title: Spatial distribution and cluster analysis of retail drug shop characteristics and antimalarial behaviors as reported by private medicine retailers in western Kenya: informing future interventions.
abstract: BACKGROUND
  Efforts to improve malaria case management in sub-Saharan Africa have shifted focus to private antimalarial retailers to increase access to appropriate treatment. Demands to decrease intervention cost while increasing efficacy requires interventions tailored to geographic regions with demonstrated need. Cluster analysis presents an opportunity to meet this demand, but has not been applied to the retail sector or antimalarial retailer behaviors. This research conducted cluster analysis on medicine retailer behaviors in Kenya, to improve malaria case management and inform future interventions.
  
  
  METHODS
  Ninety-seven surveys were collected from medicine retailers working in the Webuye Health and Demographic Surveillance Site. Survey items included retailer training, education, antimalarial drug knowledge, recommending behavior, sales, and shop characteristics, and were analyzed using Kulldorff's spatial scan statistic. The Bernoulli purely spatial model for binomial data was used, comparing cases to controls. Statistical significance of found clusters was tested with a likelihood ratio test, using the null hypothesis of no clustering, and a p value based on 999 Monte Carlo simulations. The null hypothesis was rejected with p values of 0.05 or less.
  
  
  RESULTS
  A statistically significant cluster of fewer than expected pharmacy-trained retailers was found (RR = .09, p = .001) when compared to the expected random distribution. Drug recommending behavior also yielded a statistically significant cluster, with fewer than expected retailers recommending the correct antimalarial medication to adults (RR = .018, p = .01), and fewer than expected shops selling that medication more often than outdated antimalarials when compared to random distribution (RR = 0.23, p = .007). All three of these clusters were co-located, overlapping in the northwest of the study area.
  
  
  CONCLUSIONS
  Spatial clustering was found in the data. A concerning amount of correlation was found in one specific region in the study area where multiple behaviors converged in space, highlighting a prime target for interventions. These results also demonstrate the utility of applying geospatial methods in the study of medicine retailer behaviors, making the case for expanding this approach to other regions.
authors.affiliation: The University of Texas School of Public Health, Pressler Dr, Houston, TX, USA. andriaerusk@gmail.com.
authors.forename: Andria
authors.initials: A
authors.lastname: Rusk
authors.affiliation: The University of Texas School of Public Health, Pressler Dr, Houston, TX, USA. Linda.D.Highfield@uth.tmc.edu.
authors.forename: Linda
authors.initials: L
authors.lastname: Highfield
authors.affiliation: The University of Texas School of Public Health, Pressler Dr, Houston, TX, USA. Johnny.M.Wilkerson@uth.tmc.edu.
authors.forename: J Michael
authors.initials: JM
authors.lastname: Wilkerson
authors.affiliation: The University of Texas School of Public Health, Pressler Dr, Houston, TX, USA. Melissa.B.Harrell@uth.tmc.edu.
authors.forename: Melissa
authors.initials: M
authors.lastname: Harrell
authors.affiliation: Moi University School of Medicine, Nandi Rd, Eldoret, Kenya. Andrew.Obala@gmail.com.|Webuye Demographic Surveillance Site Scientific Steering Committee, Eldoret, Kenya. Andrew.Obala@gmail.com.
authors.forename: Andrew
authors.initials: A
authors.lastname: Obala
authors.affiliation: Department of Health Policy and Management, Florida International University, Robert Stempel College of Public Health and Social Work, Miami, FL, USA. BAmickii@fiu.edu.|Institute for Work and Health, Toronto, Canada. BAmickii@fiu.edu.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Amick
mesh_terms: D000328:Adult; D000962:Antimalarials / Q000191:economics / Q000627:therapeutic use*; D016000:Cluster Analysis; D003132:Commerce / Q000706:statistics & numerical data* / Q000639:trends; D003710:Demography / Q000706:statistics & numerical data* / Q000639:trends; D005260:Female; D005544:Forecasting; D006801:Humans; D007630:Kenya / Q000453:epidemiology; D008288:Malaria / Q000188:drug therapy / Q000191:economics / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D010594:Pharmacies / Q000706:statistics & numerical data* / Q000639:trends; D011795:Surveys and Questionnaires
chemical_list: D000962:Antimalarials