The development of children born to young mothers with no, first- or second-generation HIV acquisition in the Eastern Cape province, South Africa: a cross-sectional study

BMJ Open, Volume 12, No. 10, Year 2022

Interpretation

Background The intergenerational effects of HIV require long-term investigation. We compared developmental outcomes of different generations impacted by HIV – children of mothers not living with HIV, the ‘second generation’ (ie, with recently infected mothers) and the ‘third generation’ (ie, children of perinatally infected mothers). Methods A cross-sectional community sample of N=1015 young mothers (12-25 years) and their first children (2-68 months, 48.2% female), from South Africa’s Eastern Cape Province. 71.3% (n=724) of children were born to mothers not living with HIV; 2.7% (n=27; 1 living with HIV) were third-generation and 26.0% (n=264; 11 living with HIV) second-generation children. Child scores on the Mullen Scales of Early Learning (MSEL), the WHO Ten Questions Screen for Disability and maternal demographics were compared between groups using χ 2 tests and univariate approach, analysis of variance analysis. Hierarchical linear regressions investigated predictive effects of familial HIV infection patterns on child MSEL composite scores, controlling for demographic and family environment variables. Results Second-generation children performed poorer on gross (M=47.0, SD=13.1) and fine motor functioning (M=41.4, SD=15.2) and the MSEL composite score (M=90.6, SD=23.0) than children with non-infected mothers (gross motor: M=50.4, SD=12.3; fine motor: M=44.4, SD=14.1; composite score: M=94.1, SD=20.7). The third generation performed at similar levels to non-exposed children (gross motor: M=52.4, SD=16.1; fine motor: M=44.3, SD=16.1, composite score: M=94.7, SD=22.2), though analyses were underpowered for definite conclusions. Hierarchical regression analyses suggest marginal predictive effects of being second-generation child compared with having a mother not living with HIV (B=-3.3, 95% CI=-6.8 to 0.1) on MSEL total scores, and non-significant predictive effects of being a third-generation child (B=1.1, 5% CI=-7.5 to 9.7) when controlling for covariates. No group differences were found for disability rates (26.9% third generation, 27.7% second generation, 26.2% non-exposed; χ 2 =0.02, p=0.90). Conclusion Recently infected mothers and their children may struggle due to the disruptiveness of new HIV diagnoses and incomplete access to care/support during pregnancy and early motherhood. Long-standing familial HIV infection may facilitate care pathways and coping, explaining similar cognitive development among not exposed and third-generation children. Targeted intervention and fast-tracking into services may improve maternal mental health and socioeconomic support. Data used within these analyses originate from the ‘Helping Empower Youth Brought up in Adversity with their Babies and Young children’ study. The study was conducted in rural and periurban health districts of South Africa’s Eastern Cape and aimed at investigating the effects of adolescent motherhood, as well as intergenerational effects of HIV exposure. A total of 1046 adolescent and young adult mothers (10–25 years) with at least one living child were interviewed between March 2018 and July 2019.23 The required sample size was estimated based on expected effect sizes for key outcomes. Participants were partially recruited from a previous study (n=159: any young mothers included), as well as through six parallel sampling strategies developed in cooperation with local experts and an adolescent mother advisory group to ensure representativeness (n=887: only adolescent mothers). This comprised sampling through 73 known health facilities within the districts, 43 secondary schools, 9 maternity units and referrals by service providers, social workers and adolescent mothers themselves. A total of 95%–98% of eligible mothers from each recruitment channel were successfully enrolled into the study. The current analyses were limited to data on first child of the young mother only (10–25 years), and children aged 68 months or younger who were within the normed range for the Mullen Scales (n=31 excluded based on this age restriction), limiting the sample to n=1015. We also performed analyses including adolescent mothers (age at pregnancy ≤19, n=972) only for the key outcomes (see online supplemental appendix 1), as these mothers had been the main recruitment focus for the study. bmjopen-2021-058340supp001.pdf The study team were advised on recruitment methods by adolescent mothers, whose suggestions were included in the study protocol. Furthermore, the team has been working with two Teen Advisory groups in the Eastern and Western Cape of South Africa, who were involved in piloting the study. Feedback was incorporated to improve relevance and acceptability of the questionnaire and the research procedure. Finally, through the engagement of community leaders, it was ensured that recruitment strategies were effective, sensitive to participant’s circumstances and minimised stigma risks. The dataset analysed combined four data sources. First, all adolescent mother participants completed a detailed study questionnaire relating to sociodemographic characteristics, sexual and reproductive health, physical and mental health, relationships and social support. Second, they completed an adolescent parent questionnaire, which collected data on maternal and child health, child development, the father of the child and maternal factors including social support, the parenting experience and violence exposure. Third, cognitive assessments of the children were performed by a trained administrator, using the Mullen Scales of Early Learning. Finally, details from the child’s medical records (Road to Health Booklet) were also included in the database. All participants provided written consent, and interviews were conducted in the language of their choice (ie, English or isiXhosa). Regardless of participation, all adolescents received a small ‘snack pack’ containing a snack and juice during the interview, and a small ‘thank you pack’, with personal products such as toothpaste and a toothbrush. Items included in these packs were selected by the adolescent advisory group as preferable and appropriate. New mothers also received a ‘baby pack’, the contents of which were also chosen by an adolescent advisory group, and included nappies and baby cream. Mode of maternal HIV acquisition (perinatal vs recent) was assessed through an algorithm, given that the study was community-based and not linked to clinical testing data. Accordingly, it was derived using a logic tree based on clinical and fieldwork experiences.17 The algorithm allowed for categorisation according to self-report, age of ART initiation, and parental death information (Tolmay, Saal et al., in preparation). For the current analyses, we compared the following three groups: children of the third generation (mother perinatally infected), children of the second generation (mother recently infected) and children of mothers not living with HIV (see figure 1). Child HIV status was not taken into account in these classifications, since absolute numbers were low (n=12 based on maternal self-reported data), with only one of these children being in the third generation. Thus, we were underpowered to study these children separately. We decided however to retain them in the analyses since they are relevant members of the second and third generations, respectively. Flow chart of classification by mode of maternal HIV acquisition. HEY BABY, Helping Empower Youth Brought up in Adversity with their Babies and Young children. Child developmental outcomes were assessed using the Mullen Scales of Early Development (MSEL).24 The MSEL is normed for children aged 0–68 months (USA) and assesses child performance across five domains: gross motor (only for age <39 months), fine motor, visual reception, receptive and expressive language (score range: 20–80). A composite score (score range: 49–155) can be derived, and the scales have been validated for use in sub-Saharan Africa.23 25 26 Child disability status was assessed using the WHO Ten Questions Screen for Disability.27 This measure can be applied to detect common disabilities (hearing, visual, physical, speech, mental and epilepsy) in children. A score indicating any disability (‘yes’ across any of the 10 items) was derived for the current analyses. Adolescent mothers were compared on various variables, including demographic factors (maternal age, maternal and paternal age at pregnancy), child feeding method used (formula, breastfeeding, combined, other), maternal school progression (self-report: repeated at least one school grade), poverty (number of the eight socially perceived necessities for children the family had access to,28 household access to any government cash transfers child support, foster child, pension, disability or care dependency grant, measured via South Africa Census item,29 food security number of days there was not enough food for the household in the past 7 days,30 full ART adherence over the past 7 days (Patient Medication Adherence Questionnaire,31 any HIV clinic appointments missed in the past year, extent of HIV-related stigma Adolescents Living with HIV Stigma Scale,32 depressive symptoms in the past 2 weeks Child Depression Inventory-Short Form,33 anxiety symptoms in the past month Revised Children’s Manifest Anxiety Scale,34 PTSD symptoms in the past month Child PTSD Checklist-Short Form,35 suicidality symptoms during the past month Mini International Psychiatric Interview for Children and Adolescents- Suicidality and Self-Harm Subscale,36 extent of community violence exposure in the past year item from the Child Exposure to Community Violence Checklist,37 intimate partner violence exposure in the past month (Juvenile Victimisation Questionnaire,38 current parenting stress (Parental Stress Scale39) and amount of social support received (adjusted version of the Medical Outcomes Study Social Support Survey,40 previously used in the South African context.41 All analyses were conducted using STATA V.16 SE. First, child and adolescent mother descriptive information for the three groups of interest (children of the third generation, children of the second generation and HIV-unexposed children) was provided. Next, child developmental outcomes and maternal factors surrounding socioeconomic status, HIV-related variables (only HIV-affected mothers), mental health and social environment were compared between groups, usingχ2 tests and univariate analyses of variance as appropriate. For continuous variables, Tukey’s range tests were used to conduct post hoc group comparisons. Effects at p<0.05 were considered relevant. Finally, a hierarchical regression model was run, with the child composite score on the Mullen scales as a key outcome. In a first step, child familial HIV exposure (third generation, second generation, HIV-unexposed) was added as a predictor. In a second step, we included relevant control variables that were found to be associated with the predictor or outcome variables in univariate analyses or were identified as relevant within the literature to see whether child familial HIV exposure would remain a relevant predictor.

AI Digest

Study Justification:

The study aimed to investigate the developmental outcomes of children born to young mothers affected by HIV in the Eastern Cape province of South Africa. This research is important because it explores the intergenerational effects of HIV and provides insights into the challenges faced by recently infected mothers and their children. By comparing the developmental outcomes of different generations impacted by HIV, the study contributes to our understanding of the long-term effects of HIV on child development.

Highlights:

1. The study included a cross-sectional community sample of 1,015 young mothers and their first children.
2. Three groups were compared: children of mothers not living with HIV, second-generation children (with recently infected mothers), and third-generation children (children of perinatally infected mothers).
3. Second-generation children performed poorer on gross and fine motor functioning, as well as overall developmental scores, compared to children with non-infected mothers.
4. Third-generation children performed at similar levels to non-exposed children, suggesting that long-standing familial HIV infection may facilitate care pathways and coping, leading to similar cognitive development.
5. No significant differences were found in disability rates among the three groups.

Recommendations:

1. Targeted interventions should be developed to support recently infected mothers and their children, who may struggle due to the disruptiveness of new HIV diagnoses and incomplete access to care/support during pregnancy and early motherhood.
2. Fast-tracking into services and improving access to care and support for recently infected mothers can help improve maternal mental health and socioeconomic support.

Key Role Players:

1. Healthcare providers: They play a crucial role in providing care, support, and interventions for recently infected mothers and their children.
2. Social workers: They can provide additional support and resources to help address the challenges faced by recently infected mothers and their families.
3. Community leaders: Their engagement is important in ensuring effective recruitment strategies, sensitivity to participants’ circumstances, and minimizing stigma risks.

Cost Items for Planning Recommendations:

1. Development and implementation of targeted interventions: This includes the cost of program development, training of healthcare providers and social workers, and ongoing support services.
2. Access to care and support services: This includes the cost of improving access to healthcare facilities, HIV testing, antiretroviral therapy, mental health services, and socioeconomic support programs.
3. Community engagement and awareness campaigns: This includes the cost of community outreach, education, and awareness initiatives to reduce stigma and promote understanding of the challenges faced by recently infected mothers and their children.
Please note that the provided cost items are general categories and do not represent actual cost figures. Actual cost estimates would require a detailed budget analysis based on specific intervention plans and local context.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some limitations. The study is a cross-sectional design, which limits the ability to establish causality. Additionally, the sample size for the third-generation children is small, leading to underpowered analyses. To improve the evidence, a longitudinal study design could be implemented to examine the long-term effects of HIV exposure on child development. Additionally, increasing the sample size for third-generation children would provide more robust findings. Finally, including a control group of children without any HIV exposure would allow for better comparisons and interpretation of the results.

Abstract

Data used within these analyses originate from the ‘Helping Empower Youth Brought up in Adversity with their Babies and Young children’ study. The study was conducted in rural and periurban health districts of South Africa’s Eastern Cape and aimed at investigating the effects of adolescent motherhood, as well as intergenerational effects of HIV exposure. A total of 1046 adolescent and young adult mothers (10–25 years) with at least one living child were interviewed between March 2018 and July 2019.23 The required sample size was estimated based on expected effect sizes for key outcomes. Participants were partially recruited from a previous study (n=159: any young mothers included), as well as through six parallel sampling strategies developed in cooperation with local experts and an adolescent mother advisory group to ensure representativeness (n=887: only adolescent mothers). This comprised sampling through 73 known health facilities within the districts, 43 secondary schools, 9 maternity units and referrals by service providers, social workers and adolescent mothers themselves. A total of 95%–98% of eligible mothers from each recruitment channel were successfully enrolled into the study. The current analyses were limited to data on first child of the young mother only (10–25 years), and children aged 68 months or younger who were within the normed range for the Mullen Scales (n=31 excluded based on this age restriction), limiting the sample to n=1015. We also performed analyses including adolescent mothers (age at pregnancy ≤19, n=972) only for the key outcomes (see online supplemental appendix 1), as these mothers had been the main recruitment focus for the study. bmjopen-2021-058340supp001.pdf The study team were advised on recruitment methods by adolescent mothers, whose suggestions were included in the study protocol. Furthermore, the team has been working with two Teen Advisory groups in the Eastern and Western Cape of South Africa, who were involved in piloting the study. Feedback was incorporated to improve relevance and acceptability of the questionnaire and the research procedure. Finally, through the engagement of community leaders, it was ensured that recruitment strategies were effective, sensitive to participant’s circumstances and minimised stigma risks. The dataset analysed combined four data sources. First, all adolescent mother participants completed a detailed study questionnaire relating to sociodemographic characteristics, sexual and reproductive health, physical and mental health, relationships and social support. Second, they completed an adolescent parent questionnaire, which collected data on maternal and child health, child development, the father of the child and maternal factors including social support, the parenting experience and violence exposure. Third, cognitive assessments of the children were performed by a trained administrator, using the Mullen Scales of Early Learning. Finally, details from the child’s medical records (Road to Health Booklet) were also included in the database. All participants provided written consent, and interviews were conducted in the language of their choice (ie, English or isiXhosa). Regardless of participation, all adolescents received a small ‘snack pack’ containing a snack and juice during the interview, and a small ‘thank you pack’, with personal products such as toothpaste and a toothbrush. Items included in these packs were selected by the adolescent advisory group as preferable and appropriate. New mothers also received a ‘baby pack’, the contents of which were also chosen by an adolescent advisory group, and included nappies and baby cream. Mode of maternal HIV acquisition (perinatal vs recent) was assessed through an algorithm, given that the study was community-based and not linked to clinical testing data. Accordingly, it was derived using a logic tree based on clinical and fieldwork experiences.17 The algorithm allowed for categorisation according to self-report, age of ART initiation, and parental death information (Tolmay, Saal et al., in preparation). For the current analyses, we compared the following three groups: children of the third generation (mother perinatally infected), children of the second generation (mother recently infected) and children of mothers not living with HIV (see figure 1). Child HIV status was not taken into account in these classifications, since absolute numbers were low (n=12 based on maternal self-reported data), with only one of these children being in the third generation. Thus, we were underpowered to study these children separately. We decided however to retain them in the analyses since they are relevant members of the second and third generations, respectively. Flow chart of classification by mode of maternal HIV acquisition. HEY BABY, Helping Empower Youth Brought up in Adversity with their Babies and Young children. Child developmental outcomes were assessed using the Mullen Scales of Early Development (MSEL).24 The MSEL is normed for children aged 0–68 months (USA) and assesses child performance across five domains: gross motor (only for age <39 months), fine motor, visual reception, receptive and expressive language (score range: 20–80). A composite score (score range: 49–155) can be derived, and the scales have been validated for use in sub-Saharan Africa.23 25 26 Child disability status was assessed using the WHO Ten Questions Screen for Disability.27 This measure can be applied to detect common disabilities (hearing, visual, physical, speech, mental and epilepsy) in children. A score indicating any disability (‘yes’ across any of the 10 items) was derived for the current analyses. Adolescent mothers were compared on various variables, including demographic factors (maternal age, maternal and paternal age at pregnancy), child feeding method used (formula, breastfeeding, combined, other), maternal school progression (self-report: repeated at least one school grade), poverty (number of the eight socially perceived necessities for children the family had access to,28 household access to any government cash transfers child support, foster child, pension, disability or care dependency grant, measured via South Africa Census item,29 food security number of days there was not enough food for the household in the past 7 days,30 full ART adherence over the past 7 days (Patient Medication Adherence Questionnaire,31 any HIV clinic appointments missed in the past year, extent of HIV-related stigma Adolescents Living with HIV Stigma Scale,32 depressive symptoms in the past 2 weeks Child Depression Inventory-Short Form,33 anxiety symptoms in the past month Revised Children’s Manifest Anxiety Scale,34 PTSD symptoms in the past month Child PTSD Checklist-Short Form,35 suicidality symptoms during the past month Mini International Psychiatric Interview for Children and Adolescents- Suicidality and Self-Harm Subscale,36 extent of community violence exposure in the past year item from the Child Exposure to Community Violence Checklist,37 intimate partner violence exposure in the past month (Juvenile Victimisation Questionnaire,38 current parenting stress (Parental Stress Scale39) and amount of social support received (adjusted version of the Medical Outcomes Study Social Support Survey,40 previously used in the South African context.41 All analyses were conducted using STATA V.16 SE. First, child and adolescent mother descriptive information for the three groups of interest (children of the third generation, children of the second generation and HIV-unexposed children) was provided. Next, child developmental outcomes and maternal factors surrounding socioeconomic status, HIV-related variables (only HIV-affected mothers), mental health and social environment were compared between groups, usingχ2 tests and univariate analyses of variance as appropriate. For continuous variables, Tukey’s range tests were used to conduct post hoc group comparisons. Effects at p<0.05 were considered relevant. Finally, a hierarchical regression model was run, with the child composite score on the Mullen scales as a key outcome. In a first step, child familial HIV exposure (third generation, second generation, HIV-unexposed) was added as a predictor. In a second step, we included relevant control variables that were found to be associated with the predictor or outcome variables in univariate analyses or were identified as relevant within the literature to see whether child familial HIV exposure would remain a relevant predictor.

Materials

Innovations

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or text messaging services that provide pregnant women and new mothers with important health information, reminders for prenatal and postnatal care appointments, and access to telemedicine consultations.

2. Community Health Workers: Train and deploy community health workers to provide education, support, and referrals to pregnant women and new mothers in underserved areas. These workers can help bridge the gap between healthcare facilities and the community, ensuring that women receive the necessary care and support.

3. Telemedicine: Implement telemedicine services to enable remote consultations between healthcare providers and pregnant women or new mothers. This can be particularly beneficial for those living in rural or remote areas with limited access to healthcare facilities.

4. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to pregnant women, allowing them to access essential maternal health services, such as prenatal care, skilled birth attendance, and postnatal care.

5. Transportation Support: Establish transportation services or subsidies to help pregnant women and new mothers overcome transportation barriers and reach healthcare facilities for prenatal and postnatal care.

6. Maternal Health Education Programs: Develop comprehensive educational programs that focus on maternal health, including prenatal care, nutrition, breastfeeding, and newborn care. These programs can be delivered through community workshops, online platforms, or mobile applications.

7. Maternity Waiting Homes: Establish maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel for delivery. These homes provide a safe and supportive environment for women to stay during the final weeks of pregnancy.

8. Task-Shifting: Train and empower non-specialist healthcare providers, such as nurses and midwives, to perform certain tasks traditionally done by doctors. This can help alleviate the shortage of skilled healthcare professionals and improve access to maternal health services.

9. 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 in underserved areas.

10. Health Financing Innovations: Explore innovative financing models, such as micro-insurance or community-based health financing schemes, to make maternal health services more affordable and accessible to vulnerable populations.

These are just a few potential innovations that can be considered to improve access to maternal health. It’s important to assess the local context, resources, and needs to determine which innovations would be most effective in a specific setting.

AI Innovations Description

Based on the information provided, the study titled “The development of children born to young mothers with no, first- or second-generation HIV acquisition in the Eastern Cape province, South Africa: a cross-sectional study” aims to investigate the developmental outcomes of different generations impacted by HIV, specifically children of mothers not living with HIV (non-infected mothers), second-generation children (with recently infected mothers), and third-generation children (children of perinatally infected mothers).

The study used a cross-sectional community sample of 1,015 young mothers (aged 12-25 years) and their first children (aged 2-68 months) from South Africa’s Eastern Cape Province. The children’s developmental outcomes were assessed using the Mullen Scales of Early Learning (MSEL) and the WHO Ten Questions Screen for Disability. The study also collected data on maternal demographics and HIV infection patterns.

The findings of the study showed that second-generation children performed poorer on gross and fine motor functioning, as well as the MSEL composite score, compared to children with non-infected mothers. However, third-generation children performed at similar levels to non-exposed children. The study suggests that recently infected mothers and their children may struggle due to the disruptiveness of new HIV diagnoses and incomplete access to care/support during pregnancy and early motherhood. On the other hand, long-standing familial HIV infection may facilitate care pathways and coping, explaining similar cognitive development among non-exposed and third-generation children.

Based on these findings, a recommendation to improve access to maternal health and address the challenges faced by recently infected mothers and their children could be to implement targeted interventions and fast-track them into services. This could include providing comprehensive support and resources to recently infected mothers during pregnancy and early motherhood, ensuring access to HIV care and treatment, mental health support, and socioeconomic assistance. By addressing these specific needs, it is possible to improve maternal mental health and socioeconomic support, ultimately enhancing the well-being and developmental outcomes of both mothers and their children.

AI Innovations Methodology

To improve access to maternal health, here are some potential recommendations:

1. Mobile Clinics: Implement mobile clinics that can reach remote areas and provide essential maternal health services such as prenatal care, vaccinations, and postnatal care.

2. Telemedicine: Utilize telemedicine technologies to provide virtual consultations and follow-ups for pregnant women, reducing the need for travel and improving access to healthcare professionals.

3. Community Health Workers: Train and deploy community health workers who can provide basic maternal health services, education, and support in underserved areas.

4. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to pregnant women, enabling them to access quality maternal healthcare services.

5. Transportation Support: Establish transportation support systems to help pregnant women reach healthcare facilities, especially in areas with limited transportation options.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the indicators: Identify key indicators that reflect improved access to maternal health, such as the number of prenatal visits, percentage of women receiving skilled birth attendance, or reduction in maternal mortality rates.

2. Data collection: Gather relevant data on the current state of maternal health access in the target area, including the number of healthcare facilities, distance to facilities, transportation availability, and utilization rates.

3. Model development: Develop a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This could involve creating mathematical equations or using simulation software to simulate the effects of each recommendation on access to maternal health.

4. Parameter estimation: Estimate the parameters of the simulation model based on available data and expert knowledge. This may involve conducting surveys, interviews, or literature reviews to gather information on factors such as population demographics, healthcare facility capacity, and transportation infrastructure.

5. Scenario analysis: Run the simulation model using different scenarios that represent the implementation of the recommendations. This could include varying factors such as the number of mobile clinics, coverage of telemedicine services, or the effectiveness of community health worker programs.

6. Impact assessment: Analyze the simulation results to assess the impact of each recommendation on the selected indicators of improved access to maternal health. Compare the scenarios to the baseline data to determine the potential improvements that could be achieved.

7. Sensitivity analysis: Conduct sensitivity analysis to test the robustness of the simulation results and identify key factors that may influence the effectiveness of the recommendations.

8. Policy recommendations: Based on the simulation results, provide policy recommendations on the most effective combination of recommendations to improve access to maternal health in the target area. Consider factors such as cost-effectiveness, scalability, and sustainability.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and available data in the target area.

Author

Dima Health

Identifiers:

DOI: 10.1136/bmjopen-2021-058340

Research Areas:

Community Interventions, Disability, Disparities, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Mental Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Violence and Injury

Study Countries:

South Africa

Study Design:

Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female