Intimate partner violence and growth outcomes through infancy: A longitudinal investigation of multiple mediators in a South African birth cohort

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Study Justification:
– The study aims to investigate the relationship between intimate partner violence (IPV) and fetal and infant growth outcomes.
– The factors underlying this relationship are not well understood, particularly in the postnatal period.
– Understanding these factors is crucial for developing effective interventions to improve child health and well-being.
Study Highlights:
– The study found that both emotional and physical IPV during pregnancy were associated with reduced weight-for-age z-scores (WFAZ) at birth.
– Only physical IPV was associated with length-for-age z-scores (LFAZ) at birth.
– Maternal alcohol and tobacco use mediated the relationship between IPV and growth outcomes at birth.
– Emotional and physical IPV were associated with reduced WFAZ at 12 months, while emotional IPV was associated with reduced LFAZ at 12 months.
– Maternal tobacco use mediated the relationship between postnatal IPV and growth outcomes at 12 months.
Study Recommendations:
– The findings highlight the role of physical and emotional IPV as risk factors for compromised fetal and infant growth.
– The study emphasizes the importance of addressing interrelated risk factors, specifically IPV and substance use, in programs aimed at improving infant growth.
– Interventions should focus on preventing and addressing IPV, as well as providing support for substance use disorders in high-risk settings.
Key Role Players:
– Researchers and academics in the field of child health and development
– Public health officials and policymakers
– Healthcare providers and social workers
– Non-governmental organizations (NGOs) working on gender-based violence and substance abuse
Cost Items for Planning Recommendations:
– Training and capacity-building for healthcare providers and social workers on identifying and addressing IPV and substance use disorders
– Development and implementation of intervention programs targeting IPV and substance use
– Awareness campaigns and community outreach activities
– Data collection and monitoring systems to track the prevalence and impact of IPV and substance use
– Evaluation and research to assess the effectiveness of interventions
– Collaboration and coordination among different stakeholders and organizations

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design is longitudinal, which allows for the examination of relationships over time. The sample size is also large, with over 1,000 mother-infant pairs included in the analysis. The study uses validated measures to assess intimate partner violence (IPV), maternal depression, tobacco and alcohol use, and infant hospitalizations. The statistical analyses include linear regression and structural equation models to investigate the relationships between IPV and growth outcomes, as well as potential mediators. However, there are a few areas where the evidence could be strengthened. First, the abstract does not provide information on the representativeness of the sample, which could affect the generalizability of the findings. Second, the abstract does not mention any limitations of the study, such as potential confounding factors or biases. Finally, the abstract does not provide any information on the significance of the findings or the implications for future research or interventions. To improve the evidence, the authors could provide more details on the sample characteristics, acknowledge the study limitations, and discuss the implications of the findings.

Intimate partner violence (IPV) has been linked to poor fetal and infant growth. However, factors underlying this relationship are not well understood, particularly in the postnatal time period. In a South African cohort, we investigated (1) associations between IPV in pregnancy and growth at birth as well as postnatal IPV and child growth at 12 months and (2) whether maternal depression, tobacco or alcohol use or infant hospitalizations mediated IPV-growth relationships. Mothers were enrolled in pregnancy. Maternal IPV was measured during pregnancy and 10 weeks postpartum; depression, alcohol and tobacco use were measured during pregnancy and at 6 months postpartum. Child weight and length were measured at birth and 12 months and converted to z-scores for analysis. Linear regression and structural equation models investigated interrelationships between IPV and potential mediators of IPV-growth relationships. At birth, among 1,111 mother–infant pairs, maternal emotional and physical IPV were associated with reduced weight-for-age z-scores (WFAZ). Only physical IPV was associated with length-for-age z-scores (LFAZ) at birth. Antenatal maternal alcohol and tobacco use mediated IPV-growth relationships at birth. Postnatally, among 783 mother–infant pairs, emotional and physical IPV were associated with reduced WFAZ at 12 months. Only emotional IPV was associated with LFAZ at 12 months. Maternal tobacco use was a mediator postnatally. Findings highlight the role of physical and emotional IPV as risk factors for compromised fetal and infant growth. Findings underscore the importance of programmes to address interrelated risk factors for compromised infant growth, specifically IPV and substance use, which are prevalent in high-risk settings.

This study uses data from a birth cohort investigating the early‐life determinants of child health in a peri‐urban area in South Africa (Stein et al., 2015). The parent study collects comprehensive, longitudinal measures of key risk factors across a variety of disciplines (e.g., environmental, infectious, nutritional, genetic, psychosocial, maternal and immunological) that may impact child health. The DCHS is located 60 km outside Cape Town, South Africa with a population of approximately 200,000. The area is characterized by low socio‐economic status, low educational attainment and a high proportion of female‐headed households (Stats SA, 2021). Results from the cohort study have also shown high rates of interpersonal violence, substance use and child malnutrition (Barnett et al., 2018; Budree et al., 2017; Myers et al., 2018). This is despite a well‐established, free primary health care system, where more than 90% of the population access antenatal or child health services (Stein et al., 2015; Zar et al., 2015). Support for psychosocial issues and mental health disorders in LMIC settings such as South Africa is limited, with some studies estimating a 90% treatment gap (Demyttenaere et al., 2004). Thus, the psychosocial risk profile of this community may be an important driver of child outcomes such as growth. Pregnant women were recruited from two public primary health care clinics, Mbekweni (serving a Black African community) and TC Newman (serving a mixed‐ancestry community). Mothers were enrolled in their second trimester while attending routine antenatal care and have completed assessments antenatally and following birth (ongoing). Women were eligible for the study if they were 18 years or older, between 20 and 28 weeks of gestation, planned attendance at the two recruitment clinics and intended to remain in the area. Data included in the current study were collected antenatally at 28–32 weeks of gestation, at birth, and postnatally at 10 weeks, 6 and 12 months, Figure S1. Between March 2012 and March 2015, 1,225 pregnant women were enrolled into the DCHS, as has been described (Zar et al., 2015). Details of study attendance and loss‐to‐follow‐up are provided in Figure S2. A total of 1,111 children had growth outcome data at birth and were included in the antenatal analysis. A total of 783 children who had growth data at 12 months were included in the postnatal analyses, Figure S2. Infant birth length and weight measurements were conducted by trained labour ward staff, with a subgroup of measurements checked by study staff to confirm reliability. Postnatal anthropometric measurements were done by trained study staff at 12 months. Comprehensive quality control measures were in place including regular training and assessment of staff, routine calibration of equipment and taking multiple measurements. Measurements were performed twice in each child to ensure accuracy. Infant’s weight was measured (to the nearest 10 g) in light or no clothing using a Tanita digital platform scale (TAN1584; IL, USA). Recumbent length was measured using a Seca length‐measuring mat (Seca, Hamburg, Germany), performed on a firm surface by two staff members. Equipment was checked and calibrated weekly (Budree et al., 2017). Birth weights and lengths were converted to z‐scores based on gender and gestational age using the INTERGROWTH‐21st standards (Villar et al., 2014). Postnatal weight‐for‐age z‐scores (WFAZ) and length‐for‐age z‐scores (LFAZ) were calculated using weight and length measurements at 12 months, based on age and gender using Anthro software (World Health Organization [WHO], 2006). The Intimate Partner Violence Questionnaire (IPVQ) is a 12‐item inventory adapted from the WHO multicountry study (Jewkes, 2002) and the Women’s Health Study in Zimbabwe (Shamu et al., 2011). The IPVQ has shown high internal consistency and reliability in similar settings and has been widely used in South Africa (Schraiber et al., 2010; Shamu et al., 2016). Further, unpublished data from our cohort have shown a Cronbach’s alpha of 0.91, indicating relatively high internal consistency. The IPVQ assessed recent (past‐year) exposure to emotional, physical and sexual abuse. Mothers reported frequency of exposure to partner behaviour (‘never’, ‘once’, ‘a few times’ or ‘many times’). Items were summed to create a total score for each IPV subtype, with higher scores indicating higher frequency and severity of IPV (subtype scores ranged from 3 to 20). Above threshold IPV was defined as more than an isolated event within each subtype, specifically where mothers reported multiple responses of ‘once’ or at least one response of ‘a few times’ or ‘many times’ for each IPV subtype. Mothers completed the IPVQ at the 28‐ to 32‐week antenatal visit and at 10‐week postpartum. Socio‐demographic variables were collected from a shortened questionnaire used in the South African Stress and Health (SASH) study, a large population‐based study (Zar et al., 2015) and specifically developed for use in a South African setting. Household income and maternal education (any secondary versus completed secondary) were self‐reported antenatally at 28–32 weeks of gestation. Maternal height was measured at enrolment, using a wall‐mounted stadiometer (CE stature meter). Maternal HIV diagnosis was established at enrolment through maternal self‐report and confirmed during routine HIV testing of pregnant women per the Western Cape PMTCT guidelines. Number of hospitalizations included all‐cause child admissions to Paarl Hospital, the only hospital serving the study catchment area. Active surveillance was conducted by study staff at Paarl Hospital. In addition, at routine study visits, mothers were asked whether children had been hospitalized. Where admissions were reported that were missed by surveillance efforts, study staff abstracted relevant details from hospital folders. A score for total number of hospitalizations from 10 weeks through 12 months of child age was calculated for the postnatal analyses. Validated questionnaires were administered to mothers antenatally and at 6‐month postpartum to assess maternal substance use and depression (Stein et al., 2015). The Alcohol, Smoking and Substance Involvement Screening test (ASSIST), a tool that was developed by the WHO to detect and manage substance use among people attending primary health care services, assessed maternal alcohol and tobacco use risk. It has shown good reliability and validity in international multi‐site studies (Humeniuk et al., 2008) as well as in South Africa (Cronbach’s alpha of 0.81 and 0.95 for alcohol and illicit drugs respectively, van der Westhuizen et al., 2016). Previously published results found that self‐reported tobacco use on the ASSIST correlated well with urine cotinine measures, a biomarker of tobacco smoke (Vanker et al., 2017). Individual item responses were summed to generate total scores, with a higher score indicative of greater risk for substance‐related health problems. Scores of 0–10 for alcohol and 0–3 for tobacco have been used to indicate that a participant is at low risk for substance‐related health problems from their current pattern of use; scores of >10 for alcohol and >3 for tobacco indicate moderate or high risk (WHO, 2010). The Edinburgh Postnatal Depression Rating Scale (EPDS) is a 10‐item self‐report measure of recent depressive symptoms (Cox et al., 1987). It has been validated for use in South Africa (de Bruin et al., 2004) and shown high internal consistency (Cronbach’s alpha = 0.89). Each item is scored on a frequency scale ranging from 0 to 3, with higher total scores indicative of more severe depressive symptoms. Within a total range possible range of 0 to 30, a cut‐off score of ≥13 was used to indicate probable depression (Cox et al., 1987). Categorical variables were summarized using frequencies and percentages, while continuous variables were summarized using median (interquartile range [IQR]), where not normally distributed. Normality of data was assessed using Shapiro–Wilkes. Mann–Whitney rank sum and Kruskal–Wallis tests were used to test for associations between categorical and continuous variables. Pearson chi‐square test was used to determine if significant associations existed between categorical variables. To investigate potential mediators of IPV‐growth relationships in this study, we used an iterative approach. Specifically, we used linear regression to identify significant relationships within the proposed models (i.e., Path a, Path b and Path c) prior to formally testing mediation using structural equation models (SEMs). To do this, the following steps were taken. First, we explored bivariate relationships between IPV subtypes and growth outcomes (i.e., total effect; Path c), specifically between antenatal IPV and WFAZ and LFAZ at birth as well as between postnatal IPV and WFAZ and LFAZ at 12 months, and further analyses were only done where these relationships were significant (p < 0.05). Second, we investigated bivariate associations between IPV subtypes and each hypothesized mediator (i.e., Path a). Third, we ran adjusted linear regression models, adjusting for covariates and potential mediators to identify mediators that were significantly (p < 0.05) associated with growth outcomes considered (i.e., Path b). These relationships were then used to inform criteria, as laid out below, for running formal mediation analyses using SEM. IPV subtypes were included separately in adjusted models due to collinearity. Adjusted linear regression models considered key factors known to affect growth, namely, maternal education, household income, maternal height, maternal HIV status and child sex. Additionally, WFAZ at birth was considered in models investigating growth at 12 months. Covariates were included based on strength of association (p value < 0.05) with the growth outcome explored. Final models were estimated using the maximum likelihood method. Variance inflation factor (VIF) was used to check for multicollinearity. These analyses were run using STATA 15.0. As a final step, multiple mediation analyses were conducted using a SEM approach, which allowed for the estimation of direct and indirect effects, Figure 1. SEMs were used to investigate simultaneously maternal depression, alcohol and/or tobacco use as mediators in the IPV‐growth relationship at birth and at 12 months. Additionally, number of hospitalizations was considered as a potential mediator in IPV‐growth relationships at 12 months. Mediators were tested concurrently (VanderWeele & Vansteelandt, 2014) in final mediation models (1) where IPV sub‐type and the hypothesized mediator were associated (Path a) and (2) where the hypothesized mediator was associated with growth outcomes (p < 0.05) in adjusted models (Path b). Hypothesized mediators that did not meet these criteria but were associated with growth outcomes were included as covariates in final models. SEMs were used to estimate the indirect effect of IPV subtypes on growth outcomes via proposed mediators as well as the direct effect of IPV on growth outcomes (Path c′), Figure 1. SEMs were estimated using the maximum likelihood method to impute missing values. All mother–child pairs with anthropometry data at birth or 12 months were included in models of growth at birth and 12 months, respectively. These were conducted using R version 3.6.1 (R Core Team, 2019) and the library ‘lavaan’ version 3.5.3 (Rosseel, 2012). Confidence intervals (95%) as well as direct effects and casual mediation effects were calculated. Model fit was evaluated using root mean square error of approximation (RMSEA; acceptable fit  0.90). Each analysis was based on 5,000 bootstrapped samples (generated from the ‘lavaan’ package version 3.5.3 in R). Proportion mediated was calculated as the indirect effect/total effect and was done using estimations from the SEM models. Diagram of hypothesized pathways in the association between intimate partner violence and growth at birth or 12 months The DCHS was approved by the Human Research Ethics Committee at the University of Cape Town (401/2009) and by the Western Cape Provincial Health Research committee. Mothers completed informed consent in their preferred language: isiXhosa, Afrikaans or English. Study staff were trained on the ethical conduct of violence research, including confidentiality, mandatory reporting and safety issues. Where substance abuse or mental health issues were identified, staff referred participants to social services or appropriate care in the Paarl area.

Based on the information provided, it is not clear what specific innovations or recommendations are being sought to improve access to maternal health. However, based on the study’s findings and the context of the research, here are some potential innovations that could be considered:

1. Integrated care: Implementing integrated care models that address both physical and psychosocial aspects of maternal health, including screening and support for intimate partner violence (IPV), substance use, and mental health disorders.

2. Training and capacity building: Providing training and capacity building for healthcare providers to identify and respond to IPV, substance use, and mental health issues in pregnant women, ensuring they have the knowledge and skills to provide appropriate care and support.

3. Community-based interventions: Developing community-based interventions that raise awareness about IPV, substance use, and mental health, and provide resources and support for pregnant women and new mothers.

4. Collaboration and coordination: Promoting collaboration and coordination between healthcare providers, social services, and community organizations to ensure a comprehensive and holistic approach to maternal health.

5. Technology-based solutions: Utilizing technology, such as mobile health applications or telemedicine, to improve access to maternal health services, including screening for IPV, substance use, and mental health, and providing support and resources remotely.

6. Policy and advocacy: Advocating for policies and programs that address the social determinants of maternal health, including poverty, education, and gender inequality, to create an enabling environment for improved access to care.

It is important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of the population being served.
AI Innovations Description
Based on the description provided, the recommendation to improve access to maternal health and address the issues highlighted in the study is to implement comprehensive programs that address the interrelated risk factors for compromised infant growth, specifically intimate partner violence (IPV) and substance use. These programs should focus on the following:

1. IPV Prevention and Intervention: Implement interventions that aim to prevent and address IPV during pregnancy and the postnatal period. This can include community-based education programs, counseling services, and support groups for women experiencing IPV. It is important to raise awareness about the negative impact of IPV on maternal and child health and provide resources for women to seek help and support.

2. Substance Use Screening and Treatment: Develop screening protocols to identify pregnant women who are at risk for substance use, including alcohol and tobacco use. Provide appropriate interventions and treatment options for women who are identified as having substance use disorders. This can include counseling, referral to substance abuse treatment programs, and support for smoking cessation.

3. Mental Health Support: Enhance access to mental health services for pregnant women and new mothers, particularly those who have experienced IPV. This can involve integrating mental health screening and support into routine antenatal and postnatal care, training healthcare providers to identify and address mental health issues, and establishing partnerships with mental health professionals and organizations to provide specialized care.

4. Collaboration and Coordination: Foster collaboration between healthcare providers, social services, and community organizations to ensure a coordinated and comprehensive approach to addressing the complex needs of women experiencing IPV and substance use. This can involve establishing referral pathways, sharing information and resources, and promoting multidisciplinary teamwork.

5. Community Engagement and Empowerment: Engage the community in efforts to improve maternal health and address the underlying social determinants of health. This can include community mobilization, advocacy for policy changes, and empowering women to seek help and support. It is important to involve community leaders, organizations, and stakeholders in the design and implementation of programs to ensure their relevance and effectiveness.

By implementing these recommendations, it is possible to improve access to maternal health and support the well-being of women and their infants, particularly in high-risk settings where IPV and substance use are prevalent.
AI Innovations Methodology
Based on the information provided, here are some potential recommendations for improving access to maternal health:

1. Increase awareness and education: Implement comprehensive public health campaigns to raise awareness about maternal health issues, including the impact of intimate partner violence (IPV) on maternal and infant health. These campaigns should target both women and men to promote understanding and support for maternal health.

2. Strengthen healthcare systems: Improve the availability and accessibility of maternal health services, particularly in low-income communities. This can be achieved by increasing the number of healthcare facilities, ensuring adequate staffing and resources, and reducing barriers to care such as transportation and cost.

3. Integrate mental health services: Develop and implement integrated mental health services within maternal health programs. This includes screening for maternal depression and providing appropriate support and treatment for women experiencing mental health challenges.

4. Provide support for substance abuse: Establish programs that address substance abuse among pregnant women, including screening, counseling, and referral to treatment services. This can help reduce the negative impact of substance use on maternal and infant health outcomes.

5. Strengthen community support networks: Foster community-based support networks for pregnant women, including peer support groups, community health workers, and community outreach programs. These networks can provide emotional support, education, and referrals to appropriate services.

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

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the number of women receiving prenatal care, the percentage of women receiving postnatal care, or the rate of maternal mortality.

2. Collect baseline data: Gather data on the current state of access to maternal health services in the target population. This can be done through surveys, interviews, or analysis of existing data sources.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, healthcare infrastructure, and resource availability.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations on access to maternal health. This can involve adjusting variables such as the implementation rate of each recommendation or the target population size.

5. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on access to maternal health. This can include comparing the simulated outcomes to the baseline data and identifying any significant improvements or changes.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data or expert input. This can help ensure the accuracy and reliability of the simulation.

7. Communicate findings and make recommendations: Present the findings of the simulation and use them to inform policy and decision-making processes. Provide clear recommendations based on the simulation results to guide efforts in improving access to maternal health.

By following these steps, a methodology can be developed to simulate the impact of recommendations on improving access to maternal health. This can help inform decision-making and resource allocation to effectively address the identified challenges.

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