ObjectivesTo achieve universal health coverage, adequate geographic access to quality healthcare services is vital and should be characterized periodically to support planning. However, in Kenya, previous assessments of geographic accessibility have relied on public health facility lists only, assembled several years ago. Here, for the first time we assemble a geocoded list of public and private health facilities in 2021 and make use of this updated list to interrogate geographical accessibility to all health providers.MethodsExisting health provider lists in Kenya were accessed, merged, cleaned, harmonized, and assigned a unique geospatial location. The resultant master list was combined with road network, land use, topography, travel barriers and healthcare-seeking behavior within a geospatial framework to estimate travel time to the nearest (i) private, (ii) public, and (iii) both (public and private-PP) health facilities through a travel scenario involving walking, bicycling and motorized transport. The proportion of the population within 1 h and outside 2-h was computed at 300 × 300 spatial resolution and aggregated at subnational units used for decision-making. Areas with a high disease prevalence for common infections that were outside 1-h catchment (dual burden) were also identified to guide prioritization.ResultsThe combined database contained 13,579 health facilities, both in the public (55.5%) and private-for-profit sector (44.5%) in 2021. The private health facilities' distribution was skewed toward the urban counties. Nationally, average travel time to the nearest health facility was 130, 254, and 128 min while the population within 1-h was 89.4, 80.5, and 89.6% for the public, private and PP health facility, respectively. The population outside 2-h were 6% for public and PP and 11% for the private sector. Mean travel time across counties was heterogeneous, while the population within 1-h ranged between 38 and 100% in both the public sector and PP. Counties in northwest and southeast Kenya had a dual burden.ConclusionContinuous updating and geocoding of health facilities will facilitate an improved understanding of healthcare gaps for planning. Heterogeneities in geographical access continue to persist, with some areas having a dual burden and should be prioritized toward reducing health inequities and attaining universal health coverage.

The High Burden High Impact (HBHI) strategy for malaria encourages countries to use multiple sources of available data to define the sub-national vulnerabilities to malaria risk, including parasite prevalence. Here, a modelled estimate of Plasmodium falciparum from an updated assembly of community parasite survey data in Kenya, mainland Tanzania, and Uganda is presented and used to provide a more contemporary understanding of the sub-national malaria prevalence stratification across the sub-region for 2019. Malaria prevalence data from surveys undertaken between January 2010 and June 2020 were assembled form each of the three countries. Bayesian spatiotemporal model-based approaches were used to interpolate space-time data at fine spatial resolution adjusting for population, environmental and ecological covariates across the three countries. A total of 18,940 time-space age-standardised and microscopy-converted surveys were assembled of which 14,170 (74.8%) were identified after 2017. The estimated national population-adjusted posterior mean parasite prevalence was 4.7% (95% Bayesian Credible Interval 2.6–36.9) in Kenya, 10.6% (3.4–39.2) in mainland Tanzania, and 9.5% (4.0–48.3) in Uganda. In 2019, more than 12.7 million people resided in communities where parasite prevalence was predicted ≥ 30%, including 6.4%, 12.1% and 6.3% of Kenya, mainland Tanzania and Uganda populations, respectively. Conversely, areas that supported very low parasite prevalence (<1%) were inhabited by approximately 46.2 million people across the sub-region, or 52.2%, 26.7% and 10.4% of Kenya, mainland Tanzania and Uganda populations, respectively. In conclusion, parasite prevalence represents one of several data metrics for disease stratification at national and sub-national levels. To increase the use of this metric for decision making, there is a need to integrate other data layers on mortality related to malaria, malaria vector composition, insecticide resistance and bionomic, malaria care-seeking behaviour and current levels of unmet need of malaria interventions.

Background Understanding the age patterns of disease is necessary to target interventions to maximise cost-effective impact. New malaria chemoprevention and vaccine initiatives target young children attending routine immunisation services. Here we explore the relationships between age and severity of malaria hospitalisation versus malaria transmission intensity. Methods Clinical data from 21 surveillance hospitals in East Africa were reviewed. Malaria admissions aged 1 month to 14 years from discrete administrative areas since 2006 were identified. Each site-time period was matched to a model estimated community-based age-corrected parasite prevalence to provide predictions of prevalence in childhood (PfPR2–10). Admission with all-cause malaria, severe malaria anaemia (SMA), respiratory distress (RD) and cerebral malaria (CM) were analysed as means and predicted probabilities from Bayesian generalised mixed models. Results 52,684 malaria admissions aged 1 month to 14 years were described at 21 hospitals from 49 site-time locations where PfPR2–10 varied from < 1 to 48.7%. Twelve site-time periods were described as low transmission (PfPR2–10 < 5%), five low-moderate transmission (PfPR2–10 5–9%), 20 moderate transmission (PfPR2–10 10–29%) and 12 high transmission (PfPR2–10 ≥ 30%). The majority of malaria admissions were below 5 years of age (69–85%) and rare among children aged 10–14 years (0.7–5.4%) across all transmission settings. The mean age of all-cause malaria hospitalisation was 49.5 months (95% CI 45.1, 55.4) under low transmission compared with 34.1 months (95% CI 30.4, 38.3) at high transmission, with similar trends for each severe malaria phenotype. CM presented among older children at a mean of 48.7 months compared with 39.0 months and 33.7 months for SMA and RD, respectively. In moderate and high transmission settings, 34% and 42% of the children were aged between 2 and 23 months and so within the age range targeted by chemoprevention or vaccines. Conclusions Targeting chemoprevention or vaccination programmes to areas where community-based parasite prevalence is ≥10% is likely to match the age ranges covered by interventions (e.g. intermittent presumptive treatment in infancy to children aged 2–23 months and current vaccine age eligibility and duration of efficacy) and the age ranges of highest disease burden.

Background Access to major services, often located in urban centres, is key to the realisation of numerous Sustainable Development Goals (SDGs). In Kenya, there are no up-to-date and localised estimates of spatial access to urban centres. We estimate the travel time to urban centres and identify marginalised populations for prioritisation and targeting. Methods Urban centres were mapped from the 2019 Kenya population census and combined with spatial databases of road networks, elevation, land use and travel barriers within a cost-friction algorithm to compute travel time. Seven travel scenarios were considered: i) walking only (least optimistic), ii) bicycle only, iii) motorcycle only, iv) vehicle only (most optimistic), v) walking followed by motorcycle transport, vi) walking followed by vehicle transport, and vii) walking followed by motorcycle and then vehicle transport (most pragmatic). Mean travel time, and proportion of the population within 1-hour and 2-hours of the urban centres were summarized at sub-national units (counties) used for devolved planning. Inequities were explored and correlations between the proportion of the population within 1-hour of an urban centre and ten SDG indicators were computed. Results A total of 307 urban centres were digitised. Nationally, the mean travel time was 4.5-hours for the walking-only scenario, 1.0-hours for the vehicle only (most optimistic) scenario and 1.5-hours for the walking-motorcycle-vehicle (most pragmatic) scenario. Forty-five per cent (21.3 million people) and 87% (41.6 million people) of Kenya’s population resided within 1-hour of the nearest urban centre for the least optimistic and most pragmatic scenarios respectively. Over 3.2 million people were considered marginalised or living outside the 2-hour threshold in the pragmatic scenario, 16.0 million Kenyans for walking only, and 2.2 million for the most optimistic scenario. County-level spatial access was highly heterogeneous ranging between 8%-100% and 32%-100% of people within the 1-hour threshold for the least and most optimistic scenarios, respectively. Counties in northern and eastern parts of Kenya were generally most marginalised. The correlation coefficients for nine SDG indicators ranged between 0.45 to 0.78 and were statistically significant. Conclusion Travel time to urban centres in Kenya is heterogeneous. Therefore, marginalised populations should be prioritised during resource allocation and policies should be formulated to enhance equitable access to public services and opportunities in urban areas.

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The geographical area around a health facility characterizing the population that utilizes some or all of its services, — a health facility catchment area (HFCA) —, forms the fundamental basis of estimating reliable population denominator for disease mapping and routine healthcare planning. Consequently, the approaches used to delineate the catchment area have a direct impact on the health of a population. To date, there is no systematic literature review documenting different approaches that have been used to define HFCAs while elucidating the implications on derived population denominators. To fill this gap, we systematically reviewed literature and documented approaches that have been used to define HFCA in sub —Saharan Africa (SSA). Simple to complex approaches have been used to define catchment areas in SSA with varying degrees of complexity and limitations in the last four decades. These approaches are mainly driven by lack of geocoded data on the residential address of care seekers and their care—seeking behaviour. To generate closer—to— reality HFCA, for robust disease mapping and healthcare planning, additional data and innovative approaches balancing between model complexity and routine programmatic use are required.

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.