Beyond Their HIV Status: the Occurrence of Multiple Health Risk Behavior Among Adolescents from a Rural Setting of Sub-Saharan Africa

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Study Justification:
– The study aimed to investigate the clustering of health risk behaviors (HRB) among adolescents in a rural setting of Sub-Saharan Africa, specifically in Kenya.
– The study focused on the role of HIV in predicting HRB clustering among adolescents.
– The findings of the study aimed to inform the development of screening and intervention strategies to address psychosocial risk factors and promote adolescent health in Kenya.
Study Highlights:
– The study included 588 adolescents from rural Kenya, categorized into three groups: perinatally HIV infected, perinatally HIV exposed but uninfected, and HIV unexposed/uninfected.
– Latent class analysis identified four risk behavior classes among the adolescents.
– The study found that there were no significant differences in behavioral class membership across the three HIV groups.
– Factors associated with higher risk behavioral classes included older age, mental distress, and feeling unsafe in the neighborhood.
– Better working memory was found to be protective against higher risk behavioral classes.
Study Recommendations:
– The study recommends the inclusion of screening and interventions for internalizing mental health problems and deficits in executive functioning among adolescents in Kenya.
– The study highlights the importance of involving family members and communities in addressing psychosocial risk factors in adolescents.
Key Role Players:
– Researchers and scientists in the field of adolescent health and HIV prevention.
– Healthcare professionals and counselors who can provide screening and interventions for mental health problems.
– Family members and community leaders who can support and promote adolescent health.
Cost Items for Planning Recommendations:
– Training and capacity building for healthcare professionals and counselors.
– Development and implementation of screening tools and intervention programs.
– Community outreach and education initiatives.
– Monitoring and evaluation of the effectiveness of interventions.
– Collaboration and coordination between different stakeholders involved in adolescent health promotion.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a cross-sectional study conducted in rural Kenya among 588 adolescents. The study utilized latent class analysis and multinomial logistic regression to investigate the clustering of health risk behaviors and its associated factors. The study provides descriptive statistics, bivariate analyses, and multinomial logistic regression results. However, the evidence is limited to a single study and does not provide information on the strength of the associations or the generalizability of the findings. To improve the evidence, further research could include longitudinal studies to establish causal relationships and replicate the findings in different populations.

Background: Health risk behaviors during adolescence may cluster into patterns that might be predicted by specific factors, among which HIV may have an important role. Method: In a cross-sectional study conducted between 2017 and 2018, clustering of HRB and its associated factors was investigated in rural Kenya among 588 adolescents (36% perinatally HIV infected; 28% perinatally HIV exposed but uninfected; and 36% HIV unexposed/uninfected). Latent class analysis of 22 behaviors followed by multinomial logistic regression were conducted. Four risk behavior classes were identified. Results: No significant differences were found in behavioral class membership across the three HIV groups (p = 0.366). The risk of membership to the higher risk behavioral classes relative to class 1 (the substance and drug abstinent low risk takers) increased with older adolescent age (p = 0.047), increased among adolescent who experienced mental distress (p < 0.001), and those who felt unsafe in their neighborhood (p < 0.002). Better working memory (p = 0.0037) was found to be protective. Conclusion: The results highlight a need to include screening and interventions for internalizing mental health problems and deficits in executive functioning, as well as steps to involve family members and communities to address psychosocial risk factors in adolescents in Kenya.

The first wave of data collection was conducted between November 2017 and October 2018, providing a baseline assessment for the ongoing longitudinal study, the Adolescent Health Outcomes Study (AHOS). The study was conducted at the Centre for Geographic Medicine Research-Coast at the Kenya Medical Research Institute (CGMRC-KEMRI) and all the participants were residents of Kilifi County at the coast of Kenya. About 1.4 million people resided in Kilifi County by 2016 of whom the majority (61%) were rural dwellers and 22% were aged 10–20 years [53]. Kilifi is termed as a “medium HIV county” with 45 per 1000 affected of whom 6000 (19%) were youth and young adults, aged 15–24 years [50]. About 891 km2 of Kilifi County is covered by Kilifi Health and Demographic Surveillance System (KHDSS) [54], a region with various ongoing CGMRC-KEMRI research activities. Perinatally HIV infected and perinatally HIV exposed but uninfected adolescents and their caregivers were recruited through sequential sampling from all families that attended HIV clinic days at eight HIV treatment and care clinics at health facilities (hospitals and health centres) in Kilifi County. Recruitment was conducted by a trained research assistant in liaison with health workers at the participating HIV treatment facilities. Some of the perinatally HIV exposed but uninfected adolescents and their caregivers were also recruited by visiting families affected with HIV within their community with the assistance of a community health worker based at an HIV clinic. HIV unexposed and uninfected adolescents were randomly sampled among households within the KHDSS using the KHDSS population register [54]. Medical records at the health facilities were used to confirm perinatally HIV-infected adolescents’ HIV status. As part of the eligibility criteria, the adolescents had to be fully aware of their HIV status and that of their biological mother. The adolescent’s awareness of maternal HIV status was an important criterion for abating the ethical dilemma and emotional burden for example, tension and mistrust, that can potentially arise from the abrupt awareness of the source of HIV infection by the adolescents participating in this study and the members of their household. HIV exposure of perinatally HIV exposed but uninfected adolescents was ascertained from maternal medical records (antenatal care cards) confirming HIV infection of the mother during pregnancy. Additionally, recent medical records of the adolescent (if available) were used to ascertain that the perinatally HIV exposed but uninfected adolescent was HIV uninfected. The perinatally HIV exposed but uninfected adolescent also had to be aware of his or her biological mothers’ HIV status for study eligibility. HIV unexposed and uninfected adolescents were not directly tested for HIV but recruitment was restricted to those whose mothers willingly shared their HIV test results at the time of their pregnancy with the participating adolescent. For both perinatally HIV exposed but uninfected adolescents and HIV unexposed and uninfected adolescents, a brief screening checklist was utilized to exclude adolescents who had experienced severe childhood illness or were having recurring health problems so as to minimize the possibility of including HIV-infected adolescents among the control group. Besides, a more detailed assessment of the adolescents’ medical history, symptoms, and concerns was also done by a trained clinician during the assessments for data collection. All eligible adolescent participants had to be accompanied by a legal caretaker during their appointment for data collection at the CGMRC-KEMRI. Monetary reimbursement of 300 Kenyan shillings (about 3 US dollars) and a transport fee reimbursement were given to the accompanying caretaker and a snack was provided to all participants prior to the assessments. Ethical approval to conduct this study was obtained from the Kenya Medical Research Institute Scientific and Ethics Review Unit (KEMRI/SERU/CGMR-C/084/3454). Permission was also obtained from the Kilifi County government, department of health services (HP/KCHS/VOL.VIX/80). The current study comprises 558 (199 HIV unexposed and uninfected, 158 perinatally HIV exposed but uninfected, and 201 perinatally HIV infected) adolescents aged 12–17 years. Initially, 638 potentially eligible adolescent participants (227 HIV unexposed and uninfected, 185 perinatally HIV exposed but uninfected, and 226 perinatally HIV infected) had shown interest in participating, but 560 ultimately took part. Of these 560, data on HRB outcomes from 2 participants was completely missing, therefore they were excluded. Non-response (n = 78) was mainly attributable to silent refusal (i.e., lack of follow-up in making or attending visits (n = 39, 50%), or to direct refusal of further contact with the research team (n = 9, 11.5%), to the failure to meet inclusion criteria (n = 7, 8.9%), and to participant relocation (n = 2, 2.6%)). Some eligible participants were not scheduled for an assessment after attaining the required sample size quotas (n = 21, 27%). Overall, the non-respondents did not differ by HIV group composition (34.6% HIV unexposed and uninfected, 34.6% perinatally HIV exposed but uninfected and 30.8% perinatally HIV infected) and sex distribution (p = 0.75). However, among the non-respondents, the HIV unexposed and uninfected group was the oldest (mean = 13.9 years, SD = 1.7, p = 0.002). Informed consent was obtained from all the individual participants included in the study. Written parental or guardian consent as well as adolescents’ assent were obtained. HRB, the primary outcome of this study, was assessed using an audio-computer assisted self-interview (ACASI) of the Kilifi Health Risk Behavior Questionnaire (KRIBE-Q) in Swahili language. The KRIBE-Q was previously adapted and validated for use among the adolescent sub-population in Kilifi and is a reliable (Gwet AC1 = 0.82) measure for adolescents’ HRB [55]. Contextually relevant examples and explanations of HRB were utilized in the interviews for clarity. In summary, the reported behaviors comprised the following: Six injury and violence-related behaviors were reported: (i) was engaged in physical fights within the past 12 months; (ii) was seriously injured within the past 12 months; (iii) experienced dating violence (physical and/or sexual) within the past 12 months; (iv) was forced to have sexual intercourse; (v) was bullied within the past 12 months; and (vi) experienced suicidal behavior (ideation and/or attempt) within the past 12 months. Three behaviors reported on sexual risk behavior were (i) early sexual debut (dichotomized initiation of sex before 14 years or at 14 years and above); (ii) engagement in transactional sex (victim and/or perpetrator) in the past 12 months; and (iii) condom nonuse during most recent sex. Tobacco and alcohol use were categorized into one group as both are examples of licit substances that are largely accessible within the Kenyan context [56]. Marijuana and Khat were grouped together in a second category as central nervous stimulants [57], which was fairly accessible by youths within the study setting [58]. A third categorization was for other drug use. The five substance use behaviors reported were (i) lifetime use of tobacco or alcohol products, (ii) recent use (past 30 days) of tobacco or alcohol products, (iii) lifetime use of marijuana and Khat products, (iv) recent use (past 30 days) of marijuana and Khat products, and (v) lifetime use of other drugs. Two indications of poor oral hygiene and poor general body hygiene were reported. Respondents were asked how often they cleaned or brushed their teeth in a regular week and how often they washed their entire bodies with water and soap during a regular week. Gambling behaviors were captured by an item asking if the adolescent ever spent much more than they planned on gambling activities within the past 12 months. Three behaviors on physical activity and sedentary behavior were reported: (i) number of days of vigorous physical activity (at least 10 min at a time) during a regular week, (ii) number of days of moderate physical activity (at least 10 min at a time) during a regular week, and (iii) number of hours spent on sedentary activities during a regular day. Contextually relevant examples and explanations of sedentary behavior and vigorous and moderate forms of physical activity were utilized in the interviews for purposes of clarification. Dietary behaviors were captured by assessing the frequency of (i) fruit and vegetable consumption and (ii) fatty or fast food intake during a regular week. Three core EF domains, namely working memory, inhibitory control, and cognitive flexibility [59], were assessed. All EF assessments were administered by a research assistant (trained in psychological and cognitive assessment) in quiet and properly lit rooms, arranged to minimize any form of distractions. Standardized procedures for administration of each EF test were followed. Five trails of the comprehensive trail making test (CTMT) were administered in numerical order following the standardized procedure [60]. Raw scores for each trial (number of seconds taken by an examinee to complete the trial) were recorded by the assessor. T-scores per trial were obtained from the CTMT examiner’s manual [60] and performance summarized by the average T-scores for all completed trials. The Stroop color and word test (SCWT) [61] was administered to assess inhibitory control. Study reports have shown a link between deficits in the ability to inhibit cognitive interference (inhibitory control) and impulsivity [62, 63]. From the three sections of the test (each timed for 45 s), the raw word score (words correctly identified), raw color score (correctly identified colors), and the raw color-word score (correctly identified color of ink for a contrasting name of color) were recorded by the assessor per examinee. The interference score was computed by subtracting the raw color score from the raw color-word score. Participants were administered the backward digit span and the letter-number sequencing (LNS) subsets. These tasks have been previously modified for use with children, adolescents, and older populations within the study setting [64]. Scores were the total number of correct sequences (total correct raw scores) for both LNS and backward digit span tasks. The adolescents’ age, sex, current educational level, and orphanhood status were captured and ascertained in the presence of their caretaker as well as from records such as birth certificates. The adolescents’ caretakers were also asked about their household socio-economic status using an assets index that has been extensively utilized in the Kilifi context [65]. Adolescents’ weight and height were measured according to recommended procedures [66]. Body mass index for age (BMI for age) and height for age were then computed using the WHO standards [67]. Items assessing parent-to-adolescent interaction, peer-to-peer relationship, and school attachment were from measures previously utilized in adolescent sub-populations [68–70]. The most suitable items for each of the three components were selected based on factor analytic approach. Additional items were taken from the KRIBE-Q [55] and assessed “household food insecurity” in the past 30 days (asking if one went hungry because there was not enough food at home), “feeling unsafe in their neighborhood,” “experience of mental distress” (feeling sad/hopeless almost every day for 2 weeks or more in a row) within the past 12 month, and “use of tobacco products by their caretaker/s.” A blood sample was taken from the perinatally HIV-infected adolescents and analyzed for CD4/CD8 cell count and HIV viral load concentrations. All analyses were conducted in the STATA15 software package (StataCorp LLC). First, latent class analysis (LCA) [71] was performed to identify behavioral classes based on the 22 behaviors described above. Five models varying from one to five latent classes were generated and the Akaike information criterion (AIC) and Bayesian information criterion (BIC) were utilized to select the model with the best goodness of fit indices and lowest BIC values [72]. Assignment of participants to respective latent classes was based on their posterior probabilities of class membership. Entropy was also measured to indicate the level of separation between classes. Values of normalized entropy greater than 0.80 indicate that the latent classes are highly discriminating [73]. The HRB composition, socio-demographic factors (sex, age, socio-economic status, education level, household food insecurity), biological factors (BMI, height for age, medical history, HIV status, CD4/CD8 cell count, HIV viral load concentrations), and psychosocial factors (mental distress, orphanhood, caregiver-adolescent interaction, caregiver tobacco use, school attachment, peer-peer relationship, feelings about neighborhood safety) of each class from the optimal model were summarized using descriptive statistics of proportions (%) and means. Bivariate analyses (Chi-square test or Fisher exact test for categorical variables and analysis of variance [ANOVA] with Bonferroni correction for post-hoc analysis for continuous variables) were used to test for significance of differences in HRB composition, socio-demographic, and biological and psychosocial factors across the behavioral classes. ANOVA with Bonferroni correction for post-hoc analysis was performed to identify differences in EF outcomes across classes. A directed acyclic graph (DAG) of the hypothesized exposure-outcome relationship was generated in DAGitty V2.3 open access software [74] (see Fig. 1). The exposure-outcome relationship relies on prior knowledge from other empirical studies and assumed contributory effects which were explained in the introduction section of this article. Using the DAG, we identified variables for adjustment (also known as minimum sufficient adjustment sets) in estimating the effect of perinatal HIV status on HRB clustering. DAGs are increasingly utilized in modern epidemiology and are found to be crucial in advancing investigation of causal relationships which involve multiple interrelated variables [75, 76]. DAGs are also useful for avoiding the introduction of collider bias (i.e., conditional associations introduced by selected covariates) and identifying confounding [77]. Multinomial logistic regression was conducted to investigate the association between perinatal HIV infection and HRB clustering while controlling for the variables identified from the DAG model as minimum sufficient adjustment sets. Multiple imputation was used for data missing at random on individual behavioral variables due to non-response [78]. A directed acyclic graph (DAG) conceptualizing the effect of perinatal HIV infection on health risk behavior clustering among adolescents. HIV: perinatal HIV infection, HRB clustering: Health risk behavior clustering, EF: Measure of executive functioning domains of working memory, inhibitory control and cognitive flexibility, Low_SES: Low household socio-economic status, Sex: sex of the adolescent, Age: age of the adolescent, HIV_biomarkers: HIV treatment outcomes (CD4/CD8 cell count and HIV viral load concentrations), Poor_anthropometry: poor adolescent anthropometric measures of body mass index and height for age, Orphanhood: being an orphan, Education: adolescent’s current educational level, Food_insecurity: household food insecurity in the past 30 days, Mental_distress: Experience of mental distress within the past 12 months, Caregiver_substance use: Use of substances by the caretaker, Insecurity: Feeling unsafe in their neighborhood, Parent_adolescent interaction: Parent-to-adolescent interaction, Peer-peer: Peer-to-peer relationship, School_attachment: School attachment, Exposure, : Outcome, : Other Variables, : Causal path, : Biasing path

Based on the provided information, here are some potential innovations that can be used to improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with access to information, resources, and support related to maternal health. These apps can provide guidance on prenatal care, nutrition, exercise, and common pregnancy concerns.

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 medical advice and support, especially in rural areas with limited healthcare facilities.

3. Community Health Workers: Train and deploy community health workers who can provide education, counseling, and basic healthcare services to pregnant women in their communities. These workers can help bridge the gap between healthcare facilities and remote areas, ensuring that pregnant women receive the necessary care and support.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access maternal healthcare services. These vouchers can cover the cost of prenatal check-ups, delivery, and postnatal care, making healthcare more affordable and accessible.

5. Maternal Health Clinics: Establish specialized maternal health clinics that offer comprehensive care for pregnant women. These clinics can provide prenatal check-ups, ultrasounds, childbirth classes, and postnatal care in one location, making it easier for women to access the services they need.

6. Transportation Support: Develop transportation programs that provide pregnant women with reliable and affordable transportation to healthcare facilities. This can help overcome transportation barriers, especially in areas with limited public transportation options.

7. Maternal Health Education Programs: Implement educational programs that focus on raising awareness about maternal health and empowering women to take control of their own health. These programs can provide information on prenatal care, nutrition, breastfeeding, and postpartum care.

8. Maternal Health Hotlines: Establish hotlines or helplines that pregnant women can call to receive immediate support and guidance from healthcare professionals. These hotlines can provide information, answer questions, and address concerns related to maternal health.

9. Partnerships with Non-Governmental Organizations (NGOs): Collaborate with NGOs that specialize in maternal health to leverage their expertise, resources, and networks. These partnerships can help expand access to maternal healthcare services and improve the overall quality of care.

10. Data Collection and Analysis: Improve data collection and analysis systems to better understand the barriers and challenges faced by pregnant women in accessing maternal healthcare. This data can inform the development of targeted interventions and policies to address these issues effectively.
AI Innovations Description
Based on the information provided, the recommendation to improve access to maternal health would be to implement screening and interventions for internalizing mental health problems and deficits in executive functioning among adolescents in Kenya. This recommendation is based on the findings that older adolescent age, mental distress, and feeling unsafe in their neighborhood were associated with higher risk behavioral classes. Additionally, better working memory was found to be protective. By addressing these psychosocial risk factors and providing support for mental health and executive functioning, it is expected that maternal health outcomes can be improved. It is also suggested to involve family members and communities in addressing these risk factors to create a supportive environment for adolescents.
AI Innovations Methodology
Based on the provided information, the study focuses on health risk behaviors among adolescents in rural Kenya, with a specific emphasis on the role of HIV in predicting these behaviors. The study utilized latent class analysis to identify four risk behavior classes and conducted multinomial logistic regression to explore the associated factors. The findings suggest that older age, mental distress, and feeling unsafe in the neighborhood increase the risk of membership in higher risk behavioral classes, while better working memory is protective.

To improve access to maternal health, it is important to consider innovations that address the identified risk factors and promote positive health behaviors among adolescents. Here are some potential recommendations:

1. Integrated mental health screening and interventions: Implementing routine mental health screening for adolescents and providing appropriate interventions can help identify and address mental distress, which is associated with higher risk behaviors. This can be done through collaboration between healthcare providers, schools, and community organizations.

2. Community-based interventions: Engage families, caregivers, and community members in promoting positive health behaviors and creating a supportive environment for adolescents. This can include educational programs, peer support groups, and community events that focus on health promotion and risk prevention.

3. Strengthening neighborhood safety: Collaborate with local authorities and community organizations to improve safety measures in neighborhoods, such as increased street lighting, community policing, and safe recreational spaces. This can help reduce feelings of insecurity and create a safer environment for adolescents.

4. Enhancing executive functioning skills: Develop interventions that target executive functioning skills, such as working memory, inhibitory control, and cognitive flexibility. These skills play a crucial role in decision-making and self-regulation, which can influence health behaviors. Interventions can include cognitive training programs, educational workshops, and mentoring programs.

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

1. Define the outcome: Clearly define the desired outcome of improved access to maternal health, such as increased utilization of antenatal care services, reduced maternal mortality rates, or improved maternal health indicators.

2. Identify relevant indicators: Select indicators that capture the key aspects of access to maternal health, such as the number of antenatal care visits, percentage of births attended by skilled health personnel, or maternal mortality rates. These indicators should align with the desired outcome and be measurable.

3. Collect baseline data: Gather baseline data on the selected indicators to establish a starting point for comparison. This can be done through surveys, medical records, or existing data sources.

4. Implement interventions: Implement the recommended innovations, such as mental health screening and interventions, community-based programs, neighborhood safety initiatives, and executive functioning skill development programs. Ensure proper implementation and monitor the progress of each intervention.

5. Collect post-intervention data: After a sufficient period of time, collect post-intervention data on the selected indicators. This can be done using the same methods as the baseline data collection.

6. Analyze and compare data: Analyze the baseline and post-intervention data to assess the impact of the recommendations on improving access to maternal health. Compare the indicators before and after the interventions to determine if there have been any significant changes.

7. Interpret and report findings: Interpret the findings of the data analysis and report the results, highlighting the impact of the recommendations on improving access to maternal health. Provide clear and concise information on the changes observed and any lessons learned from the implementation process.

By following this methodology, it is possible to simulate the impact of the recommended innovations on improving access to maternal health and assess their effectiveness in addressing the identified risk factors among adolescents.

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