Improving the spatial accessibility of healthcare in North Kivu, Democratic Republic of Congo

Pu, Qiang; Yoo, Eun-Hye; Rothstein, David H.; Cairo, Sarah B.; Malemo, Luc

doi:10.1016/j.apgeog.2020.102262

Cited by 49 publications

(33 citation statements)

References 67 publications

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“…We assume that health facilities in the study area share common geographic, socio-economic, and demographic characteristics that attain spatial efficiency in the spatial distribution of health facilities [24], given that the cost of developing new health facilities in LMICs is high. For example, both Mishra et al [21] and Pu et al [25] identified key factors that are important to consider for optimal locations for new health facilities in LMICs. They include the distance to the nearest health facility, population ratio, connection to road networks, land use type, and elevation.…”

Section: Geostatistical Linkage Methods and Performance Evaluationmentioning

confidence: 99%

Geostatistical linkage of national demographic and health survey data: a case study of Tanzania

Yoo

Palermo

Maluka

2021

Popul Health Metrics

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Background When Service Provision Assessment (SPA) surveys on primary health service delivery are combined with the nationally representative household survey—Demographic and Health Survey (DHS), they can provide key information on the access, utilization, and equity of health service availability in low- and middle-income countries. However, existing linkage methods have been established only at aggregate levels due to known limitations of the survey datasets. Methods For the linkage of two data sets at a disaggregated level, we developed a geostatistical approach where SPA limitations are explicitly accounted for by identifying the sites where health facilities might be present but not included in SPA surveys. Using the knowledge gained from SPA surveys related to the contextual information around facilities and their spatial structure, we made an inference on the service environment of unsampled health facilities. The geostatistical linkage results on the availability of health service were validated using two criteria—prediction accuracy and classification error. We also assessed the effect of displacement of DHS clusters on the linkage results using simulation. Results The performance evaluation of the geostatistical linkage method, demonstrated using information on the general service readiness of sampled health facilities in Tanzania, showed that the proposed methods exceeded the performance of the existing methods in terms of both prediction accuracy and classification error. We also found that the geostatistical linkage methods are more robust than existing methods with respect to the displacement of DHS clusters. Conclusions The proposed geospatial approach minimizes the methodological issues and has potential to be used in various public health research applications where facility and population-based data need to be combined at fine spatial scale.

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Section: Geostatistical Linkage Methods and Performance Evaluationmentioning

confidence: 99%

Geostatistical linkage of national demographic and health survey data: a case study of Tanzania

Yoo

Palermo

Maluka

2021

Popul Health Metrics

Self Cite

View full text Add to dashboard Cite

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“…The supplydemand ratio is calculated based on the catchment area, which is the area of space covered by facilities or people within a spatial barrier threshold (travel time or distance). The travel-cost approach [3], the isochrone approach [14], the gravity-based approach, and the raster-based model [15] are also commonly used methods for research on the accessibility of medical facilities. These methods realize the evaluation of the ease of access to medical services by residents and the impact of land use on accessibility by integrating location-allocation models and objective measurements.…”

Section: Literature Reviewmentioning

confidence: 99%

A Data-Driven Method for Measuring Accessibility to Healthcare Using the Spatial Interpolation Model

et al. 2021

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The evaluation of accessibility to healthcare greatly influences public policy. However, existing approaches mainly rely on network topology technology to estimate the driving paths available to healthcare users and the spatial impedances that they may encounter; these approaches do not suffice to represent the real life travel scenarios of urban traffic. This paper proposes a data-driven method to measure healthcare accessibility. The travel records related to visiting a health facility via taxi (VHT) were identified based on the travel destination, and the travel time for each trip was accurately recorded. The spatial interpolation model converts discrete taxi trajectory data into continuous data surfaces, and an improved cumulative accessibility measure method is applied to determine the accessibility to healthcare. A case study using four months of actual Ningbo taxi data shows that the mean absolute percentage error (MAPE) is 7.1%, and the root mean square error (RMSE) is approximately 2.57min. The case study results highlight that changes in travel speed over time have a significant impact on accessibility to healthcare facilities. This method measures the spatial impedance encountered by residents visiting medical facilities by capturing real travel scenarios and presenting the methodological implications of evaluating healthcare accessibility.

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“…Interactive visualizations can be particularly useful for location-allocation analyses given that these analyses can result in many charts and maps depending on the number of evaluated scenarios and optimization criteria (e.g., [22,23]). Importantly, previous research has demonstrated that interactive information can have greater impact than passive information (e.g., [24,25]).…”

Section: Introductionmentioning

confidence: 99%

Guiding placement of health facilities using malaria criteria and interactive tool

Toh

Millar

Psychas³

et al. 2021

Preprint

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Background: Access to healthcare is important in controlling malaria burden and, as a result, distance or travel time to health facilities is often a significant predictor in modeling malaria prevalence. Adding new health facilities may reduce overall travel time to health facilities and may decrease malaria transmission. To help guide local decision makers as they scale up community-based accessibility, we explore how the allocation of new health facilities might influence malaria prevalence in Bunkpurugu-Yunyoo district in northern Ghana. We perform a location-allocation analysis to find optimal locations of new health facilities by minimizing three district-wide objectives separately: malaria prevalence, malaria incidence, and average travel time to health facilities. Methods: We used generalized additive model to model malaria prevalence as a function of travel time to health facility and other geospatial covariates. The model predictions are used to calculate the optimization criteria and to conduct spatial optimization. This analysis was performed for two scenarios: adding new health facilities to the existing ones, and a hypothetical scenario in which the community-based healthcare facilities would be allocated anew. We created an interactive web application to facilitate efficient presentation of this analysis and allow users to experiment with their choice of health facility location and optimization criteria. Results: Using malaria prevalence and travel time as optimization criteria, we found two locations that were not covered by existing community-based health services that would benefit from new health facilities, regardless of scenarios. Due to the non-linear relationship between malaria incidence and prevalence, the optimal locations chosen by using incidence criterion tend to be inequitable and are different from those based on the other optimization criteria. Conclusion: Our findings underscore the importance of using multiple optimization criteria in the decision-making process. We believe that our analysis and interactive application can be repurposed for other regions and criteria, bridging the gap between science, models and decisions.

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Improving the spatial accessibility of healthcare in North Kivu, Democratic Republic of Congo

Cited by 49 publications

References 67 publications

Geostatistical linkage of national demographic and health survey data: a case study of Tanzania

Geostatistical linkage of national demographic and health survey data: a case study of Tanzania

A Data-Driven Method for Measuring Accessibility to Healthcare Using the Spatial Interpolation Model

Guiding placement of health facilities using malaria criteria and interactive tool

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