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

10.21203/rs.3.rs-306572/v1 ◽

2021 ◽

Author(s):

Kok Ben Toh ◽

Justin Millar ◽

Paul Psychas ◽

Benjamin Abuaku ◽

Collins Ahorlu ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Malaria Incidence ◽

Spatial Optimization ◽

Malaria Prevalence ◽

Health Facilities ◽

Northern Ghana ◽

Community Based ◽

Optimization Criteria ◽

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 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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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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Estimating malaria incidence from routine health facility-based surveillance data in Uganda

Malaria Journal ◽

10.1186/s12936-020-03514-z ◽

2020 ◽

Vol 19 (1) ◽

Author(s):

Adrienne Epstein ◽

Jane Frances Namuganga ◽

Emmanuel Victor Kamya ◽

Joaniter I. Nankabirwa ◽

Samir Bhatt ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Health Management ◽

Malaria Incidence ◽

Additive Models ◽

Surveillance Data ◽

Health Management Information System ◽

Care Seeking ◽

Catchment Areas ◽

Over Time

Abstract Background Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility. Methods Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011–2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts. Results A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera). Conclusions Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

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Estimating Malaria Incidence From Routine Health Facility-Based Surveillance Data in Uganda

10.21203/rs.3.rs-70803/v1 ◽

2020 ◽

Author(s):

Adrienne Epstein ◽

Jane Frances Namuganga ◽

Emmanuel Victor Kamya ◽

Joaniter I Nankabirwa ◽

Samir Bhatt ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Health Management ◽

Malaria Incidence ◽

Additive Models ◽

Surveillance Data ◽

Health Management Information System ◽

Care Seeking ◽

Catchment Areas ◽

Over Time

Abstract Background. Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviors are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility.Methods. Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a three year period (2011-2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts. Results. A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the three years in Kihihi (0.5 cases per person-year [PPY] versus 1.7 cases PPY) and Nagongera (0.3 cases PPY versus 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera).Conclusions. Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

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Estimating malaria incidence from routine health facility-based surveillance data in Uganda

10.21203/rs.3.rs-70803/v2 ◽

2020 ◽

Author(s):

Adrienne Epstein ◽

Jane Frances Namuganga ◽

Emmanuel Victor Kamya ◽

Joaniter I Nankabirwa ◽

Samir Bhatt ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Health Management ◽

Malaria Incidence ◽

Additive Models ◽

Surveillance Data ◽

Health Management Information System ◽

Care Seeking ◽

Catchment Areas ◽

Over Time

Abstract Background: Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviors are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility. Methods: Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a three year period (2011-2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts. Results: A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the three years in Kihihi (0.5 cases per person-year [PPY] versus 1.7 cases PPY) and Nagongera (0.3 cases PPY versus 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera). Conclusions: Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

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Estimating malaria incidence from routine health facility-based surveillance data in Uganda

10.21203/rs.3.rs-70803/v3 ◽

2020 ◽

Author(s):

Adrienne Epstein ◽

Jane Frances Namuganga ◽

Emmanuel Victor Kamya ◽

Joaniter I Nankabirwa ◽

Samir Bhatt ◽

...

Keyword(s):

Travel Time ◽

Health Facility ◽

Health Management ◽

Malaria Incidence ◽

Additive Models ◽

Surveillance Data ◽

Health Management Information System ◽

Care Seeking ◽

Catchment Areas ◽

Over Time

Abstract Background Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility. Methods Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011-2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts. Results A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi=0.64, p <0.001; correlation in Nagongera=0.34, p=0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera). Conclusions Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

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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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Modeled Impact of Seasonal Malaria Chemoprevention on District-Level Suspected and Confirmed Malaria Cases in Chad Based on Routine Clinical Data (2013–2018)

American Journal of Tropical Medicine and Hygiene ◽

10.4269/ajtmh.21-0314 ◽

2021 ◽

Author(s):

Sol Richardson ◽

Azoukalne Moukenet ◽

Mahamat Saleh Issakha Diar ◽

Monica Anna de Cola ◽

Christian Rassi ◽

...

Keyword(s):

Health Management ◽

Malaria Incidence ◽

Routine Data ◽

Health Facilities ◽

Additive Models ◽

District Level ◽

Sensitivity Analyses ◽

Health Management Information System ◽

Seasonal Malaria Chemoprevention ◽

The Impact

Sulfadoxine-pyrimethamine plus amodiaquine to children aged 3–59 months is delivered as seasonal malaria chemoprevention (SMC) in areas where transmission is highly seasonal such as Chad and other Sahelian countries. Although clinical trials show a 75% reduction in malaria cases, evidence of SMC’s impact at scale remains limited. Using data from the Chadian National Health Management Information System, we analyzed associations between SMC implementation during July–October and monthly district-level malaria incidence (suspected and confirmed outpatient cases) among children aged 0–59 months at health facilities in 23 health districts with SMC implementation during 2013–2018. Generalized additive models were fitted with separate cyclic cubic spline terms for each district to adjust for seasonality in cases. SMC implementation in Chad was associated, compared with no implementation, with lower monthly counts of both suspected (rate ratio [RR]: 0.82, 95% CI: 0.72–0.94. P = 0.006) and confirmed malaria cases (RR: 0.81, 95% CI: 0.71–0.93, P = 0.003), representing around 20% reduction in malaria incidence. Sensitivity analyses showed effect sizes of up to 28% after modifying model assumptions. Caution should be exercised in interpreting our findings, which may not be comparable with other studies, and may over- or underestimate impact of SMC; not all malaria cases present at health facilities, not all suspected cases are tested, and not all facilities report cases consistently. This study’s approach presents a solution for employing readily available routine data to evaluate the impact of health interventions at scale without extensive covariate data. Further efforts are needed to improve the quality of routine data in Chad and elsewhere.

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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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