Impact of a health system strengthening intervention on maternal and child health outputs and outcomes in rural Rwanda 2005-2010

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Study Justification:
The study evaluates the impact of a health system strengthening intervention on maternal and child health outcomes in rural Rwanda from 2005 to 2010. This evaluation is important because although Rwanda’s health system had undergone improvements, the southeast region still lagged behind. The study aims to assess the potential impacts of the intervention and provide evidence for future health system strengthening efforts.
Highlights:
– Overall health system coverage improved in both the intervention area and other rural areas between 2005 and 2010.
– The intervention area saw a significant decline in under-five mortality, with an annual rate of 12.8% and a decrease from 229.8 to 83.2 deaths per 1000 live births.
– Improvements in health outcomes were most pronounced among the poorest households.
Recommendations:
– Based on the positive impact observed, it is recommended to continue and expand health system strengthening interventions in rural areas of Rwanda.
– Efforts should focus on improving access to maternal and child health services, including antenatal care, skilled birth attendance, and vaccination coverage.
– Targeting interventions towards the poorest households can help address health inequities and further improve health outcomes.
Key Role Players:
– Rwandan Ministry of Health: Responsible for coordinating and implementing health system strengthening interventions.
– Partners In Health: Collaborated with the Ministry of Health to implement the intervention and provide technical expertise.
– National Institute of Statistics-Rwanda: Conducted the Demographic and Health Surveys to collect data for the study.
– ICF International: Assisted in data collection and analysis for the Demographic and Health Surveys.
Cost Items for Planning Recommendations:
– Training and capacity building for healthcare workers to provide quality maternal and child health services.
– Infrastructure development, including the construction or renovation of health facilities.
– Procurement of medical equipment, vaccines, and essential medicines.
– Community outreach and education programs to increase awareness and utilization of health services.
– Monitoring and evaluation activities to assess the impact of interventions and ensure quality improvement.
Please note that the cost items provided are general categories and not specific cost estimates. Actual costs will vary depending on the scale and scope of the interventions.

The strength of evidence for this abstract is 9 out of 10.
The evidence in the abstract is strong because it presents a clear methodology and uses data from nationally representative surveys. However, to improve the evidence, the abstract could include more details on the specific health system strengthening intervention implemented and the potential limitations of the study.

Introduction Although Rwanda’s health system underwent major reforms and improvements after the 1994 Genocide, the health system and population health in the southeast lagged behind other areas. In 2005, Partners In Health and the Rwandan Ministry of Health began a health system strengthening intervention in this region. We evaluate potential impacts of the intervention on maternal and child health indicators. Methods Combining results from the 2005 and 2010 Demographic and Health Surveys with those from a supplemental 2010 survey, we compared changes in health system output indicators and population health outcomes between 2005 and 2010 as reported by women living in the intervention area with those reported by the pooled population of women from all other rural areas of the country, controlling for potential confounding by economic and demographic variables. results Overall health system coverage improved similarly in the comparison groups between 2005 and 2010, with an indicator of composite coverage of child health interventions increasing from 57.9% to 75.0% in the intervention area and from 58.7% to 73.8% in the other rural areas. Under-five mortality declined by an annual rate of 12.8% in the intervention area, from 229.8 to 83.2 deaths per 1000 live births, and by 8.9% in other rural areas, from 157.7 to 75.8 deaths per 1000 live births. Improvements were most marked among the poorest households. Conclusion We observed dramatic improvements in population health outcomes including under-five mortality between 2005 and 2010 in rural Rwanda generally and in the intervention area specifically.

We evaluated the impact of the RMOH-PIH interventions by comparing the temporal trends in health outputs and outcomes between 2005 and 2010 in the intervention target region to those in the pooled population of all ORAs. First, we assessed health system outputs, focusing on a set of indicators meant to capture the coverage of maternal and child health services and then we assessed population outcomes including neonatal, infant and under-five mortality. We used Rwanda Demographic and Health Survey (RDHS) data collected from 21 338 women living in Kirehe/S. Kayonza (K/SK) and ORAs in 2005 (K/SK: 418, ORA: 8217) and 2010 (K/SK: 2073, ORA: 10 630) (table 1). RDHSs are nationally and subnationally representative two-stage cluster samples conducted roughly every 5 years by the RMOH, National Institute of Statistics-Rwanda (NISR) and ICF International. The surveys collect information from women aged 15–49 on their reproductive health histories, practices and desires; household composition; siblings’ survival; and children’s health and survival. The DHS birth history module included the date of the birth and death of each child born alive, and through the sibling module, the age and date of death for each biological sibling (see online supplement). The 2005 RDHS was underway at the onset of the RMOH-PIH collaboration. In order to expand the sample size to allow us to compare the intervention area to other areas, we coordinated with the NISR immediately following the 2010 RDHS to collect a supplemental sample of 1391 households from 54 primary sampling units (PSUs) in Kirehe/S. Kayonza using the same sampling frame, staff and questionnaires as the 2010 RDHS (figure 1).12 Most data were collected consistently across the three surveys (2005 RDHS, 2010 RDHS and Supplemental RDHS) although neither stunting and wasting was assessed in the supplemental survey. The response rates for surveyed women were 98% in 2005 and 99% in 2010. The RDHS urban/rural boundaries were adopted from the Rwandan government. Exact urban boundaries are not published, however, urban areas are described as being built up, population dense and having public services and facilities. All areas not meeting this definition are considered rural.14 Maps of RDHS strata and primary sampling units in 2005 (left) and 2010 (right) and the PIH-RMOH intervention area in southeastern Rwanda (green). PIH, Partners In Health; RDHS, Rwanda Demographic and Health Survey; RMOH, Rwandan Ministry of Health. Summary of sociodemographic characteristics in 2005 and 2010 *Based on t-test from an OLS regression with year, group and year–group interaction terms. †With respect to 2005 wealth score definition. K/SK, Kirehe/S. Kayonza; OLS, ordinary least squares; ORA, other rural area. We assessed the following child health-related health system output indicators: whether treatment was provided for recent episodes of ARI, diarrhoea or fever in children under age 5 years; whether children under age 2 years received the recommended three doses of diphtheria-polio-tetanus (DPT) or measles vaccine; whether children received vitamin A supplementation between age 6 months and 1 year; whether children were exclusively breastfed for the first 6 months of life; whether at least one, or the recommended four, antenatal care visits took place during the last pregnancy; whether the most recent birth was attended by a skilled health worker; whether the birth was delivered by caesarean section; whether women received postnatal care within 24 hours of delivery; women’s current contraceptive use; and their unmet need for contraception. The following population health outcome indicators were assessed: neonatal, infant and under-five mortality; adult mortality (men, women and combined); recent occurrence of ARI, diarrhoea or fever in children under age 5 years; and stunting and wasting in children under age 5 years.15 We calculated a composite coverage index (CCI) for both groups to monitor overall healthcare coverage across time in the intervention and comparison areas based on that proposed by Barros and Victoria (2013) but modified to exclude BCG tuberculosis vaccination coverage (which was not available) as an indicator.16 The modified CCI is a weighted average for a group that includes the prevalence of met need for contraception (FPS), skilled birth attendance (SBA), at least one ANC visit with a skilled provider (ANCS), third DTP vaccination (DPT3), measles vaccination (MSL), oral rehydration therapy (ORT) for children with diarrhoea and care seeking for pneumonia symptoms (CPNM) as follows: We integrated the data from the supplemental survey with that from the 2010 RDHS as follows. Sampling probability weights were recalculated for the combined 2010 dataset. To protect respondent confidentiality, we randomly geodisplaced PSU latitude/longitude coordinates in the supplemental survey up to 5 km within district boundaries according to DHS guidelines.17 All geographic information was linked to displaced PSU locations in a geographic information system (ArcGIS V.10, ESRI). We combined latitude/longitude coordinates and rural residence information to identify respondents living in Kirehe/S. Kayzona and ORAs. Additional PSU geographic characteristics included straight-line distance to the nearest main road in metres (downloaded from DIVA-GIS database), straight-line distance to Kigali province in kilometres (downloaded from Map Library database), elevation above sea level in metres (from RDHS) and 30-year (1971–2000) average total rainfall in millimetres during the months of January, April, July and October (downloaded from US National Oceanic and Atmospheric Administration Climate Prediction Center). We generated comparable household wealth scores for 2010 using the principle components generated from the 2005 RDHS,18 and we considered a household to be ‘poor’ if it ranked in the bottom 20% of wealth scores of the pooled 2005, 2010 and supplemental survey datasets. Three rural PSUs (representing 69 households and 70 women) were excluded from the 2005 dataset because the PSU GPS coordinate was missing, preventing the PSU from being linked to either Kirehe/S. Kayonza or ORA. We compared baseline differences in woman, household and community (PSU) characteristics between the two comparison groups using χ2 tests and t-tests and temporal changes in and between groups using ordinary least squares regression with a year, group and year-by-group interaction term. Since the intervention area and ORAs may differ by factors that would have an impact on child mortality but which would not be altered by our health system strengthening intervention, we also compared the following social and geographic characteristics of sampled communities: fraction of each PSU with improved water, fraction of PSU adults who received a primary education, distance of PSU to a main road and to Kigali, elevation and average total rainfall in specified months. Finally, we compared health system outputs and population health measures at baseline. Kirehe/S. Kayonza had been chosen as the site of the intervention, because it had the highest rate of under-five mortality in Rwanda in 2005; thus, it was not possible to identify another Rwandan rural site with comparable under-five mortality. In an attempt to identify an optimal comparison group for Kirehe/S. Kayonza, we first limited our comparison area to Eastern Province in proximity to the intervention area (see figure 1). We found under-five mortality was higher in the intervention area than the rest of Eastern Province or any other subregion, and comparisons of other indicators were mixed (see online supplement). When a subregion was not identified for comparison, we used propensity score matching with inverse probability of treatment weights to identify comparable communities from ORAs by assessing PSU characteristics that might differ between the intervention and comparison areas but which would not be expected to be altered by the intervention (distance to road, distance to Kigali, elevation and average rainfall). We generated a bias B value to capture the difference in the standard deviation between the means of the groups and an R value that is the ratio of variances in the two groups. Following Rubin, we considered the groups to be balanced if B was less than 25% and R was between 0.5 and 2.19 Kirehe/S. Kayonza and ORAs were balanced by the R value but not by the B bias value (B=340.9, R=0.54) (see online supplement). Since neither approach identified a more appropriate comparison group, we compared Kirehe/S. Kayonza to all ORAs adjusting models for sociodemographic characteristics that followed different trends in Kirehe/S. Kayonza and ORAs over time. We used ordinary least squares regression with group, year and group–year interaction terms to model changes in binary health outputs and outcomes, controlling for differences in woman’s age and household wealth. We modelled change in childhood mortality rates using the DHS synthetic life-table approach, which uses the histories of all children a mother reports to have been alive during the previous 5 years.20 Adult mortality rates were based on a 5-year synthetic cohort of respondent’s siblings’ births and deaths.21 Expected mortality rates were calculated by standardising mortality rates of ORAs to the age structure in Kirehe/S. Kayonza. We estimated mean changes between 2005 and 2010 as the absolute difference in rates, and we calculated variances of trends as the sum of year–group variances. We adjusted for clustering of observations by PSU using Taylor linearised variance estimation in regression models and jackknife repeated replications to estimate variance in all other analyses.21 We conducted regressions in Stata V.13 and mortality analyses in SAS V.9.2. Verbal consent was obtained for all respondents before interviews took place. Protocols for the Rwanda 2005 and 2010 DHSs were approved by the Rwandan government. Protocols for the 2010 supplemental survey were reviewed and approved by the Partners HealthCare Internal Review Board (protocol #: 2009 P-001941/8) and the Rwanda National Ethics Committee. The funders had no role in study design, data collection, data analysis, data interpretation, writing of this report, nor the decision to submit this paper for publication. The corresponding author (DRT) had full access to all the data in the study and had final responsibility for the decision to submit for publication.

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

1. Mobile health clinics: Implementing mobile health clinics that can travel to rural areas, providing access to maternal health services such as prenatal care, postnatal care, and family planning.

2. Telemedicine: Utilizing telemedicine technology to connect pregnant women in remote areas with healthcare providers, allowing them to receive virtual consultations and guidance throughout their pregnancy.

3. Community health workers: Training and deploying community health workers in rural areas to provide education, support, and basic maternal health services to pregnant women and new mothers.

4. Maternal health vouchers: Introducing a voucher system that provides pregnant women with access to essential maternal health services, including antenatal care, skilled birth attendance, and postnatal care.

5. Transportation support: Establishing transportation support systems to ensure that pregnant women in remote areas can easily access healthcare facilities for prenatal check-ups, delivery, and emergency obstetric care.

6. Health information systems: Implementing robust health information systems to track and monitor maternal health indicators, identify gaps in service delivery, and inform evidence-based decision-making for improving access to maternal health services.

7. Public-private partnerships: Collaborating with private sector organizations to leverage their resources, expertise, and networks to improve access to maternal health services in underserved areas.

8. Maternal waiting homes: Establishing maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away, providing them with a safe and comfortable place to stay before and after giving birth.

9. Task-shifting: Training and empowering non-physician healthcare providers, such as nurses and midwives, to perform certain tasks traditionally done by doctors, thereby increasing the availability of skilled maternal healthcare providers in rural areas.

10. Health education and awareness campaigns: Conducting targeted health education and awareness campaigns to educate communities about the importance of maternal health, promote early antenatal care, and encourage facility-based deliveries.

These innovations can help address the challenges of improving access to maternal health in rural areas, ultimately leading to improved maternal and child health outcomes.
AI Innovations Description
The recommendation that can be developed into an innovation to improve access to maternal health based on the provided description is to implement a health system strengthening intervention. This intervention should focus on improving the coverage of maternal and child health services in rural areas, particularly in regions where access to healthcare is lagging behind. The intervention should aim to increase the number of antenatal care visits, ensure skilled attendance at births, provide postnatal care, and promote exclusive breastfeeding. Additionally, efforts should be made to improve access to contraception and family planning services to address the unmet need for contraception. This recommendation is supported by the findings of the evaluation, which showed that the health system strengthening intervention led to significant improvements in population health outcomes, including a decline in under-five mortality rates. By implementing this recommendation, it is possible to replicate the success observed in rural Rwanda and improve access to maternal health in other regions as well.
AI Innovations Methodology
Based on the provided description, the study evaluated the impact of a health system strengthening intervention on maternal and child health indicators in rural Rwanda between 2005 and 2010. The methodology involved comparing changes in health system output indicators and population health outcomes between the intervention area and other rural areas of the country. The data used for the analysis were collected from the Rwanda Demographic and Health Surveys (RDHS) conducted in 2005 and 2010, as well as a supplemental survey conducted in 2010.

To simulate the impact of recommendations on improving access to maternal health, a similar methodology can be employed. Here is a brief description of the methodology:

1. Identify the target population: Determine the specific population group or region for which access to maternal health needs improvement. This could be based on factors such as high maternal mortality rates or limited access to healthcare facilities.

2. Define the intervention: Identify the specific recommendations or interventions that can be implemented to improve access to maternal health. These could include measures such as increasing the number of healthcare facilities, training healthcare providers, implementing community health programs, or improving transportation infrastructure.

3. Data collection: Collect relevant data on health system output indicators and population health outcomes before and after the implementation of the intervention. This could involve conducting surveys, interviews, or analyzing existing data sources.

4. Comparison group: Select a comparison group or region that is similar to the target population but did not receive the intervention. This will allow for a comparison of the impact of the intervention on access to maternal health.

5. Analyze the data: Compare changes in health system output indicators and population health outcomes between the intervention group and the comparison group. Use statistical methods to control for potential confounding factors such as economic and demographic variables.

6. Assess the impact: Evaluate the impact of the recommendations or interventions on improving access to maternal health. This could involve analyzing changes in maternal mortality rates, access to antenatal care, skilled birth attendance, postnatal care, and other relevant indicators.

7. Interpret the findings: Interpret the results of the analysis to determine the effectiveness of the recommendations or interventions in improving access to maternal health. Identify any limitations or challenges encountered during the study.

By following this methodology, researchers can simulate the impact of recommendations on improving access to maternal health and assess the effectiveness of different interventions. This information can then be used to inform policy decisions and guide the implementation of strategies to improve maternal health outcomes.

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