District decision-making for health in low-income settings: A case study of the potential of public and private sector data in India and Ethiopia

  • Health Policy and Planning, Volume 31, Year 2016

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Study Justification:
– Pluralistic health systems in low-income countries often have untapped potential for data sharing and decision-making.
– This study aims to document the nature and type of data collected by public and private health systems in India and Ethiopia.
– By understanding data flow, sharing, and use, this study highlights the potential for increased use of health data in district decision-making.
Study Highlights:
– Data flow in the public health sectors of India and Ethiopia is sequential, formal, and systematic.
– There is little formal sharing of data between sectors, although Ethiopia has better-developed structures for data sharing.
– Both public and private sectors collect health data across all six health system categories, with a focus on maternal and child health.
– India’s private sector has a better balance of data across categories compared to the public sector.
– Both countries have the potential to use health data to guide district decision-making.
Recommendations:
– Increase formal sharing of data between public and private sectors to improve decision-making.
– Strengthen data sharing structures in India to match Ethiopia’s more developed system.
– Expand the collection of health data beyond maternal and child health to include other health system categories.
– Encourage the use of health data in district-level planning and decision-making.
Key Role Players:
– Central Government in India responsible for developing national standards and sponsoring key programs.
– State governments in India responsible for healthcare delivery.
– Districts in India act as a link between the state and local health centers.
– District Programme Management Unit (DPMU) in India monitors and supports health programs.
– Federal Ministry of Health and Regional Health Bureaus in Ethiopia share decision-making for health program development and implementation.
– Zonal Health Departments and District Health Offices in Ethiopia manage health service delivery at the district level.
Cost Items for Planning Recommendations:
– Budget allocation for strengthening data sharing structures in India.
– Funding for training and capacity building to improve data collection and use.
– Resources for expanding the collection of health data in both countries.
– Investment in technology and infrastructure to support data sharing and analysis.
– Funding for coordination and collaboration between public and private sectors.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study provides detailed information on the type of data collected, data flow and sharing, and inter-sectoral linkages in India and Ethiopia. However, the abstract does not mention the sample size or the methodology used to conduct the study. To improve the evidence, the authors could include this information in the abstract, as well as provide more specific details on the findings and implications of the study.

Many low-and middle-income countries have pluralistic health systems where private for-profit and not-for-profit sectors complement the public sector: data shared across sectors can provide information for local decision-making. The third article in a series of four on district decision-making for health in low-income settings, this study shows the untapped potential of existing data through documenting the nature and type of data collected by the public and private health systems, data flow and sharing, use and inter-sectoral linkages in India and Ethiopia. In two districts in each country, semi-structured interviews were conducted with administrators and data managers to understand the type of data maintained and linkages with other sectors in terms of data sharing, flow and use. We created a database of all data elements maintained at district level, categorized by form and according to the six World Health Organization health system blocks. We used content analysis to capture the type of data available for different health system levels. Data flow in the public health sectors of both counties is sequential, formal and systematic. Although multiple sources of data exist outside the public health system, there is little formal sharing of data between sectors. Though not fully operational, Ethiopia has better developed formal structures for data sharing than India. In the private and public sectors, health data in both countries are collected in all six health system categories, with greatest focus on service delivery data and limited focus on supplies, health workforce, governance and contextual information. In the Indian private sector, there is a better balance than in the public sector of data across the six categories. In both India and Ethiopia the majority of data collected relate to maternal and child health. Both countries have huge potential for increased use of health data to guide district decision-making.

In India, the central Government is mainly responsible for developing national standards, and sponsoring key programmes while health is a state subject and the state holds primary responsibility for healthcare delivery. The district acts as a link between the state and the local health centres, and is responsible for coordinating with state governments for programme implementation. The service delivery structure in a district comprises primary and community health centres at sub-district level and the sub-centre facility and community level workers at the community level. Through the health sector reform programme the National Rural Health Mission [later renamed the National Health Mission (NHM)] has sought to decentralize planning and increase community involvement, particularly planning and decision-making at district level. Accordingly, a District Programme Management Unit (DPMU) monitors and supports health programmes, collates data and makes plans and budgetary allocation (Ministry of Health and Family Welfare 2006). The NHM further aims to integrate district health plans with those of other sectors such as water, sanitation and nutrition, and to include partnership with non-governmental organizations and coordination with the private health sector (NRHM Division 2007; Ministry of Health and Family Welfare 2012; Prasad et al. 2013). The Ethiopian Government has also taken measures to decentralize the health care system (Earth Institute at Colombia University and Center for National Health Development in Ethiopia). The process of decision-making for health programme development and implementation is shared between the Federal Ministry of Health and the Regional Health Bureaus (RHBs), which also manage policy matters and provide technical support. Zonal Health Departments support the RHBs and District Health Offices in the management of health service delivery, while the District Health Offices are also tasked to manage and coordinate the operation of the primary health care services (Federal Ministry of Health, Ethiopia website). Health services at district level are delivered through Primary Health Care Units (PHCUs). Each PHCU is comprised of one health centre and five satellite health posts. These local health needs are determined through a district-based planning system where the objective is to meet the local health needs within the context of national targets. Health budgets are allocated by the governing body; the District Cabinet, which is responsible for dividing the district budget among different sectors including health, education and agriculture. The study was undertaken in Sitapur and Unnao districts in Uttar Pradesh, India and in Dendi district in Oromia region and Basso district in Amhara region in Ethiopia (IDEAS 2012a,b). Districts were selected in consultation with NHM representatives in India and Federal Ministry of Health and RHB representatives in Ethiopia, and based on variability in the functioning of health facilities and district health administration, which can have an effect on linkages with different sectors and also the nature and type of health data they maintained. We sought state (regional in the Ethiopian context) and zonal government support to facilitate visits to health facilities for meetings with key staff. We conducted an initial scoping visit to meet key informants in the public and private sectors in each district, identified on the basis of their role, knowledge and relevance in terms of managing health data. The team visited both strong and weak facilities, determined by the government representatives, at every level of service delivery, to solicit their cooperation. At this stage we outlined the structure of the health system, linkages between central, state (regional) and district levels and the various non-health departments and ministries in operation. After the scoping visit, data collection was conducted between June and September 2012. In India, we visited eight public health facilities at primary and secondary care levels and in Ethiopia we visited eight public health facilities at the primary care level. A complete listing of private sector organizations, both for-profit and not-for-profit, working on MCH in the selected districts was carried out and from that three private sector organizations in Ethiopia and four in India were included as case studies from the two countries. Private sector organizations were selected with the assistance of the district level health offices, using the selection criteria of having a district level office, a registered license to operate and a major presence in the community. At each selected facility we interviewed administrative heads and data managers, in all 35 respondents in Ethiopia and 18 respondents in India. Semi-structured interview guides were used to understand the structure and functions of the organizations, their activities and the type of data collected and maintained, the use of data for preparing district health plans, and linkages with the other sectors in terms of data sharing and flow. The team collected templates of all the data forms that the facility maintained, both article-based and online. Ethical approval for the study was obtained from the corresponding author’s institute, the Health Ministry Screening Committee in India, and the Science and Technology Ministry in Ethiopia. Verbal consent was obtained for the interviews. A Microsoft Access database was created of all the data forms that are maintained at district level by the public and private health sector. Each data form was given a unique number and was categorized based on its source, level of completion (within the health system) and frequency of reporting. The health system categories were adapted from the WHO framework of health system building blocks (WHO 2007). Thematic areas were first identified (e.g. immunization, human resources and expenditure) and sorted into one of the WHO health system categories. Each data element from the collected forms was then categorized according to thematic area (Table 1). Content analysis of the data elements in each form was conducted to capture the type of data available for different health system levels, the level of data sharing and the flow (Weber 1990). An in-depth analysis was done to understand the MCH service delivery data and distal services affecting MCH outcomes such as nutrition, water and sanitation, family planning and abortion care. Framework for health system data.

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

1. Integrated Health Information System: Develop and implement a comprehensive health information system that integrates data from both the public and private health sectors. This system would allow for seamless sharing and flow of data between sectors, enabling better coordination and decision-making at the district level.

2. Mobile Health Technology: Utilize mobile health technology, such as smartphones and apps, to collect and manage maternal health data. This would enable real-time data collection, improve data accuracy, and facilitate data sharing between health facilities and districts.

3. Public-Private Partnerships: Foster partnerships between the public and private sectors to leverage their respective strengths and resources. This collaboration could include joint data collection and sharing initiatives, as well as coordinated efforts to address maternal health challenges.

4. Data-driven Decision Making: Promote the use of health data to inform decision-making at the district level. This could involve training district administrators and data managers on data analysis and interpretation, as well as providing them with the necessary tools and resources to utilize data effectively.

5. Strengthening Health System Categories: Focus on improving data collection and reporting in all six health system categories, with particular emphasis on maternal and child health. This would ensure that comprehensive and accurate data is available to guide district-level decision-making and resource allocation.

6. Capacity Building: Invest in training and capacity building initiatives for health workers involved in data management and analysis. This would enhance their skills and knowledge in utilizing health data to improve maternal health outcomes.

7. Standardized Data Collection: Establish standardized data collection protocols and tools across the public and private health sectors. This would ensure consistency and comparability of data, making it easier to analyze and share information between sectors.

8. Data Sharing Policies: Develop clear policies and guidelines for data sharing between the public and private health sectors. This would address any legal and ethical concerns, while promoting the exchange of relevant and useful data for maternal health improvement.

9. Community Engagement: Involve local communities in the data collection and decision-making processes. This could be done through community-based data collection initiatives and participatory approaches, ensuring that the needs and perspectives of the community are taken into account.

10. Monitoring and Evaluation: Establish robust monitoring and evaluation systems to track the impact of data-driven interventions on maternal health outcomes. This would enable continuous learning and improvement, and ensure accountability in the use of health data.
AI Innovations Description
The recommendation to improve access to maternal health based on the provided information is to enhance data sharing and collaboration between the public and private sectors in low-income settings. This can be achieved by implementing the following strategies:

1. Establish formal structures for data sharing: Develop clear guidelines and protocols for sharing health data between the public and private sectors. This will ensure that relevant and accurate information is shared in a timely manner.

2. Strengthen inter-sectoral linkages: Foster collaboration and coordination between different sectors involved in maternal health, such as healthcare, water, sanitation, and nutrition. This will enable a comprehensive approach to addressing the various factors that impact maternal health outcomes.

3. Improve data collection and management: Enhance the capacity of both public and private health systems to collect, manage, and analyze data. This includes training healthcare providers and data managers on data collection methods, data quality assurance, and data analysis techniques.

4. Increase focus on key maternal health indicators: Prioritize the collection and analysis of data related to maternal and child health. This will provide valuable insights into the effectiveness of existing interventions and help identify areas for improvement.

5. Promote data-driven decision-making: Encourage the use of health data in the decision-making process at the district level. This involves providing training and support to district administrators and policymakers on how to interpret and utilize health data to inform resource allocation and program planning.

By implementing these recommendations, access to maternal health can be improved by leveraging the untapped potential of existing data and promoting evidence-based decision-making at the district level.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations for improving access to maternal health:

1. Strengthen data sharing between public and private sectors: Encourage formal sharing of data between the public and private health sectors in low-income settings. This can be done through the establishment of standardized protocols and mechanisms for data exchange.

2. Enhance inter-sectoral linkages: Promote collaboration and coordination between the health sector and other sectors such as water, sanitation, and nutrition. This can help address the broader determinants of maternal health and improve access to essential services.

3. Increase community involvement: Involve local communities in the planning and decision-making processes at the district level. This can ensure that maternal health services are tailored to the specific needs and preferences of the community, leading to improved access and utilization.

4. Strengthen district-level health planning: Support the capacity of district-level health offices to effectively plan and allocate resources for maternal health programs. This can be achieved through training and technical assistance, as well as the integration of health data into the planning process.

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

1. Define indicators: Identify key indicators that measure access to maternal health, such as the number of antenatal care visits, skilled birth attendance, and postnatal care coverage.

2. Collect baseline data: Gather existing data on the selected indicators from the public and private health sectors in the target districts. This can include data from health facilities, surveys, and other relevant sources.

3. Analyze data: Use statistical analysis techniques to examine the current status of access to maternal health and identify any gaps or disparities. This can involve comparing data between different sectors, geographic areas, or population groups.

4. Simulate interventions: Develop a simulation model that incorporates the recommended interventions. This can involve adjusting the input parameters based on the expected impact of each intervention, such as increased data sharing or improved community involvement.

5. Project outcomes: Run the simulation model to project the potential impact of the interventions on access to maternal health. This can provide estimates of the expected changes in the selected indicators, allowing for comparisons between different scenarios.

6. Validate and refine the model: Validate the simulation results by comparing them with real-world data or expert opinions. Refine the model based on feedback and make adjustments as necessary.

7. Communicate findings: Present the simulation results in a clear and concise manner, highlighting the potential benefits of the recommended interventions for improving access to maternal health. This can help inform policy and decision-making processes at the district and national levels.

Statistics:
Citations: 23
Authors: 7
Identifiers:
DOI: 10.1093/heapol/czw017
ISSN: 02681080
Research Areas:
Community Interventions, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Technology and Innovations
Study Countries:
Ethiopia
Study Design:
Case Study, Grounded Theory
Study Approach:
Qualitative
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