Delineating natural catchment health districts with routinely collected health data from women’s travel to give birth in Ghana

BMC Health Services Research, Volume 22, No. 1, Year 2022

Interpretação

Background: Health service areas are essential for planning, policy and managing public health interventions. In this study, we delineate health service areas from routinely collected health data as a robust geographic basis for presenting access to maternal care indicators. Methods: A zone design algorithm was adapted to delineate health service areas through a cross-sectional, ecological study design. Health sub-districts were merged into health service areas such that patient flows across boundaries were minimised. Delineated zones and existing administrative boundaries were used to provide estimates of access to maternal health services. We analysed secondary data comprising routinely collected health records from 32,921 women attending 27 hospitals to give birth, spatial demographic data, a service provision assessment on the quality of maternal healthcare and health sub-district boundaries from Eastern Region, Ghana. Results: Clear patterns of cross border movement to give birth emerged from the analysis, but more women originated closer to the hospitals. After merging the 250 sub-districts in 33 districts, 11 health service areas were created. The minimum percent of internal flows of women giving birth within any health service area was 97.4%. Because the newly delineated boundaries are more “natural” and sensitive to observed flow patterns, when we calculated areal indicator estimates, they showed a marked improvement over the existing administrative boundaries, with the inclusion of a hospital in every health service area. Conclusion: Health planning can be improved by using routine health data to delineate natural catchment health districts. In addition, data-driven geographic boundaries derived from public health events will improve areal health indicator estimates, planning and interventions. Data documenting births of 32,921 women from 27 hospitals in the Eastern region, Ghana, from 1st January to 31st December 2017 were used to analyse women’s travel to give birth. The Ghana Health Service (GHS) collects birth data using book registers. Midwives record details of a woman into these registers when they give birth in a health facility. Information collected includes the woman’s residential address, age, parity, complications, birth outcomes and other relevant maternal health information. Subsequently, the data is entered into the electronic District Health Information Management System (DHIMS). First, the women are counted and reported as monthly aggregates. Secondly, the individual records as they appear in the register are also captured in the DHIMS system. Currently, only hospitals enter the individual women’s data into the DHIMS, as health centres use paper registers only. The individual records transferred from paper to electronic registers at hospitals can differ from routine aggregate reports. The two key variables used in this analysis are the woman’s community of residence and the health facility she gave birth in. District and sub-district boundaries from the GHS were included in the analysis. In the GHS, sub-districts are the lowest administrative areal unit and are formed by a group of health facilities and communities. We used WorldPop gridded (100 m by 100 m) estimated population, the number of midwives in health facilities, and the spatial distribution of the hospitals to estimate access to adequately staffed birthing services. The WorldPop group produces the population estimates by disaggregating census data into 100 square meter grids within built settlements using Random Forest machine learning methods [31]. In order to construct indicators that provide a good measure of human resource availability and quality of care, data were collected for this study in September 2021. An emergency obstetric and newborn care (EmONC) service provision assessment survey (SPA) was conducted. The survey data was used to determine if hospitals provided care to the level of Comprehensive Emergency Obstetric and Newborn Care (CEmONC). A hospital is classified as CEmONC ready if they administered parenteral antibiotics, uterotonics, parenteral anticonvulsants, removed placenta manually, removed retained products, performed assisted vaginal delivery, neonatal resuscitation, caesarean section and blood transfusion in the last 3 months [22]. CEmONC designated health facilities are supposed to be ready for all major obstetric complications, including the need for surgery and blood transfusion. We used a list of place names with geographic coordinates from the GHS to locate the residential towns of the women. However, we could not find some addresses due to spelling errors, unavailable town names or address mismatches. The town names were manually matched with the reference list as automated geocoding performs poorly because official standardised address lists are unavailable in Ghana [32]. The geographic locations of the hospitals were collected during the SPA. The flows of women initially captured between residential communities and hospitals were aggregated to sub-districts. The aggregation was carried out for two reasons: to reduce the complexity of flows and, secondly, to be consistent with the geographic unit used for delineating HSAs in subsequent analysis. For mappings, only flows of six or more women between sub-districts were shown to avoid graphical complexity [33]. The TTWA zoning method used in this study is a criteria-based zoning process originally used to delineate labour market [14] and retail [15] areas from flow data. The TTWA was used to analyse labour markets delineated to have the majority of people living and working within the zones generated. Similarly, this study utilises it to create health service areas where people live and use birthing services in an area. The building block for our zoning analysis is sub-districts, which were merged into larger areal units. Sub-districts without any facilities reporting births were first merged to a nearby one within the same district. Figure 1 shows the steps involved in developing the HSAs, implemented via Visual Basic within a Microsoft Access database. Zoning procedure used to delineate natural catchment health districts in Eastern Region, Ghana The women were first assigned to the hospital they used. Where women in a subdistrict used more than one hospital, they were assigned to the hospital that received the most flows from that subdistrict to form the first set of zones. Then, the self-containment or localisation index was calculated (Step 1) to determine which zone will be merged in the next step. Next, demand and supply-side self-containment are calculated from patient flows (Step 2). Demand-side self-containment is calculated as the number of internal flows starting and ending within a zone as a proportion of all flows ending in the zone. In contrast, supply-side self-containment comprises internal flows as a proportion of all flows originating in a zone. The zone with the minimum demand or supply-side self-containment is the candidate for merging. The next step (Step 3) calculates the connectance (connectivity strength) between zones [15] to identify the zone with the highest connectance (Step 4). The connectance flows are calculated between the zone with the lowest self-containment in step two and all other zones. In Step 5, we merged the least self-contained zone in Step 2 with the best connected in Step 4. This analysis used the connectance flow function (Eq. 1) by Pratt and colleagues [15] derived from Coombes [34]: Where Cij is the connectance flows between zone i and zone j. Tij are the flows from zone i to zone j. Tji are the opposite from zone j to zone i. The sum of flows at the origin ∑iTij contains internal flows from i to i. The sum of flows at the destination ∑jTij contains internal flows from j to j. The sum of reverse flows at the origin ∑iTji contains internal flows from i to i. The sum of reverse flows at the destination ∑jTji includes internal flows from j to j. Steps one to five are repeated until a minimum self-containment criterion is met. Zones were only progressively merged. They were not dissolved as implemented in the original TTWA process. The minimum supply/demand self-containment threshold for all zones to qualify as an HSA was set at 96%. The self-containment was high because the initial self-containment was high (70.5%), and a high value optimises health services planning by limiting cross border patient movement in the output zones. The final step used manual interventions to make all zones contiguous. There were instances where most of the women in a sub-district attended the regional hospital or a hospital farther away, resulting in an outlier island zone. These inconsistent zones were corrected by assigning them to the nearby zone with the highest contiguity to ensure homogeneity. Henceforth, the study introduces new terminology and refers to HSAs as Natural Catchment Health Districts (NCHD) as it differentiates them from HSAs delineated from mandatory catchments. NCHD and Zones are used interchangeably. A simpler comparable set of zones were delineated to assess the effect of scale on self-containment. The flows were assigned to the destinations where most women went to give birth from a sub-district. The zone with the smallest supply-side self-containment was a candidate for merging. The candidate zone was merged to the contiguous zone with the least supply-side self-containment. The process was repeated until a comparable number of zones was achieved. Geographic access indicators were calculated using municipal and district assembly boundaries (MDA) and NCHD. MDAs are the government geographical boundaries for local governance. Two indicators of access to birthing services were calculated for each geographic boundary using the 2021 service provision assessment survey and gridded population data: Although the most appropriate indicators of geographic access are travel time to health facilities [5], the analysis used a provider to population ratio. Provider to population ratio is suitable because it is the primary indicator health managers use, simple to calculate and a recommended benchmark by the WHO [23, 35].

AI Digest

Study Justification:

– Health service areas are crucial for planning, policy-making, and managing public health interventions.
– This study aims to delineate health service areas using routinely collected health data to provide a robust geographic basis for assessing access to maternal care indicators.
– By using data-driven geographic boundaries, the study seeks to improve areal health indicator estimates, planning, and interventions.

Highlights:

– The study analyzed data from 32,921 women attending 27 hospitals in the Eastern Region of Ghana to understand their travel patterns for giving birth.
– Clear patterns of cross-border movement for giving birth were identified, with more women originating closer to the hospitals.
– By merging sub-districts, the study created 11 health service areas with a minimum internal flow of 97.4% of women giving birth within each area.
– The newly delineated boundaries, based on observed flow patterns, showed a marked improvement over existing administrative boundaries, including a hospital in every health service area.
– The study demonstrates that health planning can be improved by using routine health data to delineate natural catchment health districts.

Recommendations:

– Incorporate the use of routine health data in health planning to delineate natural catchment health districts.
– Implement data-driven geographic boundaries derived from public health events to improve areal health indicator estimates, planning, and interventions.

Key Role Players:

– Ghana Health Service (GHS): Responsible for collecting and managing health data.
– Hospitals and health facilities: Provide data on births and maternal healthcare.
– Midwives: Record details of women giving birth in health facilities.
– District and sub-district health authorities: Provide administrative boundaries and data for analysis.
– Researchers and analysts: Conduct the study and analyze the data.

Cost Items for Planning Recommendations:

– Data collection and management systems: Budget for improving the collection and management of health data, including electronic systems for recording and reporting.
– Training and capacity building: Allocate funds for training healthcare providers and staff on data collection, analysis, and interpretation.
– Research and analysis: Budget for conducting studies and analyses to inform health planning and interventions.
– Infrastructure and resources: Consider the need for additional healthcare facilities, equipment, and resources based on the delineated health service areas.
– Monitoring and evaluation: Allocate resources for ongoing monitoring and evaluation of the implemented recommendations to assess their effectiveness.
Please note that the provided cost items are general suggestions and may vary depending on the specific context and requirements of the implementation.

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a cross-sectional, ecological study design and utilizes a large dataset of 32,921 women attending 27 hospitals in the Eastern Region of Ghana. The study uses routinely collected health records, spatial demographic data, and a service provision assessment to delineate health service areas and estimate access to maternal health services. The analysis shows clear patterns of cross-border movement for giving birth and demonstrates the improvement of areal health indicator estimates using the newly delineated boundaries. To improve the evidence, the study could consider including a comparison group or conducting a longitudinal study to assess the long-term impact of the delineated health service areas on maternal health outcomes.

Resumo

Data documenting births of 32,921 women from 27 hospitals in the Eastern region, Ghana, from 1st January to 31st December 2017 were used to analyse women’s travel to give birth. The Ghana Health Service (GHS) collects birth data using book registers. Midwives record details of a woman into these registers when they give birth in a health facility. Information collected includes the woman’s residential address, age, parity, complications, birth outcomes and other relevant maternal health information. Subsequently, the data is entered into the electronic District Health Information Management System (DHIMS). First, the women are counted and reported as monthly aggregates. Secondly, the individual records as they appear in the register are also captured in the DHIMS system. Currently, only hospitals enter the individual women’s data into the DHIMS, as health centres use paper registers only. The individual records transferred from paper to electronic registers at hospitals can differ from routine aggregate reports. The two key variables used in this analysis are the woman’s community of residence and the health facility she gave birth in. District and sub-district boundaries from the GHS were included in the analysis. In the GHS, sub-districts are the lowest administrative areal unit and are formed by a group of health facilities and communities. We used WorldPop gridded (100 m by 100 m) estimated population, the number of midwives in health facilities, and the spatial distribution of the hospitals to estimate access to adequately staffed birthing services. The WorldPop group produces the population estimates by disaggregating census data into 100 square meter grids within built settlements using Random Forest machine learning methods [31]. In order to construct indicators that provide a good measure of human resource availability and quality of care, data were collected for this study in September 2021. An emergency obstetric and newborn care (EmONC) service provision assessment survey (SPA) was conducted. The survey data was used to determine if hospitals provided care to the level of Comprehensive Emergency Obstetric and Newborn Care (CEmONC). A hospital is classified as CEmONC ready if they administered parenteral antibiotics, uterotonics, parenteral anticonvulsants, removed placenta manually, removed retained products, performed assisted vaginal delivery, neonatal resuscitation, caesarean section and blood transfusion in the last 3 months [22]. CEmONC designated health facilities are supposed to be ready for all major obstetric complications, including the need for surgery and blood transfusion. We used a list of place names with geographic coordinates from the GHS to locate the residential towns of the women. However, we could not find some addresses due to spelling errors, unavailable town names or address mismatches. The town names were manually matched with the reference list as automated geocoding performs poorly because official standardised address lists are unavailable in Ghana [32]. The geographic locations of the hospitals were collected during the SPA. The flows of women initially captured between residential communities and hospitals were aggregated to sub-districts. The aggregation was carried out for two reasons: to reduce the complexity of flows and, secondly, to be consistent with the geographic unit used for delineating HSAs in subsequent analysis. For mappings, only flows of six or more women between sub-districts were shown to avoid graphical complexity [33]. The TTWA zoning method used in this study is a criteria-based zoning process originally used to delineate labour market [14] and retail [15] areas from flow data. The TTWA was used to analyse labour markets delineated to have the majority of people living and working within the zones generated. Similarly, this study utilises it to create health service areas where people live and use birthing services in an area. The building block for our zoning analysis is sub-districts, which were merged into larger areal units. Sub-districts without any facilities reporting births were first merged to a nearby one within the same district. Figure 1 shows the steps involved in developing the HSAs, implemented via Visual Basic within a Microsoft Access database. Zoning procedure used to delineate natural catchment health districts in Eastern Region, Ghana The women were first assigned to the hospital they used. Where women in a subdistrict used more than one hospital, they were assigned to the hospital that received the most flows from that subdistrict to form the first set of zones. Then, the self-containment or localisation index was calculated (Step 1) to determine which zone will be merged in the next step. Next, demand and supply-side self-containment are calculated from patient flows (Step 2). Demand-side self-containment is calculated as the number of internal flows starting and ending within a zone as a proportion of all flows ending in the zone. In contrast, supply-side self-containment comprises internal flows as a proportion of all flows originating in a zone. The zone with the minimum demand or supply-side self-containment is the candidate for merging. The next step (Step 3) calculates the connectance (connectivity strength) between zones [15] to identify the zone with the highest connectance (Step 4). The connectance flows are calculated between the zone with the lowest self-containment in step two and all other zones. In Step 5, we merged the least self-contained zone in Step 2 with the best connected in Step 4. This analysis used the connectance flow function (Eq. 1) by Pratt and colleagues [15] derived from Coombes [34]: Where Cij is the connectance flows between zone i and zone j. Tij are the flows from zone i to zone j. Tji are the opposite from zone j to zone i. The sum of flows at the origin ∑iTij contains internal flows from i to i. The sum of flows at the destination ∑jTij contains internal flows from j to j. The sum of reverse flows at the origin ∑iTji contains internal flows from i to i. The sum of reverse flows at the destination ∑jTji includes internal flows from j to j. Steps one to five are repeated until a minimum self-containment criterion is met. Zones were only progressively merged. They were not dissolved as implemented in the original TTWA process. The minimum supply/demand self-containment threshold for all zones to qualify as an HSA was set at 96%. The self-containment was high because the initial self-containment was high (70.5%), and a high value optimises health services planning by limiting cross border patient movement in the output zones. The final step used manual interventions to make all zones contiguous. There were instances where most of the women in a sub-district attended the regional hospital or a hospital farther away, resulting in an outlier island zone. These inconsistent zones were corrected by assigning them to the nearby zone with the highest contiguity to ensure homogeneity. Henceforth, the study introduces new terminology and refers to HSAs as Natural Catchment Health Districts (NCHD) as it differentiates them from HSAs delineated from mandatory catchments. NCHD and Zones are used interchangeably. A simpler comparable set of zones were delineated to assess the effect of scale on self-containment. The flows were assigned to the destinations where most women went to give birth from a sub-district. The zone with the smallest supply-side self-containment was a candidate for merging. The candidate zone was merged to the contiguous zone with the least supply-side self-containment. The process was repeated until a comparable number of zones was achieved. Geographic access indicators were calculated using municipal and district assembly boundaries (MDA) and NCHD. MDAs are the government geographical boundaries for local governance. Two indicators of access to birthing services were calculated for each geographic boundary using the 2021 service provision assessment survey and gridded population data: Although the most appropriate indicators of geographic access are travel time to health facilities [5], the analysis used a provider to population ratio. Provider to population ratio is suitable because it is the primary indicator health managers use, simple to calculate and a recommended benchmark by the WHO [23, 35].

Materiais

N/A

Inovações

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

1. Mobile Health (mHealth) Solutions: Develop and implement mobile health applications or platforms that provide pregnant women with access to information, resources, and support for maternal health. This can include features such as appointment reminders, educational materials, and communication with healthcare providers.

2. Telemedicine Services: Establish telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide timely access to prenatal care and consultations.

3. Community Health Worker Programs: Implement community health worker programs to provide maternal health education, support, and referrals in local communities. Trained community health workers can bridge the gap between healthcare facilities and pregnant women, particularly in areas with limited healthcare infrastructure.

4. Transportation Solutions: Develop innovative transportation solutions to improve access to healthcare facilities for pregnant women. This can include initiatives such as mobile clinics, ambulance services, or partnerships with transportation providers to ensure reliable and affordable transportation options.

5. Data-Driven Planning and Resource Allocation: Utilize data analytics and geographic information systems (GIS) to identify areas with high maternal health needs and allocate resources accordingly. This can help optimize the distribution of healthcare facilities, staff, and supplies to areas where they are most needed.

6. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers, technology companies, and philanthropic organizations to expand and enhance maternal health services.

7. Financial Incentives and Insurance Programs: Implement financial incentives or insurance programs that reduce the financial burden of maternal healthcare for women, particularly those from low-income backgrounds. This can help remove financial barriers and increase access to essential maternal health services.

8. Maternal Health Education and Awareness Campaigns: Launch targeted education and awareness campaigns to promote maternal health knowledge and encourage early and regular prenatal care. These campaigns can be tailored to specific communities and address cultural, social, and linguistic barriers to accessing maternal healthcare.

9. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to enhance the overall quality of maternal health services. This can involve training healthcare providers, improving infrastructure and equipment, and implementing evidence-based practices for maternal care.

10. Collaboration with Traditional Birth Attendants: Foster collaboration and training programs between healthcare professionals and traditional birth attendants to ensure safe and culturally sensitive maternal care practices. This can help integrate traditional practices with modern healthcare approaches and improve access to skilled birth attendance.

It’s important to note that the specific implementation of these innovations would require further research, planning, and collaboration with relevant stakeholders in the maternal health sector in Ghana.

AI Innovations Description

The recommendation to improve access to maternal health based on the described study is to develop and implement natural catchment health districts (NCHDs) using routinely collected health data. These NCHDs would serve as geographic units for planning, policy-making, and managing public health interventions related to maternal care.

The study utilized data from 32,921 women who gave birth in 27 hospitals in the Eastern region of Ghana. The data included information on the women’s residential addresses, age, complications, birth outcomes, and other relevant maternal health information. By analyzing this data and using a zone design algorithm, the researchers were able to delineate 11 health service areas within the region.

The newly delineated NCHDs showed clear patterns of cross-border movement for giving birth, with more women originating closer to the hospitals. The minimum percent of internal flows of women giving birth within any NCHD was 97.4%. This indicates that the NCHDs effectively captured the majority of women accessing maternal health services within their respective areas.

Compared to existing administrative boundaries, the NCHDs provided more accurate estimates of access to maternal health services. The inclusion of a hospital in every NCHD improved the areal health indicator estimates. This data-driven approach to defining geographic boundaries based on observed flow patterns can enhance health planning, interventions, and resource allocation.

To implement this recommendation, it would be necessary to collect and analyze routine health data, including birth records, from health facilities. The data should include information on the woman’s residential address and the health facility where she gave birth. This data can be entered into an electronic health information system to facilitate analysis and mapping.

The zone design algorithm used in the study, such as the TTWA zoning method, can be adapted and implemented to delineate NCHDs in other regions or countries. The algorithm involves merging sub-districts into larger areal units based on patient flows and self-containment indices. The process can be repeated until a minimum self-containment criterion is met, ensuring that each NCHD captures a high percentage of internal flows.

By implementing NCHDs, health planners and policymakers can have a more accurate understanding of the geographic distribution of maternal health services and the population they serve. This information can inform decisions on resource allocation, infrastructure development, and the provision of quality maternal healthcare.

AI Innovations Methodology

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

1. Strengthening Health Service Areas (HSAs): Use the methodology described in the study to delineate natural catchment health districts based on patient flows and existing administrative boundaries. This will provide a more accurate and robust geographic basis for presenting access to maternal care indicators.

2. Improving Data Collection and Management: Enhance the collection and management of maternal health data by ensuring that all health facilities, including health centers, enter individual women’s data into the electronic District Health Information Management System (DHIMS). This will improve the accuracy and consistency of data for analysis and planning purposes.

3. Addressing Address Matching Challenges: Develop standardized address lists or improve automated geocoding methods to overcome challenges in matching residential addresses of women with geographic coordinates. This will ensure accurate mapping of women’s residential towns and improve the precision of access indicators.

4. Enhancing Human Resource Availability: Utilize data on the number of midwives in health facilities and their spatial distribution to estimate access to adequately staffed birthing services. Identify areas with shortages of skilled birth attendants and develop strategies to address these gaps, such as recruitment and deployment of midwives.

To simulate the impact of these recommendations on improving access to maternal health, the following methodology can be used:

1. Define Key Indicators: Identify key indicators that reflect access to maternal health services, such as the provider to population ratio and the proportion of women giving birth within a specific geographic boundary.

2. Baseline Assessment: Collect baseline data on the identified indicators using existing administrative boundaries (e.g., municipal and district assembly boundaries) and compare it with the data obtained from the newly delineated Natural Catchment Health Districts (NCHDs). This will provide a benchmark for evaluating the impact of the recommendations.

3. Simulation Modeling: Use simulation modeling techniques to estimate the potential impact of the recommendations on the identified indicators. This can involve creating scenarios where the recommendations are implemented and comparing the simulated results with the baseline data.

4. Analyze Results: Analyze the simulated results to assess the potential improvements in access to maternal health services. This can include evaluating changes in the provider to population ratio, the proportion of women giving birth within the NCHDs, and other relevant indicators.

5. Interpretation and Recommendations: Interpret the results of the simulation analysis and provide recommendations based on the findings. This can include identifying areas where the recommendations have the greatest impact and suggesting strategies for implementation.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and data availability.

Autores & Coautores

Statistics:

Authors: 6

Identifiers:

DOI: 10.1186/s12913-022-08125-9

Research Areas:

Community Interventions, Health System and Policy, Maternal and Child Health, Quality of Care, Social Determinants, Technology and Innovations, Workforce

Study Countries:

Ghana

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

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