Violence prevention accelerators for children and adolescents in South Africa: A path analysis using two pooled cohorts.

BACKGROUND: The INSPIRE framework was developed by 10 global agencies as the first global package for preventing and responding to violence against children. The framework includes seven complementary strategies. Delivering all seven strategies is a challenge in resource-limited contexts. Consequently, governments are requesting additional evidence to inform which 'accelerator' provisions can simultaneously reduce multiple types of violence against children. METHODS AND
FINDINGS: We pooled data from two prospective South African adolescent cohorts including Young Carers (2010-2012) and Mzantsi Wakho (2014-2017). The combined sample size was 5,034 adolescents. Each cohort measured six self-reported violence outcomes (sexual abuse, transactional sexual exploitation, physical abuse, emotional abuse, community violence victimisation, and youth lawbreaking) and seven self-reported INSPIRE-aligned protective factors (positive parenting, parental monitoring and supervision, food security at home, basic economic security at home, free schooling, free school meals, and abuse response services). Associations between hypothesised protective factors and violence outcomes were estimated jointly in a sex-stratified multivariate path model, controlling for baseline outcomes and socio-demographics and correcting for multiple-hypothesis testing using the Benjamini-Hochberg procedure. We calculated adjusted probability estimates conditional on the presence of no, one, or all protective factors significantly associated with reduced odds of at least three forms of violence in the path model. Adjusted risk differences (ARDs) and adjusted risk ratios (ARRs) with 95% confidence intervals (CIs) were also calculated. The sample mean age was 13.54 years, and 56.62% were female. There was 4% loss to follow-up. Positive parenting, parental monitoring and supervision, and food security at home were each associated with lower odds of three or more violence outcomes (p < 0.05). For girls, the adjusted probability of violence outcomes was estimated to be lower if all three of these factors were present, as compared to none of them: sexual abuse, 5.38% and 1.64% (ARD: -3.74% points, 95% CI -5.31 to -2.16, p < 0.001); transactional sexual exploitation, 10.07% and 4.84% (ARD: -5.23% points, 95% CI -7.26 to -3.20, p < 0.001); physical abuse, 38.58% and 23.85% (ARD: -14.72% points, 95% CI -19.11 to -10.33, p < 0.001); emotional abuse, 25.39% and 12.98% (ARD: -12.41% points, 95% CI -16.00 to -8.83, p < 0.001); community violence victimisation, 36.25% and 28.37% (ARD: -7.87% points, 95% CI -11.98 to -3.76, p < 0.001); and youth lawbreaking, 18.90% and 11.61% (ARD: -7.30% points, 95% CI -10.50 to -4.09, p < 0.001). For boys, the adjusted probability of violence outcomes was also estimated to be lower if all three factors were present, as compared to none of them: sexual abuse, 2.39% to 1.80% (ARD: -0.59% points, 95% CI -2.24 to 1.05, p = 0.482); transactional sexual exploitation, 6.97% to 4.55% (ARD: -2.42% points, 95% CI -4.77 to -0.08, p = 0.043); physical abuse from 37.19% to 25.44% (ARD: -11.74% points, 95% CI -16.91 to -6.58, p < 0.001); emotional abuse from 23.72% to 10.72% (ARD: -13.00% points, 95% CI -17.04 to -8.95, p < 0.001); community violence victimisation from 41.28% to 35.41% (ARD: -5.87% points, 95% CI -10.98 to -0.75, p = 0.025); and youth lawbreaking from 22.44% to 14.98% (ARD -7.46% points, 95% CI -11.57 to -3.35, p < 0.001). Key limitations were risk of residual confounding and not having information on protective factors related to all seven INSPIRE strategies.
CONCLUSION: In this cohort study, we found that positive and supervisory caregiving and food security at home are associated with reduced risk of multiple forms of violence against children. The presence of all three of these factors may be linked to greater risk reduction as compared to the presence of one or none of these factors. Policies promoting action on positive and supervisory caregiving and food security at home are likely to support further efficiencies in the delivery of INSPIRE.

Introduction

We are at a pivotal moment of opportunity and risk in preventing and responding to violence against children. A billion children globally experience emotional, physical, or sexual violence annually, with evidence of increased rates of violence associated with the COVID-19 epidemic [1]. Severe, long-term impacts include higher mortality, morbidity, school dropout, unemployment, neurological deficits, sexual risks, HIV infection, adolescent pregnancy, and violence in the next generation [2]. This has led to global commitments within the Sustainable Development Goals (SDGs) to prevent and respond to violence against children and to the development led by the World Health Organization (WHO) in 2016 of a set of seven strategies for achieving this—INSPIRE.

INSPIRE was launched alongside the Global Partnership to End Violence Against Children by WHO, United States Centers for Disease Control and Prevention (CDC), the Pan American Health Organization (PAHO), President’s Emergency Plan for AIDS Relief (PEPFAR), Together for Girls, United Nations International Children’s Fund (UNICEF), UN Office on Drugs and Crime (UNODC), US Agency for International Development (USAID), and the World Bank. It includes seven strategies for ending violence against children: Implementation and enforcement of laws, Norms and values, Safe Environments, Parent and caregiver support, Income and economic strengthening, Response and support services, and Education and life skills (Fig 1) [3]. These strategies are based on reviews of all effective interventions in the growing evidence base for prevention and response to violence against children [4], including youth violence and sexual abuse [5]. INSPIRE has gained widespread uptake globally, with commitments from 27 ‘pathfinding’ countries.

Fig 1

INSPIRE strategies for preventing and responding to violence against children.

However, implementing the full range of INSPIRE strategies and approaches is a challenge in contexts of limited financial and human resources. This may become even more acute with lasting global economic impacts of the COVID-19 epidemic, limiting budgets and services for prevention of violence against children. In parallel, governments, donors, and nongovernmental organisations (NGOs) have committed to a new agenda of integrated and connected SDGs. If we are to meet these commitments, we need to address multiple forms of violence—across several SDG targets—simultaneously. Consequently, governments are asking for a next step in the evidence base: the identification of solutions that can have impact across not one but multiple childhood violence targets. Such multiple-win actions are described by the UN as ‘development accelerators’ and have the potential to provide efficiencies for governments [6].

Testing a large number of interventions in isolation and in multiple different combinations in a randomised control trial is neither feasible nor affordable. As a first step, observational data can be used to evaluate associations between community- and family-level protective factors and multiple outcomes. Such evidence could guide future policy or programme selection. In 2019, a pioneering analysis evaluated the association between six protective factors and 11 SDG targets, including emotional and physical violence [7]. However, this study focused on a subpopulation of adolescents living with HIV and lacked power to study rarer forms of violence such as sexual abuse or transactional sexual exploitation. The present study responds to policy requests to identify ‘accelerators’ for violence prevention and response, by evaluating which INSPIRE-aligned protective factors may act on multiple violence types.

In any analysis of adolescent violence, differences in rates of exposure and ramifications mean that it is important to examine violence prevention and response for girls and boys separately [8]. To this end, we identified and pooled two existing prospective cohorts. Our aim was to guide the choice of ‘best buy’ INSPIRE provisions for governments. The study objectives were to (1) evaluate the association between INSPIRE-aligned protective factors and multiple violence outcomes and (2) assess whether experiencing multiple INSPIRE-aligned protective factors might be linked to greater reductions in the risk of violence.

Methods

We use secondary data with self-reported measures to examine how variation in protective factors between individuals is associated with multiple forms of violence. All of the protective factors examined are possible targets of INSPIRE interventions. Identifying which protective factors predict lower rates of violence is intended to inform efforts to identify and prioritise the most efficient subset of interventions. The study setting was South Africa—a country with high levels of government commitment to preventing violence against children but also facing implementation challenges shared by many low- and middle-income contexts: limited service delivery capacity, infrastructure, and financial resources. The study was reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for cohort studies (S1 Checklist) [9].

Data

Study data were pooled from two large prospective cohorts (Young Carers and Mzantsi Wakho) spread across three South African Provinces: Eastern Cape, Mpumalanga, and Western Cape. The Young Carers and Mzantsi Wakho cohorts were designed to allow data merging, with shared investigators, measures, data collection procedures, and sampling in urban and rural sites. The Young Carers recruitment took place between 1 January 2010 and 7 December 2011, with follow-up between 5 January 2011 and 15 December 2012. Participants included N = 3,515 children and adolescents in Mpumalanga and Western Cape Provinces. In each province, census enumeration areas were randomly selected within one urban and one rural health district, and all households with a resident 10- to 17-year-old were recruited. The cohort had 97% uptake during recruitment and 97% retention at 12- to 18-month follow-up [10]. The Mzantsi Wakho (‘Our South Africa’) recruitment took place between 4 March 2014 and 25 September 2015, with follow-up between 10 November 2015 and 5 April 2017. Participants included N = 1,519 children and adolescents in the Eastern Cape Province, living with and without HIV. Across two urban and rural health districts, all children and adolescents (aged 10–19 years) who had ever initiated HIV care in 53 health facilities were recruited, alongside their closest child or adolescent neighbours. The cohort had 90% uptake and 94% retention at 12- to 18-month follow-up (with 2.4% mortality) [11].

For both cohorts, interviews took approximately 45–70 minutes and were conducted in a location of the adolescent’s choice. For Young Carers, interviews were administered using self-interviewing on paper forms, and for Mzantsi Wakho, interviews were administered using audio mobile-assisted self-interviewing on electronic tablets. All interviews were supported by trained local interviewers, and the level of assistance was adjusted according to the age and literacy level of participants. Questionnaires were available in the languages of the adolescent’s choice, including Xhosa, Swati, Tsonga, Pedi, and English. There was no financial payment for participation, but all participants received certificates and snacks, regardless of whether they completed interviews. All participants were asked whether they would be happy to be contacted or visited again for a follow-up at baseline. For those consenting, telephone numbers, addresses, or GPS location was collected for tracing.

Ethics

Ethical approvals were gained for Young Carers and Mzantsi Wakho from the University of Oxford (SSD/CUREC2/11-40; SSD/CUREC2/12-21) and University of Cape Town (REC REF: CSSR 389/2009; REC REF CSSR 2013/04) and provincial South African Departments of Health, Basic Education, and Social Development. In both cases, ethical approval included examination of predictors of violence with collected data. Both studies obtained written consent from all adolescents and their primary caregivers at both baseline and follow-up. Owing to varying levels of literacy, informed consent information was read aloud to potential participants. Confidentiality was maintained except when participants disclosed serious risk of harm to themselves or others. In such circumstances, safeguarding processes were followed. For those reporting current abuse, recent rape, or suicidality, immediate responses included support to access postexposure prophylaxis, pregnancy prevention, and child protection measures in conjunction with government services. Findings of the studies are reported back to communities and health facilities in research areas as part of ongoing local knowledge-sharing.

Adolescent involvement

Children and adolescents were involved in the conceptualisation, design, conduct, and dissemination of these two studies and of this paper. This took place through 11 years of Adolescent Advisory Group weekend workshops in urban and rural areas of South Africa, in which children and adolescents (10–20 years old) engaged in planning research studies, designing content and appearance of questionnaires, planning dissemination, and engaging with the South African National Ministry of Health who attended these workshops. In addition, Young Carers and Mzantsi Wakho questionnaires were piloted with 20 and 25 adolescents, respectively. Adolescents were from local areas involved in the respective studies. All comments were taken into consideration and errors amended.

Measures

Input to questionnaire design in both studies was given by the South African National Departments of Health, Basic Education, and Social Development; the South African National AIDS Council; UNICEF; PEPFAR; USAID; and local NGOs. All questionnaires are available at www.youngcarers.org.za: Young Carers baseline [12] and follow-up [13] and Mzantsi Wakho baseline [14] and follow-up [15].

Outcomes

Six violence outcomes were identified in the data, including five measures of violence victimisation and one of violence perpetration. For the analysis, all six outcomes were coded by collapsing responses to multiple self-reported items into binary indicators of any experience. The six outcomes were (1) sexual abuse, measured as reporting either sexual assault and/or completed rape in the last year using items from the Sexual Victimization module of the Juvenile Victimization Questionnaire [16]; (2) transactional sexual exploitation, measured as reporting any lifetime receipt of money, drinks, clothes, cell phone airtime, a place to stay, lifts in a car/taxi, better marks at school, school fees, food, or anything else for having sex with someone (this question was drawn from the National Survey of Risk Behaviour Amongst Young South Africans [17]); (3) physical abuse, measured as reporting a caregiver or other adult to have either used a stick, belt, or other hard item to hit you or slapped, punched, hit, pinched, or pulled your ear/hair so that it hurt or left marks in the last year, as in UNICEF’s Psychosocial Vulnerability and Resilience Measures For National-Level Monitoring of Vulnerable Children [18]; (4) emotional abuse, defined as reporting a caregiver or other adult to have said they would call ghosts or evil spirits or harmful people, said you would be sent away or kicked out of the house, and/or called you dumb, lazy, or other names in the last year, as in the same UNICEF Psychosocial Vulnerability and Resilience Measures [18]; (5) community violence victimisation, defined as reporting having had things stolen in the last year, having ever been hit or attacked outside, or having ever seen someone stabbed or shot, as in the Child Exposure to Community Violence checklist [19] (these three items reflect the most common community traumas in South Africa, as identified by police statistics); (6) youth lawbreaking as reporting hanging around with kids that get in trouble, stealing at home, stealing things from places other than home, fighting a lot, and carrying a gun and/or knife for protection in the past 6 months, as in the Child Behaviour Checklist Delinquency subscale [20]. Items about gang affiliation and carrying of a knife and/or gun were added during questionnaire piloting.

Hypothesised protective factors for violence

As data collection was initiated before the publication of the INSPIRE framework in 2016, we retrospectively identified seven protective factors that were aligned with its strategies. (1) We measured positive parenting/caregiving using a continuous sum of items 2, 13, 16, and 27 from the child form of the Alabama Parenting Questionnaire [21], which consider warmth and praise from a primary caregiver (range: 0–16). Given high rates of informal fostering in Southern Africa, we note that ‘parents’ were defined as any primary caregiver and there was no requirement for a biological relationship. (2) We measured parental/caregiver monitoring and supervision using a continuous sum of items 10, 17, and 19, from the child form of the Alabama Parenting Questionnaire, which include setting rules about coming home in evenings, and knowing who an adolescent is friends with (range: 0–12). (3) We measured food security at home as a binary indicator of 6 or 7 days in the past week with enough food in the home. (4) We measured basic economic security at home as a binary indicator of access to all six of the top socially perceived necessities for youth in the South African Social Attitudes Survey: a doctor when needed, school uniform, basic clothing, toiletries to wash, school equipment, and a pair of shoes [22]. (5) We measured free schooling as a binary indicator of enrolment in a no-fees school, or receipt of a fee exemption. (6) We measured free school meals as a binary indicator of daily free lunches or breakfasts at school [23]. Exposure to these protective factors was measured as consistent reporting at both baseline and follow-up, based on evidence that such factors need to be predictable and sustained in order to protect children and adolescents in high-risk settings [24, 25]. For this, continuous variables were combined additively. (8) We also describe the prevalence of adolescent engagement with response services for children who are victims of violence, measured as any social or justice service response at follow-up related to emotional, physical, or sexual abuse (Young Carers data) and sexual abuse (Mzantsi Wakho data). Because experiencing study outcomes of abuse was a condition for being asked about access to response services, we were unable to evaluate it as a protective factor.

Covariates

Control variables included 10 key sociodemographic variables and violence-associated factors: participant sex, age, HIV status, rural/urban household location, household size, informal/shack housing, maternal orphanhood, paternal orphanhood, and province of residence. Participant HIV status was assessed using self-report (Young Carers data) and clinical records (Mzantsi Wakho data). Baseline measures of self-reported sexual abuse, emotional abuse, physical abuse (all last year), and youth lawbreaking in the last 6 months were also included as covariates in regression models investigating associations between hypothesised protective factors and these forms of violence at follow-up.

Analysis

Analysis took place in seven steps in Stata 15, all stratified by sex. First, we compared baseline characteristics of participants retained and lost to follow-up. Second, we described baseline and follow-up characteristics of retained participants using count (percent) for binary and categorical variables and mean (standard deviation [SD]) for continuous variables. Third, we evaluated univariable associations between the six hypothesised protective factors and six binary violence outcomes. Fourth, we investigated multivariable associations between hypothesised protective factors and violence outcomes. For this, we used path analysis consisting of six single-outcome multivariable logistic regression models, each regressing one of the six violence outcomes at follow-up on the six hypothesised protective factors, controlling for outcome-specific baseline exposure to violence and for common sociodemographic factors. Missing data were handled using listwise deletion. Fifth, to account for risk of type I error from multiple-hypothesis testing, we adjusted estimated p-values using the Benjamini-Hochberg procedure specified with a false discovery rate of 5% [26]. The analysis had six regression models, each containing six predictors of scientific interest. Hence, a family of tests consisted of six values, one for each protective factor, across regression models. Sixth, hypothesised protective factors significantly associated (for either sex or both) with three or more violence outcomes were defined as protective factors that had potential to indicate development ‘accelerators’. Seventh, we estimated adjusted probabilities for experiencing each violence outcome under three scenarios: (1) experiencing no ‘accelerator’ protective factors, (2) experiencing each ‘accelerator’ protective factor, and (3) experiencing all ‘accelerator’ protective factors together. Positive parenting and parental monitoring and supervision were the only hypothesised protective factors modelled as continuous scales. For these two variables, adjusted probabilities were estimated at the sample mean value and at their maximum scores (32 for positive parenting and 24 for parental monitoring and supervision). Finally, adjusted risk ratios (ARRs) and adjusted risk differences (ARDs) were used to compare scenarios 2 and 3 relative to scenario 1. All confidence intervals (CIs) are given at 95% confidence level.

Although the specific analysis reported in this study was not prespecified, it followed a prespecified methodological approach that has been developed to investigate factors associated with multiple SDG outcomes in observational data as part of the UKRI GCRF ‘Accelerate Hub’ [27]. This approach is laid out on Open Science Framework [28].

Sensitivity analysis

Multiple-outcome models that include correlation between the error terms of outcomes can be more accurate when modelling highly correlated outcomes with different sets of predictors per outcome [29]. As a sensitivity analysis, we calculated adjusted probabilities of experiencing each violence outcome from a multiple-outcome probit model that correlated the error terms of our six different regression models. This was done using the mvprobit command in Stata 15 set at 100 random draws [30]. As in our main analysis, each regression regressed one of the six violence outcomes at follow-up on the six hypothesised protective factors, controlling for outcome-specific baseline exposure to violence and for common sociodemographic factors.

Results

Participants

The pooled data provided a sample of N = 5,034 children and adolescents at baseline. Across the two pooled cohorts, median time to follow-up was 353 days, and 4% of participants (N = 223) were lost to follow-up. At baseline, participants lost to follow-up were more likely to live in the Eastern Cape Province (p < 0.001), be older (p < 0.001), be living with HIV (p < 0.001), and live in an urban area (p = 0.002) (S1 Table). In terms of violence, at baseline, participants lost to follow-up were equally likely to experience sexual abuse, transactional sexual exploitation, physical abuse, and youth lawbreaking but more likely to experience emotional abuse (p = 0.022) and community violence victimisation (p = 0.012) (S1 Table). The final analysis included 4,811 participants. Missing values for all variables were <1%.

Descriptive data

Baseline and follow-up characteristics of study participants are summarised in Table 1. Fifty-six percent of participants were female. At baseline, the average age was 13.54 years (SD: 2.41 years), 19% of participants were maternally orphaned, 23% paternally orphaned, 7% doubly orphaned, and 22% living with HIV. Mean household size including the participant was 5.72 (SD: 2.56). Forty-three percent lived in rural areas and 28% in informal shacks. Girls were slightly older than boys (13.68 years compared to 13.36 years, p < 0.001) and more likely to live in informal housing (p = 0.03) and households with a larger number of persons (p < 0.001). Baseline comparison of study participants by province is reported in S3 Table.

Table 1

Characteristics of study participants.

	Baseline				Follow-up
	Total n = 4,811	Boys n = 2,087	Girls n = 2,724	Sex difference p-value	Total n = 4,811	Boys n = 2,087	Girls n = 2,724	Sex difference p-value
Sociodemographic characteristics
Province				0.096				0.096
Eastern Cape	1,410 (29)	612 (29)	798 (29)		1,410 (29)	612 (29)	798 (29)
Western Cape	1,753 (36)	729 (34)	1,024 (37)		1,753 (36)	729 (35)	1,024 (38)
Mpumalanga	1,648 (34)	746 (35)	902 (33)		1,648 (34)	746 (36)	902 (33)
Age				<0.001				<0.001
Mean (SD)	13.54 (2.41)	13.36 (2.27)	13.68 (2.51)		14.85 (2.47)	14.68 (2.34)	14.99 (2.57)
Maternal orphan				0.064				0.09
Yes	913 (19)	421 (20)	492 (18)		1,060 (22)	484 (23)	576 (21)
Paternal orphan				0.168				0.40
Yes	1,090 (23)	453 (22)	637 (23)		1,321 (27)	560 (27)	761 (28)
Living with HIV				0.388				0.47
Yes	1,042 (22)	464 (22)	578 (21)		1,058 (22)	469 (22)	589 (22)
Rural location				0.086				0.083
Yes	2,067 (43)	867 (42)	1,200 (44)		2,048 (43)	859 (41)	1,189 (44)
Informal housing				0.03				0.058
Yes	1,322 (28)	540 (26)	782 (29)		1,257 (26)	518 (25)	739 (27)
Household size				<0.001				0.005
Mean (SD)	5.72 (2.56)	5.58 (2.44)	5.83 (2.64)		5.44 (2.74)	5.32 (2.53)	5.54 (2.88)
Hypothesised protective factors for violence*
Positive parenting				0.784				0.664
Mean (SD)	12.27 (3.68)	12.29 (3.61)	12.26 (3.74)		24.51 (5.89)	24.55 (5.71)	24.48 (6.02)
Parental monitoring and supervision				0.001				<0.001
Mean (SD)	9.79 (2.62)	9.65 (2.66)	9.90 (2.59)		19.53 (3.89)	19.19 (3.98)	19.79 (3.80)
Food security at home				0.002				<0.001
Yes	3,782 (79)	1,684 (81)	2,098 (77)		3,101 (64)	1,421 (68)	1,680 (62)
Basic economic security at home				0.416				0.177
Yes	1,960 (41)	865 (41)	1,095 (40)		1,112 (23)	503 (24)	609 (22)
Free schooling				0.318				0.533
Yes	2,338 (49)	997 (48)	1,341 (49)		1,892 (39)	810 (39)	1,082 (40)
Free school meals				0.566				0.019
Yes	4,149 (86)	1,807 (87)	2,342 (86)		3,847 (80)	1,703 (82)	2,144 (79)
Abuse response services†				-				<0.001
Yes	-	-	-		133 (3)	31 (1)	102 (4)
Violence outcomes
Sexual abuse				<0.001				<0.001
Yes	180 (4)	45 (2)	135 (5)		163 (3)	49 (2)	114 (4)
Transactional sexual exploitation				<0.001				<0.001
Yes	190 (4)	44 (2)	146 (5)		381 (8)	133 (6)	248 (9)
Physical abuse				0.849				0.365
Yes	1,648 (34)	718 (34)	930 (34)		1,595 (33)	707 (34)	888 (33)
Emotional abuse				0.002				<0.001
Yes	1,343 (28)	536 (26)	807 (30)		1,023 (21)	394 (19)	629 (23)
Community violence victimisation				<0.001				<0.001
Yes	1,965 (41)	911 (44)	1,054 (39)		1,757 (37)	826 (40)	931 (34)
Youth lawbreaking				<0.001				<0.001
Yes	1,297 (27)	648 (31)	649 (24)		1,089 (23)	565 (27)	524 (19)

Data are mean (SD) for continuous variables and n (%) for categorical variables.

*Hypothesised protective factors at follow-up were measured as consistent access at both baseline and follow-up. For example, 1,684 boys experienced food security at T1, of whom 1,421 also experienced it at T2.

†Measured in the Young Carers data as any social or justice service response at follow-up related to emotional, physical, or sexual abuse and in Mzantsi Wakho as accessing any social or justice services at follow-up related to sexual abuse.

Abbreviation: SD, standard deviation

Consistent exposure to hypothesised protective factors at both baseline and follow-up was most common for free school meals (80%), food security at home (64%), free schooling (39%), and basic economic security at home (23%). The mean score of parental monitoring and supervision across baseline and follow-up was 19.53 (SD: 3.89), and 22% reported the maximum score of 24.00. The mean score of positive parenting was 24.51 (SD: 5.89), and 16% reported the maximum score of 32.00. Girls were more likely to experience better parental monitoring and supervision (p < 0.001) but had less food security at home (p < 0.001) or free school meals (p = 0.019). Access to any kind of support service in response to abuse was very rare (3% of those reporting abuse).

At follow-up, the most common form of reported violence was community violence victimisation (37%), followed by physical abuse (33%), youth lawbreaking (23%), emotional abuse (21%), transactional sexual exploitation (8%), and sexual abuse (3%). Girls were more likely to experience sexual abuse, transactional sexual exploitation, and emotional abuse (p < 0.001), whereas boys were more likely to experience community violence victimisation, and youth lawbreaking (p < 0.001).

Multivariable associations between hypothesised protective factors and violence outcomes

Of the six hypothesised protective factors from the INSPIRE framework, three factors—positive parenting, parental monitoring and supervision, and food security—were significantly associated with lower likelihood of three or more violence outcomes at follow-up for either girls or boys (Table 2). For girls, each unit increase in positive parenting was associated with lower odds of sexual abuse (AOR: 0.95, 95% CI 0.92–0.98, p < 0.001), physical abuse (AOR: 0.98, 95% CI 0.97–0.99, p = 0.009), emotional abuse (AOR: 0.96, 95% CI 0.95–0.98, p < 0.001), and youth lawbreaking (AOR 0.96, 95% CI 0.95–0.98, p < 0.001); each unit increase in parental monitoring and supervision was associated with lower odds of sexual abuse (AOR: 0.94, 95% CI 0.90–0.99, p = 0.02), transactional sexual exploitation (AOR: 0.94, 95% CI 0.91–0.97, p < 0.001), physical abuse (AOR: 0.95, 95% CI 0.93–0.97, p < 0.001), emotional abuse (AOR: 0.94, 95% CI 0.92–0.97, p < 0.001), community violence victimisation (AOR: 0.97, 95% CI 0.94–0.99, p = 0.012), and youth lawbreaking (AOR: 0.94, 95% CI 0.92–0.97, p < 0.001); food security at home was associated with lower odds of sexual abuse (AOR: 0.54, 95% CI 0.35–0.81, p = 0.003), transactional sexual exploitation (AOR: 0.55, 95% CI 0.40–0.76, p < 0.001), physical abuse (AOR: 0.70, 95% CI 0.58–0.84, p < 0.001), emotional abuse (AOR: 0.74, 95% CI 0.61–0.91, p = 0.004), and community violence victimisation (AOR: 0.73, 95% CI 0.60–0.89, p = 0.002). For boys, each unit increase in positive parenting was associated with lower odds of emotional abuse (AOR: 0.98, 95% CI 0.96–1.00, p = 0.028) and youth lawbreaking (AOR: 0.97, 95% CI 0.95–0.99, p = 0.001); each unit increase in parental monitoring and supervision was associated with lower odds of transactional sexual exploitation (AOR: 0.93, 95% CI 0.89–0.97, p = 0.001), physical abuse (AOR: 0.96, 95% CI 0.94–0.99, p = 0.005), emotional abuse (AOR: 0.94, 95% CI 0.91–0.97, p < 0.001), community violence victimisation (AOR: 0.94, 95% CI 0.92–0.97, p < 0.001), and youth lawbreaking (AOR: 0.91, 95% CI 0.88–0.93, p < 0.001); food security at home was associated with lower odds of physical abuse (AOR: 0.72, 95% CI 0.58–0.90, p = 0.003) and emotional abuse (AOR: 0.59, 95% CI 0.46–0.76, p < 0.001). Univariable associations between hypothesised protective factors and violence outcomes are reported in S3 Table.

Table 2

Summary of multivariable associations between hypothesised protective factors and violence outcomes.

	Boys		Girls		Sex interactionp-value
	AOR (95% CI)	p-value	AOR (95% CI)	p-value
Sexual abuse
Positive parenting	0.97 (0.92–1.02)	0.176	0.95 (0.92–0.98)	<0.001*	0.61
Parental monitoring and supervision	0.99 (0.92–1.07)	0.83	0.94 (0.90–0.99)	0.02	0.27
Food security at home	1.00 (0.50–2.01)	0.999	0.54 (0.35–0.81)	0.003*	0.13
Basic economic security at home	1.13 (0.56–2.27)	0.74	0.84 (0.48–1.46)	0.538	0.52
Free schooling	1.99 (1.00–3.97)	0.051	1.27 (0.79–2.05)	0.327	0.27
Free school meals	0.51 (0.24–1.09)	0.084	0.96 (0.58–1.60)	0.872	0.16
Transactional sexual exploitation
Positive parenting	0.98 (0.95–1.02)	0.359	0.99 (0.97–1.02)	0.56	0.69
Parental monitoring and supervision	0.93 (0.89–0.97)	0.001*	0.94 (0.91–0.97)	<0.001*	0.63
Food security at home	0.94 (0.62–1.45)	0.792	0.55 (0.40–0.76)	<0.001*	0.05
Basic economic security at home	0.71 (0.42–1.21)	0.205	0.88 (0.56–1.36)	0.561	0.54
Free schooling	1.00 (0.65–1.56)	0.985	0.54 (0.37–0.81)	0.002*	0.04
Free school meals	0.68 (0.42–1.11)	0.12	0.84 (0.58–1.21)	0.343	0.47
Physical abuse
Positive parenting	0.99 (0.97–1.00)	0.135	0.98 (0.97–0.99)	0.009*	0.59
Parental monitoring and supervision	0.96 (0.94–0.99)	0.005*	0.95 (0.93–0.97)	<0.001*	0.41
Food security at home	0.72 (0.58–0.90)	0.003*	0.70 (0.58–0.84)	<0.001*	0.78
Basic economic security at home	1.15 (0.90–1.46)	0.274	1.00 (0.80–1.25)	0.98	0.42
Free schooling	1.25 (1.01–1.54)	0.042	1.06 (0.87–1.28)	0.574	0.24
Free school meals	1.32 (1.00–1.74)	0.053	1.29 (1.02–1.64)	0.032	0.92
Emotional abuse
Positive parenting	0.98 (0.96–1.00)	0.028*	0.96 (0.95–0.98)	<0.001*	0.26
Parental monitoring and supervision	0.94 (0.91–0.97)	<0.001*	0.94 (0.92–0.97)	<0.001*	0.90
Food security at home	0.59 (0.46–0.76)	<0.001*	0.74 (0.61–0.91)	0.004*	0.16
Basic economic security at home	1.20 (0.88–1.62)	0.247	0.84 (0.64–1.10)	0.199	0.08
Free schooling	1.34 (1.04–1.73)	0.022	0.94 (0.76–1.16)	0.555	0.03
Free school meals	0.88 (0.64–1.22)	0.444	1.02 (0.79–1.32)	0.854	0.46
Community violence victimisation
Positive parenting	1.00 (0.98–1.02)	0.861	1.00 (0.98–1.01)	0.707	0.70
Parental monitoring and supervision	0.94 (0.92–0.97)	<0.001*	0.97 (0.94–0.99)	0.012*	0.18
Food security at home	0.91 (0.72–1.16)	0.458	0.73 (0.60–0.89)	0.002*	0.15
Basic economic security at home	0.76 (0.57–1.01)	0.058	0.85 (0.64–1.14)	0.276	0.56
Free schooling	0.86 (0.68–1.09)	0.215	1.16 (0.94–1.44)	0.175	0.06
Free school meals	0.76 (0.56–1.03)	0.076	0.83 (0.63–1.08)	0.167	0.65
Youth lawbreaking
Positive parenting	0.97 (0.95–0.99)	0.001*	0.96 (0.95–0.98)	<0.001*	0.73
Parental monitoring and supervision	0.91 (0.88–0.93)	<0.001*	0.94 (0.92–0.97)	<0.001*	0.04
Food security at home	1.19 (0.94–1.51)	0.141	0.96 (0.77–1.19)	0.71	0.18
Basic economic security at home	0.92 (0.70–1.20)	0.526	0.80 (0.60–1.07)	0.132	0.50
Free schooling	0.64 (0.50–0.80)	<0.001*	0.64 (0.51–0.81)	<0.001*	0.98
Free school meals	1.05 (0.79–1.40)	0.730	1.08 (0.83–1.41)	0.583	0.90

With listwise deletion N = 4,641. AORs and 95% CIs were estimated jointly in a single multivariate multivariable path model. Associations between hypothesised protective factors and the six outcomes are mutually adjusted for all other hypothesised protective factors and for participant sociodemographic characteristics: age, maternal orphanhood, paternal orphanhood, HIV status, number of people living in household, rural/urban household location, informal housing type (shack), and province of residence. Associations between hypothesised protective factors and sexual abuse, emotional abuse, physical abuse, and youth lawbreaking are also adjusted for self-reported exposure to these forms of violence at baseline.

*p-Values are significant after applying the Benjamini-Hochberg procedure specified with a false discovery rate of 5%.

Abbreviations: AOR, adjusted odds ratio; CI, confidence interval

Adjusted probability of violence outcomes comparing different combinations of ‘accelerator’ protective factors

For girls, the adjusted probability of all six types of violence was estimated to be significantly lower for a scenario in which high positive parenting, high parental monitoring and supervision, and food security at home were experienced together as compared to a scenario of average positive parenting, average parental monitoring, and no food security at home (Table 3 and Fig 2A). With no protective factors, the adjusted probability of sexual abuse was 5.38%; with all three, it was 1.64% (ARD: −3.74% points, 95% CI −5.31 to −2.16, p < 0.001). With no protective factors, the adjusted probability of transactional sexual exploitation was 10.07%; with all three, it was 4.84% (ARD: −5.23% points, 95% CI −7.26 to −3.20, p < 0.001). With no protective factors, the adjusted probability of physical abuse was 38.58%; with all three, it was 23.85% (ARD: −14.72% points, 95% CI −19.11 to −10.53, p < 0.001). With no protective factors, the adjusted probability of experiencing emotional abuse was 25.39%; with all three, it was 12.98% (ARD: −12.41% points, 95% CI −16.00 to −8.83, p < 0.001). With no protective factors, the adjusted probability of community violence victimisation was 36.25%; with all three, it was 28.37% (ARD: −7.87% points, 95% CI −11.98 to −3.76, p < 0.001). With no protective factors, the adjusted probability of youth lawbreaking was 18.90%; with all three, it was 11.61% (ARD: −7.30% points, 95% CI −10.50 to −4.09, p < 0.001).

Table 3

Summary of adjusted probabilities, risk ratios, and risk differences for violence outcomes.

	Boys			Girls
	Adjusted probability, %*	Exposure versus no exposure		Adjusted probability, %*	Exposure versus no exposure
		ARR (95% CI)	ARD (95% CI)		ARR (95% CI)	ARD (95% CI)
Sexual abuse
No protective factors	2.39	-	-	5.38	-	-
Positive parenting	1.86	0.78 (0.50–1.06)	−0.53 (−1.24 to 0.19)	3.81	0.71 (0.56–0.86)	−1.57 (−2.43 to −0.71)
Parental monitoring and supervision	2.31	0.97 (0.66–1.27)	−0.08 (−0.81 to 0.64)	4.24	0.79 (0.63–0.95)	−1.14 (−2.01 to −0.26)
Food security at home	2.39	1.00 (0.33–1.67)	0.00 (−1.59 to 1.59)	3.00	0.56 (0.34–0.78)	−2.38 (−4.09 to −0.67)
All three protective factors	1.80	0.75 (0.16–1.34)	−0.59 (−2.24 to 1.05)	1.64	0.30 (0.16–0.45)	−3.74 (−5.31 to −2.16)
Transactional sexual exploitation
No protective factors	6.97	-	-	10.07	-	-
Positive parenting	6.35	0.91 (0.73–1.09)	−0.63 (−1.91 to 0.66)	9.67	0.96 (0.83–1.09)	−0.40 (−1.73 to 0.93)
Parental monitoring and supervision	5.26	0.76 (0.63–0.88)	−1.71 (−2.55 to −0.86)	8.15	0.81 (0.71–0.91)	−1.92 (−2.94 to −0.91)
Food security at home	6.66	0.95 (0.63–1.28)	−0.31 (−2.67 to 2.04)	6.38	0.63 (0.48–0.79)	−3.70 (−5.73 to −1.66)
All three protective factors	4.55	0.65 (0.38–0.93)	−2.42 (−4.77 to −0.08)	4.84	0.48 (0.33–0.63)	−5.23 (−7.26 to −3.20)
Physical abuse
No protective factors	37.19	-	-	38.58	-	-
Positive parenting	35.00	0.94 (0.86–1.02)	−2.19 (−5.01 to 0.64)	35.33	0.92 (0.85–0.98)	−3.25 (−5.63 to −0.87)
Parental monitoring and supervision	33.71	0.91 (0.84–0.97)	−3.48 (−5.80 to −1.15)	33.70	0.87 (0.82–0.93)	−4.88 (−7.04 to −2.71)
Food security at home	30.44	0.82 (0.71–0.93)	−6.75 (−11.31 to −2.19)	30.85	0.80 (0.71–0.89)	−7.73 (−11.63 to −3.82)
All three protective factors	25.44	0.68 (0.57–0.80)	−11.74 (−16.91 to −6.58)	23.85	0.62 (0.52–0.71)	−14.72 (−19.11 to −10.33)
Emotional abuse
No protective factors	23.72	-	-	25.39	-	-
Positive parenting	20.78	0.88 (0.77–0.98)	−2.93 (−5.43 to −0.44)	20.51	0.81 (0.73–0.88)	−4.88 (−6.83 to −2.93)
Parental monitoring and supervision	19.25	0.81 (0.73–0.90)	−4.47 (−6.42 to −2.51)	20.86	0.82 (0.75–0.89)	−4.53 (−6.36 to −2.70)
Food security at home	15.68	0.66 (0.53–0.79)	−8.04 (−12.07 to −4.01)	20.27	0.80 (0.68–0.92)	−5.12 (−8.62 to −1.61)
All three protective factors	10.72	0.45 (0.33–0.57)	−13.00 (−17.04 to −8.95)	12.98	0.51 (0.40–0.62)	−12.41 (−16.00 to −8.83)
Community violence victimisation
No protective factors	41.28	-	-	36.25	-	-
Positive parenting	41.51	1.01 (0.94–1.07)	0.23 (−2.36 to 2.82)	35.84	0.99 (0.93–1.05)	−0.40 (−2.51 to 1.70)
Parental monitoring and supervision	36.72	0.89 (0.84–0.94)	−4.56 (−6.66 to −2.45)	33.79	0.93 (0.88–0.99)	−2.46 (−4.36 to −0.55)
Food security at home	39.69	0.96 (0.86–1.06)	−1.59 (−5.81 to 2.63)	31.05	0.86 (0.77–0.94)	−5.20 (−8.54 to −1.86)
All three protective factors	35.41	0.86 (0.74–0.97)	−5.87 (−10.98 to −0.75)	28.37	0.78 (0.68–0.89)	−7.87 (−11.98 to −3.76)
Youth lawbreaking
No protective factors	22.44	-	-	18.90	-	-
Positive parenting	18.62	0.83 (0.74–0.92)	−3.82 (−5.90 to −1.74)	15.14	0.80 (0.72–0.89)	−3.76 (−5.36 to −2.16)
Parental monitoring and supervision	15.84	0.71 (0.63–0.78)	−6.61 (−8.21 to −5.00)	15.17	0.80 (0.72–0.88)	−3.74 (−5.26 to −2.21)
Food security at home	25.52	1.14 (0.94–1.33)	3.08 (−0.96 to 7.12)	18.30	0.97 (0.80–1.13)	−0.60 (−3.78 to 2.58)
All three protective factors	14.98	0.67 (0.51–0.82)	−7.46 (−11.57 to −3.35)	11.61	0.61 (0.48–0.75)	−7.30 (−10.50 to −4.09)

*Adjusted probabilities were estimated with basic economic security at home, free schooling, free school meals, and all other covariates at observed values. Not experiencing positive parenting or parental monitoring and supervision was considered to be equivalent to the sample mean score of these factors (24.51 for positive parenting and 19.53 for parental monitoring and supervision). Experiencing these protective factors was considered to be equivalent to experiencing the maximum score of these factors (32.00 for positive parenting and 24.00 for parental monitoring and supervision).

Abbreviations: ARD, adjusted risk difference; ARR, adjusted risk ratio; CI, confidence interval

Fig 2

Adjusted probabilities and risk differences for experiencing violence outcomes in (A) girls and (B) boys. Percentages joined by an arrow are adjusted probabilities for the scenarios (1) experiencing no ‘accelerator’ protective factors and (2) experiencing all three ‘accelerator’ protective factors. Data in the brackets are adjusted risk differences, and 95% CIs. Double lines between central and outer circles indicate an additive effect of two accelerators; triple lines indicate an additive effect of three accelerators. Blue circles correspond to outcomes related to SDG 16: Peace, Justice, and Strong Institutions. Red circles correspond to outcomes related to SDG 5: Gender Equality. Adjusted probabilities were estimated with basic economic security at home, free schooling, free school meals, and all other covariates at observed values. CI, confidence interval; SDG, Sustainable Development Goal.

For boys, the adjusted probability of transactional sexual exploitation, physical abuse, emotional abuse, community violence victimisation, and youth lawbreaking was estimated to be significantly lower for a scenario in which high positive parenting, high parental monitoring and supervision, and food security at home were experienced together as compared to a scenario of average positive parenting, average parental monitoring, and no food security at home (Table 3 and Fig 2B). With no protective factors, the adjusted probability of engaging in transactional sexual exploitation was 6.97%; with all three, it was 4.55% (ARD: −2.42% points, 95% CI −4.77 to −0.08, p = 0.043). With no protective factors, the adjusted probability of experiencing physical abuse was 37.19%; with all three, it was 25.44% (ARD: −11.74% points, 95% CI −16.91 to −6.58, p < 0.001). With no protective factors, the adjusted probability of experiencing emotional abuse was 23.72%; with all three, it was 10.72% (ARD: −13.00% points, 95% CI −17.04 to −8.95, p < 0.001). With no protective factors, the adjusted probability of experiencing community violence victimisation was 41.28%; with all three, it was 35.41% (ARD: −5.87% points, 95% CI −10.98 to −0.75, p = 0.025). With no protective factors, the adjusted probability of youth lawbreaking was 22.44%; with all three, it was 14.98% (ARD: −7.46% points, 95% CI −11.57 to −3.35, p < 0.001).

Accounting for correlation between our outcomes using a multiple-outcome model showed equivalent results, with no differences in significant findings between the two methods. However, the combination of positive parenting, parental monitoring and supervision, and food security was associated with even lower adjusted probability of community violence victimisation in both sexes (ARD: −8.78% points in boys and −8.22% points in girls) (S4 Table).

Discussion

Findings from our analysis provide important new evidence on the association between protective factors and multiple violence outcomes. They suggest that food security, positive parenting, and parental monitoring and supervision may be associated with reduced risk of three or more violence outcomes related to SDGs 5 ‘Gender equality’ and 16 ‘Peace, justice, and strong institutions’. Results also indicate that experiencing all three of these factors may be associated with lower risk of violence as compared to experiencing any one of them alone. The large sample used in this analysis enabled disaggregation by sex, and investigation of underreported forms of violence including sexual abuse and transactional sexual exploitation. Although some differences by sex reflect distinct experiences and prevalence of violence victimisation, there was strong similarity in protective factors across sexes. Food security and positive and supervisory caregiving were shared ‘accelerators’ across girls and boys—suggesting that the same protective factors can be beneficial for both sexes. This is especially important when considering implications of these findings for future interventions. Provisions to support food security and parenting primarily act at a household level, where targeting of one sex can be problematic. Evidence of protective associations for both boys and girls can increase both the value and feasibility of delivering INSPIRE-associated provisions. It is notable that in girls, free schooling was also significantly associated with lower odds of transactional sexual exploitation and youth lawbreaking.

To our knowledge, this study provides the first evidence on which protective factors may be targeted to achieve improvements across diverse violence types simultaneously. Although desirable, it would have been too costly to test all protective factors, and their various combinations, using experimental methods. Instead, we took advantage of observed variation between individuals to provide critical guidance on which protective factors could be considered as effective targets for governments seeking efficiencies in the delivery of INSPIRE. A drawback of this observational approach is that it is unable to say whether identified relationships between self-reported protective factors and violence outcomes are causal. In relation to existing literature, study findings are supported by evidence that positive and supervisory caregiving are important mediators of the effects of parenting support interventions on physical abuse, emotional abuse, and behaviour problems [31]. They are also consistent with growing observational evidence linking parenting to adolescent violence victimisation and perpetration across settings [32]. With regards to food security, a recent systematic review found that cash transfer interventions, which are known to increase food security, are linked to reduced risk of sexual abuse amongst female adolescents [33, 34]. Following up on this review, a more recent study in Zimbabwe identified food security as a potential mediator of the effect of economic strengthening on physical violence among adolescents [35]. Food security may reduce pressure on adolescent girls to work outside the home or empower them to leave exploitative relationships [36].

Although our study is unable to establish whether estimated associations are causal, rigorous research methods ensure the highest possible internal validity. The research was conducted using strong longitudinal data, linking repeatedly measured protective factors to prospective violence outcomes. Both cohorts had high rates of uptake (>90%) and retention (>94%) and minimal missing data. Risk of reverse causality is further reduced by evaluation of protective factors reflecting circumstances external to adolescent participants. The study also adjusts for the common confounding variables including participant sex, age, caregiver status, and socioeconomic status. A threat to internal validity is risk of residual confounding from unmeasured variables such as caregiver attitudes and knowledge, and future research should consider this further. Generalisability of study findings is supported by both strong internal validity as well as evidence of the role of parent-child relationships and poverty in determining violence against adolescents across diverse settings and groups in Africa [37, 38]. Known contextual differences in the prevalence of protective factors and violence, as well as the interplay between them, mean that further work is needed to confirm this in other regions [39].

The study has a number of limitations. Because the data used in this analysis were collected prior to the publication of the INSPIRE framework, it did not include potential protective factors within the ‘Norms and Values’ or ‘Safe Environments’ strategies. Our measure of access to abuse response services was also not perfectly matched across our two cohorts. Despite this, the study highlights that the proportion of victims reaching any services may be extremely small (<3%). Indeed, such negligible access rates reflect an important need for additional focus on these services. As with all violence research in low-resource settings, this study used self-reported violence outcomes and therefore risks underreporting, particularly of outcomes such as sexual abuse. Evaluating use of justice or social service records is difficult in contexts of very limited service availability. We attempted to reduce bias by using questions previously validated in this population and setting, and by building trust with respondents. For transactional sexual exploitation, participants were asked about ever experiencing this outcome, and it is possible that household food security and economic security were influenced by exposure to transactional sexual exploitation prior to baseline. Any measurement error from this is expected to be minimal, as qualitative data from the region stress the financial and emotional dependence of adolescents on transactional sexual relationships once initiated [40]. In the Young Carers data, HIV status was only measured at follow-up, which may have introduced some measurement error. Considering the expected rate of new infections over the study period, this will have been minimal.

This study provides some of the first evidence, to our knowledge, on which protective factors could be effective targets for interventions aiming to address multiple violence outcomes simultaneously. Each of the self-reported protective factors found to be associated with multiple violence outcomes have corresponding interventions with randomised evidence of effectiveness in low- and middle-income countries. Parenting support programmes improve both positive and supervisory caregiving [41], and household food security may be increased by either cash transfers or other nutrition-sensitive programmes [33, 42]. Combining parenting support with economic strengthening is also an effective way to address these household-level factors in an integrated way [43-46]. Before strong policy recommendations can be made, a number of further research steps are necessary. Additional analysis is needed to disaggregate within- and between-person effects of protective factors and thus strengthen causal claim. Such research should aim to explore hypothesised protective factors across the full spectrum of recommended INSPIRE strategies and focus on a range of settings where the interplay between family, community, or cultural factors and violence may differ from South Africa. Randomised control trials are considered the ‘gold-standard’ approach for investigating causal effects but, as discussed, can be prohibitively complex and expensive when considering multiple combinations of distinct treatment components. As evidence linking intermediary protective factors to multiple violence outcomes grows, and a reduced set of intervention targets emerges, experimental evaluation of interventions designed to impact on these factors will be necessary. It would also be valuable to consider if and how services known to modify these protective factors could be implemented at scale effectively and affordably.

The INSPIRE framework has been important in vision and impact: identifying the best and most recent evidence, intensifying the focus on evidence-based programmes, and increasing country-level commitments to prevent violence against children. But for many governments, delivering the full package is challenged by limited resources. These findings suggest a valuable additional step in the process from evidence review to implementation. By identifying INSPIRE-aligned protective factors associated with multiple violence outcomes, it may be possible to pinpoint interventions that could bring efficiency gains and contribute to our growing understanding of ‘development accelerators’. Within the broader development agenda of interconnected Global Goals, we are moving towards a new generation of violence prevention programming that is not only effective but also scalable.

Study STROBE Statement.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

Click here for additional data file.

Baseline characteristics of study participants by loss to follow-up. Data are mean (SD) for continuous variables and n (%) for categorical variables. SD, standard deviation.

(DOCX)

Click here for additional data file.

Baseline characteristics of study participants by province of residence.

Data are mean (SD) for continuous variables and n (%) for categorical variables. SD, standard deviation.

(DOCX)

Click here for additional data file.

Summary of univariable associations between hypothesised protective factors and violence outcomes.

With listwise deletion N = 4,641. CI, confidence interval; OR, odds ratio.

(DOCX)

Click here for additional data file.

Sensitivity analysis of adjusted probabilities comparing single- and multiple-outcome modelling approach.

(DOCX)

Click here for additional data file.

23 Apr 2020

Dear Dr Cluver,

Thank you for submitting your manuscript entitled "Violence prevention accelerators for children and adolescents in Africa" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by .

Login to Editorial Manager here: https://www.editorialmanager.com/pmedicine

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Artur Arikainen,

Associate Editor

PLOS Medicine

25 Jun 2020

Dear Dr. Cluver,

Thank you very much for submitting your manuscript "Violence prevention accelerators for children and adolescents in Africa" (PMEDICINE-D-20-01470R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by Jul 16 2020 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Emma Veitch, PhD

PLOS Medicine

On behalf of Clare Stone, PhD, Acting Chief Editor,

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

*Please revise your title according to PLOS Medicine's style, this would normally include some indication of the study design (eg, here, an analysis based on 2 prospective cohorts) at the end of the title after a colon.

*Please structure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions - "Methods and Findings" is a single subsection). In the last sentence of the Abstract Methods and Findings section, please include a brief comment about any key limitation(s) of the study's methodology.

*In the abstract, the acronym CI is used without having previously defined it (presumably, "confidence interval"). In addition, it would be good to explicitly state what the limits for the CIs are, presumably this is 95% but not always, sometimes 99% is seen.

*We'd suggest ensuring that the study is reported according to the STROBE guideline; in which case please include the completed STROBE checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (SChecklist)." The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/. When completing the checklist, please use section and paragraph numbers, rather than page numbers.

*Please clarify if the analysis reported in this paper corresponded to that laid out in a prospectively set-out protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

-----------------------------------------------------------

Comments from the reviewers:

Reviewer #1: See attachment

Michael Dewey

-----------------------------------------------------------

Reviewer #2: Thank you for the opportunity to review the manuscript titled, "Violence prevention accelerators for children and adolescents in Africa" (PMEDICINE-D-20-01470R1). The manuscript reports on an innovative and compelling approach to informing the potential for policy and programmatic impacts on youth experiences of violence. The study has the potential to make significant contributions to the literature and address key questions about effective strategies to improve youth well-being and prevent violence. I recommend acceptance of the paper and note some points for the authors to consider in a revision.

1. Though WHO is the publisher of INSPIRE, WHO was not the sole institution to develop and release the technical package. The development was co-led by CDC and WHO, and all of the signatory agencies listed in INSPIRE co-released it, including UNICEF, PEPFAR, USAID, the World Bank, and others. This is one of the key hallmarks of INSPIRE, that it has been co-endorsed and co-released by multiple signatory agencies, allowing for consensus among key institutions in the most effective strategies to address violence against children (page 3).

2. Though the content of INSPIRE is primarily focused on prevention, there are also strategies (Response and support services) and programs directed at services for children who have experienced violence. I recommend revising the introduction to use language inclusive of both prevention and response, in places where only prevention of violence is noted (page 3).

3. I recommend including a bit more detail on the data collection procedures for the two studies. Were the questionnaires self-administered? What method of administration was used (paper and pencil, tablet, interview, etc.)? (Page 4)

4. From the description of the measures, it is not clear which are dichotomous/categorical and which are continuous. I checked the questionnaire on the Young Carers website, and given some of the measures use multiple items it is not possible to determine how the responses were coded and used. I recommend including more detail on how the specific outcomes and hypothesized violence prevention provisions were coded for the analyses (page 5).

5. The overall pattern of findings suggests that there are more similarities in "accelerator" effects across boys and girls than differences. I think this is a critical point that should be clearly noted in the discussion. However, I found this point to be less prominent. The first paragraph notes that parenting and food security were both accelerators. Based on the lack of significant differences in the gender interaction term it seems to be worth noting more clearly that the differences were in fact few (page 10).

-----------------------------------------------------------

Reviewer #3: I think that this complex analysis is very valuable to the field and worthy of publication. It is valuable to understand which protective factors are protective for multiple violence outcomes and how that can be leveraged to inform violence prevention programming and policy, particularly in low and middle income settings. I have some fairly minor questions/comments/suggestions for consideration below.

1. There are several references to "gender", when I think what is actually meant is biological sex in both the narrative (methods and results) and in the tables. I recommend changing "gender" to "sex".

2. The rates of sexual abuse and of transactional sex seem fairly low compared to other global surveys (e.g. VACS) and other literature from South Africa. This may be because of the young age of the sample, but it might be helpful to include the actual measures. For example, under sexual abuse in the methods section the sexual abuse measure is described as "two items from the Sexual Victimization module of the Juvenile Victimization Questionnaire". It would be helpful to include what those two measures are.

3. My largest comment is in the conceptualization of protective factors (self-reported on questionnaires) as being accelerators of INSPIRE. Based on my reading of the methods section, these study populations did not receive any violence interventions. Rather, they (general population youth) were asked about their experiences with violence victimization and their experience with various protective factors. These protective factors could have been resultant from interventions or not- we do not know because that was not assessed as far as I can gather. Therefore, I am struggling a little bit with the jump from showing in analysis that certain types of protective factors are protective for multiple types of violence to saying that these protective factors are therefore INSPIRE accelerators. In other words, I don't think that we can assume that because something was protective against violence outcomes means that it can therefore be conceptualized as an accelerator for prevention interventions. I actually re-read the methods section several times to understand if there was an intervention, but the way I read the methods section there was no intervention of positive parenting, supervision or food security- these were just self-reported. So I think there might need to be some additional explanation in the discussion section as to why the authors believe that evidence of existing protective factors (without known intervention) having an impact on violence outcomes in the general population can therefore be justification for assuming that interventions that target those protective factors will therefore have the same multiplicative effect on violence outcomes. I also found the term "provisions" to be a bit broad and recommend a different term such as "protective factors" or a similar term.

-----------------------------------------------------------

Any attachments provided with reviews can be seen via the following link:

[LINK]

Submitted filename: cluver.pdf

Click here for additional data file.

28 Jul 2020

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

18 Aug 2020

Dear Dr. Cluver,

Thank you very much for re-submitting your manuscript "Violence prevention accelerators for children and adolescents: A path analysis using two pooled cohorts in South Africa" (PMEDICINE-D-20-01470R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by two reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Aug 25 2020 11:59PM.

Sincerely,

Artur Arikainen,

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

1. Title: Please amend to: “Violence prevention accelerators for children and adolescents in South Africa: a path analysis using two pooled cohorts”

2. Abstract:

a. Please include the cohort recruitment dates.

b. Please include summary cohort demographics, eg. age, sex, n and % lost to follow-up.

c. Please quantify all results with p values.

d. Please replace “95% CIs” with “95% CI”

e. Line 71: "…predicted to significantly lower the prevalence…" should become "…was associated with a lower prevalence..." or similar

f. Please present the results for boys and sexual abuse prevalence, to match results presented for girls; mention whether or not significant.

g. In the last sentence of the Methods and Findings subsection, please include key limitation(s) of the study's methodology.

h. Conclusion: Please start this section with a summary of your findings, eg. “In this cohort study, we found that…”.

i. Avoid the use of causal language here and throughout the manuscript, eg. “may be effective” (line 87); use “may be associated with” or similar instead.

3. At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

4. Methods:

a. Please give exact cohort recruitment dates, including day and month.

b. Please specify whether informed consent was written or oral.

c. Please provide more direct URL links to the participant questionnaires in all languages, or provide them as Supporting Information file(s).

d. Please provide more information on the pilot testing of questionnaires (line 209) in a Supporting Information file or cite published work, as appropriate.

e. Please state whether ethics approval was required or waived for this analysis.

5. When completing the STROBE checklist, please use section and paragraph numbers, rather than page numbers. Please upload the checklist as a separate file named ‘S1 Checklist’.

6. Results: Please quantify all results with p values.

7. Please avoid causal language, eg. “may protect against” (line 480); your study can only show association.

8. Some of the language is a little extreme (e.g., "groundbreaking in vision and impact" at line 574; "superb" at line 586) – please tone down as appropriate.

9. Please provide full access details (eg. DOI or URL) for references 3, 5, 6, 8, 12, 13, 24, 15, 18, 27, 33, 37, and 40. Re: references 27, 33 listed as forthcoming/in press, papers cannot be listed in the reference list until they have been accepted for publication or are otherwise publicly accessible (for example, in a preprint archive). The information may be cited in the text as a personal communication with the author if the author provides written permission to be named. Alternatively, please provide a different appropriate reference.

----

Comments from Reviewers:

Reviewer #1: The authors have addressed my points.

Michael Dewey

Reviewer #3: The manuscript is an important contribution to better understand how violence against children prevention programming can be maximized for the greatest impact. The author was very receptive to reviewers' feedback and made adjustments that addressed all of the feedback. I recommend publication of this important work.

Any attachments provided with reviews can be seen via the following link:

[LINK]

3 Sep 2020

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

18 Sep 2020

Dear Professor Cluver,

On behalf of my colleagues and the academic editor, Dr. Greta Massetti, I am delighted to inform you that your manuscript entitled "Violence prevention accelerators for children and adolescents in South Africa: a path analysis using two pooled cohorts" (PMEDICINE-D-20-01470R3) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

Before publication you will see the copyedited word document (in around 1-2 weeks from now) and a PDF galley proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at the copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors.

If you are likely to be away when either this document or the proof is sent, please ensure we have contact information of a second person, as we will need you to respond quickly at each point.

PRESS

A selection of our articles each week are press released by the journal. You will be contacted nearer the time if we are press releasing your article in order to approve the content and check the contact information for journalists is correct. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact.

PROFILE INFORMATION

Now that your manuscript has been accepted, please log into EM and update your profile. Go to https://www.editorialmanager.com/pmedicine, log in, and click on the "Update My Information" link at the top of the page. Please update your user information to ensure an efficient production and billing process.

Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Artur Arikainen,

Associate Editor

PLOS Medicine

plosmedicine.org