Landscape risk factors for Lyme disease in the eastern broadleaf forest province of the Hudson River valley and the effect of explanatory data classification resolution.
Spat Spatiotemporal Epidemiol
; 12: 9-17, 2015 Jan.
Article
en En
| MEDLINE
| ID: mdl-25779905
ABSTRACT
This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R(2) (Rα(2)) and AIC. The maximum Rα(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. The aggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The individual model variables included deciduous forest, deciduous forest-squared, developed low intensity, pasture, y-coordinate, y-coordinate-squared, income, and income-squared. Results indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Enfermedad de Lyme
/
Bosques
Tipo de estudio:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Spat Spatiotemporal Epidemiol
Año:
2015
Tipo del documento:
Article
País de afiliación:
Estados Unidos