Environmental risk factors for Lyme disease identified with geographic information systems
A geographic information system was used to identify and locate residential environmental risk factors for Lyme disease. 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 se...
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| Veröffentlicht in: | American journal of public health (1971) 1995-07, Vol.85 (7), p.944-948 |
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| creator | Glass, G E Schwartz, B S Morgan, J M, III Johnson, D T Noy, P M Israel, E |
| description | A geographic information system was used to identify and locate residential environmental risk factors for Lyme disease.
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.
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.
Combining a geographic information system with epidemiologic methods can be used to rapidly identify risk factors of zoonotic disease over large areas. |
| doi_str_mv | 10.2105/AJPH.85.7.944 |
| format | Article |
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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.
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.
Combining a geographic information system with epidemiologic methods can be used to rapidly identify risk factors of zoonotic disease over large areas.</description><identifier>ISSN: 0090-0036</identifier><identifier>EISSN: 1541-0048</identifier><identifier>DOI: 10.2105/AJPH.85.7.944</identifier><identifier>PMID: 7604918</identifier><identifier>CODEN: AJPEAG</identifier><language>eng</language><publisher>Washington, DC: Am Public Health Assoc</publisher><subject>Agriculture ; Arachnids ; Bacterial diseases ; Baltimore ; Biological and medical sciences ; Borrelia burgdorferi ; Borrelia infections ; Data analysis ; Digitization ; Environment ; Environmental Exposure - adverse effects ; Environmental Exposure - statistics & numerical data ; Environmental protection ; Forests ; Geographic information systems ; Geography ; Health risk assessment ; Human bacterial diseases ; Humans ; Identification ; Infectious diseases ; Information Systems ; Land use ; Logistic Models ; Lyme disease ; Lyme Disease - epidemiology ; Maryland - epidemiology ; Medical sciences ; Odds Ratio ; Patients ; Physical properties ; Public health ; Regression analysis ; Residence Characteristics ; Risk Factors ; Social research ; Software ; Trends ; Tropical bacterial diseases ; Variables ; Watershed management ; Watersheds</subject><ispartof>American journal of public health (1971), 1995-07, Vol.85 (7), p.944-948</ispartof><rights>1995 INIST-CNRS</rights><rights>Copyright American Public Health Association Jul 1995</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c559t-59b82ce17d0bf79f9885da7f1a12f4da13880567985518252994d949d467eba33</citedby><cites>FETCH-LOGICAL-c559t-59b82ce17d0bf79f9885da7f1a12f4da13880567985518252994d949d467eba33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC1615529/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC1615529/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,27843,27846,27901,27902,30977,53766,53768</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=3599238$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/7604918$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Glass, G E</creatorcontrib><creatorcontrib>Schwartz, B S</creatorcontrib><creatorcontrib>Morgan, J M, III</creatorcontrib><creatorcontrib>Johnson, D T</creatorcontrib><creatorcontrib>Noy, P M</creatorcontrib><creatorcontrib>Israel, E</creatorcontrib><title>Environmental risk factors for Lyme disease identified with geographic information systems</title><title>American journal of public health (1971)</title><addtitle>Am J Public Health</addtitle><description>A geographic information system was used to identify and locate residential environmental risk factors for Lyme disease.
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.
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.
Combining a geographic information system with epidemiologic methods can be used to rapidly identify risk factors of zoonotic disease over large areas.</description><subject>Agriculture</subject><subject>Arachnids</subject><subject>Bacterial diseases</subject><subject>Baltimore</subject><subject>Biological and medical sciences</subject><subject>Borrelia burgdorferi</subject><subject>Borrelia infections</subject><subject>Data analysis</subject><subject>Digitization</subject><subject>Environment</subject><subject>Environmental Exposure - adverse effects</subject><subject>Environmental Exposure - statistics & numerical data</subject><subject>Environmental protection</subject><subject>Forests</subject><subject>Geographic information systems</subject><subject>Geography</subject><subject>Health risk assessment</subject><subject>Human bacterial diseases</subject><subject>Humans</subject><subject>Identification</subject><subject>Infectious diseases</subject><subject>Information Systems</subject><subject>Land use</subject><subject>Logistic Models</subject><subject>Lyme disease</subject><subject>Lyme Disease - epidemiology</subject><subject>Maryland - epidemiology</subject><subject>Medical sciences</subject><subject>Odds Ratio</subject><subject>Patients</subject><subject>Physical properties</subject><subject>Public health</subject><subject>Regression analysis</subject><subject>Residence Characteristics</subject><subject>Risk Factors</subject><subject>Social research</subject><subject>Software</subject><subject>Trends</subject><subject>Tropical bacterial diseases</subject><subject>Variables</subject><subject>Watershed 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J Public Health</addtitle><date>1995-07-01</date><risdate>1995</risdate><volume>85</volume><issue>7</issue><spage>944</spage><epage>948</epage><pages>944-948</pages><issn>0090-0036</issn><eissn>1541-0048</eissn><coden>AJPEAG</coden><abstract>A geographic information system was used to identify and locate residential environmental risk factors for Lyme disease.
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.
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.
Combining a geographic information system with epidemiologic methods can be used to rapidly identify risk factors of zoonotic disease over large areas.</abstract><cop>Washington, DC</cop><pub>Am Public Health Assoc</pub><pmid>7604918</pmid><doi>10.2105/AJPH.85.7.944</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 0090-0036 |
| ispartof | American journal of public health (1971), 1995-07, Vol.85 (7), p.944-948 |
| issn | 0090-0036 1541-0048 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_1615529 |
| source | Applied Social Sciences Index & Abstracts (ASSIA); MEDLINE; PAIS Index; Business Source Complete; PubMed Central; Alma/SFX Local Collection; Periodicals Index Online |
| subjects | Agriculture Arachnids Bacterial diseases Baltimore Biological and medical sciences Borrelia burgdorferi Borrelia infections Data analysis Digitization Environment Environmental Exposure - adverse effects Environmental Exposure - statistics & numerical data Environmental protection Forests Geographic information systems Geography Health risk assessment Human bacterial diseases Humans Identification Infectious diseases Information Systems Land use Logistic Models Lyme disease Lyme Disease - epidemiology Maryland - epidemiology Medical sciences Odds Ratio Patients Physical properties Public health Regression analysis Residence Characteristics Risk Factors Social research Software Trends Tropical bacterial diseases Variables Watershed management Watersheds |
| title | Environmental risk factors for Lyme disease identified with geographic information systems |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T17%3A17%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Environmental%20risk%20factors%20for%20Lyme%20disease%20identified%20with%20geographic%20information%20systems&rft.jtitle=American%20journal%20of%20public%20health%20(1971)&rft.au=Glass,%20G%20E&rft.date=1995-07-01&rft.volume=85&rft.issue=7&rft.spage=944&rft.epage=948&rft.pages=944-948&rft.issn=0090-0036&rft.eissn=1541-0048&rft.coden=AJPEAG&rft_id=info:doi/10.2105/AJPH.85.7.944&rft_dat=%3Cproquest_pubme%3E1298323689%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=215105572&rft_id=info:pmid/7604918&rfr_iscdi=true |