1. ♦ Data Source : Blue Cross Blue Shield of Texas (BCBSTX), and Medicaid Texas 2008 – 2012 administrative data ♦ Population: Women aged 12 – 55 at the time of delivery ♦ Inclusion criteria: Inpatient and professional claims with following codes were included: ♦ Analysis: DRG’s and ICD-9-CM diagnosis codes were applied to inpatient claims data of BCBSTX and Medicaid as well as to Medicaid managed care encounter data. CPT codes were applied to professional claims in all datasets. ICD-9-CM procedure codes were applied to only BCBSTX due to the unavailability of the code in Medicaid. We calculated the number of individuals and deliveries identified using each set of codes. For each dataset, we conducted cross-tabulation of deliveries by type of code to analyze the reasons behind the differences. ALGORITHMS TO IDENTIFY DELIVERIES USING DIFFERENT ADMINISTRATIVE DATASETS L. Nguyen MS, S. Rajan PhD, E. Weber PhD and C. Ganduglia Cazaban MD DRPH The University of Texas Health Science Center at Houston (UTHealth) School of Public Health and Blue Cross Blue Shield of Texas Research Programs in Payment Systems ABSTRACTRESULTSBACKGROUNDDISCUSSION & CONCLUSION ♦ To compare different algorithms to identify deliveries in private and public administrative datasets and describe the differences in the number of deliveries identified using these different algorithms. OBJECTIVE: To compare different algorithms to identify deliveries in private and public administrative datasets. METHODS: As part of a study on deliveries, we used Blue Cross Blue Shield Texas (BCBSTX) and Medicaid Texas datasets to identify births occurring between 2008 and 2012. We selected validated algorithms 1,5 based on clinical diagnosis (ICD-9-CM), procedures (ICD-9-CM procedures and CPT) and diagnostic related groups (DRGs) codes. These were applied to BCBSTX and Medicaid Fee For Service (FFS) claims data as well as to Medicaid managed care (MC) encounter data. The analysis was limited to women aged 12 to 55. We accounted for code modifications that occurred during the studied period. We compared the total number of deliveries identified using each search algorithm and analyzed the reasons behind the differences. RESULTS: During 2008-2012, we found approximately 150,000 deliveries in BCBSTX and 1 million deliveries in Medicaid Texas. Among BCBSTX, 148,705 deliveries were identified using DRGs, 150,521 using diagnosis codes, 134,947 using ICD-9 procedures and 157,409 using CPT. On the Medicaid FFS, we detected 584,152 using DRGs and 27,545 more when using selected diagnosis codes. Among encounters, however, there were considerable differences, with 302,888 deliveries identified using DRGs and 486,986 deliveries using diagnosis codes. The almost 200,000 cases difference was mainly driven by missing DRGs among encounter observations (93% of all not identified with DRGs). Other reasons include non-delivery related DRGs and errors in data entry (partial codes and non-valid characters). CONCLUSION: Selection of algorithms did not considerably affect delivery identification among commercially insured population but did affect that in Medicaid. It appears that best methods for identifying cases vary across types of administrative data as well as payers. REFERENCES 1.Palmsten K, Huybrechts KF, Mogun H, Kowal MK, Williams PL, et al. (2013) Harnessing the Medicaid Analytic eXtract (MAX) to Evaluate Medications in Pregnancy: Design Considerations. PLoS ONE 8(6): e67405. doi:10.1371/journal.pone.0067405 2.Kimberly D Gregory, Lisa M Korst, Jeffrey A Gornbein, and Lawrence D Platt. Using Administrative Data to Identify Indications for Elective Primary Cesarean Delivery. Health Serv Res. 2002 Oct; 37(5): 1387–1401. 3.Devine S, West S, Andrews E, Tennis P, Hammad TA, Eaton S, Thorp J, Olshan A. The identification of pregnancies within the general practice research database. Pharmacoepidemiol Drug Saf. 2010 Jan;19(1):45-50. doi: 10.1002/pds.1862. 4.Goff SL, Pekow PS, Markenson G, Knee A, Chasan-Taber L, Lindenauer PK. Validity of using ICD-9-CM codes to identify selected categories of obstetric complications, procedures and co-morbidities. Paediatr Perinat Epidemiol. 2012 Sep;26(5):421-9. doi: 10.1111/j.1365-3016.2012.01303.x. Epub 2012 Jul 23. 5.Shagufta Yasmeen et al. Accuracy of obstetric diagnoses and procedures in hospital discharge data. American Journal of Obstetrics and Gynecology (2006) 194, 992–1001. doi:10.1016/j.ajog.2005.08.058 For further information contact via e-mail at : linh.nguyen@uth.tmc.edu ♦ Across the five years, using BCBSTX dataset, 148,705 deliveries were identified by using DRGs, 150,521 deliveries by diagnosis codes, 134,947 deliveries by ICD-9-CM procedure codes and 157,409 deliveries by CPT codes. ♦ Using Medicaid FFS claims, there were 584,152 deliveries detected using DRGs, 611,697 using diagnosis codes and 363,300 using CPT. There is not much variation in the number of deliveries using DRGs versus diagnosis codes but much lower number of deliveries using CPT. The reasons for the great difference (almost 300,000 cases) using CPT codes remain unclear to us. ♦ In Medicaid encounters, the number of deliveries vary substantially by algorithm: 302,888 deliveries identified by DRG versus 486,986 deliveries using diagnosis codes. The large difference was attributed to missing DRGs among encounter observations (93% of observations not identified by DRG), non-delivery related DRG, partial codes and non-valid character codes. ♦ Among commercially insured population, the total number of deliveries were slightly different (less than 2%) using different algorithms (DRG vs. ICD-9-CM diagnosis vs. CPT). ♦ However in Medicaid data, selection of algorithms did considerably affect the total number of deliveries when applied to FFS claims versus to MC encounters. Differences in payment methods have generated differences in type of data collected. ♦ Researchers should take into consideration different methods, type of administrative data as well as payers when identifying cases for their investigation of pregnancy treatment and birth outcomes. RESEACH OBJECTIVE ♦ Correct identification of events in administrative claims datasets has become critical given its increased utilization in research. 1-3 ♦ Identifying deliveries has been a critical step in studies that aim to investigate pregnancy treatments and birth outcomes. 1-4 The accuracy of the analysis depends on the codes used to define deliveries and on the characteristics of the dataset. METHOD RESULTS Table 1: Comparison of the number of deliveries identified BCBSTX during 2008-2012 Table 2: Comparison of the number of deliveries identified in Medicaid Texas during 2008-2012 DRG ICD-9-CM Diagnosis ICD-9-CM Procedure CPT No. of Individuals135,295136,770123,635143,157 No. of deliveries148,705150,521134,947157,409 Claims (FFS)Encounter (MC) DRG ICD-9-CM Diagnosis CPTDRG ICD-9-CM Diagnosis CPT No. of Individuals499,318519,939313,435267,632412,787437,093 No. of deliveries584,152611,697363,300302,888486,986519,713 Inpatient Claims Diagnosis Related Groups (DRG) Vaginal delivery: 767, 768, 774, 775, 372 – 375 (expired in 2010) C-section delivery: 765, 766, 370 – 371 (expired in 2010) International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM) Diagnosis Codes 5 Principal or secondary diagnosis of 640-676 with a fifth digit of 1 or 2 or diagnosis of 650. International Classification of Diseases, 9th edition, Clinical Modification (ICD- 9-CM) Procedure Codes 1 72.xx-74.xx, 75.4 Professional Claims Current Procedural Terminology (CPT): 1 01960 - 01963, 01967-01969, 59050, 59051, 59400, 59409, 59410, 59412, 59414, 59430, 59510, 59514, 59515, 59525, 59610, 59612, 59614, 59618, 59620, 59622, 99436, 99440.