Malaria vaccine coverage estimation using age-eligible populations and service user denominators in Kenya

Background The World Health Organization approved the RTS,S/AS01 malaria vaccine for wider rollout, and Kenya participated in a phased pilot implementation from 2019 to understand its impact under routine conditions. Vaccine delivery requires coverage measures at national and sub-national levels to evaluate progress over time. This study aimed to estimate the coverage of the RTS,S/AS01 vaccine during the first 36 months of the Kenyan pilot implementation. Methods Monthly dose-specific immunization data for 23 sub-counties were obtained from routine health information systems at the facility level for 2019–2022. Coverage of each RTS,S/AS01 dose was determined using reported doses as a numerator and service-based (Penta 1 and Measles) or population (projected infant populations from WorldPop) as denominators. Descriptive statistics of vaccine delivery, dropout rates and coverage estimates were computed across the 36-month implementation period. Results Over 36 months, 818,648 RTSS/AS01 doses were administered. Facilities managed by the Ministry of Health and faith-based organizations accounted for over 88% of all vaccines delivered. Overall, service-based malaria vaccine coverage was 96%, 87%, 78%, and 39% for doses 1–4 respectively. Using a population-derived denominator for age-eligible children, vaccine coverage was 78%, 68%, 57%, and 24% for doses 1–4, respectively. Of the children that received measles dose 1 vaccines delivered at 9 months (coverage: 95%), 82% received RTSS/AS01 dose 3, only 66% of children who received measles dose 2 at 18 months (coverage: 59%) also received dose 4. Conclusion The implementation programme successfully maintained high levels of coverage for the first three doses of RTSS/AS01 among children defined as EPI service users up to 9 months of age but had much lower coverage within the community with up to 1 in 5 children not receiving the vaccine. Consistent with vaccines delivered over the age of 1 year, coverage of the fourth malaria dose was low. Vaccine uptake, service access and dropout rates for malaria vaccines require constant monitoring and intervention to ensure maximum protection is conferred

Geographic accessibility and hospital competition for emergency blood transfusion services in Bungoma, Western Kenya

Background Estimating accessibility gaps to essential health interventions helps to allocate and prioritize health resources. Access to blood transfusion represents an important emergency health requirement. Here, we develop geo-spatial models of accessibility and competition to blood transfusion services in Bungoma County, Western Kenya. Methods Hospitals providing blood transfusion services in Bungoma were identified from an up-dated geo-coded facility database. AccessMod was used to define care-seeker’s travel times to the nearest blood transfusion service. A spatial accessibility index for each enumeration area (EA) was defined using modelled travel time, population demand, and supply available at the hospital, assuming a uniform risk of emergency occurrence in the county. To identify populations marginalized from transfusion services, the number of people outside 1-h travel time and those residing in EAs with low accessibility indexes were computed at the sub-county level. Competition between the transfusing hospitals was estimated using a spatial competition index which provided a measure of the level of attractiveness of each hospital. To understand whether highly competitive facilities had better capacity for blood transfusion services, a correlation test between the computed competition metric and the blood units received and transfused at the hospital was done. Results 15 hospitals in Bungoma county provide transfusion services, however these are unevenly distributed across the sub-counties. Average travel time to a blood transfusion centre in the county was 33 min and 5% of the population resided outside 1-h travel time. Based on the accessibility index, 38% of the EAs were classified to have low accessibility, representing 34% of the population, with one sub-county having the highest marginalized population. The computed competition index showed that hospitals in the urban areas had a spatial competitive advantage over those in rural areas. Conclusion The modelled spatial accessibility has provided an improved understanding of health care gaps essential for health planning. Hospital competition has been illustrated to have some degree of influence in provision of health services hence should be considered as a significant external factor impacting the delivery, and re-design of available services

Spatial variation and clustering of anaemia prevalence in school-aged children in Western Kenya

Anaemia surveillance has overlooked school-aged children (SAC), hence information on this age group is scarce. This study examined the spatial variation of anaemia prevalence among SAC (5–14 years) in western Kenya, a region associated with high malaria infection rates. A total of 8051 SAC were examined from 82 schools across eight counties in Western Kenya in February 2022. Haemoglobin (Hb) concentrations were assessed at the school and village level and anaemia defined as Hb<11.5g/dl for age 5-11yrs and Hb <12.0g/dl for 12-14yrs after adjusting for altitude. Moran’s I analysis was used to measure spatial autocorrelation, and local clusters of anaemia were mapped using spatial scan statistics and local indices of spatial association (LISA). The prevalence of anaemia among SAC was 27.8%. The spatial variation of anaemia was non-random, with Global Moran’s I 0.2 (p-value < 0.002). Two significant anaemia cluster windows were identified: Cluster 1 (LLR = 38.9, RR = 1.4, prevalence = 32.0%) and cluster 2 (LLR = 23.6, RR = 1.6, prevalence = 45.5%) at schools and cluster 1 (LLR = 41.3, RR = 1.4, prevalence = 33.3%) and cluster 2 (LLR = 24.5, RR = 1.6, prevalence = 36.8%) at villages. Additionally, LISA analysis identified ten school catchments as anaemia hotspots corresponding geographically to SatScan clusters. Anaemia in the SAC is a public health problem in the Western region of Kenya with some localised areas presenting greater risk relative to others. Increasing coverage of interventions, geographically targeting the prevention of anaemia in the SAC, including malaria, is required to alleviate the burden among children attending school in Western Kenya

Malaria vaccine coverage estimation using age-eligible populations and service user denominators in Kenya

Abstract Background The World Health Organization approved the RTS,S/AS01 malaria vaccine for wider rollout, and Kenya participated in a phased pilot implementation from 2019 to understand its impact under routine conditions. Vaccine delivery requires coverage measures at national and sub-national levels to evaluate progress over time. This study aimed to estimate the coverage of the RTS,S/AS01 vaccine during the first 36 months of the Kenyan pilot implementation. Methods Monthly dose-specific immunization data for 23 sub-counties were obtained from routine health information systems at the facility level for 2019–2022. Coverage of each RTS,S/AS01 dose was determined using reported doses as a numerator and service-based (Penta 1 and Measles) or population (projected infant populations from WorldPop) as denominators. Descriptive statistics of vaccine delivery, dropout rates and coverage estimates were computed across the 36-month implementation period. Results Over 36 months, 818,648 RTSS/AS01 doses were administered. Facilities managed by the Ministry of Health and faith-based organizations accounted for over 88% of all vaccines delivered. Overall, service-based malaria vaccine coverage was 96%, 87%, 78%, and 39% for doses 1–4 respectively. Using a population-derived denominator for age-eligible children, vaccine coverage was 78%, 68%, 57%, and 24% for doses 1–4, respectively. Of the children that received measles dose 1 vaccines delivered at 9 months (coverage: 95%), 82% received RTSS/AS01 dose 3, only 66% of children who received measles dose 2 at 18 months (coverage: 59%) also received dose 4. Conclusion The implementation programme successfully maintained high levels of coverage for the first three doses of RTSS/AS01 among children defined as EPI service users up to 9 months of age but had much lower coverage within the community with up to 1 in 5 children not receiving the vaccine. Consistent with vaccines delivered over the age of 1 year, coverage of the fourth malaria dose was low. Vaccine uptake, service access and dropout rates for malaria vaccines require constant monitoring and intervention to ensure maximum protection is conferred

Additional file 1 of Malaria vaccine coverage estimation using age-eligible populations and service user denominators in Kenya

Additional file 1: Table S1. Kenya national Extended Programme for Immunisation (EPI) vaccine schedule. Figure S1. Flowchart of the selection process to determine facilities routinely offering vaccination services in RTS, S/AS01 intervention areas (23 sub-counties) from DHIS2 listing. Figure S2. Gantt chart illustration of numerator and denominator counts used for coverage computation. Figure S3. Annual population density maps of children under 1 year within RTS,S/AS01 implementation sub-counties for the years 2019-2022. Table S2. Characteristics of 537 vaccinating facilities within the 23 implementation sub-counties. Figure S4. Animation of cumulative Penta 1 and RTS,S/AS01 vaccines administered at facility level from September 2019 to August 2022 (N = 537). Figure S5. Chart showing sub-county coverage rankings of RTS,S/AS01 vaccine doses for each denominator

Fig 4 -

(A) Anaemia prevalence for each school catchment (computed from empirical Hb measurements), (B) Spatial scan statistics results of anaemia clusters for schools, (C) Spatial scan statistics results of anaemia clusters for villages and (D) LISA cluster map showing anaemia hotspots (red) and cold spots (blue). The corresponding LISA significance map in shown in S2 Fig. The Western Kenya county level shapefile was based on County Integrated Development Plans 2021 [22].</p

LISA significance map.

The Western Kenya county level shapefile was based on the County Integrated Development Plans 2021 [22]. (TIF)</p

Combined bar and line charts.

The bar chart shows the variation in anaemia prevalence by age (5–14 yrs) and the line graph shows mean Hb (adjusted for altitude) by gender across all age groups (5–14 yrs).</p

S1 File -

Anaemia surveillance has overlooked school-aged children (SAC), hence information on this age group is scarce. This study examined the spatial variation of anaemia prevalence among SAC (5–14 years) in western Kenya, a region associated with high malaria infection rates. A total of 8051 SAC were examined from 82 schools across eight counties in Western Kenya in February 2022. Haemoglobin (Hb) concentrations were assessed at the school and village level and anaemia defined as Hb</div

Box plot of the prevalence rates across clusters identified by SatScan and LISA cluster analysis.

(A) shows distribution of anaemia prevalence across schools and villages in cluster 1 and cluster 2 identified from SatScan analysis; (B) shows distribution of anaemia prevalence across High-High, High-Low, Low-High and Low-Low LISA clusters.</p