Risky sexual behaviours among Ugandan university students: A pilot study exploring the role of adverse childhood experiences, substance use history, and family environment

PLoS ONE, Volume 17, No. 11 November, Year 2022

Interpretation

University students are known to have risky sexual behaviours (RSBs). The severity of the RSB is influenced by many factors, including the family environment, exposure to adverse childhood events (ACEs), and the use of addictive substances. However, there is limited information about the influence of ACEs and the family environment of these students in low-and medium-income countries (LMICs). Therefore, a pilot study was conducted among university students from a LMIC, Uganda. Methods The present study comprised a cross-sectional online survey among Ugandan students at a public university (N = 316; 75% male; 52.2% aged between 18–22 years). The survey included questions relating to socio-demographic information, family environmental information, the Sexual Risk Survey (SRS), and the Adverse Childhood Experiences-International Questionnaire (ACE-IQ). Results Over half (53.8%) reported having had sexual intercourse. Males reported over two times higher mean total SRS score compared to females (χ2 = 4.06, p = 0.044). Approximately one-sixth of the sample had drunk alcohol or used illicit psychoactive substances in the past six months (16.1%). Among four regression analysis models, sociodemographic variables predicted the highest variance (13%), followed by family environment variables (10%), and both psychoactive substance use history (past six months) and ACEs individually explained approximately 5% variance in total SRS score, with the final model predicting 33% of the variance in RSB. Conclusions The present study demonstrated a gender disparity with males involved in more RSB than females, as has been reported in most previous RSB studies. Family environment, sociodemographic factors, substance use, and ACEs all appear to contribute to RSB among university students. These findings will benefit other researchers exploring factors associated with RSB among university students and will help develop interventions to reduce RSB to protect students from unwanted pregnancies, sexually transmitted diseases, and HIV/AIDS. The present pilot study was a cross-sectional online survey conducted among students of Mbarara University of Science and Technology (MUST), a public university in Southwestern Uganda. Data were collected from April 3 to May 23, 2021, using Google Forms. The survey link was shared on online platforms like WhatsApp groups and personal student emails to students in the university’s six faculties (i.e., Medicine; Computing and Informatics; Business and Management Sciences; Science; Applied Sciences and Technology; and Interdisciplinary Studies), and its two institutions (i.e., Tropical Forest Conservation; and Maternal New-born and Child Health). MUST had over 4,269 undergraduate students enrolled in the academic year 2019/2020, and all were eligible to participate in the study. A total of 316 students participated in the study. The data were collected during the second year of the COVID-19 pandemic when students had just started returning to in-person teaching, and most of the restrictions concerning COVID-19 prevention, such as spatial distancing, had been relaxed. The participants were enrolled using a snowball convenience sampling technique where students who were approached could forward the survey link to other students in the university. To avoid physical contact and to include as many eligible students as possible, snowball convenience sampling was employed to enable efficient recruitment of university students during the COVID-19 pandemic as has been employed in previous studies conducted inside or outside Uganda [31–34]. The online survey link was circulated on the different faculty and student social media platforms like end-to-end encrypted WhatsApp groups and students’ personal emails. The survey tool was designed to only allow a single response from each student participant. Potential participants received a message requesting them to participate and to share the survey link with their fellow students at MUST. The survey was in English (the language of all teaching in Ugandan universities). Questions were pretested among the students before the commencement of the study to ensure that they were all well understood. The online survey tool included a participant information page, which provided participants with information to understand the intentions of the study, and an informed consent page which all participants completed before responding and participating in the study. As there were no mandatory questions to respond to, participants were free to leave questions unanswered if they were not comfortable and/or sure with the response. However, all participants responded to the questions except one question about the number of sexual partners. In addition, the survey included a sociodemographic questionnaire, family environment questions, the Adverse Childhood Experiences-International Questionnaire (ACE-IQ), and the Sexual Risk Survey (SRS). Given that participants responded to the tool items at their time of convenience, participants were advised to use a calendar of the past six months to accurately remember their past sexual experiences and to minimize memory recall bias (i.e., enhance accurate recall). Sociodemographic data collected included relevant personal information regarding basic participant characteristics; participant’s age (in years), gender (female, male), marital status (single, separated/divorced, married/cohabiting), and the region of the country of origin (Central, Western, Eastern, and Northern Uganda). A single question (i.e., “In the past six months have you used alcohol or illicit drugs?”) with a binary response (yes/no) was used to assess recent substance use history. Those with a ‘yes’ response selected the substances used (i.e., alcohol and/or illicit drugs). Family environment data collected included information on the family type (i.e., nuclear family, extended family, step-parent family, grandparent family, and single parent family); the number of family members; the number of children; primary care provider (i.e., parent, step-parent, uncle/aunt, sibling, guardian, grandparent, NGO, and self-sponsored); birth position in the family; parent’s highest level of education; having a family member with mental illness, or who abuses drugs/substance, or with a criminal record; and whether a parent died before 18 years of age. The 23-item SRS [3] was used to assess sexual risk behaviour among college students over a period of six months prior to the study. It comprises five subscales of risky sexual behaviours: sexual risk-taking with uncommitted partners (e.g., “How many times have you had sex with someone you don’t know well or just met?”), risky sex acts (e.g., “How many times have you or your partner used alcohol or drugs before or during sex?”), impulsive sexual behaviours (e.g., “How many times have you had an unexpected and unanticipated sexual experience?”), intent to engage in risky sexual behaviours (e.g., “How many times have you gone out to bars/parties/ social events with the intent of ‘‘hooking up” and having sex with someone?”) and risky anal sex acts (e.g., “How many times have you had anal sex without a condom?”) [35], for details, see S1 Table. Raw response frequencies were recorded and converted into ordinal categories which assign weights to the level of sexual risk-taking, ranging from 0 to 4, employing a method used by the scale developers [35]. This approach addresses the skewness of frequency data commonly used in sexual risk assessment studies. The total sexual risk score is calculated as a sum of all raw items’ responses, with total scores ranging from 0 to 92. Higher scores indicate higher sexual risk riskiness. The SRS has shown very good psychometric properties [3], although the Cronbach alpha was 0.69 in the present study. However, the Cronbach alphas for the five subscales were good to excellent: risk-taking with uncommitted partners (α = 0.92), risky sex acts (α = 0.75), impulsive sexual behaviours (α = 0.83), intent to engage in risky sexual behaviours (α = 0.82), and risky anal sex acts (α = 0.82). The 29-item ACE-IQ [36] was used to assess 13 childhood adversities. Items (e.g., “During the first 18 years of your life, did someone actually have oral, anal, or vaginal intercourse with you when you did not want them to?”) are responded to on a binary (yes/no) scale. Consequently, total scores range from 0 to 13, where a higher score indicates greater childhood adversity. In previous sub-Saharan African studies, the ACE-IQ has demonstrated good psychometric properties among adolescents and young adults [37–39]. The Cronbach alpha of the ACE-QI in the present study was 0.82. The present study received formal ethical approval from Mbarara University of Science and Technology research ethics committee (MUSTREC#04/01-21). Participants were informed about the sensitive nature of the questions on the SRS and the ACE-IQ due to the potential of some questions to give rise to distressing and negative emotions. Consequently, participants did not have to respond to such questions and were free to end the survey at any point with absolutely no penalty whatsoever. Data confidentiality and anonymity were emphasized. Participation was voluntary, and participants provided informed consent. The survey included a detailed consent form that informed the participants about the study, the risks, and the benefits. All participants were adults who provided their written informed consent to participate in the study; these were automatically granted entry to the study survey. A link to the departmental psychiatry team was provided within the survey, and participants could access the link for help and support if they needed it. Data were imported into STATA Version.15 statistical software, where data were cleaned and analysed. Descriptive statistics are presented in percentages, frequencies, medians, ranges and interquartile ranges. The total score on the SRS and its subscales were analysed as continuous variables. Gender differences in sexual risk-taking and behaviours were assessed by Wilcoxon rank-sum (total scores of SRS and all SRS subscales) and chi-square tests (age at which sexual intercourse first occurred and the number of sexual partners). The Gaussian assumption was used to test for normality of continuous data and was confirmed with Shapiro-Wilks’s test and histograms. Hierarchical Poisson regression was used to determine the predictors of RSBs, and four models were generated. All statistics were calculated at a 95% level of confidence and 5% statistical error.

AI Digest

Study Justification:

– Risky sexual behaviors (RSBs) among university students are a significant public health concern.
– Limited information exists about the influence of adverse childhood experiences (ACEs) and family environment on RSBs in low- and medium-income countries (LMICs).
– This pilot study aimed to explore the role of ACEs, substance use history, and family environment in RSBs among Ugandan university students.

Highlights:

– Over half (53.8%) of the participants reported having had sexual intercourse.
– Males had higher mean total RSB scores compared to females.
– Approximately 16.1% of the sample had used alcohol or illicit drugs in the past six months.
– Sociodemographic variables predicted the highest variance in RSBs, followed by family environment variables.
– Psychoactive substance use history and ACEs individually explained approximately 5% variance in RSBs.
– The final regression model predicted 33% of the variance in RSBs.

Recommendations:

– Develop interventions to reduce RSBs among university students.
– Address sociodemographic factors, family environment, substance use, and ACEs in these interventions.
– Focus on protecting students from unwanted pregnancies, sexually transmitted diseases, and HIV/AIDS.

Key Role Players:

– Researchers specializing in sexual health and behavior.
– Public health officials and policymakers.
– University administrators and student support services.
– NGOs and community organizations working on sexual health education and prevention.

Cost Items for Planning Recommendations:

– Research funding for further studies and intervention development.
– Training and capacity building for researchers and healthcare professionals.
– Implementation of educational programs and campaigns.
– Access to healthcare services, including contraception and STI testing.
– Monitoring and evaluation of intervention effectiveness.
– Collaboration and coordination between stakeholders.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a pilot study conducted among university students in Uganda. The study utilized a cross-sectional online survey and included questions on socio-demographic information, family environment, sexual risk behavior, and adverse childhood experiences. The study found that sociodemographic variables, family environment variables, substance use history, and adverse childhood experiences all contributed to risky sexual behaviors among university students. The study had a sample size of 316 students and used statistical analysis to determine the predictors of risky sexual behaviors. While the study provides valuable insights into the factors influencing risky sexual behaviors among university students in Uganda, it is important to note that it is a pilot study with a relatively small sample size. Therefore, further research with a larger and more diverse sample is needed to validate and generalize the findings. Additionally, the study relied on self-reported data, which may be subject to recall bias and social desirability bias. To improve the strength of the evidence, future studies could consider using a larger sample size, employing a longitudinal design to establish causal relationships, and incorporating objective measures of risky sexual behaviors.

Abstract

The present pilot study was a cross-sectional online survey conducted among students of Mbarara University of Science and Technology (MUST), a public university in Southwestern Uganda. Data were collected from April 3 to May 23, 2021, using Google Forms. The survey link was shared on online platforms like WhatsApp groups and personal student emails to students in the university’s six faculties (i.e., Medicine; Computing and Informatics; Business and Management Sciences; Science; Applied Sciences and Technology; and Interdisciplinary Studies), and its two institutions (i.e., Tropical Forest Conservation; and Maternal New-born and Child Health). MUST had over 4,269 undergraduate students enrolled in the academic year 2019/2020, and all were eligible to participate in the study. A total of 316 students participated in the study. The data were collected during the second year of the COVID-19 pandemic when students had just started returning to in-person teaching, and most of the restrictions concerning COVID-19 prevention, such as spatial distancing, had been relaxed. The participants were enrolled using a snowball convenience sampling technique where students who were approached could forward the survey link to other students in the university. To avoid physical contact and to include as many eligible students as possible, snowball convenience sampling was employed to enable efficient recruitment of university students during the COVID-19 pandemic as has been employed in previous studies conducted inside or outside Uganda [31–34]. The online survey link was circulated on the different faculty and student social media platforms like end-to-end encrypted WhatsApp groups and students’ personal emails. The survey tool was designed to only allow a single response from each student participant. Potential participants received a message requesting them to participate and to share the survey link with their fellow students at MUST. The survey was in English (the language of all teaching in Ugandan universities). Questions were pretested among the students before the commencement of the study to ensure that they were all well understood. The online survey tool included a participant information page, which provided participants with information to understand the intentions of the study, and an informed consent page which all participants completed before responding and participating in the study. As there were no mandatory questions to respond to, participants were free to leave questions unanswered if they were not comfortable and/or sure with the response. However, all participants responded to the questions except one question about the number of sexual partners. In addition, the survey included a sociodemographic questionnaire, family environment questions, the Adverse Childhood Experiences-International Questionnaire (ACE-IQ), and the Sexual Risk Survey (SRS). Given that participants responded to the tool items at their time of convenience, participants were advised to use a calendar of the past six months to accurately remember their past sexual experiences and to minimize memory recall bias (i.e., enhance accurate recall). Sociodemographic data collected included relevant personal information regarding basic participant characteristics; participant’s age (in years), gender (female, male), marital status (single, separated/divorced, married/cohabiting), and the region of the country of origin (Central, Western, Eastern, and Northern Uganda). A single question (i.e., “In the past six months have you used alcohol or illicit drugs?”) with a binary response (yes/no) was used to assess recent substance use history. Those with a ‘yes’ response selected the substances used (i.e., alcohol and/or illicit drugs). Family environment data collected included information on the family type (i.e., nuclear family, extended family, step-parent family, grandparent family, and single parent family); the number of family members; the number of children; primary care provider (i.e., parent, step-parent, uncle/aunt, sibling, guardian, grandparent, NGO, and self-sponsored); birth position in the family; parent’s highest level of education; having a family member with mental illness, or who abuses drugs/substance, or with a criminal record; and whether a parent died before 18 years of age. The 23-item SRS [3] was used to assess sexual risk behaviour among college students over a period of six months prior to the study. It comprises five subscales of risky sexual behaviours: sexual risk-taking with uncommitted partners (e.g., “How many times have you had sex with someone you don’t know well or just met?”), risky sex acts (e.g., “How many times have you or your partner used alcohol or drugs before or during sex?”), impulsive sexual behaviours (e.g., “How many times have you had an unexpected and unanticipated sexual experience?”), intent to engage in risky sexual behaviours (e.g., “How many times have you gone out to bars/parties/ social events with the intent of ‘‘hooking up” and having sex with someone?”) and risky anal sex acts (e.g., “How many times have you had anal sex without a condom?”) [35], for details, see S1 Table. Raw response frequencies were recorded and converted into ordinal categories which assign weights to the level of sexual risk-taking, ranging from 0 to 4, employing a method used by the scale developers [35]. This approach addresses the skewness of frequency data commonly used in sexual risk assessment studies. The total sexual risk score is calculated as a sum of all raw items’ responses, with total scores ranging from 0 to 92. Higher scores indicate higher sexual risk riskiness. The SRS has shown very good psychometric properties [3], although the Cronbach alpha was 0.69 in the present study. However, the Cronbach alphas for the five subscales were good to excellent: risk-taking with uncommitted partners (α = 0.92), risky sex acts (α = 0.75), impulsive sexual behaviours (α = 0.83), intent to engage in risky sexual behaviours (α = 0.82), and risky anal sex acts (α = 0.82). The 29-item ACE-IQ [36] was used to assess 13 childhood adversities. Items (e.g., “During the first 18 years of your life, did someone actually have oral, anal, or vaginal intercourse with you when you did not want them to?”) are responded to on a binary (yes/no) scale. Consequently, total scores range from 0 to 13, where a higher score indicates greater childhood adversity. In previous sub-Saharan African studies, the ACE-IQ has demonstrated good psychometric properties among adolescents and young adults [37–39]. The Cronbach alpha of the ACE-QI in the present study was 0.82. The present study received formal ethical approval from Mbarara University of Science and Technology research ethics committee (MUSTREC#04/01-21). Participants were informed about the sensitive nature of the questions on the SRS and the ACE-IQ due to the potential of some questions to give rise to distressing and negative emotions. Consequently, participants did not have to respond to such questions and were free to end the survey at any point with absolutely no penalty whatsoever. Data confidentiality and anonymity were emphasized. Participation was voluntary, and participants provided informed consent. The survey included a detailed consent form that informed the participants about the study, the risks, and the benefits. All participants were adults who provided their written informed consent to participate in the study; these were automatically granted entry to the study survey. A link to the departmental psychiatry team was provided within the survey, and participants could access the link for help and support if they needed it. Data were imported into STATA Version.15 statistical software, where data were cleaned and analysed. Descriptive statistics are presented in percentages, frequencies, medians, ranges and interquartile ranges. The total score on the SRS and its subscales were analysed as continuous variables. Gender differences in sexual risk-taking and behaviours were assessed by Wilcoxon rank-sum (total scores of SRS and all SRS subscales) and chi-square tests (age at which sexual intercourse first occurred and the number of sexual partners). The Gaussian assumption was used to test for normality of continuous data and was confirmed with Shapiro-Wilks’s test and histograms. Hierarchical Poisson regression was used to determine the predictors of RSBs, and four models were generated. All statistics were calculated at a 95% level of confidence and 5% statistical error.

Materials

https://efashare.b-cdn.net/materials/e60154741ccc3c513a840eea4b15497c457003963296b8952915aa9ee0753eaa/pone.0277129.s001.docx

Innovations

Based on the provided information, here are some potential recommendations for innovations to improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide information and resources related to maternal health, including prenatal care, postnatal care, family planning, and breastfeeding. These apps can also offer reminders for appointments and medication, as well as access to telemedicine consultations with healthcare providers.

2. Telemedicine Services: Implement telemedicine services that allow pregnant women to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to prenatal care, monitoring, and advice without the need for in-person visits.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and basic healthcare services to pregnant women in underserved areas. These workers can conduct home visits, offer antenatal and postnatal care, and refer women to healthcare facilities when necessary.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access maternal health services. These vouchers can cover the cost of prenatal care, delivery, and postnatal care, ensuring that women can afford the necessary healthcare services.

5. Maternal Health Clinics: Establish dedicated maternal health clinics in areas with limited access to healthcare facilities. These clinics can provide comprehensive maternal health services, including prenatal care, delivery, postnatal care, family planning, and breastfeeding support.

6. Health Education Programs: Develop and implement health education programs that focus on maternal health and target both women and their families. These programs can provide information on prenatal care, nutrition, hygiene, and the importance of seeking timely healthcare during pregnancy.

7. Transportation Support: Address transportation challenges by providing transportation support to pregnant women in remote areas. This can involve organizing community transportation services or partnering with existing transportation providers to ensure that women can reach healthcare facilities for prenatal care and delivery.

8. Maternal Health Hotlines: Establish hotlines staffed by trained healthcare professionals who can provide information, advice, and support to pregnant women. These hotlines can be accessed via phone or messaging platforms and can help address concerns, provide guidance, and connect women with appropriate healthcare services.

9. Maternal Health Monitoring Systems: Develop innovative monitoring systems that use technology to track and monitor the health of pregnant women. This can include wearable devices, remote monitoring tools, and data analytics to detect potential complications and provide timely interventions.

10. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private sector resources, expertise, and infrastructure to expand healthcare services and reach underserved populations.

It is important to note that the implementation of these innovations should be context-specific and consider the unique needs and challenges of the target population.

AI Innovations Description

The pilot study conducted among university students in Uganda identified several factors that contribute to risky sexual behaviors (RSBs) among this population. These factors include the family environment, exposure to adverse childhood experiences (ACEs), substance use history, and sociodemographic variables. The study found that males reported higher levels of RSB compared to females. Approximately one-sixth of the participants reported alcohol or illicit drug use in the past six months.

The study recommends the development of interventions to reduce RSB among university students in order to protect them from unwanted pregnancies, sexually transmitted diseases, and HIV/AIDS. These interventions should take into account the influence of family environment, ACEs, substance use, and sociodemographic factors on RSB. By addressing these factors, access to maternal health can be improved by reducing the occurrence of unintended pregnancies and the transmission of sexually transmitted infections.

It is important to note that this study was a pilot study and further research is needed to validate and expand on these findings. However, the results provide valuable insights into the factors contributing to RSB among university students in Uganda and can guide the development of targeted interventions to improve access to maternal health.

AI Innovations Methodology

The pilot study described in the provided text aimed to explore the role of adverse childhood experiences (ACEs), substance use history, and family environment in influencing risky sexual behaviors (RSBs) among university students in Uganda. The study utilized a cross-sectional online survey methodology to collect data from 316 students at Mbarara University of Science and Technology (MUST). The survey included questions on socio-demographic information, family environment, the Sexual Risk Survey (SRS), and the Adverse Childhood Experiences-International Questionnaire (ACE-IQ).

To simulate the impact of recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Identify the recommendations: Based on the findings of the pilot study and existing literature, identify potential recommendations that could improve access to maternal health. These recommendations could include interventions targeting family environment, substance use prevention, ACEs mitigation, and education on safe sexual practices.

2. Define the simulation parameters: Determine the variables and parameters that will be used to simulate the impact of the recommendations. This could include factors such as the number of women accessing maternal health services, the reduction in risky sexual behaviors, the improvement in family environment, and the decrease in substance use.

3. Develop a simulation model: Create a mathematical or computational model that incorporates the identified variables and parameters. This model should simulate the potential impact of the recommendations on improving access to maternal health. The model could use statistical techniques, such as regression analysis or predictive modeling, to estimate the effects of the recommendations.

4. Validate the model: Validate the simulation model by comparing its outputs with real-world data or existing evidence. This step ensures that the model accurately represents the potential impact of the recommendations on improving access to maternal health.

5. Run simulations: Use the validated simulation model to run multiple simulations with different scenarios and assumptions. This will allow for a comprehensive analysis of the potential impact of the recommendations under various conditions.

6. Analyze the results: Analyze the results of the simulations to assess the potential effectiveness of the recommendations in improving access to maternal health. This analysis could include evaluating the magnitude of the impact, identifying any potential limitations or barriers, and exploring the cost-effectiveness of the recommendations.

7. Communicate the findings: Present the findings of the simulation analysis in a clear and concise manner. This could include visualizations, reports, or presentations that highlight the potential benefits of the recommendations and provide actionable insights for policymakers, healthcare providers, and other stakeholders.

By following this methodology, policymakers and stakeholders can gain valuable insights into the potential impact of recommendations on improving access to maternal health. This information can guide the development and implementation of effective interventions and policies to address the identified issues and promote better maternal health outcomes.

Author

Dima Health

Statistics:

Citations: 1

Identifiers:

DOI: 10.1371/journal.pone.0277129

Research Areas:

Cancer, Community Interventions, COVID, Disparities, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Mental Health, Sexual and Reproductive Health, Social Determinants, Substance Abuse, Technology and Innovations

Study Countries:

Uganda

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female