title: High-Resolution Spatial Distribution and Estimation of Access to Improved Sanitation in Kenya.
abstract: BACKGROUND
  Access to sanitation facilities is imperative in reducing the risk of multiple adverse health outcomes. A distinct disparity in sanitation exists among different wealth levels in many low-income countries, which may hinder the progress across each of the Millennium Development Goals.
  
  
  METHODS
  The surveyed households in 397 clusters from 2008-2009 Kenya Demographic and Health Surveys were divided into five wealth quintiles based on their national asset scores. A series of spatial analysis methods including excess risk, local spatial autocorrelation, and spatial interpolation were applied to observe disparities in coverage of improved sanitation among different wealth categories. The total number of the population with improved sanitation was estimated by interpolating, time-adjusting, and multiplying the surveyed coverage rates by high-resolution population grids. A comparison was then made with the annual estimates from United Nations Population Division and World Health Organization /United Nations Children's Fund Joint Monitoring Program for Water Supply and Sanitation.
  
  
  RESULTS
  The Empirical Bayesian Kriging interpolation produced minimal root mean squared error for all clusters and five quintiles while predicting the raw and spatial coverage rates of improved sanitation. The coverage in southern regions was generally higher than in the north and east, and the coverage in the south decreased from Nairobi in all directions, while Nyanza and North Eastern Province had relatively poor coverage. The general clustering trend of high and low sanitation improvement among surveyed clusters was confirmed after spatial smoothing.
  
  
  CONCLUSIONS
  There exists an apparent disparity in sanitation among different wealth categories across Kenya and spatially smoothed coverage rates resulted in a closer estimation of the available statistics than raw coverage rates. Future intervention activities need to be tailored for both different wealth categories and nationally where there are areas of greater needs when resources are limited.
authors.affiliation: Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, 7500, The Netherlands.
authors.forename: Peng
authors.initials: P
authors.lastname: Jia
authors.affiliation: Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States of America.|Department of Environmental and Global Health, University of Florida, Gainesville, FL, United States of America.
authors.forename: John D
authors.initials: JD
authors.lastname: Anderson
authors.affiliation: Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA, United States of America.|Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, Austria.
authors.forename: Michael
authors.initials: M
authors.lastname: Leitner
authors.affiliation: Goodnight Sustainable Development Department, Appalachian State University, Boone, NC, United States of America.
authors.forename: Richard
authors.initials: R
authors.lastname: Rheingans
mesh_terms: D003625:Data Collection; D006801:Humans; D007630:Kenya; D011156:Population Density; D012499:Sanitation / Q000706:statistics & numerical data*; D062206:Spatial Analysis