{"id":2079,"date":"2022-07-09T00:00:00","date_gmt":"2022-07-09T04:00:00","guid":{"rendered":"https:\/\/icesontario.wpengine.com\/journal-articles\/validation-of-algorithms-to-identify-gestational-diabetes-from-population-level-healthcare-administrative-data\/"},"modified":"2023-12-21T11:59:34","modified_gmt":"2023-12-21T16:59:34","slug":"validation-of-algorithms-to-identify-gestational-diabetes-from-population-level-healthcare-administrative-data","status":"publish","type":"journal_article","link":"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/validation-of-algorithms-to-identify-gestational-diabetes-from-population-level-healthcare-administrative-data\/","title":{"rendered":"Validation of algorithms to identify gestational diabetes from population-level healthcare administrative data"},"content":{"rendered":"

Aims<\/strong> \u2014 To determine the test characteristics of algorithms using hospitalization and physician claim data to predict gestational diabetes (GDM).<\/p>\n

Methods<\/strong> \u2014 Using population-level healthcare administrative data, we identified all pregnant women in Ontario in 2019. The presence of GDM was determined based on glucose screening laboratory results. Algorithms using hospitalization records and\/or physician claims were tested against this gold standard. The selected algorithm was applied to administrative data records from 1999 to 2019 to determine GDM prevalence in each year.<\/p>\n

Results<\/strong> \u2014 Identifying GDM based on either a diabetes mellitus code on the delivery hospitalization record, OR at least 1 physician claim with a diabetes diagnosis code with a 90 day lookback before delivery yielded a sensitivity of 95.9%, specificity of 99.2%, and positive predictive value of 87.6%. The prevalence of GDM increased from 4.2% of pregnancies in 1999 to 12.0% in 2019.<\/p>\n

Conclusions<\/strong> \u2014 Algorithms using hospitalization or physician claims administrative data can accurately identify GDM<\/p>\n","protected":false},"excerpt":{"rendered":"

Aims \u2014 To determine the test characteristics of algorithms using hospitalization and physician claim data to predict gestational diabetes (GDM). Methods \u2014 Using population-level healthcare administrative data, we identified all pregnant women in Ontario in 2019. The presence of GDM was determined based on glucose screening laboratory results. Algorithms using hospitalization records and\/or physician claims […]<\/p>\n","protected":false},"template":"","migration-helper-automated":[],"migration-manual":[],"topic":[],"migration-helper-qa-sample-set":[],"class_list":["post-2079","journal_article","type-journal_article","status-publish","hentry"],"acf":{"citation":"Shah BR, Booth GL, Feig DS, Lipscombe LL. Can J Diabetes<\/em>. 2023; 47(1):25-30. Epub 2022 Jul 9.","source_url":"https:\/\/doi.org\/10.1016\/j.jcjd.2022.06.010","ices_scientist":[1361,1295,1232,1175],"site":[6733],"research_program":[6746],"news_release":"","journal_article":"","atlas":"","research_report":"","infographic":"","video":"","downloads":null,"links":null,"sitecore_item_id":"A692E824-2917-49D9-8E0E-D60630606394","sitecore_item_name":"Validation-of-algorithms-to-identify-gestational-diabetes-from-population-level-healthcare","sitecore_field_values":"{\n \"Title\": \"Validation of algorithms to identify gestational diabetes from population-level healthcare administrative data\",\n \"Short title\": \"Validation of algorithms to identify\",\n \"Summary\": \"The study aim was to determine the test characteristics of algorithms using hospitalization and physician claim data to predict gestational diabetes.\",\n \"Citation\": \"

Shah BR, Booth GL, Feig DS, Lipscombe LL. Can J Diabetes<\/em>. 2022; Jul 9 [Epub ahead of print]. DOI: https:\/\/doi.org\/10.1016\/j.jcjd.2022.06.010<\/a><\/p>\",\n \"Abstract\": \"

Aims<\/strong> — To determine the test characteristics of algorithms using hospitalization and physician claim data to predict gestational diabetes (GDM).<\/p>n

nMethods<\/strong> — Using population-level healthcare administrative data, we identified all pregnant women in Ontario in 2019. The presence of GDM was determined based on glucose screening laboratory results. Algorithms using hospitalization records and\/or physician claims were tested against this gold standard. The selected algorithm was applied to administrative data records from 1999 to 2019 to determine GDM prevalence in each year.<\/p>n

nResults<\/strong> — Identifying GDM based on either a diabetes mellitus code on the delivery hospitalization record, OR at least 1 physician claim with a diabetes diagnosis code with a 90 day lookback before delivery yielded a sensitivity of 95.9%, specificity of 99.2%, and positive predictive value of 87.6%. The prevalence of GDM increased from 4.2% of pregnancies in 1999 to 12.0% in 2019.<\/p>n

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