Effects of scaling up various community-level interventions on child mortality in Burundi, Kenya, Rwanda, Uganda and Tanzania: a modeling study

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Study Justification:
– Improving child health is a significant challenge in sub-Saharan Africa, which has a high burden of under-five mortality.
– Access to evidence-based community-level interventions can help reduce child mortality and achieve the Sustainable Development Goal (SDG) target.
– However, coverage of these interventions is currently suboptimal.
– This study aims to estimate the potential impact of scaling up community-level interventions on child mortality in five East African Community (EAC) countries.
Highlights:
– The study identified ten preventive and curative community-level interventions that have been shown to reduce child mortality.
– Using the Lives Saved Tool, the study modeled the impact of scaling up these interventions from baseline coverage to ideal coverage by 2030.
– The results showed that scaling up the interventions could prevent approximately 74,200 child deaths by 2030 across the five EAC countries.
– The top four interventions (oral antibiotics for pneumonia, oral rehydration solution, hand washing with soap, and treatment for moderate acute malnutrition) accounted for over 75% of all deaths prevented in each country.
Recommendations:
– The study recommends scaling up interventions that can be delivered at the community level by community health workers to reduce child mortality in the EAC region.
– The findings suggest that focusing on the top four community-level interventions could prevent a significant number of child deaths.
– Policy decisions should be guided by estimating the costs of scaling up each intervention to allocate health resources effectively.
Key Role Players:
– Community health workers: They play a crucial role in delivering community-level interventions.
– Ministries of Health: They are responsible for implementing and coordinating health programs.
– Non-governmental organizations (NGOs): They can provide support and resources for scaling up interventions.
– International organizations: They can provide technical assistance and funding for implementing interventions.
Cost Items for Planning Recommendations:
– Training and capacity building for community health workers.
– Procurement and distribution of intervention supplies (e.g., oral antibiotics, oral rehydration solution, insecticide-treated nets).
– Monitoring and evaluation of intervention coverage and impact.
– Communication and awareness campaigns to promote behavior change.
– Support for health system strengthening to ensure effective implementation of interventions.
Please note that the cost items provided are general examples and may vary depending on the specific context and interventions being implemented.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a modeling study using the Lives Saved Tool. The study identifies ten community-level interventions that have been reported to reduce child mortality and estimates the potential impact of scaling up these interventions on child mortality in five East African Community countries. The study provides specific numbers of child deaths that could be prevented by scaling up the interventions. To improve the evidence, the study could include more details on the methodology used and provide information on the limitations of the modeling approach.

Background: Improving child health remains one of the most significant health challenges in sub-Saharan Africa, a region that accounts for half of the global burden of under-five mortality despite having approximately 13% of the world population and 25% of births globally. Improving access to evidence-based community-level interventions has increasingly been advocated to contribute to reducing child mortality and, thus, help low-and middle-income countries (LMICs) achieve the child health related Sustainable Development Goal (SDG) target. Nevertheless, the coverage of community-level interventions remains suboptimal. In this study, we estimated the potential impact of scaling up various community-level interventions on child mortality in five East African Community (EAC) countries (i.e., Burundi, Kenya, Rwanda, Uganda and the United Republic of Tanzania). Methods: We identified ten preventive and curative community-level interventions that have been reported to reduce child mortality: Breastfeeding promotion, complementary feeding, vitamin A supplementation, Zinc for treatment of diarrhea, hand washing with soap, hygienic disposal of children’s stools, oral rehydration solution (ORS), oral antibiotics for treatment of pneumonia, treatment for moderate acute malnutrition (MAM), and prevention of malaria using insecticide-treated nets and indoor residual spraying (ITN/IRS). Using the Lives Saved Tool, we modeled the impact on child mortality of scaling up these 10 interventions from baseline coverage (2016) to ideal coverage (99%) by 2030 (ideal scale-up scenario) relative to business as usual (BAU) scenario (forecasted coverage based on prior coverage trends). Our outcome measures include number of child deaths prevented. Results: Compared to BAU scenario, ideal scale-up of the 10 interventions could prevent approximately 74,200 (sensitivity bounds 59,068–88,611) child deaths by 2030 including 10,100 (8210–11,870) deaths in Burundi, 10,300 (7831–12,619) deaths in Kenya, 4350 (3678–4958) deaths in Rwanda, 20,600 (16049–25,162) deaths in Uganda, and 28,900 (23300–34,002) deaths in the United Republic of Tanzania. The top four interventions (oral antibiotics for pneumonia, ORS, hand washing with soap, and treatment for MAM) account for over 75.0% of all deaths prevented in each EAC country: 78.4% in Burundi, 76.0% in Kenya, 81.8% in Rwanda, 91.0% in Uganda and 88.5% in the United Republic of Tanzania. Conclusions: Scaling up interventions that can be delivered at community level by community health workers could contribute to substantial reduction of child mortality in EAC and could help the EAC region achieve child health-related SDG target. Our findings suggest that the top four community-level interventions could account for more than three-quarters of all deaths prevented across EAC countries. Going forward, costs of scaling up each intervention will be estimated to guide policy decisions including health resource allocations in EAC countries.

Headquartered in Arusha, Tanzania, the East African Community (EAC) is a regional intergovernmental organization bringing together Kenya, Uganda, the United Republic of Tanzania (henceforth referred to Tanzania), Burundi and Rwanda for a wider and deeper cooperation among these countries and other regional economic communities for mutual economic, social and political benefit (https://au.int/en/recs/eac). In the health sector, Yamin et al. (2017) argue that achieving universal health coverage (UHC) in EAC would require EAC countries to put in place human rights-based approaches for ensuring the health needs and rights of the people are being met at the community level. This would alsofoster community ownership and legitimacy of health reforms [39]. Child mortality remains one of the primary public health challenges faced by the region and consequently programs related to the prevention and reduction of child mortality require a combined effort at all levels of goverment. Despite the remarkable progress made by three EAC countries (Rwanda, Uganda, Tanzania) to achieve the MDG 4 (Table 1), there is still a lot to be done in order to reduce preventable child mortality among these countries and across the EAC region as a whole. Table ​Table11 summarizes the EAC context including population size, economic and key health indicators. With a median age ranging from 15.9 years to 19.6 years, the EAC has one of the youngest populations globally (Table ​(Table1).1). Similarly, the region has one of the world highest birth rates (Table ​(Table11). Characteristics of the EAC countries included in our analysis GDP, gross domestic product; OOP, out of pocket; HDI, health development index; MDG, millennium development goal; US$, United States dollar; EAC, East African Community. Data presented in Table ​Table11 were abstracted from various publications [26, 40–45] Drawing on prior research [37, 38, 46, 47], we identified 10 preventive and curative CLIs that have been reported to reduce child mortality: Breastfeeding promotion, complementary feeding, vitamin A supplementation, Zinc for treatment of diarrhea, hand washing with soap, hygienic disposal of children’s stools, oral rehydration solution (ORS), oral antibiotics for treatment of pneumonia, treatment for moderate acute malnutrition (MAM) and prevention of malaria using insecticide-treated nets and indoor residual spraying (ITN/IRS). These interventions can be classified into three categories: Each of these interventions has impact on specific cause(s) of death and/or risk factors [37, 38, 46–51]. For example, vitamin A supplementation, Zinc for treatment of diarrhea, hand washing with soap, hygienic disposal of children’s stools, and ORS interventions reduce child mortality by decreasing diarrhea. Oral antibiotics for treatment of pneumonia intervention reduce child mortality by decreasing deaths due to pneumonia, while ITN/IRS prevent malaria and related deaths. Interventions that have impact on risk factors for disease (for example, breastfeeding and complementary feeding) affect multiple causes of child mortality by modifying the probability of death due to specific causes of death. For example, interventions that reduces stunting and wasting will also indirectly reduce the probability of dying of diarrhea, pneumonia and malaria. We focused on interventions that can be delivered at community level by CHWs. Nine of the 10 CLIs that we selected are delivered at community level at least 50% (Table 2). We retrieved data on the percent of each interventions per delivery channel from Lives Saved Tool (described below) and, our modeling exercise assumed that the delivery channel for each intervention would remain constant over the study horizon. Similarly, it is assumed that variations in intervention coverage drive mortality changes, and the impacts on mortality of distal factors (for example, socioeconomic status) are mediated by changes in intervention coverage [49–52]. Percent of each intervention delivered at each level of healthcare delivery channels across EAC aIncluded interventions that are offered at community at 40% or more Breastfeeding promotion (exclusive breastfeeding 1-5 months). ITN/IRS insecticide-treated bed nets (ITNs) and indoor residual spraying (IRS); EAC East African Community. Source: Lives Saved Tool We used the Lives Saved Tool (LiST) [53, 54] – one of the modules in the Spectrum software package – to model the number of deaths among children younger than five years that could be prevented across EAC as a result of expanding proven effective CLIs (change in coverage), while accounting for EAC country specific health status (Table ​(Table1)1) and distribution of cause-specific mortality (Figs. 1 and ​and2).2). LiST has been used widely in lower- and middle-income countries (LMICs) to estimate the potential impact and cost of expanding maternal, newborn and child health interventions across the continuum of care [37, 38, 55–57]. Percent of neonatal deaths by proximate causes across East African Community (2014/2015). Source: Lives Saved Tool Percent of child death-post neonatal by proximate causes across East African Community (2014/2015). Source: Lives Saved Tool. While the details for ‘Other’ in the Fig. 2 was not provided in LiST, drawing on existing literature of global burden of diseases, injuries and risk factors, we believe that this section would include malnutrition, congenital anomalies, drowning, and foreign bodies [58] To make the projections, LiST employs a linear deterministic model and links with other modules (e.g., Family Planning module, AIDS Impact module and Demographic Projections module) available in the Spectrum package [53]. Our LiST model input include estimates of intervention effects and intervention coverage – defined as “the proportion of women and children in need of life–saving intervention who actually receive it” [37]. The model output was the number of deaths prevented disaggregated by each CLI. Estimates of the effects of interventions on cause specific child mortality were generated using the Child Health Epidemiology Reference Group intervention review process that draws on Cochrane Collaboration and the Working Group for Grading of Recommendations Assessment, Development and Evaluation (GRADE) [59]. The baseline population level coverage data for each intervention were derived from the most recent nationally representative surveys including demographic and health survey (DHS) and world population prospects (WPP) [37, 53]. Using LiST, we modeled the impact on under-five child mortality of scaling up the 10 CLIs from baseline coverage (2016) to ideal coverage (99%) by 2030 (Table 3). To estimate the impact under the ideal scale up scenario, we increased the coverage only for the 10 interventions that can be delivered by CHWs at the community level (Table ​(Table3),3), while holding all baseline population level coverage for other interventions in LiST module constant. We increased the coverage of our target interventions gradually using linear interpolation from 2016 to 2030 (i.e., study time horizon) (Table ​(Table3).3). We selected the study time horizon to cover the period post MDG era through the end of SGD era. To estimate the counterfactual (what would happen under business as usual (BAU) scenario), we forecasted coverage of the 10 interventions from 2016 to 2030 based upon existing trends in coverage for these interventions from 2010 to 2016 (7 years) using exponential smoothing methods and adjusted for seasonality as appropriate. We then calculated (and report in the results) number of deaths that could be prevented by ideal scale up of the 10 CLIs relative to scale up under business as usual scenario (Table 4). Baseline coverage and percent scale-up for community level interventions across EAC aExcluding breastfeeding; bSupplementary feeding and education; CLIs community-level interventions, EAC East African Community, ORS oral rehydration solution, ITN/IRS insecticide-treated bed nets (ITNs) and indoor residual spraying (IRS) Number of deaths averted by target year (2030) by intervention under ideal coverage scenario relative to BAU scenario ITN/IRS insecticide-treated bed nets (ITNs) and indoor residual spraying (IRS), BAU business as usual. *Sensitivity bounds were derived from sensitivity analyses that estimated effects of interventions based upon the highest level of effectiveness reported for all interventions (upper bound) relative to the lowest levels of effectiveness (lower bound). An em dash (─) indicates that the item is not applicable, or the value is zero, because the coverage under BAU scenario reached 99% by 2030, which is equivalent to the coverage under the ideal scale up scenario For intervention coverage where the existing trends were decreasing in the period of 2010–2016, forecasting the coverage from 2016 to 2030 would have led to considerably lower coverage by 2030 under BAU scenario, thus overestimating the number of deaths prevented under ideal scale up scenario relative to BAU scenario. Given ongoing emphasis on increasing coverage community level interventions to help LMICs achieve universal health coverage by 2030, it is unlikely that the decreasing trend in coverage reported for some interventions (from 2010 to 2016) would continue to 2030. As such, we used a more conservative approach by using mean coverage from the existing trends over 7 years (2010–2016) instead of the decreasing forecasted values. We assumed the percent delivery of each CLI at various delivery channels constant throughout the time horizon (Table ​(Table3).3). Using autoregressive integrated moving average (ARIMA) time series approach and reported under-five mortality from 2000 to 2017, we forecasted under-five mortality trends in EAC up to 2030 (Fig. 3). We used Spectrum software v5.753 (https://www.livessavedtool.org/listspectrum) and R software 3.4.4 for all analyses [60]. Reported and forecasted trends in under-five mortality across EAC (UNICEF reported estimates, 2000–2017, and forecasted estimates, 2018–2030). We forecasted under-five mortality trends in EAC from 2018 to 2030 using UNICEF reported under-five mortality from 2000 to 2017 and autoregressive integrated moving average time series approach. Based on our forecasted estimates, Rwanda and Uganda would meet the SDG target for under-five mortality of at least as low as 25 per 1000 live births

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

1. Mobile health (mHealth) applications: Develop mobile applications that provide pregnant women with information on prenatal care, nutrition, and access to healthcare services. These apps can also send reminders for appointments and medication.

2. Telemedicine: Implement telemedicine programs that allow pregnant women in remote areas to consult with healthcare professionals through video calls. This can help overcome geographical barriers and provide access to specialized care.

3. Community health worker training: Enhance the training of community health workers (CHWs) to provide comprehensive maternal health services, including prenatal care, delivery assistance, and postnatal care. This can improve access to care in underserved areas.

4. Transportation solutions: Develop transportation solutions, such as ambulances or mobile clinics, to ensure that pregnant women can reach healthcare facilities in a timely manner, especially in rural areas with limited transportation options.

5. Maternal health financing models: Explore innovative financing models, such as microinsurance or community-based health financing, to make maternal health services more affordable and accessible to low-income women.

6. Maternal health information systems: Implement robust information systems that track maternal health indicators and outcomes, allowing for better monitoring and evaluation of interventions. This can help identify gaps in access and target resources more effectively.

7. Maternal health education campaigns: Launch targeted education campaigns to raise awareness about the importance of maternal health and encourage women to seek timely care. These campaigns can address cultural and social barriers that prevent women from accessing healthcare services.

8. Public-private partnerships: Foster collaborations between the public and private sectors to improve access to maternal health services. This can involve leveraging private sector resources and expertise to expand healthcare infrastructure and service delivery.

9. Maternal health task-shifting: Explore the potential for task-shifting, where certain responsibilities traditionally performed by doctors are delegated to other healthcare professionals, such as nurses or midwives. This can help alleviate workforce shortages and improve access to care.

10. Maternal health quality improvement initiatives: Implement quality improvement initiatives that focus on enhancing the quality of care provided during pregnancy, childbirth, and postpartum. This can involve training healthcare providers, improving facility infrastructure, and implementing evidence-based practices.

These innovations have the potential to address the challenges faced in improving access to maternal health in the East African Community countries and contribute to reducing maternal mortality rates.
AI Innovations Description
The recommendation to improve access to maternal health based on the study is to scale up various community-level interventions in East African Community (EAC) countries. These interventions include breastfeeding promotion, complementary feeding, vitamin A supplementation, Zinc for treatment of diarrhea, hand washing with soap, hygienic disposal of children’s stools, oral rehydration solution (ORS), oral antibiotics for treatment of pneumonia, treatment for moderate acute malnutrition (MAM), and prevention of malaria using insecticide-treated nets and indoor residual spraying (ITN/IRS).

By scaling up these interventions from baseline coverage to ideal coverage (99%) by 2030, it is estimated that approximately 74,200 child deaths could be prevented in the EAC countries. The top four interventions that account for over 75% of all deaths prevented are oral antibiotics for pneumonia, ORS, hand washing with soap, and treatment for MAM.

Implementing these community-level interventions, delivered by community health workers, could contribute to a substantial reduction in child mortality in the EAC region and help achieve the child health-related Sustainable Development Goal (SDG) target. It is important for EAC countries to prioritize these interventions and allocate health resources accordingly.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations for improving access to maternal health:

1. Strengthening community health worker (CHW) programs: Investing in training, equipping, and supporting CHWs to provide essential maternal health services at the community level can improve access to care, especially in remote or underserved areas.

2. Mobile health (mHealth) interventions: Utilizing mobile technology to deliver maternal health information, reminders, and teleconsultations can help overcome barriers to access, such as distance and transportation challenges.

3. Community-based antenatal care: Shifting antenatal care services from health facilities to community settings, where pregnant women can receive care closer to their homes, can improve access and reduce missed appointments.

4. Task-shifting and task-sharing: Expanding the roles and responsibilities of non-physician healthcare providers, such as nurses and midwives, can help alleviate the shortage of skilled birth attendants and increase access to maternal health services.

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

1. Define the target population: Identify the specific population group or geographic area for which access to maternal health services needs to be improved.

2. Collect baseline data: Gather data on the current coverage and utilization of maternal health services in the target population, including indicators such as antenatal care attendance, skilled birth attendance, and postnatal care.

3. Develop intervention scenarios: Create different scenarios that represent the implementation of the recommended interventions. For example, one scenario could involve scaling up the CHW program, while another scenario could focus on implementing mHealth interventions.

4. Model the impact: Use a modeling tool, such as the Lives Saved Tool (LiST), to estimate the potential impact of each intervention scenario on maternal health outcomes. This could include projecting changes in key indicators like maternal mortality ratio, antenatal care coverage, and skilled birth attendance.

5. Compare scenarios: Compare the projected outcomes of each intervention scenario to the baseline data and assess the potential impact of each recommendation on improving access to maternal health services.

6. Sensitivity analysis: Conduct sensitivity analyses to explore the uncertainty and variability in the projected outcomes. This could involve testing different assumptions or parameters to assess the robustness of the results.

7. Policy implications: Use the findings from the simulation to inform policy decisions and resource allocations. Consider the cost-effectiveness of each intervention and prioritize those with the greatest potential impact on improving access to maternal health.

By following this methodology, policymakers and stakeholders can gain insights into the potential benefits of implementing specific recommendations and make informed decisions to improve access to maternal health services.

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