The Science of the total environment · 2019
The effects of multiyear and seasonal weather factors on incidence of Lyme disease and its vector in New York State.
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Lin Shao, Shrestha Srishti, Prusinski Melissa A, White Jennifer L, Lukacik Gary, Smith Maggie, Lu Jianhai, Backenson Bryon
Аннотация
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: 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: 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: 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: Extended spring and summer days and mild winter temperatures appear to increase Lyme disease cases and tick exposure risk in NYS.
OBJECTIVES: 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: 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: 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: Extended spring and summer days and mild winter temperatures appear to increase Lyme disease cases and tick exposure risk in NYS.