Does a complex intervention targeting communities, health facilities and district health managers increase the utilisation of community-based child health services? A before and after study in intervention and comparison areas of Ethiopia

listen audio

Study Justification:
The study aimed to evaluate the effectiveness of the Optimising the Health Extension Programme (OHEP) intervention in increasing the utilization of community-based child health services in Ethiopia. Despite previous reductions in child mortality, the utilization of child health services remained low. The study sought to address this issue by implementing a 2-year intervention in 26 districts, focusing on community engagement, capacity strengthening of primary care workers, and reinforcement of district accountability for child health services.
Study Highlights:
– The study included a representative sample of households and under-five children in intervention and comparison areas.
– The intervention consisted of 31 activities, but many were implemented late and interrupted in some districts.
– Care-seeking for any illness in children aged 2-59 months was higher in intervention areas compared to comparison areas at baseline, but the difference decreased at endline.
– The intervention did not have a significant effect on care-seeking for sick neonates, household participation in community engagement forums, supportive supervision of primary care workers, or indicators of district accountability for child health services.
Study Recommendations:
Based on the findings, the study recommends:
1. Considering an extended implementation period for complex interventions that involve behavior change.
2. Strengthening the implementation of intervention activities to ensure timely and uninterrupted delivery.
3. Exploring additional strategies to improve care-seeking for sick neonates and enhance community engagement and district accountability for child health services.
Key Role Players:
1. Ethiopian Government
2. Non-governmental organizations (PATH, UNICEF, Save the Children, Last 10 Kilometres/John Snow Inc.)
3. Implementing partners
4. London School of Hygiene and Tropical Medicine
5. Ethiopian Public Health Institute
6. Gondar, Jimma, Mekelle, and Hawassa Universities
Cost Items for Planning Recommendations:
1. Training and capacity building for primary care workers
2. Community engagement forums and activities
3. Supervision and monitoring of primary care workers
4. Equipment, supplies, and drugs for child health services
5. Transportation, such as ambulances, for sick under-five children
6. Communication and awareness campaigns on child health issues
Please note that the provided information is a summary of the study and may not include all details.

The strength of evidence for this abstract is 6 out of 10.
The evidence in the abstract is moderately strong, but there are areas for improvement. The study design is a before and after study, which is a weaker design compared to randomized controlled trials. The sample size is representative and includes both intervention and comparison areas. However, there were implementation issues, such as late and interrupted activities, which may have affected the results. To improve the evidence, future studies could consider using a randomized controlled trial design and ensure proper implementation of intervention activities.

INTRODUCTION: Ethiopia successfully reduced mortality in children below 5 years of age during the past few decades, but the utilisation of child health services was still low. Optimising the Health Extension Programme was a 2-year intervention in 26 districts, focusing on community engagement, capacity strengthening of primary care workers and reinforcement of district accountability of child health services. We report the intervention’s effectiveness on care utilisation for common childhood illnesses. METHODS: We included a representative sample of 5773 households with 2874 under-five children at baseline (December 2016 to February 2017) and 10 788 households and 5639 under-five children at endline surveys (December 2018 to February 2019) in intervention and comparison areas. Health facilities were also included. We assessed the effect of the intervention using difference-in-differences analyses. RESULTS: There were 31 intervention activities; many were one-off and implemented late. In eight districts, activities were interrupted for 4 months. Care-seeking for any illness in the 2 weeks before the survey for children aged 2-59 months at baseline was 58% (95% CI 47 to 68) in intervention and 49% (95% CI 39 to 60) in comparison areas. At end-line it was 39% (95% CI 32 to 45) in intervention and 34% (95% CI 27 to 41) in comparison areas (difference-in-differences -4 percentage points, adjusted OR 0.49, 95% CI 0.12 to 1.95). The intervention neither had an effect on care-seeking among sick neonates, nor on household participation in community engagement forums, supportive supervision of primary care workers, nor on indicators of district accountability for child health services. CONCLUSION: We found no evidence to suggest that the intervention increased the utilisation of care for sick children. The lack of effect could partly be attributed to the short implementation period of a complex intervention and implementation interruption. Future funding schemes should take into consideration that complex interventions that include behaviour change may need an extended implementation period. TRIAL REGISTRATION NUMBER: ISRCTN12040912.

The Ethiopian Government initiated the OHEP intervention in 26 districts of Amhara, Southern Nation, Nationalities and Peoples, Oromia and Tigray regions with an approximate population of 3.5 million (figure 1). Intervention districts were selected by the government of Ethiopia and implementing partners for having both a relatively low utilisation of primary child health services and the availability and ability of partners to support implementation. The implementers were four non-governmental organisations (PATH, UNICEF, Save the Children and Last 10 Kilometres/John Snow Inc.). The intervention started in 2016 and lasted for a duration of 2.5 years and had three components: 1) community engagement, 2) primary care level capacity building and 3) ownership and accountability of child health services at the district level. The intervention activities under these components, along with the underlying assumptions, intermediate indicators and outcomes are detailed in table 1. Map of Ethiopia showing all regions (left) and the intervention and comparison districts within the four study regions (right). Optimising the Health Extension Programme intervention implemented in 26 districts of Ethiopia, the assumptions and the expected intermediate indicators and outcomes The protocol for the evaluation of the OHEP implementation has been published.20 This study was based on a plausibility design with 26 intervention and 26 comparison districts (woredas) in four regions of Ethiopia. The baseline survey was conducted from December 2016 to February 2017 and the endline survey from December 2018 to February 2019 (figure 1). The surveys were conducted by the London School of Hygiene and Tropical Medicine and Ethiopian Public Health Institute along with representatives from Gondar, Jimma, Mekelle and Hawassa Universities. Intervention districts had a low coverage of maternal, newborn, and child health indicators. Comparison districts were selected by the Regional Health Bureaus to be similar to the size of the population, the burden of diseases, number of primary healthcare units, health service coverage, length of iCCM and CBNC service delivery and absence of partners implementing other demand generation activities. We used a two-stage stratified cluster sampling to select a representative sample of households within intervention and comparison areas. In the first stage, a list of all enumeration areas of the study districts was obtained based on the 2007 Ethiopian Housing and Population Census. Two hundred enumeration areas (clusters) were selected with probability proportional to the size of the district. Each cluster served as the primary sampling unit. Within clusters, households were selected by systematic random sampling. The Women’s Development Army leaders, HEWs, health posts, health centres with staff and woreda health offices serving the selected clusters were also surveyed. The sample size was calculated to measure changes with adequate power in a fixed number of percentage points of key indicators between intervention and comparison areas at baseline and endline. Based on the Ethiopian DHS data, a cross-sectional survey of 3000 households in 100 intervention and 100 comparison clusters was expected to find 1747 children under the age of 5 years in each arm.21 A Tanzanian childhood study found that 50% of under-fives had an illness in the 2 weeks before the survey.22 The current research assumed more conservatively 30% of children 2–59 months to have had any illness during the 2 weeks before the interview. This sample size of 3000 households per group with 90% completeness and a design effect of 1.3 would give 80% power to detect differences of 10–20 percentage points across a range of child health indicators (5% significance level). The baseline survey found fewer than the expected number of sick children in the 2 weeks before the survey. As a result, the household sample size for the endline survey was doubled. We used the baseline list of households to select 66 households randomly in each enumeration area. For every selected household, we interviewed the household head, listed residents and collected sociodemographic characteristics. The interviews included caregivers of children aged 2–59 months to assess their knowledge of childhood illnesses and care-seeking for sick under-five children in the 2 weeks before the survey. Furthermore, women of reproductive age (13–49 years) were interviewed to identify births in the 12 months before the survey, with additional questions on care-seeking for illness in the neonatal period. Up to three visits were made to each participant to ensure the study reached its target sample size. The health facility questionnaires assessed the infrastructure, equipment, supplies and staff available on the day of the survey. Also, data were collected from facility registers on services provided to sick children at health posts and health centres in the 3 months preceding the survey. The health centre staff, HEWs and Women’s Development Army leader modules covered their background, knowledge, training in the last 12 months, supervision in the previous 6 months and the services they provided in the last 3 months. Interviewers collected information at the district health office on demography and characteristics that might affect services for under-five children. The questions and content of each survey module were based on existing large-scale survey tools and the authors’ previous evaluation of the iCCM and CBNC programmes.22 23 All questionnaires were translated into three local languages (Amharic, Oromifa and Tigrigna), pretested and revised. Data collectors and supervisors were trained over 10 days, including field training before the start of data collection. They were not provided information on whether a district was an intervention or comparison area. Data were collected on personal digital assistants (Companion Touch 8), and tablets (Toshiba and Hewlett Packard) programmed with CSPro 6.3 at baseline and CSPro 7.1 at the endline. Data collectors sent encrypted data from the field to the password-protected server at the Ethiopian Public Health Institute. Data managers conducted quality checks and feedback to field teams. Data were cleaned, checked for errors, including consistency and completeness. Since OHEP was a community and health system level intervention, a data monitoring committee was not deemed necessary. The primary outcome was the proportion of children aged 2–59 months who were reported to have had any illness in the 2 weeks before the survey for whom advice or treatment was sought from an appropriate provider (health post, health centre, hospital and private clinic). Secondary outcomes included: 1) the proportion of sick children aged 2–59 months who were reported to have received appropriate treatment for diarrhoea (oral rehydration solution (ORS) with or without zinc tablets) and possible pneumonia (antibiotics), 2) the proportion of infants born in the 12 months before the survey who were reported to have had any illness in the first 28 days of life for whom advice or treatment was sought from an appropriate provider and 3) the proportion of infants born in the 12 months before the survey who received adequate treatment for suspected neonatal sepsis (antibiotics). Since malaria was not a common illness in the study areas at the time of the surveys, the assessment of appropriate treatment for this illness was excluded. Additionally, the registers for children aged 0–59 days and 2–59 months were reviewed to assess the median number of children seeking care at health centres and health posts in the 3 months before the surveys. We also evaluated intermediate indicators that included, at the community level, the proportion of caregivers that knew signs of illness in children and the proportion that cited appropriate action to be taken for a sick child under 5 years of age. We also assessed the proportion of caregivers that reported receiving health messages on common childhood illnesses and those attending community meetings to discuss maternal, newborn and child health issues. At the health system level, we assessed the proportion of HEWs that had received training and the proportion that had attended performance review and clinical mentoring meetings. We also evaluated the proportion of health centres and health posts that had received supervision and the proportion that had the necessary equipment, supplies and drugs for the provision of child health services. District-level ownership and accountability for child health programmes were reflected in the proportion of districts that had iCCM and CBNC scorecards. These cards were programme management tools for setting targets and monitoring performance. Information was also collected on the average number of ambulances available in districts to transport sick under-five children and whether there were standardised hours of operation for health posts. Descriptive analysis of baseline and endline characteristics in intervention and comparison areas was conducted at the household, caregiver, child, health facility and district levels. Categorical variables were summarised using percentages with 95% CIs. We used means, with SEs, or medians, with IQRs, to summarise continuous variables. At the district level, the demographic and health system-level characteristics were examined. For households, the characteristics of mothers or caregivers of children aged 2–59 months, and women who had a delivery in the 12 months before the survey were assessed. Distribution of age, religion, education, self-reported distance to the nearest health post and socioeconomic status was analysed in intervention and comparison areas at baseline and endline surveys. Similar assessments were done for the distribution of age and sex among children 2–59 months of age and infants born during the 12 months before the survey. Household socioeconomic status was captured by asset ownership, access to utilities and household characteristics. These were aggregated into a single wealth index score using principal component analysis.24 The household aggregated scores were grouped into wealth quintiles, where quintile 1 represented the poorest fifth of the households, and quintile 5 represented the least poor fifth. A linear or logistic regression model was fitted, depending on the variable type, to assess if there were any differences between intervention and comparison areas that changed over time. A variable was considered a potential confounder if the differences between intervention and comparison areas showed a statistically significant (p<0.05) change over time. Caregivers’ knowledge, practice and community participation relating to child health were assessed. Furthermore, the health system level factors associated with child health services, including training and supervision of health workers providing under-five services, and the observed availability of infrastructure, equipment, supplies and drugs for the treatment of childhood illnesses at health posts and health centres were assessed. Using data from facility registers, we also compared the median number of young infants and children 2–59 months of age who received care in the 3 months before the survey in intervention and comparison areas at baseline and endline. We analysed differences between intervention and comparison areas over time using quantile regression analysis. Difference-in-differences analyses were used to estimate the effect of the OHEP intervention on care-seeking for sick under-five children. Binary outcome indicators were used to capture whether a sick child had sought care or received treatment. The key independent variable for the outcomes of this study was whether the child lived in the OHEP intervention or comparison area. A model was then created that included an interaction term for the timing of the survey (baseline or endline) and the survey area (OHEP intervention or comparison area). This model allowed for the calculation of the odds of care-seeking or treatment for under-five children in intervention areas as compared with comparison areas, accounting for any differences between baseline and endline survey areas, with adjustment for the cluster sampling and identified confounding factors. The Stata V.13 (StataCorp, College Station, Texas, USA) svy commands were used to adjust for clustering. The assessment used a blinded analysis. The code identifying the intervention and comparison areas were revealed after the analysis and interpretations were completed. Patient and/or the public were not involved in the design or conduct, or reporting or dissemination plans of this research.

Based on the provided information, it appears that the study evaluated the effectiveness of the Optimising the Health Extension Programme (OHEP) intervention in improving access to child health services in Ethiopia. However, the study did not specifically focus on innovations to improve access to maternal health. Therefore, there are no specific innovations mentioned in the description that can be recommended for improving access to maternal health.
AI Innovations Description
The study described in the provided text evaluated the effectiveness of the Optimising the Health Extension Programme (OHEP) intervention in improving the utilization of community-based child health services in Ethiopia. The intervention lasted for 2.5 years and focused on community engagement, capacity building of primary care workers, and district-level ownership and accountability of child health services.

The study found that the intervention did not significantly increase the utilization of care for sick children. The care-seeking rate for any illness in children aged 2-59 months was 58% in intervention areas and 49% in comparison areas at baseline. At endline, the rates were 39% in intervention areas and 34% in comparison areas. The difference-in-differences analysis showed no significant effect of the intervention on care-seeking for sick children.

The study also evaluated secondary outcomes, such as the proportion of sick children receiving appropriate treatment for diarrhea and possible pneumonia, and the proportion of infants receiving adequate treatment for suspected neonatal sepsis. However, no significant differences were observed between intervention and comparison areas for these outcomes.

The study highlighted several challenges in implementing the intervention, including late implementation of activities and interruptions in some districts. These factors may have contributed to the lack of effect on care utilization.

Based on the findings, it is recommended that future interventions aiming to improve access to maternal health services should consider the following:

1. Longer implementation period: Complex interventions, particularly those involving behavior change, may require an extended implementation period to achieve desired outcomes.

2. Timely implementation: Activities should be implemented according to the planned timeline to ensure maximum impact.

3. Continuous monitoring and evaluation: Regular monitoring and evaluation of intervention activities can help identify challenges and make necessary adjustments to improve effectiveness.

4. Strengthening health systems: Efforts should be made to strengthen the capacity of primary care workers, improve infrastructure and equipment, and ensure the availability of necessary supplies and drugs for the provision of maternal health services.

5. Community engagement: Engaging communities and promoting community participation in decision-making processes can help increase awareness and utilization of maternal health services.

Overall, the study highlights the importance of carefully planning and implementing interventions to improve access to maternal health services, taking into account the specific context and challenges of the target population.
AI Innovations Methodology
Based on the provided information, it seems that the study evaluated the effectiveness of the Optimising the Health Extension Programme (OHEP) intervention in improving the utilization of child health services in Ethiopia. The intervention consisted of three components: community engagement, capacity building of primary care workers, and ownership and accountability of child health services at the district level.

To improve access to maternal health, here are some potential recommendations based on the study:

1. Strengthen community engagement: Enhance community involvement and participation in maternal health programs by promoting awareness, education, and mobilization. This can be achieved through community health workers, community-based organizations, and community outreach programs.

2. Improve capacity building of healthcare providers: Provide comprehensive training and continuous professional development for healthcare providers, including midwives, nurses, and doctors, to enhance their skills and knowledge in providing quality maternal health services.

3. Enhance ownership and accountability: Strengthen the accountability mechanisms at the district level to ensure that maternal health services are prioritized and effectively delivered. This can involve regular monitoring and evaluation, performance reviews, and feedback mechanisms.

4. Increase availability and accessibility of maternal health services: Improve the infrastructure and resources of health facilities, including the availability of skilled birth attendants, essential medicines, and equipment. Additionally, ensure that health facilities are geographically accessible to pregnant women, especially in remote and underserved areas.

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

1. Baseline data collection: Gather information on the current utilization of maternal health services, including the number of pregnant women accessing antenatal care, skilled birth attendance, and postnatal care. This can be done through surveys, interviews, and analysis of existing health facility records.

2. Intervention implementation: Implement the recommended interventions, such as community engagement activities, capacity building programs, and accountability mechanisms. Ensure that these interventions are implemented consistently and effectively across the target areas.

3. Monitoring and evaluation: Continuously monitor the implementation of the interventions and collect data on key indicators related to access to maternal health services. This can include the number of pregnant women attending antenatal care, the percentage of births attended by skilled birth attendants, and the utilization of postnatal care services.

4. Data analysis: Analyze the collected data to assess the impact of the interventions on improving access to maternal health services. This can be done through statistical analysis, such as difference-in-differences analysis, to compare the changes in utilization between the intervention areas and comparison areas.

5. Interpretation and reporting: Interpret the findings of the data analysis and report the results, highlighting the effectiveness of the interventions in improving access to maternal health services. This information can be used to inform future policy and programmatic decisions to further enhance maternal health services.

It is important to note that the specific methodology may vary depending on the context and resources available. Additionally, ethical considerations should be taken into account, including informed consent, privacy, and confidentiality of the participants involved in the study.

Yabelana ngalokhu:
Facebook
Twitter
LinkedIn
WhatsApp
Email