A process evaluation plan for assessing a complex community-based maternal health intervention in Ogun State, Nigeria

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Study Justification:
– Process evaluations of community-level maternal health interventions are uncommon, but they can provide valuable insights into the factors that contribute to outcomes.
– Nigeria has a high maternal mortality ratio, and the CLIP study aimed to reduce maternal and neonatal mortality and morbidity through a complex intervention.
– This study provides a methodology to evaluate the implementation processes of the CLIP intervention, assess mechanisms of impact, and identify unintended causal pathways.
Highlights:
– The study was conducted from 2013-2016 in five Local Government Areas in Ogun State, Nigeria.
– A six-step approach was developed to evaluate context, implementation, and mechanisms of impact.
– Quantitative and qualitative data were collected from various sources, including antenatal and postnatal visits, community engagement sessions, interviews, and observations.
– Data will be analyzed using R and NVivo, and diffusions of innovations and realist evaluation theories will guide the analysis.
Recommendations:
– This comprehensive approach can serve as a guide for researchers and policymakers to evaluate similar complex health interventions in resource-constrained settings.
– It can help measure the effectiveness of interventions, not just their efficacy.
Key Role Players:
– Community health care providers, such as community health extension workers, health assistants, and staff nurses, were the primary implementers of the intervention.
– Researchers, policymakers, and relevant stakeholders will play a crucial role in planning and implementing the evaluation.
Cost Items:
– Budget items for planning the recommendations will include resources for data collection, such as personnel, equipment, and materials.
– Other cost items may include data management strategies, analysis software, and collaboration with stakeholders.
– The actual cost will depend on the specific context and requirements of the evaluation.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a detailed description of the methodology and data collection methods used in the study. However, it does not provide specific results or findings from the study. To improve the evidence, the abstract could include a summary of the main findings and their implications.

Background: Despite increased investment in community-level maternal health interventions, process evaluations of such interventions are uncommon, and can be instrumental in understanding mediating factors leading to outcomes. In Nigeria, where an unacceptably number of maternal deaths occur (maternal mortality ratio of 814/100,000 livebirths), the Community Level Interventions for Pre-eclampsia (CLIP) study (NCT01911494) aimed to reduce maternal and neonatal mortality and morbidity with a complex intervention of five interrelated components. Building from previous frameworks, we illustrate a methodology to evaluate implementation processes of the complex CLIP intervention, assess mechanisms of impact and identify emerging unintended causal pathways. Methods: The study was conducted from 2013-2016 in five Local Government Areas in Ogun State, Nigeria. A six-step approach was developed to evaluate key constructs of context (external factors related to intervention), implementation (fidelity, dose, reach, and adaption) and mechanisms of impact (unintended outcomes and mediating pathways). The steps are: 1) describing the intervention by a logic model, 2) defining acceptable delivery, 3) formulating questions, 4) determining methodology, 5) planning resources in context, lastly, step 6) finalising the plan in consideration with relevant stakeholders. Results: Quantitative data were collected from 32,785 antenatal and postnatal visits at the primary health care level, from 66 community engagement sessions, training assessments of community health workers, and standard health facility questionnaires. Forty-three focus group discussions, 38 in-depth interviews, and 23 structured observations were conducted to capture qualitative data. A total of 103 community engagement reports and 182 suspected pre-eclampsia case reports were purposively collected. Timing of data collection was staggered to understand feedback mechanisms that may have resulted from the delivery of the intervention. Data will be analysed using R and NVivo. Diffusions of innovations and realist evaluation theories will underpin analysis of the interaction between context, mechanisms and outcomes. Conclusion: This comprehensive approach can serve as a guide for researchers and policy makers to plan the evaluation of similar complex health interventions in resource-constrained settings, and to aid in measuring ‘effectiveness’ of interventions and not just ‘efficacy’. Trial registration: This research is a part of the Community Level Interventions for Pre-eclampsia Study, NCT01911494. The trial is registered in Clinicaltrials.gov, the URL is https://clinicaltrials.gov/ct2/show/NCT01911494 The trial was registered on June 28, 2013 and the first participant was enrolled for intervention on March 1, 2014.

Five Local Government Areas (LGAs) in Ogun State, Nigeria were chosen to receive the CLIP intervention by stratified random sampling. The CLIP (Community Level Interventions for Pre-eclampsia) intervention was delivered as part of the CLIP cluster randomized pilot trial from March 2014 to May 2015 in two Local Government Areas, Yewa South and Remo North, and later expanded to an additional three Local Government Areas(I,e, Ijebu North East, Odeda, and Ogun Waterside) from May 2015 to January 2016 as part of the definitive CLIP cluster randomized trial. In Nigeria, the primary implementers of the intervention were the community health care providers- community health extension workers (CHEWs), health assistants (HAs) and staff nurses. The process evaluation protocol covers data gathered during the Feasibility Study (2013–2014) [11], and during delivery of the CLIP intervention (2014–2016). The Research ethics boards at UBC Children’s and Women’s Health Centre of British Columbia and Olabisi Obabanjo University Teaching Hospital in Sagamu Nigeria provided ethical approval for the CLIP Cluster Randomized Controlled Trial (Number: H12-03497). The methods used to develop this process evaluation were adapted from Saunders et al [12] and tailored to the CLIP intervention in accordance with the Medical Research Council guidance [5, 8, 13]. Six steps were undertaken to: (i) to describe the intervention using a logic model to represent intervention activities, intended outcomes, theoretical constructs, and mediating factors [12]; (ii) to define complete and acceptable delivery of the intervention, in order to understand how the intervention may interact with the external MRC framework of process evaluation [5–9, 7, 13–17]; (iii) to develop process evaluation questions (iv) consider the relevant program resources (v) develop data management strategies (i.e., data sources, timing, and planning of data collection tools) using mixed-methods to answer the questions outlined in Step iii [10]; and (vi) to finalise the evaluation plan within an interdisciplinary team in collaboration with relevant stakeholders [9]. Quantitative data (such as that obtained using the PIERS on the Move mHealth platform), trial logs to monitor delivery of the intervention (community engagement logs, staff training logs, pre-post test questionnaires, drugs and devices tracking logs), observations checklists, budgets, and facility assessment data) will be analysed using simple descriptive analyses using Microsoft Access or R. Qualitative data (focus group discussions, key informant interviews, non-participant observations, pre-eclampsia case reports, community engagement field reports) will be analysed using thematic analysis in NVivo qualitative software. The use of established social theories is widely encouraged for process evaluations to allow for comparisons [8, 9, 18]. Therefore, building upon the Nigerian CLIP Feasibility Study [11], an adaptation of the diffusion of innovation theory [18, 19] was used to assess the CLIP intervention interacts with the system antecedents (context) to diffuse with the system (health system and community) for adoption by users (health workers and participants who receive the intervention). Realist theory was used to expand on the interaction between context and mechanisms to analyse identified mechanisms of action. The interaction of how ‘mechanisms’ and ‘context’ interact to produce ‘outcomes’ is represented in Fig. 2. Adaptability will be assessed to evaluate pragmatic contextual factors with delivery of implementation. Constructs of process evaluation for the CLIP intervention in Ogun State: The key functions assessed will be implementation (the infrastructure through which intervention is delivered, how it is delivered and the ‘what’ ‘quantity and quality’ of intervention), mechanisms of impact (how interaction between intervention activities and participants effect outcomes), and context (evaluating external factors which shape or may be shaped by intervention). As evident, these functions are non-linear and mutually-informative

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

1. Mobile Health (mHealth) Platforms: Utilizing mobile technology to provide maternal health information, reminders, and access to healthcare services through mobile applications or SMS messaging.

2. Community Health Workers (CHWs): Training and deploying community health workers to provide maternal health education, antenatal and postnatal care, and referrals to healthcare facilities.

3. Telemedicine: Implementing telemedicine services to enable remote consultations between pregnant women and healthcare providers, reducing the need for travel to healthcare facilities.

4. Maternal Health Vouchers: Introducing voucher programs that provide pregnant women with subsidized or free access to maternal health services, including antenatal care, delivery, and postnatal care.

5. Transport Solutions: Developing transportation systems or partnerships to ensure pregnant women have access to reliable and affordable transportation to healthcare facilities, particularly in rural areas.

6. Maternal Health Clinics: Establishing dedicated maternal health clinics that offer comprehensive services, including antenatal care, delivery, postnatal care, family planning, and counseling.

7. Maternal Health Education Programs: Implementing community-based education programs to raise awareness about maternal health, pregnancy complications, and the importance of seeking timely healthcare.

8. Maternal Health Hotlines: Setting up helplines or hotlines where pregnant women can seek advice, ask questions, and receive guidance on maternal health issues.

9. Public-Private Partnerships: Collaborating with private healthcare providers to expand access to maternal health services, particularly in underserved areas.

10. Maternal Health Financing: Developing innovative financing mechanisms, such as microinsurance or community-based health financing, to make maternal health services more affordable and accessible.

These innovations can help address barriers to accessing maternal health services and improve the overall quality of care for pregnant women.
AI Innovations Description
The recommendation to improve access to maternal health based on the provided information is to conduct a comprehensive process evaluation of community-based maternal health interventions. This evaluation should focus on assessing the implementation processes, mechanisms of impact, and unintended causal pathways of the intervention.

The process evaluation plan should include the following steps:

1. Describe the intervention using a logic model: This will help to clearly outline the intervention activities, intended outcomes, theoretical constructs, and mediating factors.

2. Define complete and acceptable delivery of the intervention: Understand how the intervention interacts with the external framework of process evaluation and ensure that it is delivered in a consistent and effective manner.

3. Develop process evaluation questions: Formulate specific questions that will guide the evaluation and provide insights into the effectiveness of the intervention.

4. Consider relevant program resources: Take into account the resources needed for data collection, analysis, and evaluation, including data sources, timing, and data collection tools.

5. Develop data management strategies: Plan how quantitative and qualitative data will be collected, analyzed, and managed using appropriate software and analysis techniques.

6. Finalize the evaluation plan in collaboration with relevant stakeholders: Engage with key stakeholders, such as researchers, policy makers, and community members, to ensure that the evaluation plan is comprehensive, relevant, and aligned with their needs and priorities.

The evaluation should collect both quantitative and qualitative data. Quantitative data can be collected through antenatal and postnatal visits, community engagement sessions, training assessments of community health workers, and standard health facility questionnaires. Qualitative data can be collected through focus group discussions, in-depth interviews, structured observations, community engagement reports, and suspected pre-eclampsia case reports.

Data analysis can be conducted using software such as R for quantitative data and NVivo for qualitative data. The analysis should be guided by diffusions of innovations and realist evaluation theories to understand the interaction between context, mechanisms, and outcomes.

By conducting a comprehensive process evaluation, researchers and policy makers can gain valuable insights into the effectiveness of community-based maternal health interventions and identify areas for improvement. This evaluation approach can serve as a guide for similar interventions in resource-constrained settings, ultimately leading to improved access to maternal health services.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for innovations to improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women with access to important maternal health information, appointment reminders, and emergency services.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women in remote areas to consult with healthcare providers and receive prenatal care through video calls or phone consultations.

3. Community Health Worker Training: Develop innovative training programs for community health workers (CHEWs) to enhance their knowledge and skills in providing maternal health services, including prenatal care, delivery assistance, and postnatal care.

4. Transportation Solutions: Implement transportation solutions, such as mobile clinics or ambulance services, to ensure that pregnant women can easily access healthcare facilities for prenatal check-ups, delivery, and emergency care.

5. Maternal Health Education Campaigns: Launch community-based education campaigns to raise awareness about the importance of maternal health, promote early prenatal care, and encourage women to seek professional healthcare services during pregnancy.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the Objectives: Clearly outline the specific objectives of the simulation, such as measuring the increase in the number of pregnant women accessing prenatal care or the reduction in maternal mortality rates.

2. Identify Key Indicators: Determine the key indicators that will be used to measure the impact of the recommendations, such as the number of prenatal visits, the percentage of women receiving skilled birth attendance, or the maternal mortality ratio.

3. Collect Baseline Data: Gather baseline data on the current state of maternal health access in the target area, including the number of prenatal visits, the availability of healthcare facilities, and the transportation infrastructure.

4. Develop a Simulation Model: Create a simulation model that incorporates the recommended innovations and their potential impact on maternal health access. This model should consider factors such as population demographics, geographical distribution, and healthcare resource allocation.

5. Input Data and Run Simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. Vary the parameters, such as the coverage of mHealth applications or the availability of transportation services, to understand their influence on the outcomes.

6. Analyze Results: Analyze the results of the simulations to identify trends, patterns, and potential bottlenecks in improving access to maternal health. Evaluate the effectiveness of each recommendation and determine which combination of innovations yields the best outcomes.

7. Refine and Validate the Model: Refine the simulation model based on the analysis of the results and validate it against real-world data. Ensure that the model accurately represents the complexities of the maternal health system and provides reliable predictions.

8. Communicate Findings and Recommendations: Present the findings of the simulation study to stakeholders, policymakers, and healthcare providers. Use the results to inform decision-making, prioritize interventions, and allocate resources effectively to improve access to maternal health.

By following this methodology, policymakers and researchers can gain insights into the potential impact of innovations on improving access to maternal health and make informed decisions to implement the most effective interventions.

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