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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.

Monitoring changes in evapotranspiration (ET) is useful in the management of water resources in irrigated agricultural landscapes and in the assessment of crop stress and vegetation conditions of drought-vulnerable regions. Information on the impacts of climate variability on ET dynamics is profitable in developing water management adaptation strategies. Such impacts, however, are generally unreported and not conclusively determined in some regions. In this study, changes in MODIS (Moderate Resolution Imaging Spectroradiometer)-derived ET (2000–2014) over large proportions of Sub-Sahara Africa (SSA) are explored. The multivariate analyses of ET over SSA showed that four leading modes of observed dynamics in ET, accounting for about 90% of the total variability, emanated mostly from some sections of the Sudano-Sahel and Congo basin. Based on Man-Kendall’s statistics, significant positive trends ( α = 0.05) in ET over the Central African Republic and most parts of the Sahel region were observed. Over much of the Congo basin nonetheless, ET showed significant ( α = 0.05) distributions of widespread negative trends. These trends in ET were rather found to be consistent with observed changes in model soil moisture but not in all locations, perhaps due to inconsistent trends in maximum rainfall and land surface temperature. However, the results of spatio-temporal drought analysis confirm that the extensive ET losses in the Congo basin were somewhat induced by soil moisture deficits. Amidst other prominent drivers of ET, the dynamics of ET over the terrestrial ecosystems of SSA appear to be a more complex phenomenon that may transcend natural climate variations.

Background Neonatal mortality rate in Kenya continues to be unacceptably high. In reducing newborn deaths, inequality in access to care and quality care have been identified as current barriers. Contributing to these barriers are the bypassing behaviour and geographical access which leads to delay in seeking newborn care. This study (i) measured geographical accessibility of inpatient newborn care, and (ii), characterized bypassing behaviour using the geographical accessibility of the inpatient newborn care seekers. Methods Geographical accessibility to the inpatient newborn units was modelled based on travel time to the units across Bungoma County. Data was then collected from 8 inpatient newborn units and 395 mothers whose newborns were admitted in the units were interviewed. Their spatial residence locations were geo-referenced and were used against the modelled travel time to define bypassing behaviour. Results Approximately 90% of the sick newborn population have access to nearest newborn units (< 2 h). However, 36 % of the mothers bypassed their nearest inpatient newborn facility, with lack of diagnostic services (28%) and distrust of health personnel (37%) being the major determinants for bypassing. Approximately 75% of the care seekers preferred to use the higher tier facilities for both maternal and neonatal care in comparison to sub-county facilities which mostly were bypassed and remained underutilised. Conclusion Our findings suggest that though majority of the population have access to care, sub-county inpatient newborn facilities have high risk of being bypassed. There is need to improve quality of care in maternal care, to reduce bypassing behaviour and improving neonatal outcome.

Background Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. Methods A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. Results The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2  = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. Conclusion Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

Introduction Many HIV‐positive individuals in Africa have advanced disease when initiating antiretroviral therapy (ART) so have high risks of opportunistic infections and death. The REALITY trial found that an enhanced‐prophylaxis package including fluconazole reduced mortality by 27% in individuals starting ART with CD4 <100 cells/mm3. We investigated the cost‐effectiveness of this enhanced‐prophylaxis package versus other strategies, including using cryptococcal antigen (CrAg) testing, in individuals with CD4 <200 cells/mm3 or <100 cells/mm3 at ART initiation and all individuals regardless of CD4 count. Methods The REALITY trial enrolled from June 2013 to April 2015. A decision‐analytic model was developed to estimate the cost‐effectiveness of six management strategies in individuals initiating ART in the REALITY trial countries. Strategies included standard‐prophylaxis, enhanced‐prophylaxis, standard‐prophylaxis with fluconazole; and three CrAg testing strategies, the first stratifying individuals to enhanced‐prophylaxis (CrAg‐positive) or standard‐prophylaxis (CrAg‐negative), the second to enhanced‐prophylaxis (CrAg‐positive) or enhanced‐prophylaxis without fluconazole (CrAg‐negative) and the third to standard‐prophylaxis with fluconazole (CrAg‐positive) or without fluconazole (CrAg‐negative). The model estimated costs, life‐years and quality‐adjusted life‐years (QALY) over 48 weeks using three competing mortality risks: cryptococcal meningitis; tuberculosis, serious bacterial infection or other known cause; and unknown cause. Results Enhanced‐prophylaxis was cost‐effective at cost‐effectiveness thresholds of US $300 and US$ 500 per QALY with an incremental cost‐effectiveness ratio (ICER) of US $157 per QALY in the CD4 <200 cells/mm3 population providing enhanced‐prophylaxis components are sourced at lowest available prices. The ICER reduced in more severely immunosuppressed individuals (US$ 113 per QALY in the CD4 <100 cells/mm3 population) and increased in all individuals regardless of CD4 count (US $722 per QALY). Results were sensitive to prices of the enhanced‐prophylaxis components. Enhanced‐prophylaxis was more effective and less costly than all CrAg testing strategies as enhanced‐prophylaxis still conveyed health gains in CrAg‐negative patients and savings from targeting prophylaxis based on CrAg status did not compensate for costs of CrAg testing. CrAg testing strategies did not become cost‐effective unless the price of CrAg testing fell below US$ 2.30. Conclusions The REALITY enhanced‐prophylaxis package in individuals with advanced HIV starting ART reduces morbidity and mortality, is practical to administer and is cost‐effective. Efforts should continue to ensure that components are accessed at lowest available prices.