Detection of Pelvic Inflammatory Disease: Development of an Automated Case-Finding Algorithm Using Administrative Data

• Main Authors: Catherine L. Satterwhite (Author), Onchee Yu (Author), Marsha A. Raebel (Author), Stuart Berman (Author), Penelope P. Howards (Author), Hillard Weinstock (Author), David Kleinbaum (Author), Delia Scholes (Author)
• Format: Book
• Published: Hindawi Limited, 2011-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1064-7449; 1098-0997; 10.1155/2011/428351
• Keywords: Gynecology and obstetrics; RG1-991; Infectious and parasitic diseases; RC109-216
• Source: Infectious Diseases in Obstetrics and Gynecology, Vol 2011 (2011)

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.