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Passive Surveillance of Human-Biting Ixodes scapularis Ticks in Massachusetts from 2015–2019

Author

Listed:
  • Alexandra Sack

    (Clinical and Translational Science Graduate Program, Tufts University Graduate School of Biomedical Sciences, Boston, MA 02111, USA
    Department of Biological Sciences, Eck Institute of Global Health, University of Notre Dame, Notre Dame, IN 46556, USA)

  • Elena N. Naumova

    (Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA)

  • Lori Lyn Price

    (Tufts Institute of Clinical and Translational Science, Tufts University, Boston, MA 02111, USA
    Institute of Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA)

  • Guang Xu

    (Laboratory of Medical Zoology, Department of Microbiology, University of Massachusetts, Amherst, MA 01003, USA)

  • Stephen M. Rich

    (Laboratory of Medical Zoology, Department of Microbiology, University of Massachusetts, Amherst, MA 01003, USA)

Abstract

This study aimed to analyze human-biting Ixodes scapularis ticks submitted to TickReport tick testing service from 2015–2019 in Massachusetts to (1) examine possible patterns of pathogen-positive adult and nymphal ticks over time and (2) explore how socioeconomic factors can influence tick submissions. A passive surveillance data set of ticks and tick-borne pathogens was conducted over 5 years (2015–2019) in Massachusetts. The percentages of four tick-borne pathogens: Borrelia burgdorferi, Anaplasma phagocytophilum, Babesia microti , and Borrelia miyamotoi were determined by Massachusetts county and by month and year. Regression models were used to examine the association between zip-code-level socioeconomic factors and submissions. A total of 13,598 I. scapularis ticks were submitted to TickReport from Massachusetts residents. The infection rate of B. burgdorferi, A. phagocytophilum , and B. microti was 39%, 8%, and 7% in adult ticks; 23%, 6%, and 5% in nymphal ticks, respectively. A relatively higher level of education was associated with high tick submission. Passive surveillance of human-biting ticks and associated pathogens is important for monitoring tick-borne diseases, detecting areas with potentially high risks, and providing public information. Socioeconomic factors should be considered to produce more generalizable passive surveillance data and to target potentially underserved areas.

Suggested Citation

  • Alexandra Sack & Elena N. Naumova & Lori Lyn Price & Guang Xu & Stephen M. Rich, 2023. "Passive Surveillance of Human-Biting Ixodes scapularis Ticks in Massachusetts from 2015–2019," IJERPH, MDPI, vol. 20(5), pages 1-11, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4306-:d:1083240
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    References listed on IDEAS

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    1. Yan Liu & Stella C Watson & Jenna R Gettings & Robert B Lund & Shila K Nordone & Michael J Yabsley & Christopher S McMahan, 2017. "A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-18, July.
    2. Yuting Dong & Zheng Huang & Yong Zhang & Yingying X.G. Wang & Yang La, 2020. "Comparing the Climatic and Landscape Risk Factors for Lyme Disease Cases in the Upper Midwest and Northeast United States," IJERPH, MDPI, vol. 17(5), pages 1-10, February.
    3. Cameron, A Colin & Windmeijer, Frank A G, 1996. "R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 209-220, April.
    4. Stella C Watson & Yan Liu & Robert B Lund & Jenna R Gettings & Shila K Nordone & Christopher S McMahan & Michael J Yabsley, 2017. "A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-22, May.
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