‘The stars seem aligned’: A qualitative study to understand the effects of context on scale-up of maternal and newborn health innovations in Ethiopia, India and Nigeria

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Study Justification:
This study aims to understand the effects of context on the scale-up of maternal and newborn health innovations in Ethiopia, India, and Nigeria. The study is justified by the need to identify key contextual factors that influence the adoption and implementation of effective health interventions in low-income settings. By exploring these factors, the study can provide insights into how to successfully position innovations for scale-up.
Highlights:
1. The study conducted 150 semi-structured interviews with stakeholders from government, development partner agencies, implementers, academic institutions, and professional associations.
2. Multiple contextual factors were found to enable and undermine the scale-up of donor-funded maternal and newborn health innovations.
3. Factors influencing government decisions to accept innovations at scale included health policy decision-making, prioritization and funding of maternal and newborn health, and development partner harmonization.
4. Factors influencing the implementation of innovations at scale included health systems capacity and security in northeast Nigeria.
5. Contextual factors influencing beneficiary communities’ uptake of innovations at scale included sociocultural contexts and access to healthcare.
6. The study concludes that context is critical, and implementers need to assess and adapt innovations for specific contexts to achieve successful scale-up.
Recommendations:
1. Externally funded implementers should conduct thorough assessments of the contextual factors in target settings before positioning innovations for scale-up.
2. Governments should prioritize and allocate sufficient funding for maternal and newborn health interventions.
3. Development partners should harmonize their efforts and support the scale-up of effective innovations.
4. Health systems in low-income settings should be strengthened to ensure the successful implementation of innovations.
5. Efforts should be made to address security challenges in conflict-affected areas to facilitate the scale-up of innovations.
6. Sociocultural barriers to the uptake of innovations should be addressed, and access to healthcare should be improved.
Key Role Players:
1. Government officials responsible for health policy and decision-making.
2. Development partner agencies providing funding and technical support.
3. Implementers, including civil society organizations, academic institutions, and professional associations.
4. Health system administrators and healthcare providers.
5. Community leaders and members.
Cost Items for Planning Recommendations:
1. Research and data collection expenses.
2. Capacity-building and training programs for implementers and healthcare providers.
3. Funding for the scale-up of maternal and newborn health interventions.
4. Investments in health system strengthening.
5. Security measures in conflict-affected areas.
6. Community engagement and awareness campaigns.
7. Infrastructure and equipment upgrades in healthcare facilities.
8. Monitoring and evaluation activities to assess the impact of scale-up efforts.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a qualitative study with 150 semi-structured interviews conducted in three low-income settings. The study identifies key contextual factors influencing the scale-up of maternal and newborn health innovations. The use of a common analytic framework and coding themes in Nvivo enhances the rigor of the analysis. However, the abstract does not provide specific details about the sampling strategy or the representativeness of the interviewees. To improve the evidence, the authors could provide more information about the selection criteria for interviewees and the steps taken to ensure the validity of the findings.

Background: Donors commonly fund innovative interventions to improve health in the hope that governments of low and middle-income countries will scale-up those that are shown to be effective. Yet innovations can be slow to be adopted by country governments and implemented at scale. Our study explores this problem by identifying key contextual factors influencing scale-up of maternal and newborn health innovations in three low-income settings: Ethiopia, the six states of northeast Nigeria and Uttar Pradesh state in India. Methods: We conducted 150 semi-structured interviews in 2012/13 with stakeholders from government, development partner agencies, externally funded implementers including civil society organisations, academic institutions and professional associations to understand scale-up of innovations to improve the health of mothers and newborns these study settings. We analysed interview data with the aid of a common analytic framework to enable cross-country comparison, with Nvivo to code themes. Results: We found that multiple contextual factors enabled and undermined attempts to catalyse scale-up of donor-funded maternal and newborn health innovations. Factors influencing government decisions to accept innovations at scale included: how health policy decisions are made; prioritising and funding maternal and newborn health; and development partner harmonisation. Factors influencing the implementation of innovations at scale included: health systems capacity in the three settings; and security in northeast Nigeria. Contextual factors influencing beneficiary communities’ uptake of innovations at scale included: sociocultural contexts; and access to healthcare. Conclusions: We conclude that context is critical: externally funded implementers need to assess and adapt for contexts if they are to successfully position an innovation for scale-up.

We adopted a health policy analysis approach informed by the stages heuristic framework [13] that identifies sequential stages in the policy process: agenda setting, policy formulation and policy implementation, and on the literature on scale-up and context to frame different contextual domains. From this we developed a framework to guide our study consisting of three distinct stages that are critical to scale-up: Having used these categories to develop a topic guide, researchers from Nigeria, Ethiopia, India and the UK piloted it at a workshop in Addis Ababa leading to minor adaptations being made to reflect different country contexts. Researchers used the guide to conduct semi-structured interviews with purposively selected stakeholders working in MNH, or having substantial experience and/or knowledge of issues relating to scale-up of MNH innovations including policy, financing and health systems issues. The interviewees were drawn from different sectors: government, development partners, civil society organisations (CSOs) including implementers of donor- funded MNH programmes, academic institutions and professional associations. Interviewees were managers and directors, programme officers and research and evaluation and technical officers. Fifty interviews were conducted in each of the three settings between July 2012 and April 2013. Our sample of interviewees represent the majority of implementer and development partner organisations working on MNH in each of our three settings. We have deliberately not named specific organisations in our paper because of our commitment to maintaining respondent confidentiality. The MNH implementers we sampled are characterised as follows: the majority were large international nongovernmental organisations or large local nongovernmental organisations, together with a smaller number of US-based universities and for-profit consultancy companies implementing MNH programmes. Most of these implementers had in the past received large grants from different donors to maintain particular interventions and some were receiving multiple grants for separate pieces of work at the time of the interviews. Many implementers also worked with smaller local CSOs to implement work packages in particular locations. While a substantial amount of externally funded MNH-related work in the three settings took the form of projects to develop innovative interventions, some implementers also received donor funding for direct technical support to government agencies as well as advocacy work. The development partners we sampled included a mix of donors – bilateral agencies and philanthropic foundations – and UN agencies, some of which also funded MNH innovations. In addition to funding projects some development partners also contributed to larger health programmes, provided technical support for government, and in Ethiopia contributed to a pooled fund for work corresponding to the Millennium Development Goals. The MNH projects we explored in our interviews generally lasted up to five years and more commonly three to four years. The scale varied from a small handful of districts to several districts across multiple states or regions, and some were part of large multi-country grants. Some projects involved single innovations, while others involved a package of connected innovations. The interviewers included NS, RDTG, DBh and ATW, and other researchers with training in qualitative methods. The interviews were conducted in private spaces to preserve confidentiality and all respondents gave informed consent before the interview. Where it was agreed with the respondent, a sound recorder was used for data capture. Interviewers wrote ‘expanded field notes’ [6] shortly after the interview comprising detailed notes arranged under thematic headings, with direct quotes to illustrate respondents’ voices. Through simultaneously capturing and analysing data, interviewers identified emerging interpretations and hypotheses to explore in ensuing interviews. We adopted several steps to maximise the validity of our findings. We adopted an investigator triangulation approach to compare and agree researchers’ interpretations; this helped reinforce the validity of the results reported because each set of expanded field notes was the work of multiple researchers. Moreover, an analysis workshop enabled us to reach consensus on interpretations among researchers involved in the study and cross-country comparisons. Our relatively large qualitative sample, with interviewees from a variety of organisations, helped balance the views we present, and cross-checks of interviewees’ views enabled us to triangulate findings. We also conducted member checks: we presented emerging findings to interviewees and other relevant country stakeholders in Addis Ababa, Abuja and Lucknow who were invited to comment on the accuracy of our messages. The analysis of the interview data was undertaken in five stages: 1) an analysis workshop in London at which NS, DW ATW, RD and DBh reviewed and agreed emerging findings and developed an analytic framework to enable us to directly compare our three study settings; 2) using Nvivo Version 10, NS and DW analysed the expanded field notes, using a framework approach to code a priori and emerging themes; 3) the analytic framework was used to organise the emerging themes ; 4) NS drafted the paper, which was then reviewed by all authors to ensure that the findings are represented coherently and accurately. In order to maintain anonymity of our interviewees it is not appropriate to make the qualitative dataset supporting the conclusions of this article publically available.

Based on the provided description, the study identified several contextual factors that influenced the scale-up of maternal and newborn health innovations in low-income settings. These factors include:

1. Health policy decisions: The way health policy decisions are made can influence the acceptance and adoption of innovations at scale. Understanding the decision-making processes and engaging with policymakers can help position innovations for scale-up.

2. Prioritizing and funding maternal and newborn health: Governments need to prioritize and allocate sufficient funding for maternal and newborn health. Adequate financial resources are essential for implementing and scaling up innovations in this area.

3. Development partner harmonization: Collaboration and coordination among development partners are crucial for successful scale-up. Harmonization of efforts and alignment with national health priorities can facilitate the adoption of innovations.

4. Health systems capacity: The capacity of health systems to implement and sustain innovations is a critical factor. Strengthening health systems, including infrastructure, human resources, and supply chains, is necessary for successful scale-up.

5. Security: In certain settings, security issues can pose challenges to the implementation of innovations. Addressing security concerns and ensuring a safe environment for healthcare delivery is important for scale-up.

6. Sociocultural contexts: The sociocultural context of beneficiary communities can influence their uptake of innovations. Understanding cultural beliefs, practices, and preferences is essential for designing and implementing effective interventions.

7. Access to healthcare: Improving access to healthcare services, including antenatal care, skilled birth attendance, and postnatal care, is crucial for the successful scale-up of maternal and newborn health innovations.

In conclusion, considering the contextual factors mentioned above and adapting innovations to specific country contexts are important for positioning innovations for scale-up and improving access to maternal health.
AI Innovations Description
The study mentioned in the description explores the contextual factors that influence the scale-up of maternal and newborn health innovations in low-income settings such as Ethiopia, Nigeria, and India. The study found that multiple contextual factors can either enable or undermine the scale-up of donor-funded innovations. Factors influencing government decisions to accept innovations at scale include how health policy decisions are made, prioritizing and funding maternal and newborn health, and development partner harmonization. Factors influencing the implementation of innovations at scale include health systems capacity and security in certain regions. Contextual factors influencing beneficiary communities’ uptake of innovations at scale include sociocultural contexts and access to healthcare. The study concludes that context is critical, and externally funded implementers need to assess and adapt to different contexts in order to successfully position an innovation for scale-up.
AI Innovations Methodology
The study described in the provided text aims to understand the effects of context on the scale-up of maternal and newborn health (MNH) innovations in low-income settings. The researchers conducted 150 semi-structured interviews with stakeholders from government, development partner agencies, civil society organizations, academic institutions, and professional associations in Ethiopia, Nigeria, and India. The interviews were analyzed using a common analytic framework and NVivo software to identify key contextual factors influencing the scale-up of MNH innovations.

To simulate the impact of recommendations on improving access to maternal health, a methodology could be developed based on the findings of the study. Here is a brief outline of a possible methodology:

1. Identify key contextual factors: Based on the analysis of the interview data, identify the key contextual factors that enable or undermine the scale-up of MNH innovations. These factors could include health policy decisions, funding priorities, development partner harmonization, health systems capacity, security, sociocultural contexts, and access to healthcare.

2. Develop recommendations: Based on the identified contextual factors, develop specific recommendations to improve access to maternal health. These recommendations should address the barriers and challenges identified in the study and aim to leverage the enabling factors.

3. Quantify the impact: Use available data and evidence to quantify the potential impact of the recommendations on improving access to maternal health. This could involve analyzing existing data on maternal health indicators, such as maternal mortality rates, access to antenatal care, skilled birth attendance, and postnatal care. The impact could be measured in terms of improvements in these indicators.

4. Conduct modeling and simulation: Utilize modeling and simulation techniques to simulate the impact of the recommendations on improving access to maternal health. This could involve developing mathematical models or using existing simulation tools to project the potential outcomes of implementing the recommendations. The models could take into account factors such as population demographics, healthcare infrastructure, resource allocation, and behavior change.

5. Sensitivity analysis: Perform sensitivity analysis to assess the robustness of the simulation results. This could involve varying key parameters and assumptions in the models to understand the potential range of outcomes and identify the most influential factors.

6. Interpretation and communication of results: Analyze the simulation results and interpret the findings in the context of the study settings. Communicate the results to relevant stakeholders, including policymakers, healthcare providers, and development partners, to inform decision-making and prioritize actions to improve access to maternal health.

It is important to note that the methodology outlined above is a general framework and would need to be adapted and tailored to the specific recommendations and context of the study. Additionally, the availability and quality of data will play a crucial role in the accuracy and reliability of the simulation results.

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