly higher incidences of transactional sexual relationships, sexual violence, and substance use, with the engagement in transactional relationships showing a notably higher F-statistic (F\u0026thinsp;=\u0026thinsp;85,902.655, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that economic hardship plays a crucial role in driving these behaviors. These findings highlight a disproportionate vulnerability within the high-risk group, emphasizing the need for targeted interventions to address the socio-economic factors contributing to these risky behaviors and improve the well-being of AGYW, particularly those facing financial instability and other forms of adversity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTransactional Sexual Relationships and Socio-Economic Vulnerabilities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe high-risk group demonstrated a significantly greater likelihood of engaging in transactional sexual relationships, as indicated by the variable \"Have you stayed in a relationship expecting gifts, favors, or financial help?\" (F\u0026thinsp;=\u0026thinsp;85,902.655, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that economic hardships and limited access to resources are driving factors behind these relationships. Additionally, the high-risk group reported higher levels of school dropout or non-attendance (\"Are you currently in school?\" F\u0026thinsp;=\u0026thinsp;105,585.313, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), emphasizing the role of socio-economic challenges such as poverty and household instability. These factors push AGYW toward risky survival strategies that expose them to further vulnerabilities, including sexual exploitation.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrevalence of Violence and Emotional Abuse\u003c/b\u003e \u003c/p\u003e \u003cp\u003eViolence, both physical and sexual, was significantly more prevalent in the high-risk group. Participants were more likely to report experiencing sexual violence (\"Have you experienced sexual violence?\" F\u0026thinsp;=\u0026thinsp;10,598.956, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and physical violence (\"Have you experienced physical violence?\" F\u0026thinsp;=\u0026thinsp;2,875.226, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Emotional abuse and neglect were also significant issues, with \"Have you experienced continuous ridicule, insults, or emotional neglect?\" (F\u0026thinsp;=\u0026thinsp;8,098.577, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showing marked differences between the groups. This compounded burden of physical and psychological abuse increases susceptibility to mental health issues such as depression, anxiety, and PTSD, impeding social and educational development.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSubstance Use as a Coping Mechanism\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSubstance use was significantly more prevalent among the high-risk group. Variables such as \"Have you ever consumed alcohol or used illicit drugs?\" (F\u0026thinsp;=\u0026thinsp;9,463.589, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \"Have you had more than 3 drinks at one time or used illicit drugs frequently?\" (F\u0026thinsp;=\u0026thinsp;1,367.420, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) distinguish this group from the low-risk group. These behaviors may serve as coping mechanisms for socio-economic and emotional hardships. Substance use impairs decision-making and increases the likelihood of engaging in unsafe sexual behaviors, exacerbating vulnerability to exploitation, health risks like HIV, and unwanted pregnancies. In contrast, the low-risk group demonstrated minimal substance use, highlighting behavioral disparities.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHousehold Headship and Family Dynamics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHousehold headship emerged as a significant factor influencing the high-risk group. The household headship variable (F\u0026thinsp;=\u0026thinsp;8.520, p\u0026thinsp;=\u0026thinsp;0.004) revealed that AGYW in the high-risk group were more likely to live in households where the head was absent or under the age of 18. This points to a lack of stable family structures and adult supervision, contributing to socio-economic and behavioral challenges. The absence of a strong parental figure or household head can lead to greater exposure to risky environments, economic hardship, limited access to education, and increased susceptibility to exploitation, compounding the vulnerabilities faced by AGYW in the high-risk group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA Results for Behavioral Risk Profiles of Adolescent Girls and Young Women (AGYW) in Rural Uganda\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRik Question\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStayed in a relationship for gifts or favors?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85,902.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced forced or coerced sexual activity?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e554,068.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol or drugs caused problems in the last three months?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,492.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently sexually active?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500,465.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngaged in transactional sex?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73,207.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently working?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,313.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced sexual violence?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,598.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced physical violence?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,875.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreatened with a knife, gun, or weapon in the last year?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e871.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTold you were unloved or undeserving of love in the last year?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,473.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTold you were unloved, wished dead, or made to feel worthless?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,590.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreatened with a knife, gun, or weapon in the past year?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,402.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePunched, kicked, whipped, or beaten with an object in the past year?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,371.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsed condoms regularly with the most recent partner?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,253.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChoked, smothered, or burned intentionally in the past year?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e714.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced ridicule, insults, or emotional neglect?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,098.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForced to have sex through physical force, harassment, threats, or tricks?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,734.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInappropriately touched (e.g., fondling or touching sexual body parts)?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,786.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad more than three drinks or used drugs frequently?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,367.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumed alcohol or used illicit drugs (e.g., marijuana, cocaine, shisha)?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,463.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAre both parents living?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19,841.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently in school?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105,585.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of sexual partners in the last 12 months?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e335,632.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e625,795.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIs the head of the household under the age of 18?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver been pregnant?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,739.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Profiling Risk Distribution Among Adolescent Girls and Young Women in Rural Uganda: A Cluster and Bivariate Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study utilized a two-step approach to identify and characterize risk factors. A cluster analysis first segmented participants into High Risk and Low-Risk groups based on their behavioral profiles. This was followed by a bivariate analysis shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which not only validated the clustering results but also provided a detailed characterization of the risk distribution across socio-economic, educational, and behavioral dimensions. The results reveal stark disparities between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;.001 for all key variables), emphasizing the robustness of the clusters and offering critical insights into the vulnerabilities faced by adolescent girls and young women (AGYW).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCluster Composition and Validation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe cluster analysis classified 24.7% (30,664) of participants into the High-Risk group and 75.3% (93,595) into the Low-Risk group. Age emerged as a key differentiator: 92.0% of High-Risk participants were aged 20\u0026ndash;25 years, compared to 84.8% of Low-Risk participants who were aged 9\u0026ndash;14 years (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The moderate Euclidean distance of 2.689 between the clusters confirms a clear separation, supporting the validity of the two-cluster solution. This segmentation highlights the life stage as a significant determinant of exposure to socio-economic and behavioral risks.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSocio-Economic Vulnerabilities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eParental status and employment status were critical socio-economic factors influencing risk profiles. Among High-Risk participants, 94.0% lacked both parents, compared to 52.4% in the Low-Risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Employment status also differed significantly, with 9.9% of High-Risk participants engaged in employment, while employment was nearly nonexistent in the Low-Risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). These findings suggest that the High-Risk cluster is characterized by greater socio-economic instability, where the absence of parental support and engagement in precarious work amplify vulnerabilities.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEducational Disparities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEducation emerged as a protective factor, predominantly defining the Low-Risk group. While 83.6% of Low-Risk participants were actively enrolled in school, 91.4% of High-Risk participants were not in school (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). This disparity underscores the role of education in mitigating risky behaviors. School attendance provides AGYW with resources, networks, and knowledge, reducing their susceptibility to risky behaviors. Conversely, school dropouts among the High-Risk group exacerbate socio-economic challenges and limit their capacity to avoid exploitative situations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBehavioral and Substance Use\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe High-Risk group exhibited significantly higher engagement in risky behaviors, including sexual activity and transactional relationships. A notable 85.3% of High-Risk participants reported being sexually active, compared to only 0.3% in the Low-Risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Furthermore, 43.9% of the High-Risk group engaged in transactional sex, highlighting economic desperation as a driving force behind these behaviors (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). In contrast, transactional relationships were virtually absent in the Low-Risk group. These findings illustrate how behavioral risks are concentrated in the High-Risk group, further entrenching socio-economic vulnerabilities.\u003c/p\u003e \u003cp\u003eSignificant disparities in alcohol and drug use were observed between the clusters. Among High-Risk participants, 24.4% reported consuming alcohol or using illicit drugs, compared to only 5.7% in the Low-Risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Frequent and heavy substance use, characterized by consuming more than three drinks at a time or using drugs regularly, was significantly higher in the High-Risk cluster (1.5%) compared to the Low-Risk cluster (0.0%) (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). These findings suggest that substance use serves as a coping mechanism for socio-economic and emotional stressors within the High-Risk group, further exacerbating their vulnerabilities.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExperience of Violence\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe High-Risk group faced a disproportionate burden of violence. All participants in this group reported experiencing physical violence, compared to 91.4% in the Low-Risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Additionally, 24.6% of High-Risk participants reported sexual violence, compared to only 5.1% in the Low-Risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Emotional abuse was also significantly higher among High-Risk participants. Exposure to violence not only increases immediate risks but also contributes to long-term psychological impacts, such as anxiety, depression, and PTSD. These patterns further highlight the compounded vulnerabilities of the High-Risk group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk Distribution Among Adolescent Girls and Young Women in Rural Uganda: A Cluster and Bivariate Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Risk (N, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow Risk (N, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge Group (years)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e9\u0026ndash;14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79,372 (84.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e15\u0026ndash;19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,462 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,223 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e20\u0026ndash;25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28,200 (92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAlcohol/Drug Use\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23,178 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88,293 (94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,486 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,302 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eParental Status\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28,821 (94.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49,000 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,843 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44,595 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHousehold Head Under 18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30,664 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93,569 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEducation Status (Currently in School)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28,020 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,378 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,644 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78,217 (83.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSexual Activity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,510 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93,299 (99.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26,154 (85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e296 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCondom Use\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27,583 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93,561 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,081 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTransactional Sex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,194 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93,586 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,470 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eThreatened with Weapon\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30,663 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89,502 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,093 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhysical Violence\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30,663 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85,571 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,024 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSexual Violence\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23,121 (75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88,783 (94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,543 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,812 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEmployment Status\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27,619 (90.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93,594 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,045 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequent Alcohol/Drug Use\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30,194 (98.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93,577 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e470 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Cluster and Bivariate Analysis of Risk Profiles Among AGYW in Rural Uganda\u003c/h2\u003e \u003cp\u003e \u003cb\u003eCollinearity Diagnostics and Model Stability\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe conducted the collinearity diagnostics for the logistic regression model and revealed no significant multicollinearity concerns, as indicated by a Condition Index ranging from 1.000 to 4.836, well below the threshold of 10. The Eigenvalues ranged from 0.095 to 2.221, reflecting distinct contributions of predictors to the model. Variance proportions confirmed predictor independence, with \"School Status\" and \"Sexual Activity\" contributing predominantly to Dimension 4 (89% and 43%, respectively; Eigenvalue\u0026thinsp;=\u0026thinsp;0.095), while \"Transactional Sex\" aligned with Dimension 3 (84%; Eigenvalue\u0026thinsp;=\u0026thinsp;0.321). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the findings demonstrate the stability and reliability of the model, ensuring that predictors independently explain risk classification among adolescent girls and young women.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCollinearity Diagnostics for Predictors of Risk Cluster Classification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCondition Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariance Proportions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLogistic Regression Results Following Cluster and Bivariate Analysis of Risk Profiles Among AGYW in Rural Uganda\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBuilding on the cluster and bivariate analysis, a binary logistic regression was conducted to identify predictors of High Risk and Low-Risk classification among adolescent girls and young women as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The model demonstrated excellent explanatory power, with a -2 Log Likelihood of 23,765.883, a Cox \u0026amp; Snell R Square of 60.4%, and a Nagelkerke R Square of 89.8%. These results indicate that the model accounts for nearly 90% of the variance in risk classification, underscoring its robustness in identifying socio-economic and behavioral predictors.\u003c/p\u003e \u003cp\u003eEducational engagement emerged as a critical protective factor. AGYW who were currently enrolled in school were 108 times more likely to be classified in the Low-Risk group compared to those not in school (AOR\u0026thinsp;=\u0026thinsp;108.154, 95% CI: 93.844\u0026ndash;124.646, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). This finding highlights the protective role of education, which reduces exposure to socio-economic vulnerabilities and risky behaviors. In contrast, sexual activity was a significant predictor of High-Risk classification. Participants who reported being sexually active were almost exclusively in the High-Risk group (AOR\u0026thinsp;=\u0026thinsp;0.000, 95% CI: 0.000\u0026ndash;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating a strong association between sexual activity and increased exposure to risks.\u003c/p\u003e \u003cp\u003eEconomic vulnerability, reflected in transactional sex, further distinguished High-Risk participants. AGYW who reported having engaged in sex in exchange for items such as airtime, food, or school fees were 1600 times more likely to belong to the High-Risk group (AOR\u0026thinsp;=\u0026thinsp;1599.966, 95% CI: 769.091\u0026ndash;3328.465, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). This underscores the role of economic hardship in driving risky behaviors, reflecting the need for interventions that address financial instability. The collective findings from the logistic regression, cluster analysis, and bivariate analysis provide a comprehensive understanding of risk profiles among AGYW, emphasizing the interplay of educational, behavioral, and economic factors in shaping vulnerabilities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted Odds Ratios (AOR) and 95% Confidence Intervals for Predictors of Risk Classification Among AGYW\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAOR (Exp(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI for AOR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.844\u0026ndash;124.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esexually activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u0026ndash;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransactional sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1599.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e769.091\u0026ndash;3328.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eOur findings revealed the intricate challenges adolescent girls and young women (AGYW) face in rural Uganda, where socio-economic, educational, and behavioral factors converge to shape their risk profiles. By clustering participants into High Risk and Low-Risk groups, we illuminated significant disparities: those in the High-Risk category were more likely to engage in transactional sex, experience violence, and drop out of school. These behaviors are not isolated but are deeply entrenched in broader issues of poverty, social inequality, and limited access to resources. These findings resonate with global evidence emphasizing the critical role of addressing socio-economic hardship to reduce risky behaviors among vulnerable populations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Interventions must target these systemic challenges to create pathways toward safer and more empowering futures for AGYW.\u003c/p\u003e \u003cp\u003eThe analysis showed that education emerged as a transformative protective factor for AGYW, significantly increasing their likelihood of belonging to the Low-Risk group. Girls enrolled in school were found to be 108 times more likely to be classified as low-risk, consistent with studies demonstrating the empowering effects of education (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Education equips young women with knowledge, critical thinking skills, and confidence to make safer decisions, shielding them from exploitative behaviors. However, persistent barriers such as poverty, early pregnancies, and entrenched cultural norms continue to limit educational opportunities for many AGYW (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Addressing these challenges requires practical measures such as scholarships, mentorship programs, and community-driven advocacy to ensure education remains accessible and inclusive, particularly in rural areas.\u003c/p\u003e \u003cp\u003eWe also noted that early and unprotected sexual activity significantly increased the vulnerabilities of AGYW, with sexually active participants overwhelmingly falling into the High-Risk category. These findings highlight the urgent need for comprehensive sexual and reproductive health (SRH) education that goes beyond biological facts to address the social and economic contexts driving risky sexual behaviors. Existing research supports the need for tailored interventions that include peer-led counseling, contraceptive access, and dismantling harmful cultural norms around sexuality (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Programs like PEPFAR have demonstrated the potential of integrated SRH initiatives to empower AGYW, making such approaches critical in this context.\u003c/p\u003e \u003cp\u003eTransactional sex emerged as a stark indicator of economic desperation among AGYW in the High-Risk group. Girls engaging in such relationships were found to be 1600 times more likely to belong to this category, illustrating the profound influence of poverty on decision-making. These findings align with other studies linking transactional sex to socio-economic hardships, including lack of parental support and limited income-generating opportunities (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The transactional nature of these relationships often places AGYW in exploitative and unsafe situations, further entrenching their vulnerabilities. To counter this, economic empowerment programs tailored for AGYW, such as vocational training, financial literacy education, and access to microfinance, are essential. These interventions should aim to provide sustainable livelihood options, enabling AGYW to break free from cycles of dependency and exploitation.\u003c/p\u003e \u003cp\u003eWe observed violence as a pervasive issue among AGYW in the High-Risk group, with many experiencing physical, sexual, and emotional abuse. This exposure not only heightens immediate risks but also leaves lasting psychological and social scars, contributing to mental health challenges like anxiety, depression, and PTSD. Evidence from similar settings highlights how violence disrupts education, limits economic opportunities, and perpetuates social exclusion (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Protecting AGYW from violence requires multi-sectoral approaches, including strengthening legal protections, community-level interventions to challenge harmful norms, and ensuring accessible support services for survivors. Survivor-centered programs, such as counseling and safe shelters, must be integrated into broader initiatives to ensure AGYW has the resources to recover and rebuild their lives.\u003c/p\u003e \u003cp\u003eSubstance use emerged as both a symptom and a driver of vulnerability among High-Risk AGYW. The significantly higher prevalence of substance use in this group underscores its role as a coping mechanism for socio-economic and emotional stressors. However, substance use exacerbates existing vulnerabilities, impairing judgment and increasing exposure to risky behaviors such as unprotected sex and violence. These findings align with studies that advocate for integrated prevention and support programs targeting AGYW struggling with substance use (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Community-based interventions, such as peer support networks and accessible counseling, could play a pivotal role in reducing substance use and addressing the root causes of emotional and psychological distress.\u003c/p\u003e"},{"header":"5.0 Practical and Policy Implications","content":"\u003cp\u003eThis study highlights the urgent need to support adolescent girls and young women (AGYW) in rural Uganda by addressing the challenges they face in their daily lives. On a practical level, keeping AGYW in school is a powerful way to shield them from risks like early pregnancies, transactional sex, and violence. Efforts to reduce school dropouts could include scholarships for vulnerable families, community mentorship programs, and flexible re-entry policies for girls who leave school due to early motherhood. Empowering AGYW economically through vocational training, small business support, and savings programs can help them build independence and avoid exploitative relationships. Additionally, sexual and reproductive health education should not only be accessible but also relatable, addressing the specific needs and experiences of AGYW. Establishing safe spaces where girls can seek mental health support and assistance after experiencing violence or substance misuse is crucial for their recovery and resilience.\u003c/p\u003e \u003cp\u003eOn a broader scale, policies must reflect a commitment to the holistic well-being of AGYW. Education policies should ensure that every girl has the opportunity to complete her studies, regardless of socio-economic barriers. Enforcing laws to prevent child marriages and gender-based violence while providing accessible and confidential reporting mechanisms can create safer communities for girls. Policies promoting reproductive health services need to account for cultural sensitivities while making care widely available and free from stigma. Economic programs must address the underlying poverty that drives many AGYW into high-risk behaviors by creating pathways to sustainable livelihoods for them and their families. A united effort involving government, civil society, and local leaders is needed to ensure these policies and interventions reach the girls who need them most. Together, we can create a future where AGYW have the resources and support to lead safe, healthy, and empowered lives.\u003c/p\u003e"},{"header":"6.0 Conclusion","content":"\u003cp\u003eThis study sheds light on the immense challenges faced by adolescent girls and young women (AGYW) in rural Uganda, revealing the harsh realities of poverty, violence, and limited opportunities that shape their lives. A significant proportion of these young women are categorized as high-risk, grappling with circumstances that no one should endure being forced to leave school, engaging in transactional sex out of economic desperation, or suffering violence that leaves both visible and invisible scars. Behind the statistics are real lives, filled with dreams and aspirations, yet constrained by systemic barriers that perpetuate inequality and vulnerability.\u003c/p\u003e \u003cp\u003eThese findings serve as a powerful reminder of the urgent need for collective action. Families, communities, policymakers, and stakeholders must collaborate to dismantle the structural challenges that perpetuate these cycles of risk. Education emerged as a significant factor in this study, offering a pathway to empowerment and reduced vulnerabilities. Ensuring access to quality education for AGYW must remain a top priority, complemented by interventions that address economic hardships through skills development, vocational training, and financial support.\u003c/p\u003e \u003cp\u003eMoreover, creating safe spaces where AGYW can thrive, free from violence and exploitation, is critical. Multi-sectoral approaches integrating legal protections, survivor-centered support, and community awareness are essential for fostering an environment where these young women can flourish. This research is a heartfelt call to action: with empathy, purpose, and investment, we can transform their stories of struggle into narratives of resilience, dignity, and hope. Together, we have the power to ensure brighter futures for AGYW.\u003c/p\u003e"},{"header":"7.0 Strengths, Limitations, and Directions for Future Research","content":"\u003cp\u003eOur study offers several strengths that enhance its reliability and impact. Using a combination of cluster analysis, bivariate analysis, and logistic regression, it provides a detailed understanding of the socio-economic, educational, and behavioral factors shaping the risk profiles of AGYW in rural Uganda. A large sample size of over 120,000 participants enhances statistical power and generalizability. The focus on key risk indicators, such as education, sexual activity, and transactional sex, alongside broader determinants like violence and substance use, ensures the findings are both actionable and contextually relevant for designing targeted interventions.\u003c/p\u003e \u003cp\u003eHowever, limitations exist. The cross-sectional design restricts causal inferences, necessitating longitudinal studies to track changes in risk factors over time. The reliance on self-reported data raises concerns about potential biases, particularly underreporting of sensitive issues like sexual activity and violence. Moreover, structural determinants, such as cultural norms or systemic barriers, are underexplored. Future research should include qualitative approaches to understand AGYW\u0026rsquo;s lived experiences and contextual dynamics. Additionally, studies focusing on marginalized sub-groups, such as AGYW with disabilities or those in conflict zones, would enhance inclusivity. Addressing these gaps can guide more comprehensive strategies to support AGYW and inform inclusive interventions in similar contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and received ethical approval from the Mildmay Uganda Research and Ethics Committee (REC REF 0804\u0026ndash;2018) and the Uganda National Council for Science and Technology (SS639ES).\u003c/p\u003e\n\u003ch2\u003eDisclosure\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest related to this study.\u003c/p\u003e\n\u003ch2\u003eData Sharing Statement\u003c/h2\u003e\n\u003cp\u003eData available from the corresponding author upon reasonable request, subject to ethical approvals and confidentiality.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable. This manuscript does not include any person\u0026rsquo;s data in any form (including individual details, images, or videos).\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis project was funded by the US Centers for Disease Control and Prevention (CDC), Kampala Office, under Cooperative Agreement GH002046. The implementation of the Mubende Region HIV Project, project code 18033, was managed by Mildmay Uganda. The study received no additional funding beyond this support.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization (Dedrix Stephenson Bindeeba, Jane Senyondo Nakawesi), Methodology (Dedrix Stephenson Bindeeba, Andrew Mugisa), Principal Investigation (Dedrix Stephenson Bindeeba), Data Extraction (Andrew Mugisa, Ronald Mulebeke), Formal Analysis (Dedrix Stephenson Bindeeba, Jane Senyondo Nakawesi), Writing Original Draft (Dedrix Stephenson Bindeeba), Validation (Catherine Senyimba, Ronald Mulebeke, Yvonne Karamagi), Writing Review \u0026amp; Editing (All authors), and Approval of Final Manuscript (All authors).\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe sincerely appreciate the leadership and communities of Mubende, Mityana, Kassanda, and Luwero Districts for their invaluable support and cooperation, which were instrumental in the successful implementation of this work. We also extend our deepest gratitude to the Centers for Disease Control and Prevention (CDC) Uganda office for funding this project under the DREAMS initiative.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData Availability StatementThis study utilized secondary data on AGYW participants. Full datasets cannot be shared publicly due to the presence of potentially identifying information. The content and responses from participants, even after anonymization, could still pose risks to confidentiality. Access to the data may be granted upon reasonable request and in alignment with the ethical approval obtained for this study. Requests can be directed to the the corresponding Author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSelin A, DeLong SM, Julien A, MacPhail C, Twine R, Hughes JP et al. Prevalence and Associations, by Age Group, of IPV Among AGYW in Rural South Africa. Sage Open. 2019;9(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChimbindi N, Birdthistle I, Shahmanesh M, Osindo J, Mushati P, Ondenge K et al. Translating DREAMS into practice: Early lessons from implementation in six settings. PLoS ONE. 2018;13(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonenberg G, Merrill KG, Atujuna M, Emerson E, Bray B, Bekker LG. Mental health outcomes of a pilot 2-arm randomized controlled trial of a HIV-prevention program for South African adolescent girls and young women and their female caregivers. BMC Public Health. 2021;21(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGodfrey C, Nkengasong J. Prioritizing Mental Health in the HIV/AIDS Response in Africa. N Engl J Med. 2023;389(7):581\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNAIDS, Global HIV. \u0026amp; AIDS statistics 2021 fact sheet. Published 2021. Accessed November 30, 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unaids.org/en/resources/fact-sheet\u003c/span\u003e\u003cspan address=\"https://www.unaids.org/en/resources/fact-sheet\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingla DR, Waqas A, Hamdani SU, Suleman N, Zafar SW, Zill-e-Huma et al. Implementation and effectiveness of adolescent life skills programs in low- and middle-income countries: A critical review and meta-analysis. Behav Res Ther. 2020;130.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYankah E, Aggleton P. EffEcts and EffEctivEnEss of LifE skiLLs Education for Hiv prEvEntion in Young pEopLE. Volume 20. AIDS Education and Prevention; 2008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMhlongo S, Mason-Jones AJ, Ford K. Sexual, reproductive and mental health among young men (10\u0026ndash;24) in low-and-middle income countries: a scoping review. Frontiers in Reproductive Health. Volume 5. Frontiers Media SA; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinskell K, Miller KS, Allen KA, Obong\u0026rsquo;o CO. Guiding and supporting adolescents living with HIV in sub-Saharan Africa: The development of a curriculum for family and community members. Child Youth Serv Rev. 2016;61:253\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhwezi WW, Abbo C, Okello ES, Akello G, Ovuga EBL. Assessment of the relationship between life events with psychosocial competence of students in selected secondary schools in northern and central Uganda. Afr Health Sci. 2020;20(3):1426\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuseno WK, Field SH, Iritani BJ, Odongo FS, Kwaro D, Amek NO, et al. Pathways to Depression and Poor Quality of Life Among Adolescents in Western Kenya: Role of Anticipated HIV Stigma, HIV Risk Perception, and Sexual Behaviors. AIDS Behav. 2021;25(5):1423\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuringe E, Materu J, Nyato D, Majani E, Ngeni F, Shao A 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).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorbu L, Gurung N. Action Research Addressing the Implementation of Life Skills Education on Adolescent Reproductive Sexual Health (Teenage Pregnancy, Sexual Activities, STIs, HIV/AIDs) for the Positive Behavioural Outcome. Asian J Biology. 2021;7\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNAIDS, Global HIV. \u0026amp; AIDS statistics 2020 fact sheet. Published 2020. Accessed November 30, 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unaids.org/en/resources/fact-sheet\u003c/span\u003e\u003cspan address=\"https://www.unaids.org/en/resources/fact-sheet\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNgoma-Hazemba A, Chavula MP, Sichula N, Silumbwe A, Mweemba O, Mweemba M et al. Exploring the barriers, facilitators, and opportunities to enhance uptake of sexual and reproductive health, HIV and GBV services among adolescent girls and young women in Zambia: a qualitative study. BMC Public Health. 2024;24(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang L, Chuang DM, Al-Raes M. Social support, mental health needs, and HIV risk behaviors: A gender-specific, correlation study. BMC Public Health. 2019;19(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWight D, Sekiwunga R, Namutebi C, Zalwango F, Siu GE. A Ugandan Parenting Programme to Prevent Gender-Based Violence: Description and Formative Evaluation. Res Soc Work Pract. 2022;32(4):448\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulstra CA, Hontelez JAC, Otto M, Stepanova A, Lamontagne E, Yakusik A, et al. Integrating HIV services and other health services: A systematic review and metaanalysis. PLoS Medicine. Volume 18. Public Library of Science; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnyanwu MU. Psychological distress in adolescents: prevalence and its relation to high-risk behaviors among secondary school students in Mbarara Municipality, Uganda. BMC Psychol. 2023;11(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePEPFAR. Annual progress report. Published 2023. Accessed November 30. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.state.gov/annual-reports-to-congress-on-the-presidents-emergency-plan-for-aids-relief/\u003c/span\u003e\u003cspan address=\"https://www.state.gov/annual-reports-to-congress-on-the-presidents-emergency-plan-for-aids-relief/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUganda Bureau of Statistics (UBOS). We Are Evidence-Based: Multidimensional Child Poverty in Uganda. Published 2024. Accessed November 30. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ubos.org\u003c/span\u003e\u003cspan address=\"https://www.ubos.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBose DL, Hundal A, Seth K, Hadi S, ul, Singh S, Singh S et al. PROTOCOL: Social and behaviour change communication interventions for strengthening HIV prevention and research among adolescent girls and young women in low- and middle-income countries: An evidence and gap map. Campbell Syst Reviews. 2022;18(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiraba A, Orindi B, Muuo S, Floyd S, Birdthistle IJ, Mumah J et al. Understanding HIV risks among adolescent girls and young women in informal settlements of Nairobi, Kenya: Lessons for DREAMS. PLoS ONE. 2018;13(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHokororo A, Kihunrwa AF, Kalluvya S, Changalucha J, Fitzgerald DW, Downs JA. Barriers to access reproductive health care for pregnant adolescent girls: A qualitative study in Tanzania. Acta Paediatr Int J Paediatrics. 2015;104(12):1291\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adolescent Girls and Young Women (AGYW), Risk Profiling, Transactional Sex, School Dropout, Violence and Vulnerability","lastPublishedDoi":"10.21203/rs.3.rs-5570319/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5570319/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdolescent girls and young women (AGYW) in rural Uganda face a range of socio-economic, educational, and behavioral challenges that heighten their vulnerability to adverse health and social outcomes. Understanding their baseline risk profiles is essential for designing effective, targeted interventions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized a retrospective, cross-sectional design to analyze secondary data from 124,258 AGYW across four districts in Uganda. K-means cluster analysis was employed to categorize participants into High-Risk and Low-Risk groups based on socio-economic, educational, and behavioral indicators. Bivariate analysis was used to validate the distinctiveness of these clusters, and logistic regression identified predictors of high-risk behaviors, including transactional sex, school dropout, and experiences of violence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe analysis revealed that 24.7% of AGYW were classified into the High-Risk group, with the majority (92%) being aged 20\u0026ndash;25 years, compared to 9\u0026ndash;14 years in the Low-Risk group. High-risk AGYW were significantly more likely to engage in transactional sex, drop out of school, and experience violence. Education emerged as a protective factor, with school enrollment increasing the likelihood of Low-Risk classification by 108 times (AOR\u0026thinsp;=\u0026thinsp;108.154, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Economic desperation, particularly transactional sex, was a strong predictor of being classified as High-Risk (AOR\u0026thinsp;=\u0026thinsp;1599.966, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, experiences of violence and substance use further compounded the vulnerabilities of this group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAdolescent girls and young women in rural Uganda, particularly those aged 20\u0026ndash;25, face significant vulnerabilities due to poverty, violence, and limited access to education. Integrated interventions focusing on education, economic empowerment, and violence prevention are critical for reducing these risks, fostering resilience, and promoting sustainable change for AGYW. Addressing these challenges through targeted strategies will improve their well-being and contribute to long-term development outcomes.\u003c/p\u003e","manuscriptTitle":"Profiling the Plight of Adolescent Girls and Young Women: Risks and Pathways for Intervention in Rural Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 08:23:58","doi":"10.21203/rs.3.rs-5570319/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8e71aa6-d5d9-4a7e-969c-0fa19589a16a","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T04:23:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 08:23:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5570319","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5570319","identity":"rs-5570319","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}