Harmonizing work history data in epidemiologic studies with overlapping employment records
Background Work history data often require major data management including handling of overlapping jobs to avoid overestimating exposure before linkage to job‐exposure matrices (JEMs) is possible. Methods In a case‐cohort study of 1825 male Norwegian offshore petroleum workers, 3979 jobs were report...
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| Veröffentlicht in: | American journal of industrial medicine 2019-05, Vol.62 (5), p.422-429 |
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| container_title | American journal of industrial medicine |
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| creator | Stenehjem, Jo Steinson Babigumira, Ronnie Friesen, Melissa C Grimsrud, Tom Kristian |
| description | Background
Work history data often require major data management including handling of overlapping jobs to avoid overestimating exposure before linkage to job‐exposure matrices (JEMs) is possible.
Methods
In a case‐cohort study of 1825 male Norwegian offshore petroleum workers, 3979 jobs were reported (mean duration 2417 days/job; maximum 8 jobs/worker). Each job was assigned to one of 27 occupation categories. Overlapping jobs of the same category (1142 jobs) were collapsed and overlapping jobs of different categories (1013 jobs) were split. The resulting durations were weighted by a factor accounting for the number of overlapping jobs.
Results
Collapsing overlapping jobs within the same category resulted in 3295 jobs (mean 2629 days/job). Splitting overlapping jobs of different categories increased the number to 4239 jobs (mean 2043 days/job), while the total duration in days dropped by 10%.
Conclusions
We demonstrated that overlapping employment data structures can be harmonized in a systematic and unbiased way, preparing work history data for linkage to several JEMs. |
| doi_str_mv | 10.1002/ajim.22965 |
| format | Article |
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Work history data often require major data management including handling of overlapping jobs to avoid overestimating exposure before linkage to job‐exposure matrices (JEMs) is possible.
Methods
In a case‐cohort study of 1825 male Norwegian offshore petroleum workers, 3979 jobs were reported (mean duration 2417 days/job; maximum 8 jobs/worker). Each job was assigned to one of 27 occupation categories. Overlapping jobs of the same category (1142 jobs) were collapsed and overlapping jobs of different categories (1013 jobs) were split. The resulting durations were weighted by a factor accounting for the number of overlapping jobs.
Results
Collapsing overlapping jobs within the same category resulted in 3295 jobs (mean 2629 days/job). Splitting overlapping jobs of different categories increased the number to 4239 jobs (mean 2043 days/job), while the total duration in days dropped by 10%.
Conclusions
We demonstrated that overlapping employment data structures can be harmonized in a systematic and unbiased way, preparing work history data for linkage to several JEMs.</description><identifier>ISSN: 0271-3586</identifier><identifier>EISSN: 1097-0274</identifier><identifier>DOI: 10.1002/ajim.22965</identifier><identifier>PMID: 30919995</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Categories ; Credit ratings ; Data management ; Data structures ; Employment ; employment spells ; Epidemiology ; Exposure ; exposure assessment ; job‐exposure matrices (JEMs) ; occupational epidemiology ; Regional stocks ; work history ; Workers</subject><ispartof>American journal of industrial medicine, 2019-05, Vol.62 (5), p.422-429</ispartof><rights>2019 Wiley Periodicals, Inc.</rights><rights>info:eu-repo/semantics/openAccess</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4725-7441d91c9a2c2d201ef17080a5e0718a35d79ee077a827d634675951557c8f833</citedby><cites>FETCH-LOGICAL-c4725-7441d91c9a2c2d201ef17080a5e0718a35d79ee077a827d634675951557c8f833</cites><orcidid>0000-0002-1964-5410</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fajim.22965$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fajim.22965$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,315,781,785,886,1418,26572,27929,27930,45579,45580</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30919995$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Stenehjem, Jo Steinson</creatorcontrib><creatorcontrib>Babigumira, Ronnie</creatorcontrib><creatorcontrib>Friesen, Melissa C</creatorcontrib><creatorcontrib>Grimsrud, Tom Kristian</creatorcontrib><title>Harmonizing work history data in epidemiologic studies with overlapping employment records</title><title>American journal of industrial medicine</title><addtitle>Am J Ind Med</addtitle><description>Background
Work history data often require major data management including handling of overlapping jobs to avoid overestimating exposure before linkage to job‐exposure matrices (JEMs) is possible.
Methods
In a case‐cohort study of 1825 male Norwegian offshore petroleum workers, 3979 jobs were reported (mean duration 2417 days/job; maximum 8 jobs/worker). Each job was assigned to one of 27 occupation categories. Overlapping jobs of the same category (1142 jobs) were collapsed and overlapping jobs of different categories (1013 jobs) were split. The resulting durations were weighted by a factor accounting for the number of overlapping jobs.
Results
Collapsing overlapping jobs within the same category resulted in 3295 jobs (mean 2629 days/job). Splitting overlapping jobs of different categories increased the number to 4239 jobs (mean 2043 days/job), while the total duration in days dropped by 10%.
Conclusions
We demonstrated that overlapping employment data structures can be harmonized in a systematic and unbiased way, preparing work history data for linkage to several JEMs.</description><subject>Categories</subject><subject>Credit ratings</subject><subject>Data management</subject><subject>Data structures</subject><subject>Employment</subject><subject>employment spells</subject><subject>Epidemiology</subject><subject>Exposure</subject><subject>exposure assessment</subject><subject>job‐exposure matrices (JEMs)</subject><subject>occupational epidemiology</subject><subject>Regional stocks</subject><subject>work history</subject><subject>Workers</subject><issn>0271-3586</issn><issn>1097-0274</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>3HK</sourceid><recordid>eNp9kU1PxCAURYnR6Pix8QcoiTuTKo9CKRsTY_yMxo1u3BAszAxjWyp0nIy_XsZRoxtXj4TD4d1chHaBHAEh9FhPXHNEqSz4ChoAkSIjVLBVNEgDspyXxQbajHFCCAAr2DrayIkEKSUfoKcrHRrfunfXjvDMhxc8drH3YY6N7jV2LbadM7ZxvvYjV-HYT42zEc9cP8b-zYZad93irW262s8b2_Y42MoHE7fR2lDX0e58zS30eHH-cHaV3d5fXp-d3mYVE5RngjEwEiqpaUUNJWCHIEhJNLdEQKlzboS06Sx0SYUpclYILjlwLqpyWOb5FjpZervpc2NNlVYIulZdcI0Oc-W1U39vWjdWI_-mZM6ASUiC_aWgCim7a1Xrg1ZASk6VYFLSRBx8fRH869TGXk38NLQplaIUipSAc5mow2-PjzHY4c8OQNSiKbVoSn02leC931v_oN_VJACWwMzVdv6PSp3eXN8tpR91N55T</recordid><startdate>201905</startdate><enddate>201905</enddate><creator>Stenehjem, Jo Steinson</creator><creator>Babigumira, Ronnie</creator><creator>Friesen, Melissa C</creator><creator>Grimsrud, Tom Kristian</creator><general>Wiley Subscription Services, Inc</general><general>Wiley-Liss Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T2</scope><scope>7U7</scope><scope>C1K</scope><scope>3HK</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1964-5410</orcidid></search><sort><creationdate>201905</creationdate><title>Harmonizing work history data in epidemiologic studies with overlapping employment records</title><author>Stenehjem, Jo Steinson ; Babigumira, Ronnie ; Friesen, Melissa C ; Grimsrud, Tom Kristian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4725-7441d91c9a2c2d201ef17080a5e0718a35d79ee077a827d634675951557c8f833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Categories</topic><topic>Credit ratings</topic><topic>Data management</topic><topic>Data structures</topic><topic>Employment</topic><topic>employment spells</topic><topic>Epidemiology</topic><topic>Exposure</topic><topic>exposure assessment</topic><topic>job‐exposure matrices (JEMs)</topic><topic>occupational epidemiology</topic><topic>Regional stocks</topic><topic>work history</topic><topic>Workers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Stenehjem, Jo Steinson</creatorcontrib><creatorcontrib>Babigumira, Ronnie</creatorcontrib><creatorcontrib>Friesen, Melissa C</creatorcontrib><creatorcontrib>Grimsrud, Tom Kristian</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Toxicology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>NORA - Norwegian Open Research Archives</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>American journal of industrial medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Stenehjem, Jo Steinson</au><au>Babigumira, Ronnie</au><au>Friesen, Melissa C</au><au>Grimsrud, Tom Kristian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Harmonizing work history data in epidemiologic studies with overlapping employment records</atitle><jtitle>American journal of industrial medicine</jtitle><addtitle>Am J Ind Med</addtitle><date>2019-05</date><risdate>2019</risdate><volume>62</volume><issue>5</issue><spage>422</spage><epage>429</epage><pages>422-429</pages><issn>0271-3586</issn><eissn>1097-0274</eissn><abstract>Background
Work history data often require major data management including handling of overlapping jobs to avoid overestimating exposure before linkage to job‐exposure matrices (JEMs) is possible.
Methods
In a case‐cohort study of 1825 male Norwegian offshore petroleum workers, 3979 jobs were reported (mean duration 2417 days/job; maximum 8 jobs/worker). Each job was assigned to one of 27 occupation categories. Overlapping jobs of the same category (1142 jobs) were collapsed and overlapping jobs of different categories (1013 jobs) were split. The resulting durations were weighted by a factor accounting for the number of overlapping jobs.
Results
Collapsing overlapping jobs within the same category resulted in 3295 jobs (mean 2629 days/job). Splitting overlapping jobs of different categories increased the number to 4239 jobs (mean 2043 days/job), while the total duration in days dropped by 10%.
Conclusions
We demonstrated that overlapping employment data structures can be harmonized in a systematic and unbiased way, preparing work history data for linkage to several JEMs.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>30919995</pmid><doi>10.1002/ajim.22965</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-1964-5410</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | American journal of industrial medicine, 2019-05, Vol.62 (5), p.422-429 |
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| language | eng |
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| source | Access via Wiley Online Library; NORA - Norwegian Open Research Archives |
| subjects | Categories Credit ratings Data management Data structures Employment employment spells Epidemiology Exposure exposure assessment job‐exposure matrices (JEMs) occupational epidemiology Regional stocks work history Workers |
| title | Harmonizing work history data in epidemiologic studies with overlapping employment records |
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