*Article* **Estimating the Spatial Accessibility to Blood Group and Rhesus Type Point-of-Care Testing for Maternal Healthcare in Ghana**

#### **Desmond Kuupiel 1,2,\*, Kwame M. Adu 3, Vitalis Bawontuo 2,4, Duncan A. Adogboba <sup>5</sup> and Tivani P. Mashamba-Thompson <sup>1</sup>**


Received: 29 September 2019; Accepted: 29 October 2019; Published: 5 November 2019

**Abstract:** Background: In Ghana, a blood group and rhesus type test is one of the essential recommended screening tests for women during antenatal care since blood transfusion is a key intervention for haemorrhage. We estimated the spatial accessibility to health facilities for blood group and type point-of-care (POC) testing in the Upper East Region (UER), Ghana. Methods: We assembled the attributes and spatial data of hospitals, clinics, and medical laboratories providing blood group and rhesus type POC testing in the UER. We also obtained the spatial data of all the 131 towns, and 94 health centres and community-based health planning and services (CHPS) compounds providing maternal healthcare in the region. We further obtained the topographical data of the region, and travel time estimated using an assumed tricycle speed of 20 km/h. We employed ArcGIS 10.5 to estimate the distance and travel time and locations with poor spatial access identified for priority improvement. Findings: In all, blood group and rhesus type POC testing was available in 18 health facilities comprising eight public hospitals and six health centres, one private hospital, and three medical laboratories used as referral points by neighbouring health centres and CHPS compounds without the service. Of the 94 health centres and CHPS compounds, 51.1% (48/94) and 66.4% (87/131) of the towns were within a 10 km range to a facility providing blood group and rhesus type testing service. The estimated mean distance to a health facility for blood group and rhesus POC testing was 8.9 ± 4.1 km, whilst the mean travel time was 17.8 ± 8.3 min. Builsa South district recorded the longest mean distance (25.6 ± 7.4 km), whilst Bongo district recorded the shortest (3.1 ± 1.9 km). The spatial autocorrelation results showed the health facilities providing blood group and rhesus type POC testing were randomly distributed in the region (Moran Index = 0.29; z-score = 1.37; *p* = 0.17). Conclusion: This study enabled the identification of district variations in spatial accessibility to blood group and rhesus type POC testing in the region for policy decisions. We urge the health authorities in Ghana to evaluate and implement recommended POC tests such as slide agglutination tests for blood group and rhesus type testing in resource-limited settings.

**Keywords:** spatial accessibility; blood group; rhesus type; point-of-care testing; maternal healthcare; Upper East Region; Ghana

#### **1. Introduction**

Since 1990, the world has made significant progress in reducing maternal mortality [1]. Despite the progress made, recent evidence shows that every eleven seconds a pregnant woman dies somewhere in the world according to the World Health Organisation (WHO) [1]. In 2017, approximately 295,000 women died from mostly preventable causes during and subsequent to pregnancy and childbirth; 94% of these mortalities occurred in resource-limited settings [2]. Sub-Saharan Africa (SSA) and Southern Asia alone accounted for 86% of all maternal deaths in the world [2]. The WHO estimates show that in 2017, SSA countries including Ghana alone accounted for almost two-thirds (196,000) of maternal deaths compared to Southern Asia which accounted for approximately one-fifth (58,000) [2]. Haemorrhage remains a major direct cause of maternal death and together with hypertensive disorders and sepsis accounts for more than 50% maternal deaths globally [3]. A recent global systematic review by Say and colleagues revealed that haemorrhage accounted for about 27.1% ahead of hypertensive disorders with 14%, and sepsis (10.7%) of the total 60,799 maternal deaths retrieved from 23 eligible studies published from 2003 to 2012 [3]. Blood transfusion service is one of the critical interventions for the management of haemorrhage [4].

Blood transfusion saves lives and improves health, but many patients, including pregnant women or women during delivery, requiring transfusion do not have timely access to safe blood [5]. A blood transfusion may become crucial at any time in both urban and rural communities [5]. Improving access to safe blood transfusion relies partly on the ability of the health facility to effectively perform point-of-care (POC) testing for blood group and rhesus type. Generally, access to healthcare services including blood group and rhesus type POC testing may also be influenced by several factors such as the availability of laboratory services, human resource capacity, availability of POC tests and supply chain management of the diagnostic tests, cost, wealth, quality of care, occupation, cultural practices, education, and the location of the service [6–9]. The nature of the road and networks, type of transport systems, topology, land use, building use, traffic condition and population density, and seasonal variations may also influence travel time to healthcare facilities, particularly in rural areas [7,10]. In some settings, these factors may interact in a complex way and geographical access in terms of distance and travel time may be insignificant [7]. Nonetheless, where the availability of POC testing for blood group and rhesus type is poor and laboratory services are sparse, the geographical location of the service becomes a key barrier to the service, particularly for the rural populations and, hence, very essential [7,11–13].

In Ghana, evidence shows that the maternal mortality ratio presently stands at 319 per 100,000 live births [1,13–15]. Haemorrhage is one of the direct causes of maternal deaths in Ghana and among the top five causes [16,17]. The Der et al. study in 2013 identified haemorrhage as the topmost cause of maternal mortality in Ghana accounting for 21.8% of all deaths with abortion, hypertensive disorders, infections, and ectopic gestation accounting for 20.7%, 19.4%, 9.1%, and 8.7%, respectively [17]. Many interventions including the implementation of a free maternal healthcare policy since 2003, provision of emergency obstetric care, expansions of health infrastructure, increased training of midwives and posting them to underserved communities, and ongoing investment in primary healthcare (PHC) facilities such as health centres and community-based health planning and service (CHPS) facilities are meant to reduce maternal deaths in the country. Pregnant women in Ghana are also expected to undertake a blood type screening test as part of the wide range of healthcare services rendered to them during the first antenatal care (ANC) visit irrespective of the level of care [18,19]. Like most SSA countries, Ghana's PHC facilities often do not have laboratories to perform blood type screening tests for patients and donors as well as facilities for safe storage of blood. In 2018, a cross-sectional study was conducted aimed to investigate the availability and use of pregnancy-related point-of-care (POC) tests in Ghana's PHC clinics [20]. Of the 100 participating PHC clinics in the survey, blood group and rhesus type testing was available in only six clinics with some form of laboratory services in the Upper East Region (UER) [20]. Whilst the average ANC clinic attendance per month was shown to be 65 pregnant women with a minimum of 30 and a maximum of 360 [20]. The results of the survey also

revealed that out of the 94 clinics without blood group and rhesus type testing, 89 demonstrated the need for it in their clinics [20]. The findings of the survey revealed blood group and rhesus type testing is still a laboratory-based test in Ghana performed by trained laboratory professionals and, hence, may not be accessible to all who need it [20].

Despite this, to date, no study has measured the spatial accessibility in terms of distance and travel time to health facilities providing blood type testing services in Ghana, especially in the UER. Knowledge of the distance and travel time to the nearest health facility for a blood type screening test is potentially essential to help the Government of Ghana implement POC testing services in rural health facilities located in geographical settings with poor access as recommended by the WHO and bring healthcare closer to where people live and work [21]. We, therefore, investigated the spatial accessibility to blood group and rhesus type POC testing during ANC in the UER of Ghana.

#### **2. Methods**

#### *2.1. Overview*

This is a follow-up on our previously published cross-sectional study which investigated the availability and use of pregnancy-related POC tests in the UER utilising a hundred PHC clinics (health centres and CHPS compounds) randomly selected from a total of 356 clinics from all the districts in the region [20]. The sampling strategy used to select the 100 PHC clinics as well as the study area (UER) has been adequately described in the previously published survey [20]. The survey revealed that of the 100 PHC clinics, blood type screening test was available only in six clinics, and all six were health centres with some form of laboratory services [20]. To measure the spatial accessibility to health facilities where blood type screening testing is available in the regions, the cost-distance algorithm was applied using the ArcGIS desktop software. The flow diagram (Figure 1) illustrates the data, methods, and models used. All the attributes and spatial data used for this study were obtained in 2018.

**Figure 1.** A flow diagram illustrating the data, methods, and models used. PHC—primary healthcare.

We extracted the attribute data on all the 100 PHC clinics and health facilities where expectant mothers (who require blood group and rhesus status testing) were referred for blood type screening tests during ANC. These data which were obtained in text data format were loaded into the ArcGIS 10.5 software and transformed into a shapefile to allow for performing spatial analysis using ArcGIS. The topographic data gathered for this current study included roads, rivers, and the slope obtained as the digital elevation model (DEM). The topographical data were obtained for the whole of West Africa and the UER processed as a subset of Ghana. Road data were obtained to inform travel routes and, in view of this, types of roads were appended as attribute data to the spatial data to inform the potential speed users are likely to experience on each road type which may as well greatly inform travel distance and time. The DEM for the West Africa region was obtained to inform the slope of every location in the UER. This was necessary because identifying valleys and hills was key to determining which areas serve as barriers and are inaccessible to users. The DEM dataset was obtained from Adu Manu Kwame (AMK) Consultancy and compared with the data obtained from the University of Ghana Remote Sensing and Geographic Information Systems Laboratory for accuracy. We then reclassified the slope data into highlands (more than 200 m high) and flatlands (between 119 and 200 m high) as informed by the DEM data which showed that the highest point in the UER was about 470 m, and 119 m was the lowest point.

#### *2.2. Spatial Data of Health Facilities*

To realise the objectives of this study, the geo-location data of all the 100 PHC clinics and health facilities such as public hospitals or clinics offering blood type testing services as well as private medical laboratories or hospital/clinics being utilised as referral points for blood type testing services were obtained from the Regional Health Directorate of the Ghana Health Service, UER. We also obtained the spatial data of all the 131 towns in the region using the global positioning system. To accurately map all the latitude and longitude to their relative location on the earth, the World Geodetic System 30 North coordinate system was applied to the entire dataset because Ghana falls within this zone.

#### *2.3. Geospatial Analysis*

#### Developing a Model for Estimating the Travel Time

As a key aspect of this study, the model for determining the travel time was carefully developed taking into consideration all the datasets. Using the PyScripter integrated development environment and relying on the Python capabilities of ArcGIS 5.0, a model was developed to calculate the travel time and for data transformation. The cost distance model which calculates the shortest time to a source based on a cost dataset was used to determine distance. A motorised tricycle was identified as the commonly used public transport for travel within UER, hence, travel time was estimated via road and via paths and tracks using an assumed motorised tricycle speed of 20 km per hour. We recalibrated travel time per pixel (10 m × 10 m grid) for both roads and paths to enable estimation of travel time from PHC clinics where blood type testing services are not available to the nearest hospital, clinic, or medical laboratory for all the districts in the region. Although travel time can be influenced by many factors, we chose to estimate travel time via roads and paths because they are the commonly used routes in the region. Likewise, the motorised transport system was chosen because we found it was the most used public mode of transport for journeys within the region. The model and procedure used to approximate the travel time for this current study have been published in our previous studies focusing on geographical access to tuberculosis diagnostic services and comprehensive ANC POC diagnostic services [12,13]. Supplementary file 1 presents a detailed description of the procedure.

#### *2.4. Bu*ff*er*

We employed the geospatial analysis proximity tool (Buffer) to identify towns and PHC clinics without a blood group and rhesus type testing service located within a 10 km radius, and those located beyond 10 km to a health facility providing the service. We additionally estimated the proximity of all the 131 towns to the nearest health facility providing blood group and rhesus type testing service. Evidence shows that access to healthcare elsewhere more than 10 km away is associated with higher risks of poor health outcomes [22]. 'Buffer' in geographic information systems (GIS) refers to a boundary defined by specific units that surround a source or feature. For the purposes of this study, the point buffer was employed because the origin feature in this study which is health facilities providing blood type testing service is a point feature (vector dataset).

To assess the accuracy, we created a set of random points from the ground truth data and compared that to the classified data in a confusion matrix using three geoprocessing tools: create accuracy assessment points, update accuracy assessment points, and compute confusion matrix.

#### *2.5. Spatial Autocorrelation*

We utilised the spatial autocorrelation tool in ArcMap 10.5 to determine the spatial distribution of the health facilities providing blood type testing services in the region. Spatial autocorrelation mirrors the first law of geography which states that "everything is related to everything else, but near things are more related than distant things" [23]. In spatial autocorrelation, the null hypothesis of the Moran's Index statistic states that the feature being measured is distributed randomly, however, when the *p*-value obtained from running the spatial analyst tool proves to be statistically significant, then the null hypothesis can be rejected [24]. Guided by this, we considered a positive correlation to mean similar values clustered together while negative correlation is representative of different values clustered in a location, and zero means no correlation.

#### *2.6. Ethics Approval*

This study was approved by the Navrongo Health Research Centre Institutional Review Board/Ghana Health Service (approval number: NHRCIRB291) on 8th January 2018 and the University of KwaZulu-Natal Biomedical Research Ethics Committee (approval number: BE565/17) on 12th February 2018

#### **3. Results**

#### *3.1. Characteristics of the Health Facilities Providing Blood Group and Rhesus Type POC Testing*

In all, blood group and rhesus type POC testing was available in 18 health facilities. Of the 18 health facilities, nine (50%) were hospitals and six (33.3%) health centres. The remaining three (16.7%) health facilities were private medical laboratories which were used as referral points by some of the PHC clinics without blood group and rhesus type POC testing service. Of the nine hospitals providing blood group and rhesus type POC testing services, the majority (77.8%) are owned and managed by the Ghana Health Service (GHS), 11.1% are owned and managed by the Christian Health Association of Ghana (CHAG), and 11.1% by private individuals. Similarly, 83.3% (5/6) of the sub-district health centres offering blood group and rhesus type POC testing are owned and managed by GHS, whilst one (16.7%) is owned by CHAG. All the 18 health facilities offering blood group and rhesus type POC testing services were distributed across nine out of the 13 districts in the region. Four (22.2%) each were in the Bongo district and Bolgatanga municipality; meanwhile, there was no health facility providing blood group and rhesus type POC testing services in Builsa South, Nabdam, Binduri, and Pusiga districts (Figure 2).

**Figure 2.** Map showing the geographical location, facility type, and ownership of health facilities providing blood type testing services in the Upper East Region (UER). CHAG—Christian Health Association of Ghana; GHS—Ghana Health Services.

#### *3.2. Spatial Distribution of Health Facilities Providing Blood Grouping and Rhesus Type Testing Services in the UER*

To determine the spatial distribution of health facilities providing blood grouping and rhesus type testing services in the region, spatial autocorrelation analysis was conducted. The results showed a positive spatial autocorrelation (Moran Index = 0.29; z-score = 1.37; *p* = 0.17) suggesting that health facilities providing blood group and rhesus type testing services were randomly distributed spatially in the region (Figure 3).

**Figure 3.** Spatial autocorrelation map showing the spatial distribution of health facilities providing blood group and rhesus type testing in the UER.

#### *3.3. Spatial Accessibility to Blood Group and Rhesus Type Testing in the UER*

We estimated the proximity (distance) of the 94 PHC clinics without blood group and rhesus type testing service from the previous cross-sectional study to the nearest health facility providing blood group and rhesus type testing services in the region. The results showed that 48 (51.1%) of the PHC clinics without blood group and rhesus type testing service were within 10 km reach to the nearest health facility offering the services. All the participating PHC clinics without blood group and rhesus type testing service from Bolgatanga Municipal and Bongo district were within 10 km range to the closest facility providing the service. However, none of the PHC clinics included in this study from the Builsa South district were within 10 km reach of any of the health facilities providing blood group and rhesus type testing service (Figure 4).

**Figure 4.** Map showing the distribution of the PHC clinics and distance within 10 km from the nearest health facility providing blood group and rhesus type testing services in the UER.

Based on the PHC clinics included in this analysis, the mean (standard deviation (SD)) distance from a PHC clinic without blood group and rhesus type testing service to the closest facility providing the service in the UER was 12.6 ± 5.2 km. The longest mean distance was recorded in the Builsa South (32.2 ± 13.2 km) district, whilst the shortest (3.3 ± 1.4 km) was in the Bongo district. The results also show that the mean travel time from a PHC clinic without blood group and rhesus type testing service to the closest facility providing the service in the UER was 37.7 ± 15.4 min. Again, Builsa South district recorded the longest travel time (96 ± 39.4 min), whilst Bongo district recorded the shortest travel time (9.9 ± 4.1 min) to the nearest facility offering blood group and rhesus type testing service (Figure 5).

This study also estimated the proximity of all the 131 towns to their nearest health facilities providing blood group and rhesus type testing services. The results showed that 87 (66.4%) of the towns were within a 10 km radius (less than 30 min travel time) to a health facility providing blood group and rhesus type testing service. Twenty-five (19.1%) of the 131 towns were located between 10 and 15 km to a health facility offering blood group and rhesus type testing service, whilst 15 (11.5%) towns were located at more than 15–25 km reach. Meanwhile, 4 (3.1%) towns in Builsa South district were found to be located more than 25 km to the nearest health facility providing blood group and rhesus type testing service (Figure 6). Supplementary file 2 provides the distance/travel time categorisation of the towns and their names.

**Figure 5.** A bar chart depicting the mean distance and travel time from PHC clinics to the nearest health facility providing blood group and rhesus type testing services per district in the UER.

**Figure 6.** Map showing the distribution of distance (km) and travel time (minutes) from towns to health facilities providing blood group and rhesus type testing services in the UER.

This study's findings further showed that the mean distance and travel time ± SD from all locations in the 131 towns of the UER to a health facility providing blood group and rhesus type testing service in the region was 8.9 ± 4.1 km and the mean travel time was 17.8 ± 8.3 min. Builsa South and Bongo districts once again recorded the longest mean distance (25.6 ± 7.4 km) and the shortest (3.1 ± 1.9 km), respectively, to a health facility providing blood group and rhesus testing service in the region. Similarly, the mean travel time from all locations in the 131 towns of the UER to a health facility providing blood group and rhesus type testing services in the region was estimated at 17.8 ± 8.3 min with Builsa South district recording the longest travel time (51.3 ± 14.8 min) and the shortest time recorded in Bongo district (6.2 ± 3.9 min) (Figure 7).

**Figure 7.** A bar chart depicting the mean distance and travel time from all 131 towns in the UER to a health facility providing blood group and rhesus type testing services per district in the region.

#### **4. Discussion**

This study estimated the spatial accessibility (distance and travel time) to health facilities for blood group and rhesus type testing during ANC in the UER of Ghana. The results showed the about 51.1% of the PHC clinics without blood group and rhesus type testing and 66.4% of the towns in the region were within 10 km range to a facility providing the service. The mean distance to a health facility providing blood group and rhesus type testing service in the region was 8.9 ± 4.1 km, whilst the mean travel time was 17.8 ± 8.3 min using a motorised tricycle speed of 20 km/hour. The results further showed that the spatial autocorrelation of the health facilities providing blood group and rhesus type testing services were randomly distributed in the region.

We found the majority of the PHC clinics without blood group and rhesus type testing and the towns in the UER were within 10 km proximity to the nearest health facility providing the service. Although this finding is fairly good, at the same time, it depicts that almost half (48.9%) of the PHC clinics are without blood group and rhesus type testing facilities; moreover, about 33.6% of the towns are still geo-located beyond 10 km to a facility providing the services. This could also result in low utilisation of blood group and rhesus type testing services, overcrowding at the health facilities, and increased waiting time for test results. Evidence shows that people tend to limit the use of health services to facilities closer to them [25]. Ferguson and colleagues also demonstrated that having

diagnostic technologies closer to populations streamlines critical care paths [26]. Hence, these results have revealed the need to equip the PHC clinics to enable them to provide POC testing for blood group and rhesus type tests in the region. Contrary to this current study finding, a previous study in the UER reported fewer PHC clinics were within 10 km reach to either a hospital or medical laboratory for a one-stop ANC POC diagnostic service [13].

We also found the mean distance to blood group and rhesus type POC testing in the region to be 8.9 ± 4.1 km and a mean travel time of 17.8 ± 8.3 min using a motorised tricycle speed of 20 km/hour. These findings suggest moderate spatial accessibility to blood group and rhesus type testing. It is possible that a substantial proportion of women of reproductive age live beyond 8.9 km from nearest health facility providing the service. The distance of 8.9 km and can take several hours for a pregnant woman to walk in the case where she is unable to afford the services of a motorised tricycle. Even when a pregnant woman can afford the services of a motorised tricycle but considering the bad state of the roads in the region, particularly in the rural areas, the speed of the tricycle may be less than the average 20 km/h used in our analysis. Therefore, the travel time to the nearest health facility may exceed the estimated travel time we found in some cases. In this case, it is possible that a significant number of referred pregnant women from PHC clinics may not go to the referral facilities for blood group and rhesus type testing during ANC and this potentially could become problematic when the need arises for urgent blood transfusion. Although we found no other study demonstrating evidence on spatial accessibility to health facilities for blood group and rhesus type testing, Gething and colleagues' study on geographical access to care at birth in Ghana reported longer journey times to emergency obstetric and neonatal care [7]. Likewise, a study in South Africa by Tanser and colleagues also demonstrated poor physical accessibility (travel time of 170 min) to healthcare [10].

We further found the spatial autocorrelation findings also demonstrated the health facilities providing blood group and rhesus type testing services were randomly distributed in the region. This suggests the health facilities were neither clustered nor dispersed in the UER. However, our analysis showed district-wise variation in spatial accessibility to public hospitals and clinics offering blood group and rhesus type POC testing services in the region. Nonetheless, we have highlighted worst-served areas for improvement by the health authorities to address the disparities revealed, particularly, in the Builsa South district. Similarly, our previous studies in the UER which assessed the geographical access to a comprehensive ANC and tuberculosis POC testing also found geographic variations of the health facilities with moderate accessibility to diagnostic services [12,13].

Knowledge of blood group and rhesus type is of paramount importance to prevent transfusionrelated complications particularly among pregnant mothers [27]. However, the findings revealed a low number of PHC facilities providing ABO and rhesus factor testing resulting in spatial variation of the service provision. Possible solutions to the geographical access and/or knowledge gap of pregnant mothers on ABO and rhesus factor type may include the implementation of POC testing for blood group and rhesus type in Ghana's PHC clinics in accordance with the WHO recommendation. The WHO recommends a slide agglutination test using capillary whole blood or venous whole blood to determine A, B, and O groups and Rh type in resource-limited settings to facilitate the screening of patients, donors, safe blood transfusion, and improve access to healthcare and outcomes [21]. This will enable every pregnant woman and blood donors to be screened during ANC services for safe blood transfusion when needed urgently. The implementation of POC testing for blood group and rhesus type in Ghana's PHC clinics may well reduce the distance and travel time to help improve access to healthcare generally and better maternal health outcomes in resource-limited settings. Additionally, the cost and risks of traveling long distances to access blood group and rhesus type testing services potentially will be reduced with a resultant increase in utilisation. Moreover, the implementation of POC testing of blood grouping and rhesus type for maternal healthcare may lead to a reduction of maternal deaths caused by haemorrhage, hence contributing to Ghana's achievement of Sustainable Development Goal 3.1 (less than 70 maternal deaths per 100,000 live births) by 2030. According to WHO, countries such as Belarus, Bangladesh, Cambodia, Kazakhstan, Malawi, Morocco, Mongolia,

Rwanda, Timor-Leste, and Zambia made substantial progress in reducing maternal mortality owing to the implementation of several interventions including their focus on improving the quality of care at the PHC level and universal health coverage [1].

Using geographical information systems (GIS) to inform the implementation of health services has been shown to be useful [28–30]. GIS effectively enables implementation of POC testing in health networks to streamline decision making at the POC [26]. Hence, GIS helps improve patient health outcomes, save time and money, and ensure that the health networks are sufficiently resourced to deliver needed health services to the population [26]. Despite these strengths, there are several limitations worth noting such as our inability to include non-spatial factors in the analysis. Non-spatial factors such as the income of patients [8], age, cultural practices, education, and others can also influence the utilisation of health services even if the service is very close to the individual [7]. Although the implementation of POC testing for blood group and rhesus type testing in PHC clinics has the potential to improve maternal health outcomes, this study did not assess the cost–benefit analysis and other challenges associated with the implementation. Nonetheless, knowledge of one's blood group and rhesus type and safe blood transfusion timely could save many lives including those of pregnant women before, during, and after delivery. The travel time estimation provided by this study was dependent on only one mode of transportation using an assumed speed which might be inaccurate. Moreover, this study was limited to only one region in Ghana, and, therefore, the findings may not necessarily apply to the remaining fifteen regions in the country. We therefore recommend future research to focus on areas such as the non-spatial factors and cost–benefit analysis of implementing POC testing for blood group and rhesus type in PHC clinics. We also recommend a similar study in the other fifteen regions of Ghana. Notwithstanding these limitations, this study has provided evidence-based information useful for policy decision-making for targeted improvement of POC testing for blood group and rhesus type testing in the UER. Since this study is the first, it may possibly stimulate more research using GIS to evaluate access to blood group and rhesus type testing services in other similar settings for improving healthcare.

#### **5. Conclusions**

A blood group and rhesus type test are a prerequisite for both blood recipients and donors prior to transfusion to prevent or reduce blood-transfusion-related complications. The current mean distance and travel time to health facilities for blood group and rhesus type testing service in the UER is estimated at 8.9 ± 4.1 km, whilst the mean travel time was 17.8 ± 8.3 min using a motorised tricycle speed of 20 km/hour. This distance and travel time possibly may reduce substantially if blood group and rhesus type POC testing services are implemented in PHC clinics, particularly in those districts with poor spatial access evidenced by this study. We recommend the establishment and implementation of an essential diagnostic list including POC tests for blood group and rhesus type test in Ghana in line with the WHO recommendation to help address diagnostic challenges in resource-limited settings.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2075-4418/9/4/175/s1, Supplementary file 1: Procedure for estimating the travel time, Supplementary file 2: Distance/travel time categorisation of the towns and their names.

**Author Contributions:** D.K. conceptualised the study and developed the analytical strategy. D.K. collected and processed the data, performed statistical analysis, interpreted the results, and wrote the first draft of the study. K.M.A. contributed to the spatial analysis and interpretation of the results. T.P.M.-T. and V.B. contributed to the analytical strategy, to the interpretation of the results, and did critical revisions. D.K. wrote the final draft and it was approved by all authors.

**Acknowledgments:** We are thankful to the University of KwaZulu-Natal for providing us with essential research resources during this study. The authors would like to thank the authorities of the Upper East Regional Health Directorate, the District Health Management Teams, and all the rural PHC managers for granting us permission to conduct this study. We also thank the University of KwaZulu-Natal, College of Health Sciences for providing funding for the conduct of this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**


#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Stakeholder Perceptions of Point-of-Care Ultrasound Implementation in Resource-Limited Settings**

#### **Anna M. Maw 1,\*, Brittany Galvin 2, Ricardo Henri 2, Michael Yao 3, Bruno Exame 2, Michelle Fleshner 4, Meredith P. Fort <sup>5</sup> and Megan A. Morris <sup>6</sup>**


Received: 30 September 2019; Accepted: 16 October 2019; Published: 18 October 2019

**Abstract:** Background: Nearly half of the world lacks access to diagnostic imaging. Point of care ultrasound (POCUS) is a versatile and relatively affordable imaging modality that offers promise as a means of bridging the radiology gap and improving care in low resource settings. Methods: We performed semi-structured interviews of key stakeholders at two diverse hospitals where POCUS implementation programs had recently been conducted: one in a rural private hospital in Haiti and the other in a public referral hospital in Malawi. Questions regarding the clinical utility of POCUS, as well as barriers and facilitators of its implementation, were asked of study participants. Using the Framework Method, analysis of interview transcripts was guided by the WHO ASSURED criteria for point of care diagnostics. Results: Fifteen stakeholders with diverse roles in POCUS implementation were interviewed. Interviewees from both sites considered POCUS a valuable diagnostic tool that improved clinical decisions. They perceived barriers to adequate training as one of the most important remaining barriers to POCUS implementation. Conclusions: In spite of the increasing affordability and portability of ultrasounds devices, there are still important barriers to the implementation of POCUS in resource-limited settings.

**Keywords:** point-of-care-ultrasound; ultrasound; implementation

#### **1. Introduction**

It has been estimated that approximately half of the world lacks access to even basic diagnostic imaging [1]. Among the factors responsible for what is termed the "radiology gap" is the high cost required to procure and maintain most radiology equipment and the specialized expertise of necessary personnel such as radiologists and technicians [2].

Studies show that 80–90% of imaging needs can be addressed with X-ray and ultrasound alone [3,4]. Ultrasound is particularly versatile and is the least expensive of all imaging modalities [2]. Common applications of ultrasound that routinely change management decisions in low resource settings [5,6] include the diagnosis of pneumonia, extrapulmonary tuberculosis, parasitic infections, rheumatic heart disease, and ectopic pregnancy [7–9]. In addition, as noncommunicable diseases such as heart disease

become more prevalent in low- and middle-income countries, so too does the utility of ultrasound in these settings [10].

Recognizing ultrasound as the most promising modality to bridge the radiology gap, the World Health Organization (WHO) published a series of documents beginning in the 1990s designed to facilitate the implementation of ultrasound more broadly [11,12]. At that time, the WHO warned that operator training was one of the biggest barriers to appropriate implementation.

Over the last 20 years, ultrasound has become even more portable and much less costly, allowing for the development of a new practice called point-of-care ultrasound (POCUS) [13], in which images are acquired and interpreted by the treating clinician at the bedside. POCUS is a growing field in Europe and the United States, with emerging evidence that it improves time to diagnosis and decreases cost in high-resource settings [14,15]. In contrast to consultative ultrasound, which is technician-performed and interpreted by a radiologist, POCUS answers focused diagnostic questions using easily performed techniques that can be quickly interpreted at the bedside. Because POCUS addresses focused questions and is not a comprehensive evaluation of an organ system, image acquisition and interpretation are easier to master than traditional consultative ultrasound. This aspect of POCUS reduces one of the remaining significant barriers to ultrasound's widespread use in resource-limited settings: extensive training.

The reduced price of hand-held ultrasound machines, which cost approximately 10,000 USD in contrast to standard ultrasound machines which are about 50,000 USD [16], makes POCUS a particularly good fit for resource-limited settings, as does its immediate availability of results and lack of reliance on additional infrastructure and personnel (e.g., technicians, radiologists, and laboratories). Continued improvements in cost, portability, and battery life all will all serve to reduce barriers to its implementation over time. Given the dynamic nature of factors that influence the implementation of POCUS and the expanding applications of its use, the purpose of this study is to determine the current barriers and facilitators of its implementation in low-resource settings.

#### **2. Materials and Methods**

#### *2.1. Study Sample and Setting*

The Institutional Review Board at University of Colorado deemed this study not Human Subjects research. We obtained written consent from all participants prior to their interview. We interviewed key stakeholders from 2 diverse hospital settings in which POCUS implementation programs had recently been conducted. One site was in a private rural 60 bed hospital in Haiti and the other was an 800 bed referral hospital in Malawi. Key stakeholders from both sites were interviewed as part of the study. We defined a key stakeholder as an individual who in some way influences the implementation of POCUS. The following types of stakeholders were included in the study: program initiators (individuals who established the POCUS implementation program), POCUS instructors, hospital administrators, provider trainees, and local physician leaders.

#### *2.2. Data Collection*

Data for this study were collected between July 2019 and September 2019. The WHO ASSURED criteria [17] (Table 1) were used to shape the interview guide, data coding and analysis. These criteria were developed by the WHO Sexually Transmitted Diseases Diagnostics Initiative to describe the characteristics of the perfect point-of-care diagnostic test in resource-limited settings. However, we feel it outlines important considerations for any type of diagnostic test and highlights many of the reasons why POCUS is uniquely positioned to be the most effective imaging modality for low-resource settings.


#### **Table 1.** WHO ASSURED Criteria.

Semistructured interviews were conducted with key stakeholders to assess their perspective on point of care ultrasound implementation in their local setting and more broadly. Interviews were conducted by phone and in person. Some interviews were conducted in English and some in Creole, depending on the language spoken by the interviewee. Those conducted in English were conducted by AMM. Those conducted in Creole were performed by BG, who both conducted the interviews and translated answers into English during the interview. Interviews were conducted until thematic saturation was reached: when no additional themes were immerging from the interviews. All interviews were conducted using the same interview guide (see Supplementary Materials).

#### *2.3. Data Entry and Analysis*

The interviews were audio recorded and transcribed verbatim. We used a framework analysis [18] and a largely deductive approach. Our coding framework was informed by the WHO ASSURED criteria. We were also open to new themes that may have arisen inductively from the data. Our coding process was guided by consensus qualitative research methods [19]. The consensus research approach has the following features: data were collected through open-ended questions in semi-structured interviews, interviews were analyzed to achieve consensus of at least 2 analysts, an outside auditor (a qualitative expert not integrally involved in the study) supervised the process to help maximize the validity of the findings, thematic content analysis was performed by AMM, study site leaders BG (Haiti) and MF (Malawi) reviewed the categorization of themes obtained from stakeholders at their respective sites to ensure the validity of the analysis, and MAM acted as the qualitative expert supervising the analysis.

Patterns of POCUS implementation facilitators and barriers were identified from participants' interviews. First, participants' responses were coded into framework categories, which were then grouped into themes.

#### **3. Results**

A total of 15 interviews were conducted by key stakeholders (Table 2) at two sites located in a small private rural hospital in Haiti and a large public referral hospital in Malawi. Interview length ranged from 20 to 45 min.


**Table 2.** Stakeholder interviewees and their role in POCUS implementation.

The study aimed to understand factors that may affect the implementation and sustainability of POCUS in low-resource settings. Guided by the WHO ASSURED criteria for point-of-care diagnostics in resource limited settings, core themes were identified (Table 3).




#### **Table 3.** *Cont.*

#### *3.1. A*ff*ordability*

The costs associated with several aspects of implementation were considered barriers. Expenditures included costs related to adequate accessibility to machines and training.

In addition, interviewees in Haiti expressed the potential of cost-savings to both the patients and hospital associated with ultrasound as it allowed for local diagnosis and treatment rather than referral to a larger facility.

#### 3.1.1. Cost Related to Machines

Stakeholders expressed that although machines were more accessible than they had been previously, this cost was still considered a barrier to implementation. This was the case in part because even if providers had received training in the past, if there was no machine in their current practice setting they would not be able to maintain their skill.

*"After the people who are training the doctors leave, we need to be sure that there are ultrasound machines available for everyone to practice. Even if there are ultrasounds at some hospitals, doctors often take di*ff*erent jobs and if those ultrasounds aren't available at all hospitals then everything that the doctor learned they might forget because they don't have the ultrasound available to them. Therefore, they would lose this skill."*

#### 3.1.2. Cost Related to Training

The cost related to training was also perceived as a significant barrier to implementation.

*"It was the gynecologist we had on sta*ff *who said, 'You know, I've always wanted to go to a more formal ultrasound program, but when I looked at them, they were like 1,500, 2,500 U.S. dollars.' He goes, 'I can't a*ff*ord that'." (Haiti)*

#### 3.1.3. Cost to the Patient

Interviewees associated with the Haiti site felt POCUS improved cost from the perspective of the patients because it allowed them to receive a diagnosis locally thereby avoiding both the expense of travel to a referral hospital and the cost of a consultative ultrasound once there.

*"Advantage was for patients because they used to have to travel and spend a lot of money to be able to have ultrasound diagnostics in other locations. Now that we have the diagnosis here, they don't have to travel and they don't have to pay a fee for the diagnostics." (Haiti)*

In contrast, in Malawi, because it was a public hospital, there was no additional cost to the patients because all care was free.

*"The hospital that I work at is a public hospital so everything is included. For things that are able to be done at the public hospital it's not an issue* ... *. for the purposes of the ultrasounds, and the X-rays, they're not charged anything." (Malawi)*

#### 3.1.4. Revenue Generation for the Hospital

Interviewees in Haiti also noted that being able to offer ultrasound may generate revenue for the hospital because when they are able to receive a diagnosis locally, they can remain at the hospital for treatment.

*"If we could increase the utilization of the service in the hospital it would be a huge benefit because we would become more competent in ultrasound and we could take on more patients* ... *. therefore, keeping more patients in the hospital. Not having to refer them. And also, the hospital would be able to make more money because we are not having to send people away." (Haiti)*

#### *3.2. Accuracy (Sensitivity and Specificity)*

All interviewees from both sites believed POCUS improved diagnosis and management decisions significantly.

*"Actually I think that the Point of Care Ultrasound is very, very important in taking care of the patient particularly in the resource limited settings. We don't really have access to imaging modalities* *like MRI, CT. With ultrasound we can get a lot of information for most parts of the body. You can scan the lungs, you can scan the heart, you can scan the abdomen, you can scan pretty much everywhere. I think that this is actually a very helpful tool that would help improving healthcare in settings like this." (Haiti)*

*"We are looking at a lot of people being misdiagnosed. We kind of have a lot of deaths that may be would have been averted if we'd have really known what was happening." (Malawi)*

*"Most people here do paras and thoras completely blind. So training them to use POCUS to do procedures more safely would be another big benefit." (Malawi)*

#### *3.3. User-Friendly*

Once proficient, trainees felt POCUS exams were quick and easy to perform. However, all interviewees felt that adequate training was currently an important barrier to use. Subthemes included (1) language barriers between trainees and instructors; (2) limited time for trainees to attend trainings when offered because of clinical responsibilities; (3) lack of continuity of staff who have been trained.

#### 3.3.1. Language

Interviewees in Haiti reported language as a barrier to training, in that the majority of instructors spoke English but almost all of the trainees spoke Creole and French. Language was not reported as a barrier by stakeholders from the hospital in Malawi where the instructors and trainees all spoke English.

*"One challenge was for the foreigners who came to teach the lesson. We don't speak the same language, so there was a language di*ffi*culty." (Haiti)*

#### 3.3.2. Time to Attend Trainings

Interviewees at both sites reported that clinical responsibilities were an important barrier to attending training sessions when offered.

*"The biggest challenge was that we were trying to take care of the patients at the same time as learning because we still had a role at the hospital. We didn't have time o*ff*." (Haiti)*

*"In general, our medical department right now is super short-sta*ff*ed and so the interns are really overworked. So the time it takes to train is another big barrier." (Malawi)*

#### 3.3.3. Lack of Clinician Continuity

Interviewees at both sites reported lack of clinician continuity was an important barrier to implementation and sustainability.

*"So it's a little bit harder because if we were to train them, then they're gone in three months. So they're not a sustainable part of the process." (Malawi)*

*"They come and go now and then, and this is actually another challenge. Some of the doctors trained have already left the hospital." (Haiti)*

#### *3.4. Rapid and Robust*

Interviewees expressed that one of the important benefits of POCUS is that results are immediately available. This was an important advantage at the rural hospital in Haiti, where there was no consultative ultrasound available and patients were often unable to return for test results. It was important at the referral hospital in Malawi as well because there were such long waits for consultative ultrasound performed by radiology.

*"The advantages are tremendous because so often our X-ray machine isn't working, and*/*or it's a terrible X-ray machine, the images aren't clear. Plus we're so limited in referral, if you're in the States, if you have a patient who is injured, even if they're not at one hospital, you can quickly transfer them to another hospital. In Haiti it's not so much the case. In addition to the cost of care can be astronomical. So free point of care testing that can be rapid, on the spot is a huge advantage for diagnosis and treatment." (Haiti)*

*"If maybe most of the clinicians, can be trained on how to use an ultrasound, it'll be far better, because instead of sending the patient to radiology they can be doing it on their own. Because most of the time it will be like, 'Okay, it'll be done like in the next three days, because there a lot of patients that are waiting.'" (Malawi)*

#### *3.5. Equipment-Free*

Interviewees expressed that the hand-held ultrasounds were easier to maintain than other forms of radiology (X-ray, commuted tomography, and consultative ultrasound). Interestingly, interviewees related times that POCUS findings were useful as surrogates for laboratory results because basic labs were not reliably available at either site. Due to its small size, loss of the hand-held ultrasound was more of a concern than need for repair or maintenance. However, although neither site reported an instance in which a hand-held ultrasound had required maintenance or repair, interviewees expressed concern that there was no local technician to repair it should the need arise.

*"I think one of the biggest challenges with respect to the ultrasound and with respect to a lot of other things is that equipment tends to walk o*ff*. And so I think in terms of buy-in, there needs to be somebody who's kind of the gatekeeper and keeps the device from disappearing." (Malawi)*

*"And if there's a problem with the equipment, who's qualified to repair it or to troubleshoot? Because we don't have technicians of that nature available in Haiti." (Haiti)*

#### *3.6. Delivered*

Interviewees at both sites reported that POCUS was extremely portable, all but eliminating time and resources spent on delivery and installation which are important barriers to implementation with other radiologic modalities [2]. Stakeholders at the Haiti site reported having a hand-held device available allowed for diagnosis locally instead of needing to refer patients to a larger hospital with consultative ultrasound or other imaging modalities. Stakeholders at the site reported being able to obtain imaging on patients too sick to come to the hospital via home visits and those in the hospital but too sick to travel to radiology.

*"For my other job* ... *they do home visits. So there have been a couple times that there are people who can't come to clinic because of whatever, because they've had a stroke or et cetera. There've been a couple times that I've gone out with residents and taken the ultrasound with me and that is really, really useful. So I mean, I think that there would be a real benefit to that." (Malawi)*

*"Now that it's in the hospital, they don't even have to leave the hospital. Therefore, they're not having to spend this money and have more money to buy the medicine that they need. Basically, they can get all the care that they need in one place inside of the hospital." (Haiti)*

#### *3.7. Inductive Themes*

#### 3.7.1. Development and Support of Local Experts

One inductive theme that emerged from stakeholders in both Haiti and Malawi was a recommendation to support a few local providers to undergo significant training in POCUS and then offer them support to train other local providers.

*"So would there have been a possibility of appointing one physician and maybe giving them an extra stipend to encourage them, or sign a contract saying 'Hey, we're going to train you in this. This is your three year contract and these are the objectives of hiring you as this. Not only are you going to see patients, but you're going to be the lead for this and you're responsible to train other physicians as they come in.' So there is, even if we have turnover, there is a plan to engage new physicians as we bring them aboard." (Haiti)*

*"Getting the Registrars to be excited about POCUS because they're the one who will really be doing it. They're the ones that teach the interns, they're the ones who teach the med students. Getting a few of them trained and then going from there would be the best strategy.* ... *The barrier is that they get paid virtually nothing so they all end up basically taking second jobs at private hospitals or private clinics and then end up not being at work as much.* ... *I think if we were able to provide one or two people with extra money to do POCUS training and that was their incentive, I think that would be huge." (Malawi)*

#### 3.7.2. Remote Learning Technology

Another inductive theme that emerged was the potential but current limitations of the internet as a means of providing remote learning opportunities and addressing the barrier of training without local experts available.

*"We did develop this way of uploading all of our images to Google Drive and then creating a log and a spreadsheet of logging all of our images. Basically 250 images QA'd by people from the states. So, the goal ultimately would be to do that with local providers and I think it would work but there's a few big barriers. The biggest of which is paying. We had to use data, straight phone data for it and it probably cost like \$100 over the course of two months. Which isn't a lot here but it's not a sustainable thing for them to do. So that's kind of a challenge." (Malawi)*

*"Actually it is a challenge, in such limited settings, even the Internet, we had the devices, they had an integrated software called 'React' that allows remote education, but we couldn't use it properly because of the Internet. We had Internet access at the hospital, but it was a very low speed. We couldn't really use it. Internet access as well is a barrier. Like with Internet we could have been able to do remote education, it would have been much easier to do it." (Haiti)*

#### **4. Discussion**

To our knowledge, this is the first study to explore the barriers and facilitators of POCUS implementation in resource-limited settings using qualitative methods. Our findings demonstrate the perceived clinical utility of POCUS, as well as the remaining barriers to implementation and sustainability. All stakeholders expressed the belief that POCUS had the potential to greatly improve management decisions and patient care but felt that the cost and time required for training were major barriers to implementation. Other barriers identified by study participants included the lack of continuity of providers at each site that would allow retention of an institutional expert to provide longitudinal mentorship as well as curriculum development and implementation which would be required for trainees to attain competency. Additional barriers included equipment maintenance and accessibility. This finding is consistent with other studies on this topic [20]. Interviewees also reported the local internet services were currently too slow, unreliable, or expensive to use effectively for remote learning. However, it is important to note that given the pace of advances in both remote learning technology and artificial intelligence applications [21], the former allowing for accelerated training and the later offering automated image interpretation, a swift decline in training as a barrier to POCUS implementation is expected.

Stakeholders at both sites recommended focusing resources on the training of a small number of local providers that could then become educators for their region, which would address many of the barriers expressed. This approach would provide: (1) longitudinal mentoring to other local providers; (2) someone who would be accountable for maintaining equipment; (3) elimination of language barriers between instructor and learner; and (4) reduce the cost related to providing training (e.g., resources that would otherwise be invested in travel and time of nonlocal experts may instead be invested into local champions).

#### *Limitations*

There are several important limitations to our study. The first is that we only interviewed stakeholders associated with two POCUS implementation program sites. This may limit the generalizability of our results. However, the sites were diverse clinical environments, one a small rural private hospital and the other a large public referral hospital, located in different countries. Additionally, the barriers that emerged as themes included the cost of the machines, language barriers, and lack of continuity of experts which would be expected to apply to many low-resource settings, suggesting many of our findings are to some extent generalizable. The study also would have been improved by gaining a broader healthcare system perspective by interviewing people within the Ministry of Health in each of the countries represented to determine whether they consider POCUS implementation worth investing in. Additionally, because a third of the study participants and most of research team is from the United States it is possible that we failed to capture all relevant themes accurately due to cultural bias. Finally, some of our interviews were done by phone as opposed to in-person which may have resulted in loss of contextual or nonverbal information. However, given the subject matter, which is of a relatively concrete and unsensitive nature, we feel it is unlikely this greatly influenced the themes that emerged.

#### **5. Conclusions**

POCUS is considered a highly effective diagnostic tool that improves patient care by all stakeholders interviewed. Although more affordable, the cost of machines is still a barrier to implementation of POCUS, though the expense and time required for optimal training of local providers seems to be the most important current barrier. Our findings highlight the remaining barriers to implementation as well as offer potential strategies to overcome them.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2075-4418/9/4/153/s1: Interview Guide.

**Author Contributions:** Study conceptualization, A.M.M., M.P.F., M.A.M.; methodology, M.A.M.; data collection A.M.M, B.G.; writing—original draft preparation, A.M.M.; writing—review and editing, M.Y., R.H., B.E., B.G., M.F., M.P.F., M.A.M.; supervision, M.A.M.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank Gordy Johnson and Sr Jacqueline Picard without whom this manuscript would not have been possible.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*
