Geographical Accessibility to District Hospitals/Medical Laboratories for Comprehensive Antenatal Point-of-Care Diagnostic Services in the Upper East Region, Ghana

EClinicalMedicine, Volume 13, Year 2019

Ukuhunyushwa

Background: Access to referral healthcare facilities from primary healthcare (PHC) clinics for diagnostic services is critical for improving maternal health outcomes. We described the geographical distribution and accessibility to district hospitals/medical laboratories for comprehensive antenatal point-of-care (POC) diagnostic services in the Upper East region (UER), Ghana. Methods: We assembled detailed spatial data on 100 participated PHC clinics in our previous survey, their nearest referral district hospitals/medical laboratories, and landscape features influencing journeys in the UER. These were used in a geospatial model to estimate actual distance and travel time from a PHC facility to the nearest referral health facility for antenatal POC diagnostic services. Spatial distribution of the facilities was determined using spatial auto-correlation tool run in ArcMap 10.4.1. We employed Stata V14 for all other analysis. Findings: Of the 100 PHC clinics included in the analysis, only 15% were located less than 10 km to their nearest referral health facilities. The mean distance ± standard deviation from a PHC clinic to the nearest referral district hospital/medical laboratory for comprehensive antenatal POC diagnostic services was 7.0 km ± 4.9. Whilst the mean travel time using a motorized tricycle speed of 20 km/h to the nearest referral health facility for comprehensive antenatal POC diagnostic was 14.0 min ± 8.8. The spatial auto-correlation results for the PHC clinics suggested that the PHC clinics were spatially distributed at random rather than clustered (MI = 0.01, z-score = 0.33, p = 0.74). Whereas the spatial distribution of the referral health facilities suggested that the hospitals or medical laboratories were spatially dispersed (MI = − 0.69 z-score = − 2.05, p = 0.04). Interpretation: Although there is moderate geographical accessibility to district hospitals/medical laboratories for comprehensive antenatal diagnostic services in the UER, targeted improvement of POC diagnostic services in PHC clinics is recommended for improved maternal healthcare. Funding: University of KwaZulu-Natal, College of Health Sciences Research Scholarship. We developed models and algorithms for measuring geographical accessibility of POC diagnostic services from rural clinics to referral health facilities. This current study is a follow-up to a prior cross-sectional survey of 100 randomly selected PHC clinics in the UER which assessed the accessibility to pregnancy-related POC diagnostic tests in UER’s PHC clinics, Ghana [7]. The sampling strategy used for the selection of the participated PHC clinics in this study its rationale is described and in published elsewhere [7]. We collected data on the nearest referral facilities, transportation options, duration of obtaining test results from expectant mothers, and electronic options for communicating test results using a survey tool (Supplementary file 2). We also obtained the geo-located data on PHC clinics and their referral facilities for comprehensive ANC POC diagnostic services from the Upper East Regional Health Directorate. Thereafter, we applied the world geodetic system (WGS) Zone 30 degrees’ North coordinate system to all spatial data to allow for results of spatial processes in a preferred unit of meters. Landscape or topographic data included roads, rivers and a digital elevation model (DEM) of the rivers and high points/elevation. We then calibrated high points in the districts as barriers. Based on information gathered on the most commonly used public transport option from PHC clinics to the referral facilities in the region, we estimated travel time via road, paths and tracks using a motorized tricycle popularly known as “Motor king” or “Mahama can do”. The estimation involved recalibrating travel time per pixel (10 m by 10 m grid) for both roads and paths. This ultimately helped to estimate travel distance and time from rural clinics to referral facilities for all the districts in the region. Geo-located data on referral facilities, which also included attribute data on the type of diagnostic tests offered at the referral facility, were extracted from the survey prior to this study [7]. We geo-referenced all data that lacked a coordinate system and loaded it in Google Earth to determine their positions. This enabled us to identify PHC clinics that fell outside the boundary of their districts and the region. The data was reconciled and imported to ArcGIS 10.4 in the point shape file format. We captured data on digitized and geo-referenced road networks and paths as well as features such as rivers and lakes. We obtained the DEM of the region, which assisted in identifying natural barriers such as hills and valleys as well as undulating land that would inform the decision on estimating travel distance and time. All the above data was obtained from Adu Manu Kwame (AMK) Consultancy. We juxtaposed these data with the data obtained from the University of Ghana Remote Sensing and Geographic Information Systems laboratory to validate accuracy. Following this, we reclassified DEM into highlands (more than 200 m high) and flatlands (between 119 m and 200 m high). This was determined by the DEM data which showed that the highest point in the region was about 470 m, while the lowest point was identified as 119 m. A model for estimating cost was accomplished using a sequence of algorithms with the final output resulting from the cost distance tool in ArcGIS 10.4. Cost distance calculates the shortest time to a source based on a cost dataset. To realize this, we designed a cost surface algorithm with the following parameters: a grid cell with the size of 10 m was assigned to the spatial features and values were then assigned to the predetermined grids. We assigned roads low values because traveling on roads is faster than travel via paths or impediments. Assigning of values to spatial features was done via the map algebra tool and, since cost distance requires the cost surface dataset and the source, the calibrated spatial dataset served as the cost surface dataset and referral facilities served as the source for calculating the cost distance. The output is a map showing the shortest travel time (cell by cell) from any point in the map to referral facilities in the region. Algorithms which allowed for carrying out conversion of data from vector to raster, map algebra (cost surface models) and cost distance were developed using Python 2.7. All the algorithms were consolidated into a single algorithm. Based on the earlier information on transportation options for longer distances (motorized tricycle), we pegged the travel speed at 20 km/h. This served as a guide for determining travel time from each PHC clinic to the referral facility. Fig. 1 shows the flow diagram for the procedures undertaken to arrive at the results of this study. Process flow diagram for the procedures undertaken to arrive at distance and travel time. The primary outcome of this study was: geographical accessibility to the nearest referral district hospital/medical laboratory for comprehensive ANC POC diagnostic services in the UER of Ghana. Geographical accessibility was measured as: ≤ 5 km = high geographical accessibility; < 5 km to 10 km was considered as moderate geographical accessibility, and > 10 km was considered to low geographical accessibility. To determine the spatial distribution/measure how close the PHC clinics and their nearest referral district hospital/medical laboratory for a diagnostic test were, a spatial autocorrelation tool or Moran’s Index (MI) [23] was run in ArcMap 10.4.1. MI, Z-scores, and p-values for both PHC clinics and the referral facilities were reported. MI value greater than zero (MI > 0) was interpreted as spatially clustered, and MI value less than zero (MI < 0) was considered as spatially dispersed and null MI score or equivalent to zero (< 0.5), was classified as spatially random or no spatial auto-correlation. MI value of 0, or very close to 0, was considered as spatial random distribution. Estimated distances and travel times were processed in Microsoft Excel and imported into Stata statistical software, version 14, for analyses. Means, standard deviations (SD), and 95% confidence interval (CI) were generated for distance and travel time. This study was funded by the University of KwaZulu-Natal, College of Health Sciences Research Scholarship. Funders played no role in data collection, analysis, and preparation of the manuscript.

I-AI Digest

Study Justification:

– Access to referral healthcare facilities for diagnostic services is crucial for improving maternal health outcomes.
– This study aims to assess the geographical distribution and accessibility of district hospitals/medical laboratories for comprehensive antenatal point-of-care (POC) diagnostic services in the Upper East region of Ghana.
– By understanding the current accessibility, targeted improvements can be recommended to enhance maternal healthcare.

Study Highlights:

– The study used spatial data on 100 primary healthcare (PHC) clinics and their nearest referral district hospitals/medical laboratories.
– Geospatial models were developed to estimate actual distance and travel time from PHC facilities to the nearest referral health facility for antenatal POC diagnostic services.
– Findings showed that only 15% of PHC clinics were located within 10 km of their nearest referral health facilities.
– The mean distance from a PHC clinic to the nearest referral facility was 7.0 km, and the mean travel time was 14.0 minutes using a motorized tricycle speed of 20 km/h.
– Spatial analysis indicated that PHC clinics were randomly distributed, while referral health facilities were spatially dispersed.

Recommendations:

– Targeted improvement of POC diagnostic services in PHC clinics is recommended to enhance maternal healthcare.
– This could involve increasing the number of diagnostic services available at PHC clinics or improving transportation options for expectant mothers.

Key Role Players:

– Ministry of Health, Ghana
– Upper East Regional Health Directorate
– District Health Directorates
– Primary Healthcare Clinic Staff
– Referral Hospital/Medical Laboratory Staff
– Transportation Authorities

Cost Items for Planning Recommendations:

– Equipment and supplies for expanding diagnostic services at PHC clinics
– Training and capacity building for PHC clinic staff
– Infrastructure improvements at PHC clinics
– Transportation infrastructure improvements
– Communication systems for transmitting test results
– Monitoring and evaluation of the implemented recommendations
Please note that the cost items provided are general categories and not actual cost estimates. The specific costs would depend on the scope and scale of the recommended improvements.

Amandla Obufakazi

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 spatial data and employs geospatial models to estimate actual distance and travel time from primary healthcare clinics to referral health facilities for antenatal point-of-care diagnostic services. The findings indicate that only 15% of the clinics were located within 10 km of their nearest referral health facility, with a mean distance of 7.0 km. The spatial auto-correlation analysis suggests that the clinics were spatially distributed at random, while the referral health facilities were spatially dispersed. The interpretation highlights the need for targeted improvement of point-of-care diagnostic services in primary healthcare clinics for improved maternal healthcare. However, the abstract could be improved by providing more specific details about the methods used, such as the geospatial model and the Stata V14 analysis. Additionally, it would be helpful to include information about the sample size and any limitations of the study. To improve the evidence, the authors could consider conducting a larger sample size study to increase the generalizability of the findings. They could also provide more information about the landscape features influencing journeys and how they were incorporated into the geospatial model. Finally, it would be beneficial to include information about the potential implications of the findings and any recommendations for policy or practice.

Abstract

We developed models and algorithms for measuring geographical accessibility of POC diagnostic services from rural clinics to referral health facilities. This current study is a follow-up to a prior cross-sectional survey of 100 randomly selected PHC clinics in the UER which assessed the accessibility to pregnancy-related POC diagnostic tests in UER’s PHC clinics, Ghana [7]. The sampling strategy used for the selection of the participated PHC clinics in this study its rationale is described and in published elsewhere [7]. We collected data on the nearest referral facilities, transportation options, duration of obtaining test results from expectant mothers, and electronic options for communicating test results using a survey tool (Supplementary file 2). We also obtained the geo-located data on PHC clinics and their referral facilities for comprehensive ANC POC diagnostic services from the Upper East Regional Health Directorate. Thereafter, we applied the world geodetic system (WGS) Zone 30 degrees’ North coordinate system to all spatial data to allow for results of spatial processes in a preferred unit of meters. Landscape or topographic data included roads, rivers and a digital elevation model (DEM) of the rivers and high points/elevation. We then calibrated high points in the districts as barriers. Based on information gathered on the most commonly used public transport option from PHC clinics to the referral facilities in the region, we estimated travel time via road, paths and tracks using a motorized tricycle popularly known as “Motor king” or “Mahama can do”. The estimation involved recalibrating travel time per pixel (10 m by 10 m grid) for both roads and paths. This ultimately helped to estimate travel distance and time from rural clinics to referral facilities for all the districts in the region. Geo-located data on referral facilities, which also included attribute data on the type of diagnostic tests offered at the referral facility, were extracted from the survey prior to this study [7]. We geo-referenced all data that lacked a coordinate system and loaded it in Google Earth to determine their positions. This enabled us to identify PHC clinics that fell outside the boundary of their districts and the region. The data was reconciled and imported to ArcGIS 10.4 in the point shape file format. We captured data on digitized and geo-referenced road networks and paths as well as features such as rivers and lakes. We obtained the DEM of the region, which assisted in identifying natural barriers such as hills and valleys as well as undulating land that would inform the decision on estimating travel distance and time. All the above data was obtained from Adu Manu Kwame (AMK) Consultancy. We juxtaposed these data with the data obtained from the University of Ghana Remote Sensing and Geographic Information Systems laboratory to validate accuracy. Following this, we reclassified DEM into highlands (more than 200 m high) and flatlands (between 119 m and 200 m high). This was determined by the DEM data which showed that the highest point in the region was about 470 m, while the lowest point was identified as 119 m. A model for estimating cost was accomplished using a sequence of algorithms with the final output resulting from the cost distance tool in ArcGIS 10.4. Cost distance calculates the shortest time to a source based on a cost dataset. To realize this, we designed a cost surface algorithm with the following parameters: a grid cell with the size of 10 m was assigned to the spatial features and values were then assigned to the predetermined grids. We assigned roads low values because traveling on roads is faster than travel via paths or impediments. Assigning of values to spatial features was done via the map algebra tool and, since cost distance requires the cost surface dataset and the source, the calibrated spatial dataset served as the cost surface dataset and referral facilities served as the source for calculating the cost distance. The output is a map showing the shortest travel time (cell by cell) from any point in the map to referral facilities in the region. Algorithms which allowed for carrying out conversion of data from vector to raster, map algebra (cost surface models) and cost distance were developed using Python 2.7. All the algorithms were consolidated into a single algorithm. Based on the earlier information on transportation options for longer distances (motorized tricycle), we pegged the travel speed at 20 km/h. This served as a guide for determining travel time from each PHC clinic to the referral facility. Fig. 1 shows the flow diagram for the procedures undertaken to arrive at the results of this study. Process flow diagram for the procedures undertaken to arrive at distance and travel time. The primary outcome of this study was: geographical accessibility to the nearest referral district hospital/medical laboratory for comprehensive ANC POC diagnostic services in the UER of Ghana. Geographical accessibility was measured as: ≤ 5 km = high geographical accessibility;  10 km was considered to low geographical accessibility. To determine the spatial distribution/measure how close the PHC clinics and their nearest referral district hospital/medical laboratory for a diagnostic test were, a spatial autocorrelation tool or Moran’s Index (MI) [23] was run in ArcMap 10.4.1. MI, Z-scores, and p-values for both PHC clinics and the referral facilities were reported. MI value greater than zero (MI > 0) was interpreted as spatially clustered, and MI value less than zero (MI < 0) was considered as spatially dispersed and null MI score or equivalent to zero (< 0.5), was classified as spatially random or no spatial auto-correlation. MI value of 0, or very close to 0, was considered as spatial random distribution. Estimated distances and travel times were processed in Microsoft Excel and imported into Stata statistical software, version 14, for analyses. Means, standard deviations (SD), and 95% confidence interval (CI) were generated for distance and travel time. This study was funded by the University of KwaZulu-Natal, College of Health Sciences Research Scholarship. Funders played no role in data collection, analysis, and preparation of the manuscript.

Izinto zokwakha

Emisha

The recommendation to improve access to maternal health in the Upper East Region, Ghana is to target the improvement of point-of-care (POC) diagnostic services in primary healthcare (PHC) clinics. This recommendation is based on a study that assessed the geographical accessibility of district hospitals/medical laboratories for comprehensive antenatal POC diagnostic services in the region.

The study found that only 15% of the PHC clinics were located within 10 km of their nearest referral health facilities. The mean distance from a PHC clinic to the nearest referral facility was 7.0 km, and the mean travel time using a motorized tricycle was 14.0 minutes. The spatial distribution analysis showed that the PHC clinics were randomly distributed, while the referral health facilities were dispersed.

To improve access to maternal health, it is recommended to focus on enhancing POC diagnostic services in PHC clinics. This could involve providing necessary equipment, training healthcare providers, and ensuring the availability of diagnostic tests. By strengthening the diagnostic capabilities of PHC clinics, pregnant women can receive timely and accurate diagnostic services closer to their communities, reducing the need for long-distance travel to referral facilities.

It is important to note that this recommendation is based on the specific context of the Upper East Region, Ghana, and may need to be adapted to suit the local healthcare system and resources available in other regions or countries.

AI Innovations Description

The recommendation to improve access to maternal health in the Upper East Region, Ghana is to target the improvement of point-of-care (POC) diagnostic services in primary healthcare (PHC) clinics. This recommendation is based on the findings of a study that assessed the geographical accessibility of district hospitals/medical laboratories for comprehensive antenatal POC diagnostic services in the region.

The study found that only 15% of the PHC clinics were located within 10 km of their nearest referral health facilities. The mean distance from a PHC clinic to the nearest referral facility was 7.0 km, and the mean travel time using a motorized tricycle was 14.0 minutes. The spatial distribution analysis showed that the PHC clinics were randomly distributed, while the referral health facilities were dispersed.

To improve access to maternal health, it is recommended to focus on enhancing POC diagnostic services in PHC clinics. This could involve providing necessary equipment, training healthcare providers, and ensuring the availability of diagnostic tests. By strengthening the diagnostic capabilities of PHC clinics, pregnant women can receive timely and accurate diagnostic services closer to their communities, reducing the need for long-distance travel to referral facilities.

It is important to note that this recommendation is based on the specific context of the Upper East Region, Ghana, and may need to be adapted to suit the local healthcare system and resources available in other regions or countries.

AI Innovations Methodology

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

1. Identify the target population: Determine the population that will benefit from the improved access to maternal health services. This could include pregnant women in the Upper East Region, Ghana.

2. Define the indicators: Determine the key indicators that will be used to measure the impact of the recommendations. This could include indicators such as the number of pregnant women receiving POC diagnostic services in PHC clinics, the reduction in travel time and distance to referral facilities, and the improvement in timely access to diagnostic test results.

3. Collect baseline data: Gather data on the current state of access to maternal health services in the region. This could include data on the number of PHC clinics, their locations, the distance and travel time to the nearest referral facilities, and the availability and quality of POC diagnostic services.

4. Implement the recommendations: Implement the recommended interventions to improve access to maternal health services. This could involve providing necessary equipment for POC diagnostic services, training healthcare providers on conducting diagnostic tests, and ensuring the availability of diagnostic tests in PHC clinics.

5. Monitor and evaluate: Continuously monitor and evaluate the impact of the interventions. Collect data on the number of pregnant women accessing POC diagnostic services in PHC clinics, the travel time and distance to referral facilities, and the timeliness of receiving test results. Compare this data to the baseline data collected in step 3 to assess the impact of the recommendations.

6. Analyze the data: Analyze the data collected to determine the extent to which the recommendations have improved access to maternal health services. Calculate indicators such as the percentage increase in the number of pregnant women accessing POC diagnostic services, the reduction in travel time and distance, and the improvement in timeliness of receiving test results.

7. Communicate the findings: Present the findings of the analysis to relevant stakeholders, such as healthcare providers, policymakers, and funding agencies. Highlight the positive impact of the recommendations on improving access to maternal health services and make recommendations for further improvements if necessary.

By following this methodology, it will be possible to simulate the impact of the main recommendations on improving access to maternal health in the Upper East Region, Ghana. This will help inform future decision-making and resource allocation to further enhance maternal healthcare in the region.

Umbhali

Dima Health

Statistics:

Citations: 9

Identifiers:

DOI: 10.1016/j.eclinm.2019.06.015

Research Areas:

Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Ghana

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

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