Guiding placement of health facilities using malaria criteria and interactive tool

Abstract 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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Guiding placement of health facilities using multiple malaria criteria and an interactive tool

Malaria Journal ◽

10.1186/s12936-021-03991-w ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Kok Ben Toh ◽

Justin Millar ◽

Paul Psychas ◽

Benjamin Abuaku ◽

Collins Ahorlu ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Malaria Incidence ◽

Malaria Prevalence ◽

Health Facilities ◽

Additive Models ◽

Northern Ghana ◽

Community Based ◽

Location Allocation ◽

Hypothetical Scenario

Abstract 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 modelling 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, the influence of the spatial allocation of new health facilities on malaria prevalence is evaluated in Bunkpurugu-Yunyoo district in northern Ghana. A location-allocation analysis is performed to find optimal locations of new health facilities by separately minimizing three district-wide objectives: malaria prevalence, malaria incidence, and average travel time to health facilities. Methods Generalized additive models was used to estimate the relationship between malaria prevalence and travel time to the nearest health facility and other geospatial covariates. The model predictions are then used to calculate the optimisation criteria for the location-allocation analysis. 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. An interactive web application was created to facilitate efficient presentation of this analysis and allow users to experiment with their choice of health facility location and optimisation criteria. Results Using malaria prevalence and travel time as optimisation criteria, two locations that would benefit from new health facilities were identified, regardless of scenarios. Due to the non-linear relationship between malaria incidence and prevalence, the optimal locations chosen based on the incidence criterion tended to be inequitable and was different from those based on the other optimisation criteria. Conclusions This study findings underscore the importance of using multiple optimisation criteria in the decision-making process. This analysis and the interactive application can be repurposed for other regions and criteria, bridging the gap between science, models and decisions.

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Effectiveness of The Innovative 1,7-Malaria Reactive Community-based Testing and Response (1, 7-mRCTR) Approach on Malaria Burden Reduction in Southeastern Tanzania

10.21203/rs.3.rs-32068/v1 ◽

2020 ◽

Author(s):

Yeromin P. Mlacha ◽

Duoquan Wang ◽

Prosper P. Chaki ◽

Tegemeo Gavana ◽

Zhengbin Zhou ◽

...

Keyword(s):

Malaria Infection ◽

Health Workers ◽

Malaria Incidence ◽

Pilot Project ◽

Malaria Prevalence ◽

Health Facilities ◽

Community Based ◽

Malaria Burden ◽

Tailored Approach ◽

The Village

Abstract Background: In 2015, a China-UK-Tanzania tripartite pilot project was implemented in south-eastern Tanzania to explore a new model for reducing malaria burden and possibly scaling-out the approach into other malaria endemic countries. The 1,7-malaria Reactive Community-based Testing and Response (1,7-RCTR) which is a locally-tailored approach for reporting febrile malaria cases in endemic villages was developed to stop transmission and plasmodium life-cycle. The (1,7-RCTR) utilizes existing health facility data and locally trained community health workers to conduct community-level testing and treatment. Methods: The pilot project was implemented from September 2015 to June 2018. Matched malaria incidence pairs of control and intervention wards were chosen. The latter arm was selected for the 1,7-mRCTR approach leaving control wards relying on existed programs. The 1,7-mRCTR activities included community testing and treatment of malaria infection. Malaria case-to-suspect ratios at health facilities (HF) were aggregated by villages, weekly to identify the village with the highest ratio. Community-based mobile test stations (cMTS) were used for conducting mass testing and treatment. Random household surveys were done in the control and intervention wards before (baseline) and after (endline) the program. The primary outcome was the baseline and endline difference of malaria prevalence in the control and intervention wards measured by the interaction term of ‘time’ (post vs. pre) and group in a logistic model. We also studied the malaria incidence reported at the health facilities during the intervention.Results: Overall 85 rounds of 1,7-mRCT conducted in the intervention wards significantly reduced the odds of malaria infection by 66% (adjusted OR 0.34, 95%CI 0.26,0.44, p<0001) beyond the effect of the standard programs. Malaria prevalence in the intervention wards declined by 81% (from 26% (95% CI, 23.7, 7.8), at baseline to 4.9% (95% CI, 4.0,5.9) at endline). Villages receiving the 1,7-mRCT had a case ratio decreased by over 15.7% (95%CI, -33, 6) compared to baseline.Conclusion: The 1,7-mRCTR approach reduced significantly the malaria burden in the areas of moderate and high transmission in southern Tanzania. This locally-tailored approach could accelerate malaria control and elimination efforts. The results provide the impetus for further evaluation of the effectiveness and scaling up of this type of approach in other high malaria burden countries in Africa, including Tanzania.

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Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

BMC Public Health ◽

10.1186/s12889-020-10007-w ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Simon P. Kigozi ◽

Ruth N. Kigozi ◽

Catherine M. Sebuguzi ◽

Jorge Cano ◽

Damian Rutazaana ◽

...

Keyword(s):

High Risk ◽

Incidence Rate ◽

Health Facility ◽

Malaria Incidence ◽

Temporal Patterns ◽

Health Facilities ◽

Moran’S I ◽

Moran's I ◽

Catchment Population ◽

Targeted Interventions

Abstract Background As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period. Methods Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index. Results An estimated 38.8 million (95% Credible Interval [CI]: 37.9–40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9–21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7–9.4) to 36.6 (95% CI: 35.7–38.5) across the study period. Strong seasonality was observed, with June–July experiencing highest peaks and February–March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0–50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p < 0.001) and districts Moran’s I = 0.4 (p < 0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions. Conclusion Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

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Spatial access inequities and childhood immunisation uptake in Kenya

BMC Public Health ◽

10.1186/s12889-020-09486-8 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Noel K. Joseph ◽

Peter M. Macharia ◽

Paul O. Ouma ◽

Jeremiah Mumo ◽

Rose Jalang’o ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Total Population ◽

Univariate Analysis ◽

Household Wealth ◽

Health Facilities ◽

Spatial Accessibility ◽

Immunisation Coverage ◽

Spatial Access ◽

Major Barrier

Abstract Background Poor access to immunisation services remains a major barrier to achieving equity and expanding vaccination coverage in many sub-Saharan African countries. In Kenya, the extent to which spatial access affects immunisation coverage is not well understood. The aim of this study was to quantify spatial accessibility to immunising health facilities and determine its influence on immunisation uptake in Kenya while controlling for potential confounders. Methods Spatial databases of immunising facilities, road network, land use and elevation were used within a cost friction algorithim to estimate the travel time to immunising health facilities. Two travel scenarios were evaluated; (1) Walking only and (2) Optimistic scenario combining walking and motorized transport. Mean travel time to health facilities and proportions of the total population living within 1-h to the nearest immunising health facility were computed. Data from a nationally representative cross-sectional survey (KDHS 2014), was used to estimate the effect of mean travel time at survey cluster units for both fully immunised status and third dose of diphtheria-tetanus-pertussis (DPT3) vaccine using multi-level logistic regression models. Results Nationally, the mean travel time to immunising health facilities was 63 and 40 min using the walking and the optimistic travel scenarios respectively. Seventy five percent of the total population were within one-hour of walking to an immunising health facility while 93% were within one-hour considering the optimistic scenario. There were substantial variations across the country with 62%(29/47) and 34%(16/47) of the counties with < 90% of the population within one-hour from an immunising health facility using scenarios 1 and 2 respectively. Travel times > 1-h were significantly associated with low immunisation coverage in the univariate analysis for both fully immunised status and DPT3 vaccine. Children living more than 2-h were significantly less likely to be fully immunised [AOR:0.56(0.33–0.94) and receive DPT3 [AOR:0.51(0.21–0.92) after controlling for household wealth, mother’s highest education level, parity and urban/rural residence. Conclusion Travel time to immunising health facilities is a barrier to uptake of childhood vaccines in regions with suboptimal accessibility (> 2-h). Strategies that address access barriers in the hardest to reach communities are needed to enhance equitable access to immunisation services in Kenya.

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The association between travel time to health facilities and childhood vaccine coverage in rural Ethiopia. A community based cross sectional study

BMC Public Health ◽

10.1186/1471-2458-12-476 ◽

2012 ◽

Vol 12 (1) ◽

Cited By ~ 33

Author(s):

Yemisrach B Okwaraji ◽

Kim Mulholland ◽

JoannaRMArmstrong Schellenberg ◽

Gashaw Andarge ◽

Mengesha Admassu ◽

...

Keyword(s):

Travel Time ◽

Vaccine Coverage ◽

Cross Sectional Study ◽

Health Facilities ◽

Sectional Study ◽

Community Based ◽

Cross Sectional ◽

Rural Ethiopia ◽

Childhood Vaccine

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Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

10.21203/rs.3.rs-54079/v3 ◽

2020 ◽

Author(s):

Simon Kigozi ◽

Ruth N Kigozi ◽

Catherine M Sebuguzi ◽

Jorge Cano ◽

Damian Rutazaana ◽

...

Keyword(s):

High Risk ◽

Incidence Rate ◽

Health Facility ◽

Malaria Incidence ◽

Temporal Patterns ◽

Health Facilities ◽

Moran’S I ◽

Moran's I ◽

Catchment Population ◽

Targeted Interventions

Abstract Background. As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period.Methods. Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index.Results. An estimated 38.8 million (95% Credible Interval [CI]: 37.9 – 40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9 - 21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7 – 9.4) to 36.6 (95% CI: 35.7 – 38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0 – 50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p<0.001) and districts Moran’s I = 0.4 (p<0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions.Conclusion. Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

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Healthcare Seeking and Access to Care for Pneumonia, Sepsis, Meningitis, and Malaria in Rural Gambia

American Journal of Tropical Medicine and Hygiene ◽

10.4269/ajtmh.21-0362 ◽

2021 ◽

Author(s):

Ilias Hossain ◽

Philip Hill ◽

Christian Bottomley ◽

Momodou Jasseh ◽

Kalifa Bojang ◽

...

Keyword(s):

Access To Care ◽

Health Facility ◽

Low Income ◽

Disease Incidence ◽

Community Survey ◽

Health Facilities ◽

Community Based ◽

Healthcare Seeking ◽

River Region ◽

Initial Care

Children with acute infectious diseases may not present to health facilities, particularly in low-income countries. We investigated healthcare seeking using a cross-sectional community survey, health facility-based exit interviews, and interviews with customers of private pharmacies in 2014 in Upper River Region (URR) The Gambia, within the Basse Health & Demographic Surveillance System. We estimated access to care using surveillance data from 2008 to 2017 calculating disease incidence versus distance to the nearest health facility. In the facility-based survey, children and adult patients sought care initially at a pharmacy (27.9% and 16.7% respectively), from a relative (23.1% and 28.6%), at a local shop or market (13.5% and 16.7%), and on less than 5% of occasions with a community-based health worker, private clinic, or traditional healer. In the community survey, recent symptoms of pneumonia or sepsis (15% and 1.5%) or malaria (10% and 4.6%) were common in children and adults. Rates of reported healthcare-seeking were high with families of children favoring health facilities and adults favoring pharmacies. In the pharmacy survey, 47.2% of children and 30.4% of adults had sought care from health facilities before visiting the pharmacy. Incidence of childhood disease declined with increasing distance of the household from the nearest health facility with access to care ratios of 0.75 for outpatient pneumonia, 0.82 for hospitalized pneumonia, 0.87 for bacterial sepsis, and 0.92 for bacterial meningitis. In rural Gambia, patients frequently seek initial care at pharmacies and informal drug-sellers rather than community-based health workers. Surveillance underestimates disease incidence by 8–25%.

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Travel time to health facilities as a marker of geographical accessibility across heterogeneous land coverage in Peru

10.1101/19007856 ◽

2019 ◽

Author(s):

Gabriel Carrasco-Escobar ◽

Edgar Manrique ◽

Kelly Tello-Lizarraga ◽

J. Jaime Miranda

Keyword(s):

Travel Time ◽

Health Facility ◽

Urban Areas ◽

Health Facilities ◽

Middle Income ◽

Healthcare Facilities ◽

Rural And Urban Areas ◽

Rural And Urban ◽

Tertiary Healthcare ◽

Geographical Accessibility

ABSTRACTThe geographical accessibility to health facilities is conditioned by the topography and environmental conditions overlapped with different transport facilities between rural and urban areas. To better estimate the travel time to the most proximate health facility infrastructure and determine the differences across heterogeneous land coverage types, this study explored the use of a novel cloud-based geospatial modeling approach and use as a case study the unique geographical and ecological diversity in the Peruvian territory. Geospatial data of 145,134 cities and villages and 8,067 health facilities in Peru were gathered with land coverage types, roads infrastructure, navigable river networks, and digital elevation data to produce high-resolution (30 m) estimates of travel time to the most proximate health facility across the country. This study estimated important variations in travel time between urban and rural settings across the 16 major land coverage types in Peru, that in turn, overlaps with socio-economic profiles of the villages. The median travel time to primary, secondary, and tertiary healthcare facilities was 1.9, 2.3, and 2.2 folds higher in rural than urban settings, respectively. Also, higher travel time values were observed in areas with a high proportion of the population with unsatisfied basic needs. In so doing, this study provides a new methodology to estimate travel time to health facilities as a tool to enhance the understanding and characterization of the profiles of accessibility to health facilities in low- and middle-income countries (LMIC), calling for a service delivery redesign to maximize high quality of care.

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Pentavalent Vaccination in Rural Kenya: Coverage and Geographical Accessibility to Health Facilities Using Data from a Community Demographic and Health Surveillance System in Kilifi County.

10.21203/rs.3.rs-122540/v1 ◽

2020 ◽

Author(s):

Morris Ogero ◽

James Orwa ◽

Rachael Odhiambo ◽

Felix Agoi ◽

Adelaide Lusambili ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Vaccination Coverage ◽

Surveillance System ◽

Immunization Coverage ◽

Health Facilities ◽

Spatial Accessibility ◽

Immunisation Coverage ◽

Spatial Access ◽

Routine Immunisation

Abstract BackgroundThere is substantial evidence that immunization is one of the most significant and cost-effective pillars of preventive and promotive health interventions. Effective childhood immunization coverage is thus essential in stemming persistent childhood illnesses. The main indicator of performance of the immunisation programme is the third dose of diphtheria-tetanus-pertussis (DTP3) vaccine for children because it mirrors the completeness of a child’s immunisation schedule. Spatial access to a health facility, especially in SSA countries, is a significant determinant of DTP3 vaccination coverage, as the vaccine is mainly administered during routine immunisation schedules at health facilities. Rural areas and densely populated informal settlements are most affected by poor access to healthcare services. We therefore sought to determine vaccination coverage of DTP3, estimate the travel time to health facilities offering immunisation services, and explore its effect on immunisation coverage in one of the predominantly rural counties on the coast of Kenya.MethodsCoordinates of health facilities, information on land cover, digital elevation models, and road networks were used to compute spatial accessibility to immunizing health facilities for eligible children within the Kaloleni-Rabai Community Health Demographic Surveillance System (HDSS). To explore the effect of travel time on DTP3 coverage, we fitted a hierarchical multivariable model adjusting for other a priori identified confounding factors.ResultsSpatial access to health facilities that offer immunization services significantly affected DTP3 coverage, with travel times of more than one hour to a health facility significantly associated with reduced odds of receiving DTP3 vaccine (AOR= 0.84 (95% CI 0.74 – 0.94).ConclusionIncreased travel time is a significant barrier to the uptake of facility-delivered immunizations in this rural community. To improve immunisation coverage, local health authorities and policy makers in remote settings can use high-resolution maps to identify areas where distance and travel time may impede the achievement of high immunization coverage and identify appropriate interventions. These could include improving the road network, establishing new health centres and/or stepping up health outreach activities that include vaccinations in hard-to-reach areas within the county.

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Adolescent and Young People's Utilization of HIV/Sexual and Reproductive Health Services: Comparing Health Facilities and Mobile Community Outreach Centers

International Journal of Translational Medical Research and Public Health ◽

10.21106/ijtmrph.74 ◽

2019 ◽

Vol 3 (2) ◽

pp. 66-74

Author(s):

Ikenna J. Nwakamma ◽

Carol S. Talla ◽

Stephanie E. Kei ◽

Genevieve C. Okoro ◽

Godwin Asuquo ◽

...

Keyword(s):

Young People ◽

Reproductive Health ◽

Health Facility ◽

Service Utilization ◽

Waiting Time ◽

Sexual And Reproductive Health ◽

Community Outreach ◽

Health Facilities ◽

Community Based ◽

Mobile Community

Background/Objectives: Demand creation for uptake of HIV and sexual reproductive health (HIV/SRH) services among adolescents and young people (AYP) in Nigeria is challenging. This study compares the reach, and utilization patterns, and factors that drive the patterns of utilization of HIV/SRH services by AYP in mobile outreach service centers and health care facilities in Nigeria's capital city. Methods: Data were obtained from service exit surveys and HIV/SRH service utilization records in selected health facilities and mobile testing outreaches from January to April 2018. The service providers were provided a checklist to capture key information during their interaction with their AYP clients. Data were captured with Microsoft Excel, imported to and analysed with Statistical Package for Social Sciences, version 16. Results: Community-based mobile outreaches reached a significantly higher proportion of participants, with 88% of them from the community HIV testing points. Among the participants in the SRH service utilization assessment, 20 (15%) and 142 (19%) voluntarily asked for SRH-related information in the health facility and mobile outreach respectively; 53 (40%), and 224 (30%) accepted offer of SRH counselling in the health facility and mobile outreach respectively. There were significant differences in the waiting time for testing and waiting time for result collection at the mobile testing posts and the health facilities. Conclusion and Implications for Translation: AYP friendly mobile community outreach model shows more promise in terms of reach and also seems to encourage voluntary request for HIV/SRH services among AYP. The costs and waiting times favor the mobile outreach model; however, the quality in terms of personnel and environment was an issue of concern. Hospitals are not providing friendly environments that encourage voluntary uptake of HIV/SRH services by AYPs. A model for AYPs should prioritize community based and friendly services with well-trained personnel in order to build the confidence of AYPs for improved SRH seeking behaviors. Key words: • HIV • Sexual and reproductive Health • Adolescents and Young People • Preferences • Mobile outreaches services • Health facility testing • Abuja Nigeria Copyright © 2019 Nwakamma et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.affect economic and health promotion.

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