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.

Geographical accessibility in assessing bypassing behaviour for inpatient neonatal care, Bungoma County-Kenya.

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.

Exploring evapotranspiration dynamics over Sub-Sahara Africa (2000-2014).

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.

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya.

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.

Influence of global climate on freshwater changes in Africa's largest endorheic basin using multi-scaled indicators.

The poor investments in gauge measurements for hydro-climatic research in Africa has necessitated the need to investigate how decision makers can leverage on sophisticated space-borne measurements to improve knowledge on surface water hydrology that can feed directly into water accounting processes, and risk assessment from extreme droughts and its impacts. To demonstrate such potential, a suite of satellite earth observations (Sentinel-2, altimetry, Landsat, GRACE, and TRMM) and model data are combined with the standardized precipitation evapotranspiration index to assess the impacts of global climate on freshwater dynamics over the LCB (Lake Chad basin), Africa's largest endorheic basin. As shown in the results of this study, the significant relationship of climate modes (AMO; r=0.68 and 0.59; and AMM; r=0.42 and 0.47) with drought patterns in the LCB highlights the evidence of global climate influence in the region. The significant declines in drought extents and their intensities (2004 - 2015) over LCB coincide with the rise in surface water extent of the Lake Chad during the same period. Change detection analysis of open water features in the southern pool of Lake Chad during the 2015 - 2019 period shows that on the average, only 28.4% of inundated areas within the vicinity of the Lake persisted during the period. While the association of terrestrial water storage (TWS) with model-derived surface water storage (SWS) is strongest (r=0.89) in the catchments that provide the most nourishment to the Lake Chad, the relationship of rainfall (2002 - 2017) with TWS (r=0.85), model TWS (r=0.87) and SWS (r=0.88) confirm that the LCB's hydrology is predominantly climate-driven. This notion is further reinforced as the predicted SWS over the LCB using a support vector machine regression scheme was found to be strongly correlated (r=0.95 at α=0.05) with observed SWS.

Modelling the impacts of global multi-scale climatic drivers on hydro-climatic extremes (1901-2014) over the Congo basin.

The knowledge of interactions between oceanic and atmospheric processes and associated influence on drought episodes is a key step toward designing robust measure that could support government and institutional measures for drought preparedness to promote region-specific drought risk-management policy solutions. This has become necessary for the Congo basin where the preponderance of evidence from few case studies shows long-term drying and hydro-climatic extremes attributed to perturbations of the nearby oceans. In this study, statistical relationships are developed between observed standardised precipitation index (SPI) and global sea surface temperature using principal component analysis as a regularization tool prior to the implementation of a canonical scheme. The connectivity between SPI patterns and global ocean-atmosphere phenomena was thereafter examined using the output from this scheme in a predictive framework based on non-linear autoregressive standard neural network. The Congo basin is shown to have been characterized by persistent and severe multi-year droughts during the earlier (1901-1930) and latter (1991-2014) decades of the last century. The impacts of these droughts were extensive affecting more than 50% of the basin between 1901 and 1930 and about 40% during the 1994-2006 period. Analysis of the latest decades (1994-2014) shows that relative to the two climatological periods between 1931 and 1990, the Congo basin has somewhat become drier. This likely contributed to the observed change in the hydrological regimes of the Congo river (after 1994) as indicated by the relationship between SPI and runoff index (r = 0.69 and 0.64 for 1931-1990 and 1961-1990 periods, respectively as opposed to r = 0.38 for 1991-2010 period). Pacific ENSO influences large departures in precipitation (r = 0.89) but prediction skill metrics demonstrate that multi-scale ocean-atmosphere phenomena (R2 = 84%, 78%, and 77% for QBO, AMO, and ENSO, respectively) significantly impact on hydro-climatic extremes, especially droughts over the Congo basin.