ArticleInfectious diseases in obstetrics and gynecology2011
Detection of pelvic inflammatory disease: development of an automated case-finding algorithm using administrative data.
Article in Infectious diseases in obstetrics and gynecology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reliability of administrative data to identify sexually transmitted infections for population health: a systematic review.BMJ health & care informatics · 2019Pooled it
- Article
- Diagnosed Gonorrhea Among Privately Insured Women: Analysis of United States Claims Data.Journal of women's health (2002) · 2023Article
- The Burden of and Trends in Pelvic Inflammatory Disease in the United States, 2006-2016.The Journal of infectious diseases · 2021Article
- Trends in Pelvic Inflammatory Disease Among American Indian and Alaska Native Women, Indian Health Service, 2001-2015.American journal of public health · 2018Article
- Heterogeneity in risk of pelvic inflammatory diseases after chlamydia infection: a population-based study in Manitoba, Canada.The Journal of infectious diseases · 2014Article
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Authors and funding
8 authors.
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Abstract
ICD-9 codes are conventionally used to identify pelvic inflammatory disease (PID) from administrative data for surveillance purposes. This approach may include non-PID cases. To refine PID case identification among women with ICD-9 codes suggestive of PID, a case-finding algorithm was developed using additional variables. Potential PID cases were identified among women aged 15-44 years at Group Health (GH) and Kaiser Permanente Colorado (KPCO) and verified by medical record review. A classification and regression tree analysis was used to develop the algorithm at GH; validation occurred at KPCO. The positive predictive value (PPV) for using ICD-9 codes alone to identify clinical PID cases was 79%. The algorithm identified PID appropriate treatment and age 15-25 years as predictors. Algorithm sensitivity (GH = 96.4%; KPCO = 90.3%) and PPV (GH = 86.9%; KPCO = 84.5%) were high, but specificity was poor (GH = 45.9%; KPCO = 37.0%). In GH, the algorithm offered a practical alternative to medical record review to further improve PID case identification.
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