Factors influencing U.S. canine heartworm (Dirofilaria immitis) prevalence

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• 作者：Dongmei Wang (1)
  Dwight D Bowman (2)
  Heidi E Brown (3)
  Laura C Harrington (4)
  Phillip E Kaufman (5)
  Tanja McKay (6)
  Charles Thomas Nelson (7)
  Julia L Sharp (1)
  Robert Lund (1)
  
  1. Department of Mathematical Sciences ; Clemson University ; Clemson ; SC ; 29634-0975 ; USA
  2. Department of Microbiology and Immunology ; College of Veterinary Medicine ; Cornell University ; Ithaca ; NY ; 14853 ; USA
  3. Division of Epidemiology and Biostatistics ; College of Public Health ; University of Arizona ; Tucson ; AZ ; 85724 ; USA
  4. Department of Entomology ; Cornell University ; Ithaca ; NY ; 14853 ; USA
  5. Entomology and Nematology Department ; University of Florida ; Gainesville ; FL ; 32611 ; USA
  6. Department of Biological Sciences ; Arkansas State University ; State University ; Kragujevac ; AR ; 72467 ; USA
  7. Animal Medical Center ; Anniston ; AL ; 36201 ; USA
  
• 关键词：Canine heartworm ; Dirofilaria immitis ; Head ; banging smoothing ; Mosquito vectors ; Prevalence rates
• 刊名：Parasites & Vectors
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：7
• 期：1
• 全文大小：12,128 KB
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  26. Shearer, P (2011) Literature review - heartworm disease. Banfield Appl Res & Knowl Team. pp. 1-16
  
• 刊物主题：Parasitology; Infectious Diseases; Tropical Medicine; Entomology;
• 出版者：BioMed Central
• ISSN：1756-3305

文摘

Background This paper examines the individual factors that influence prevalence rates of canine heartworm in the contiguous United States. A data set provided by the Companion Animal Parasite Council, which contains county-by-county results of over nine million heartworm tests conducted during 2011 and 2012, is analyzed for predictive structure. The goal is to identify the factors that are important in predicting high canine heartworm prevalence rates. Methods The factors considered in this study are those envisioned to impact whether a dog is likely to have heartworm. The factors include climate conditions (annual temperature, precipitation, and relative humidity), socio-economic conditions (population density, household income), local topography (surface water and forestation coverage, elevation), and vector presence (several mosquito species). A baseline heartworm prevalence map is constructed using estimated proportions of positive tests in each county of the United States. A smoothing algorithm is employed to remove localized small-scale variation and highlight large-scale structures of the prevalence rates. Logistic regression is used to identify significant factors for predicting heartworm prevalence. Results All of the examined factors have power in predicting heartworm prevalence, including median household income, annual temperature, county elevation, and presence of the mosquitoes Aedes trivittatus, Aedes sierrensis and Culex quinquefasciatus. Interactions among factors also exist. Conclusions The factors identified are significant in predicting heartworm prevalence. The factor list is likely incomplete due to data deficiencies. For example, coyotes and feral dogs are known reservoirs of heartworm infection. Unfortunately, no complete data of their populations were available. The regression model considered is currently being explored to forecast future values of heartworm prevalence.