The effects of multiyear and seasonal weather factors on incidence of Lyme disease and its vector in New York State.


Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
15 May 2019
Historique:
received: 15 08 2018
revised: 12 01 2019
accepted: 07 02 2019
entrez: 22 3 2019
pubmed: 22 3 2019
medline: 17 4 2019
Statut: ppublish

Résumé

More frequent extreme weather and warmer weather due to climate change might change the spatiotemporal distributions of vector-borne diseases, including Lyme disease. However, limited studies have examined the associations of Lyme disease and its vectors with weather factors, especially multi-year and multi-weather factors related to vector life cycle. We investigated the associations between multi-year, unique weather indicators (relevant to tick and host activities) and Lyme disease incidence or documented I. scapularis encounters in New York State (NYS). Using a generalized estimating equation model, we linked Lyme disease and tick (I. scapularis) data, obtained from the NYS Department of Health (NYSDOH) Communicable Disease Surveillance and Tick Identification Service, with weather data. We used a season-specific exposure index by considering days in different seasons with certain temperature and precipitation ranges, summer Palmer Hydrological Drought Index, and fitted linear regression models using generalized estimating equations. Lyme disease and I. scapularis encounters were modestly correlated (Spearman correlation = 0.60, p-value <0.001). The results indicate that summer Lyme disease cases and tick encounters may increase by 4-10%, per one day in spring with a minimum temperature range between 40 and 50 °F in the year of diagnosis and previous year. A day increase in summer with maximum temperature > 75 °F in the previous year was associated with 2% increase in summer disease counts. Mild winter days were associated with an increase in summer tick encounters. Extended spring and summer days and mild winter temperatures appear to increase Lyme disease cases and tick exposure risk in NYS.

Sections du résumé

BACKGROUND BACKGROUND
More frequent extreme weather and warmer weather due to climate change might change the spatiotemporal distributions of vector-borne diseases, including Lyme disease. However, limited studies have examined the associations of Lyme disease and its vectors with weather factors, especially multi-year and multi-weather factors related to vector life cycle.
OBJECTIVES OBJECTIVE
We investigated the associations between multi-year, unique weather indicators (relevant to tick and host activities) and Lyme disease incidence or documented I. scapularis encounters in New York State (NYS).
METHODS METHODS
Using a generalized estimating equation model, we linked Lyme disease and tick (I. scapularis) data, obtained from the NYS Department of Health (NYSDOH) Communicable Disease Surveillance and Tick Identification Service, with weather data. We used a season-specific exposure index by considering days in different seasons with certain temperature and precipitation ranges, summer Palmer Hydrological Drought Index, and fitted linear regression models using generalized estimating equations.
RESULTS RESULTS
Lyme disease and I. scapularis encounters were modestly correlated (Spearman correlation = 0.60, p-value <0.001). The results indicate that summer Lyme disease cases and tick encounters may increase by 4-10%, per one day in spring with a minimum temperature range between 40 and 50 °F in the year of diagnosis and previous year. A day increase in summer with maximum temperature > 75 °F in the previous year was associated with 2% increase in summer disease counts. Mild winter days were associated with an increase in summer tick encounters.
CONCLUSIONS CONCLUSIONS
Extended spring and summer days and mild winter temperatures appear to increase Lyme disease cases and tick exposure risk in NYS.

Identifiants

pubmed: 30893749
pii: S0048-9697(19)30605-9
doi: 10.1016/j.scitotenv.2019.02.123
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1182-1188

Subventions

Organisme : NIEHS NIH HHS
ID : R15 ES028000
Pays : United States

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Auteurs

Shao Lin (S)

Department of Environmental Health Sciences, School of Public Health, University at Albany, State University at New York, Rensselaer, NY, United States of America; Department of Epidemiology and Biostatistics, University at Albany, State University at New York, Rensselaer, NY, United States of America. Electronic address: slin@albany.edu.

Srishti Shrestha (S)

Department of Epidemiology and Biostatistics, University at Albany, State University at New York, Rensselaer, NY, United States of America.

Melissa A Prusinski (MA)

Investigations and Vector Surveillance Units, Bureau of Communicable Disease Control, New York State Department of Health, Albany, NY, United States of America.

Jennifer L White (JL)

Investigations and Vector Surveillance Units, Bureau of Communicable Disease Control, New York State Department of Health, Albany, NY, United States of America.

Gary Lukacik (G)

Investigations and Vector Surveillance Units, Bureau of Communicable Disease Control, New York State Department of Health, Albany, NY, United States of America.

Maggie Smith (M)

Department of Epidemiology and Biostatistics, University at Albany, State University at New York, Rensselaer, NY, United States of America.

Jianhai Lu (J)

Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China.

Bryon Backenson (B)

Department of Epidemiology and Biostatistics, University at Albany, State University at New York, Rensselaer, NY, United States of America; Investigations and Vector Surveillance Units, Bureau of Communicable Disease Control, New York State Department of Health, Albany, NY, United States of America.

Articles similaires

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Robotic Surgical Procedures Animals Humans Telemedicine Models, Animal

Odour generalisation and detection dog training.

Lyn Caldicott, Thomas W Pike, Helen E Zulch et al.
1.00
Animals Odorants Dogs Generalization, Psychological Smell
Animals TOR Serine-Threonine Kinases Colorectal Neoplasms Colitis Mice

Classifications MeSH