Environmental risk factors for Lyme disease identified with geographic information systems

Datos Bibliográficos

ID11032089
AutoresGregory E Glass (0000-0002-8560-2451, Johns Hopkins University), Brian S Schwartz (0000-0002-0739-9865, Johns Hopkins University), John M Morgan (Johns Hopkins University), Deborah Johnson (0000-0002-7851-7689, Johns Hopkins University), D T JOHNSON, P M Noy (Johns Hopkins University), E Israel (0000-0002-7521-0745, Johns Hopkins University)
Año1995
Volumen85
Número7
Páginas944-948
Fecha de publicación1995-07-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaAmerican Journal of Public Health (JOURNAL)
Identificadores de la revistaISSN: 0090-0036 • E-ISSN: 1541-0048
EditorialAmerican Public Health Association (PUBLISHER • US)
DOI10.2105/ajph.85.7.944
PMID7604918
OpenAlexW2054808150
IdiomaEN
Citas recibidas23
Referencias citadas16

OBJECTIVES. A geographic information system was used to identify and locate residential environmental risk factors for Lyme disease. METHODS. Data were obtained for 53 environmental variables at the residences of Lyme disease case patients in Baltimore County from 1989 through 1990 and compared with data for randomly selected addresses. A risk model was generated combining the geographic information system with logistic regression analysis. The model was validated by comparing the distribution of cases in 1991 with another group of randomly selected addresses. RESULTS. In crude analyses, 11 environmental variables were associated with Lyme disease. In adjusted analyses, residence in forested areas (odds ratio [OR] = 3.7, 95% confidence interval [CI] = 1.2, 11.8), on specific soils (OR = 2.1, 95% CI = 1.0, 4.4), and in two regions of the county (OR = 3.5, 95% CI = 1.6, 7.4) (OR = 2.8, 95% CI = 1.0, 7.7) was associated with elevated risk of getting Lyme disease. Residence in highly developed regions was protective (OR = 0.3, 95% CI = 0.1, 1.0). The risk of Lyme disease in 1991 increased with risk categories defined from the 1989 through 1990 data. CONCLUSIONS. Combining a geographic information system with epidemiologic methods can be used to rapidly identify risk factors of zoonotic disease over large areas

Disease · Environmental health · Geography · Lyme disease · Pathology · Data-Driven Disease Surveillance · Medicine · Vector-borne infectious diseases · Virology · Zoonotic diseases and public health