03227nas a2200493   4500000000100000008004100001260005300042653002700095653002400122653001800146653001400164653002200178653001000200100001300210700001400223700001200237700001300249700001300262700001300275700001300288700001200301700001400313700001200327700001200339700001600351700001200367700001200379700001300391700001300404700001300417700001500430700001300445700001300458700001400471700001500485700001300500700001900513700001300532245013000545856006900675300001100744520196400755022001402719       2026                            d  c10/2026bSpringer Science and Business Media LLC10aVisceral Leishmaniasis10aGeographical spread10aAccessibility10aTreatment10atreatment centres10aKenya1 aMutono N1 aNjuguna B1 aMwiti D1 aKibati P1 aOmondi G1 aOmondi W1 aNthiwa J1 aOchol D1 aKariuki L1 aOfire M1 aOundo J1 aNdung’u L1 aGakuo G1 aOyugi J1 aLowana F1 aOkunga E1 aLangat D1 aMuchangi J1 aSenagi K1 aSafari P1 aNzilani M1 aGathirwa J1 aMasiga D1 aMatoke-Muhia D1 aThumbi S00aSub-national estimates of visceral leishmaniasis and optimisation of geographical accessibility to treatment centres in Kenya  uhttps://www.nature.com/articles/s43856-026-01958-z_reference.pdf  a1 - 113 a<p><strong>Background</strong></p>

<p>Visceral leishmaniasis (VL) remains a major public health challenge in Kenya, disproportionately affecting populations with limited access to timely diagnosis and treatment. National case totals mask local heterogeneity in risk and access. Here we estimate sub-national VL incidence, identify its determinants and optimise geographical placement of treatment centres.</p>

<p><strong>Methods</strong></p>

<p>We analysed nine years (October 2016-September 2025) of ward-level VL surveillance data from health facilities, with WorldPop population denominators. Bayesian small-area models related case occurrence and counts to environmental, socioeconomic and accessibility covariates. Travel time to the 46 existing treatment centres was computed over a motorised friction surface, and additional sites prioritised to maximise population coverage.</p>

<p><strong>Results</strong></p>

<p>We record 11,985 cases, an estimated incidence of 10 per 100,000 population per year, with marked seasonality and epidemic periods. Burden is highly focal: five northern counties account for 86% of cases, with further clustering at sub-county and ward levels and predicted incidence exceeding 100 per 100,000 person-years in several wards in Turkana and West Pokot. Higher temperature, poverty, and poor housing are associated with greater probability of occurrence and higher case counts; higher precipitation is inversely associated. Only 45% of the population in need can reach a treatment centre within 120 minutes of motorised travel. Five optimally sited additions increase this 120-minute coverage to 76%, with diminishing gains from further expansion.</p>

<p><strong>Conclusion</strong></p>

<p>Integrating small-area disease modelling with geospatial accessibility analysis reveals hidden inequities in VL burden and supports equitable, efficient deployment of treatment services towards elimination.</p>
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