Understanding accelerators to improve SDG-related outcomes for adolescents—An investigation into the nature and quantum of additive effects of protective factors to guide policy making

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Study Justification:
The study aims to understand the nature and effectiveness of accelerators in improving outcomes related to the United Nations Sustainable Development Goals (SDGs) for adolescents. The concept of accelerators suggests that certain interventions or programmatic areas can have additive effects on multiple SDGs, leading to enhanced outcomes. However, there is a need for detailed knowledge on the optimum combinations of accelerators and their relative gains in order to guide policy-making.
Highlights:
– The study utilized pooled data from two longitudinal studies involving 1848 children and young adolescents aged 9-13 years in South Africa.
– Five accelerators were identified: caregiver praise, caregiver monitoring, food security, living in a safe community, and access to community-based organizations.
– The study investigated the additive effects of these accelerators on 14 SDG-related outcomes.
– Results showed that various accelerator combinations were effective, but different combinations were needed for different outcomes.
– The presence of up to three accelerators was associated with marked improvements across multiple outcomes.
– The study provides detailed impact information on protective factors and offers implementation guidance for policy makers to maximize the effectiveness and expenditure of interventions.
Recommendations:
– Policy makers should consider targeting and distributing interventions based on the specific accelerator combinations that are most effective for each desired outcome.
– The benefit of targeting access to additional accelerators should be weighed against the relative gains to be achieved with focused interventions.
– Future research should investigate multiplicative effects and synergistic interactions between accelerators.
Key Role Players:
– Researchers and data collectors
– Policy makers and government officials
– Community-based organizations
– Caregivers and parents
– Health and education departments
Cost Items for Planning Recommendations:
– Research and data collection expenses
– Training and capacity building for researchers and data collectors
– Implementation costs for interventions and programs
– Monitoring and evaluation expenses
– Communication and dissemination of findings
– Administrative and logistical support
Please note that the cost items provided are general categories and not actual cost estimates. The specific budget items would depend on the context and scope of the interventions and programs implemented based on the study recommendations.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study utilized pooled data from two longitudinal studies, which increases the sample size and strengthens the findings. The study also used a standardized approach and conducted multivariable logistic regressions to analyze the data. However, the abstract does not provide information on the representativeness of the sample or the generalizability of the findings. Additionally, the abstract does not mention any limitations or potential biases in the study. To improve the strength of the evidence, the authors could provide more details on the recruitment process, sample characteristics, and potential limitations of the study. They could also consider conducting sensitivity analyses to assess the robustness of the findings.

Recent evidence has shown support for the United Nations Development Programme (UNDP) accelerator concept, which highlights the need to identify interventions or programmatic areas that can affect multiple sustainable development goals (SDGs) at once to boost their achievement. These data have also clearly shown enhanced effects when interventions are used in combination, above and beyond the effect of single interventions. However, detailed knowledge is now required on optimum combinations and relative gain in order to derive policy guidance. Which accelerators work for which outcomes, what combinations are optimum, and how many combinations are needed to maximise effect? The current study utilised pooled data from the Young Carers (n = 1402) and Child Community Care (n = 446) studies. Data were collected at baseline (n = 1848) and at a 1 to 1.5- year followup (n = 1740) from children and young adolescents aged 9–13 years, living in South Africa. Measures in common between the two databases were used to generate five accelerators (caregiver praise, caregiver monitoring, food security, living in a safe community, and access to community-based organizations) and to investigate their additive effects on 14 SDG-related outcomes. Predicted probabilities and predicted probability differences were calculated for each SDG outcome under the presence of none to five accelerators to determine optimal combinations. Results show that various accelerator combinations are effective, though different combinations are needed for different outcomes. Some accelerators ramified across multiple outcomes. Overall, the presence of up to three accelerators was associated with marked improvements over multiple outcomes. The benefit of targeting access to additional accelerators, with additional costs, needs to be weighed against the relative gains to be achieved with high quality but focused interventions. In conclusion, the current data show the detailed impact of various protective factors and provides implementation guidance for policy makers in targeting and distributing interventions to maximise effect and expenditure. Future work should investigate multiplicative effects and synergistic interactions between accelerators.

The current study pooled data from two longitudinal studies, which were designed in close collaboration and utilized the same or similar measures of relevant constructs, to generate a sample of 1848 young people (n = 1740 follow-up– 94.2% rate). Of these, 446 were drawn from the Child Community Care (CCC) study, and 1,402 from the Young Carers (YC) study. The CCC study investigated effects of attendance of community-based organisations (CBOs) in three countries (South Africa, Malawi, Zambia–only South African data is used in this paper) on child and adolescent outcomes, with high study enrolment (99.0%) at baseline and retention at a 12–18 month follow-up (86.0%). The YC study focused on the well-being of adolescents from disadvantaged backgrounds living in South Africa. Participants were drawn randomly from four census enumeration areas, with one child chosen at random from all visited households. Enrolment was 97.5% at baseline, and 96.8% were retained at the 1-year follow up. Adolescents in this study received no CBO support at either baseline or follow up. Detailed methods of recruitment for both studies are described elsewhere [23,24]. To pool both databases into one overall sample, only young people living in South Africa aged 9–13 years (overlapping age range between both studies) were selected. All data were obtained by trained data collectors in participant’s language of choice and all participants and their caretakers provided written consent. Ethical approval for the YC study was obtained from the Universities of Oxford (SSD/CUREC2/11-40) and Cape Town (Ref: CSSR 389/2009), and the respective provincial Health and Education Departments. For the CCC study, ethical approval was obtained from University College London (1478/002) and Stellenbosch University (N10/04/112), as well as the funding agencies supporting the participating CBOs. The current analyses build on a previous manuscript [13], in which path analyses were used to investigate the effects of seven hypothesized protective factors on 14 SDG-related outcomes. All protective factors were hypothesized to simultaneously affect various SDG outcomes and thus to act as accelerators. Each factor had to be present at both baseline and follow-up to count as a potential accelerator, given evidence of the importance of sustained provision during child developmental progression [8]. Measures used and coding decisions for all protective factors and SDG outcomes can be found in the original paper [13]. The hypothesized protective factors comprised: 1) food security, coded as present if the child had not gone to bed hungry recently, 2) receipt of at least one of five government-provided cash grants over the past year in the household the child lived in (measured at follow up to cover the preceding year), 3) living in a safe community, indicated by children not witnessing or directly being exposed to community violence, 4) consistent access to healthcare when needed, 5) regular caregiver praise, 6) caregiver monitoring of child activities, and 7) access to CBOs, with the YC sub-sample specifically chosen to not have access to CBOs at any time-point, thus posing a comparison group. Fourteen outcomes that aligned with five SDGs were retrospectively identified (for coding details: see [13]: S1 Table). This includes no symptoms major depression (MDD) and post-traumatic stress disorder (PTSD), no suicidality, as well as overall good mental health (combined score of all three previous measures), no peer problems, high prosocial behaviour (all SDG 3.4); no substance abuse (SDG 3.5); school enrolment, school attendance, being in the right grade for age, being able to concentrate at school (all SDGs 4.1/ 4.4); no sexual debut (given that the target population was relatively young) (SDG 5.6); no delinquent behaviours (due to their common link with aggression/violence grouped under SDG 16.1, “reduce violence everywhere”); and no exposure to physical and emotional abuse by the caregiver (SDG 16.2). Measures at follow-up were used as the main outcomes. We controlled for baseline score where possible to account for potential pre-existing differences between the YC and CCC samples. Exceptions were the peer problems and prosocial behaviour subscales and the sexual debut variable, for which measures were only available at follow-up. We included seven covariates in our analyses that were measured at baseline: child age, child sex, whether the family lived in formal versus informal housing, maternal/paternal death, caregiver HIV-status and child caretaking responsibilities for other children or adults. Descriptive analyses and analyses of those retained versus lost to follow-up were conducted using χ2 tests and two-tailed t-tests as appropriate. As described above, the current analyses build on previous work using path analyses to investigate the effect of seven hypothesized protective factors on 14 SDG-related indicators (standardized approach developed by Rudgard and colleagues 2020; code: https://osf.io/n6jy7/?view_only=17f148085fde4b3fb645106c6c6e418b). After the absence of multi-collinearity between accelerators was established, separate multivariable logistic regressions were conducted, with each outcome being simultaneously regressed on all protective factors and covariates. The aim was to identify “accelerators”, defined as protective factors that were related to three or more SDG outcomes after correcting for multiple testing via Benjamini Hochberg corrections (false discovery rate: 0.1) [25]. Accelerators affecting three or more outcomes identified in these analyses were: caregiver praise, caregiver monitoring, living in a safe community, CBO access and food security. The current paper presents a more in-depth follow-up analysis, focusing on what combinations of these five accelerators affected specific outcomes, and on identifying the quantum of benefit. For this purpose, adjusted probabilities and adjusted probability differences were calculated using the “margins” command in Stata, based on the original model (including covariates). We compared adjusted probabilities for the hypothesized presence of no accelerator, single accelerators and all possible combinations of accelerators to determine optimal combinations. We also calculated probability differences and associated confidence intervals to establish whether there were significant increases in predicted probabilities through the addition of additional accelerators. For this, we focused on the most effective accelerator combinations (i.e., the single most effective accelerator, combinations of the two to four most effective accelerators, all five accelerators). All analyses were conducted in Stata SE v.16.

The study mentioned in the description investigates accelerators that can improve outcomes related to sustainable development goals (SDGs) for adolescents. The accelerators identified in the study are caregiver praise, caregiver monitoring, living in a safe community, access to community-based organizations (CBOs), and food security. These accelerators have shown to have additive effects on 14 SDG-related outcomes.

The study found that different combinations of accelerators are effective for different outcomes. The presence of up to three accelerators was associated with marked improvements across multiple outcomes. The study provides implementation guidance for policymakers to target and distribute interventions to maximize their effect and expenditure.

It’s important to note that this information is based on the specific study mentioned and may not encompass all innovations for improving access to maternal health.
AI Innovations Description
The recommendation from the study is to utilize accelerators, which are combinations of protective factors, to improve access to maternal health and achieve multiple sustainable development goals (SDGs) simultaneously. The study found that certain accelerators, such as caregiver praise, caregiver monitoring, living in a safe community, access to community-based organizations, and food security, were associated with improved outcomes related to SDGs.

To implement this recommendation, policymakers and stakeholders should consider the following:

1. Identify and prioritize the accelerators: Determine which accelerators are most relevant and feasible to implement in the context of improving access to maternal health. Consider the specific needs and challenges faced by the target population.

2. Develop comprehensive interventions: Design interventions that incorporate multiple accelerators to maximize their impact. For example, interventions could include components that promote caregiver praise, caregiver monitoring, community safety, access to community-based organizations, and food security.

3. Tailor interventions to specific outcomes: Different combinations of accelerators may be more effective for different outcomes. Therefore, it is important to tailor interventions based on the desired outcomes. For example, certain combinations of accelerators may be more effective in reducing maternal mortality rates, while others may be more effective in improving access to prenatal care.

4. Evaluate and monitor the impact: Implement a robust monitoring and evaluation system to assess the effectiveness of the interventions. Regularly collect data on key indicators related to maternal health and SDGs to measure progress and make necessary adjustments to the interventions.

5. Collaborate and coordinate efforts: Engage relevant stakeholders, including government agencies, healthcare providers, community organizations, and international partners, to collaborate and coordinate efforts in implementing the interventions. This will help ensure a comprehensive and integrated approach to improving access to maternal health.

By implementing these recommendations, policymakers and stakeholders can develop innovative approaches that leverage accelerators to improve access to maternal health and contribute to the achievement of multiple SDGs.
AI Innovations Methodology
The study described aims to investigate the impact of accelerators on improving outcomes related to sustainable development goals (SDGs) for adolescents. Accelerators are interventions or programmatic areas that can affect multiple SDGs simultaneously. The study utilizes pooled data from two longitudinal studies, the Young Carers (YC) and Child Community Care (CCC) studies, which collected data from children and young adolescents aged 9-13 years in South Africa.

The methodology used in the study involves the following steps:

1. Data Collection: Data were collected at baseline and at a 1 to 1.5-year follow-up from a sample of 1848 young people. The CCC study focused on the effects of community-based organizations (CBOs) on child and adolescent outcomes, while the YC study focused on the well-being of adolescents from disadvantaged backgrounds. Both studies obtained ethical approval and participants provided written consent.

2. Selection of Accelerators: Seven hypothesized protective factors were identified as potential accelerators, including food security, government-provided cash grants, living in a safe community, access to healthcare, caregiver praise, caregiver monitoring, and access to CBOs. These factors were chosen based on their potential to simultaneously affect various SDG outcomes.

3. Identification of SDG Outcomes: Fourteen outcomes aligned with five SDGs were retrospectively identified, including mental health indicators, school-related outcomes, substance abuse, sexual debut, delinquent behaviors, and exposure to abuse. Measures at follow-up were used as the main outcomes, and baseline scores were controlled for where possible.

4. Statistical Analysis: Multivariable logistic regressions were conducted to examine the relationship between the accelerators, covariates, and SDG outcomes. Accelerators that were related to three or more outcomes after correcting for multiple testing were identified as significant accelerators.

5. Determining Optimal Combinations: Adjusted probabilities and adjusted probability differences were calculated to determine the impact of different combinations of accelerators on specific outcomes. The study compared the probabilities and differences for no accelerator, single accelerators, and all possible combinations of accelerators to identify the most effective combinations.

6. Evaluation of Impact: The study assessed the quantum of benefit by examining significant increases in predicted probabilities through the addition of additional accelerators. Confidence intervals were calculated to establish the statistical significance of the increases.

7. Data Analysis: All analyses were conducted using Stata SE v.16.

In conclusion, this study utilizes a methodology that combines data from two longitudinal studies to investigate the impact of accelerators on improving outcomes related to SDGs for adolescents. The study identifies significant accelerators and explores the optimal combinations of these accelerators to maximize their impact on specific outcomes.

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