PMC:4101712 / 2018-6039 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4101712","sourcedb":"PMC","sourceid":"4101712","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4101712","text":"Background\nThe Companion Animal Parasite Council (CAPC) has compiled a data set of over nine million heartworm antigen tests performed on dogs in the United States during 2011 and 2012 [1]. These data are test results taken from dogs that visited veterinary clinics throughout the United States. From this data, our goal is to quantify the environmental, socio-economic, and vector factors that influence canine heartworm prevalence rates. Brown et al.[2] describe the data and list factors posited to influence canine heartworm prevalence rates (these are discussed fully below). This paper quantitatively assesses which of the factors are important (or unimportant) and in what direction the factors impact (increase or decrease) heartworm prevalence rates.\nThe data will be used to estimate the probability that a dog entering a veterinary clinic will test positive for heartworm. Although prevalence rates increase if a dog is from an area with a high heartworm transmission rate, the raw data do not describe transmission, but rather the risk that a dog’s infection is detected if it enters a clinic within the United States and is tested for heartworm for any reason whatsoever. The most common reasons for testing a dog are: assessing the negative status of a dog before it begins heartworm prevention, annual testing of dogs on preventive prophylaxis to verify that the dog has been protected, and assessing whether or not a dog with clinical signs suggestive of heartworm disease is indeed infected. In many areas of the United States, dogs are kept on prophylaxis year round; however, in some areas, veterinarians utilize heartworm prevention seasonally. Here, annual testing verifies whether or not infection occurred during the period when preventives were not taken. To assess true transmission rates, it would be more appropriate to follow dogs or other canines, specifically coyotes, that are not receiving any form of prophylaxis [3,4]— these are not the canines studied here. According to the unpublished abstract of Pulaski et al. for the 58th Annual Meeting of the American Association of Veterinary Parasitologists and the unpublished presentation of Blagburn et al. at The Triennial Symposium of the American Heartworm Society in 2013, in parts of the United States, resistance to heartworm preventatives has been recognized. Thus, future data may help detect preventive failure.\nA spatial logistic regression model will be fitted to the CAPC county-by-county heartworm test results and related to factor measurements. Logistic regression methods are used in lieu of ordinary regression techniques because prevalence probabilities, which must lie in the interval [0,1], are being modeled. A significant technical challenge involves the large number of counties reporting a small number of tests (often this count is zero). Small sample sizes from isolated counties can adversely impact results if not properly handled. Therefore, methods are developed that account for sample size issues. The head-banging algorithm, a method for smoothing the county-by-county prevalence rates, will be used to extract general spatial structure in the prevalence estimates; this procedure is adept at dealing with outlying observations and boundary (edge) features.\nOur results are useful in a variety of contexts. First and foremost, predicting heartworm prevalence rates alerts the pet owner to high-risk areas. This will be evident from the baseline risk maps constructed in Section \"Construction of the baseline heartworm prevalence map\". Second, pinpointing the factors accompanying high heartworm prevalence rates provides an opportunity to target those factors in mosquito and heartworm control programs. Third, our results provide a quantitative analysis of canine heartworm across the entire country, allowing us to confirm that cases do occur in the Western United States. Finally, the fitted regression model can be used to forecast future prevalence levels of heartworm or its response to climate change.","divisions":[{"label":"title","span":{"begin":0,"end":10}},{"label":"p","span":{"begin":11,"end":759}},{"label":"p","span":{"begin":760,"end":2400}},{"label":"p","span":{"begin":2401,"end":3270}}],"tracks":[{"project":"2_test","denotations":[{"id":"24906567-23111089-82426939","span":{"begin":453,"end":454},"obj":"23111089"},{"id":"24906567-21281213-82426940","span":{"begin":1949,"end":1950},"obj":"21281213"},{"id":"T96663","span":{"begin":453,"end":454},"obj":"23111089"},{"id":"T73970","span":{"begin":1949,"end":1950},"obj":"21281213"}],"attributes":[{"subj":"24906567-23111089-82426939","pred":"source","obj":"2_test"},{"subj":"24906567-21281213-82426940","pred":"source","obj":"2_test"},{"subj":"T96663","pred":"source","obj":"2_test"},{"subj":"T73970","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#a1ec93","default":true}]}]}}