Using hospital discharge data to identify incident pregnancy-associated cancers: a validation study

BMC Pregnancy and Childbirth

Timothy Dobbins¹  Jane Young¹  Christine L Roberts²  Yuen Yi Cathy Lee² 

[1] Cancer Epidemiology and Services Research Group, University of Sydney, New South Wales, Australia;Clinical and Population Perinatal Health Research, Kolling Institute of Medical Research, University of Sydney, New South Wales, Australia

关键词: Validation;    Positive predictive value;    Sensitivity;    Incidence;    Pregnancy;    Cancer;   

Others  :  1138232
DOI  :  10.1186/1471-2393-13-37

 received in 2012-10-23, accepted in 2013-02-09,  发布年份 2013

【 摘 要 】

Background

Pregnancy-associated cancer is associated with maternal morbidities and adverse pregnancy outcomes, and is reported to be increasing. Hospital discharge data have the potential to provide timely information on cancer incidence, which is central to evaluation and improvement of clinical care for women. This study aimed to assess the validity of hospital data for identifying incident pregnancy-associated cancers compared with incident cancers from an Australian population-based statutory cancer registry.

Methods

Birth data from 2001–2008, comprised 470,277 women with 679,736 maternities, were linked to cancer registry and hospitalisation records to identify newly diagnosed cancers during pregnancy or within 12 months of delivery. Two hospital-identified cancer groups were examined; “index cancer hospitalisation” – first cancer admission per woman per pregnancy and “all cancer hospitalisations” –the total number of hospitalisations with a cancer diagnosis and women could have multiple hospitalisations during pregnancy. The latter replicates a scenario where identification of individuals is not possible and hospitalisations are used as the unit of analysis.

Results

The incidence of pregnancy-associated cancer (according to cancer registry) was 145.4/100,000 maternities. Incidence of cancer was substantially over-estimated when using hospitalisations as the unit of analysis (incidence rate ratio, IRR 1.7) and under-estimated when using the individual (IRR 0.8). Overall, the sensitivity of “index cancer hospitalisation” was 60.4%, positive predictive value (PPV) 77.7%, specificity and negative predictive value both 100%. Melanoma ascertainment was only 36.1% and breast cancer 62.9%. For other common cancers sensitivities ranged from 72.1% to 78.6% and PPVs 56.4% to 87.3%.

Conclusion

Although hospital data provide another timely source of cancer identification, the validity is insufficient to obtain cancer incidence estimates for the obstetric population.

【 授权许可】

   
2013 Lee et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Lee YY, Roberts CL, Dobbins T, Starvou E, Black K, Morris JM, Young J: Incidence and outcomes of pregnancy-associated cancer in Australia, 1994–2008: a population-based linkage study. Int J Obstet Gynaecol 2012.
• [2]Smith LH, Danielsen B, Allen ME, Cress R: Cancer associated with obstetric delivery: results of linkage with the California cancer registry. Am J Obstet Gynecol 2003, 189(4):1128-1135.
• [3]Yuen E, Louis D, Cisbani L, Rabinowitz C, De Palma R, Maio V, Leoni M, Grilli R: Using administrative data to identify and stage breast cancer cases: implications for assessing quality of care. Tumori 2011, 97(4):428-435.
• [4]Bernal-Delgado EE, Martos C, Martinez N, Chirlaque MD, Marquez M, Navarro C, Hernando L, Palomar J, Izarzugaza I, Larranaga N, et al.: Is hospital discharge administrative data an appropriate source of information for cancer registries purposes? Some insights from four Spanish registries. BMC Health Serv Res 2010, 10:9. BioMed Central Full Text
• [5]Wang PS, Walker AM, Tsuang MT, Orav EJ, Levin R, Avorn J: Finding incident breast cancer cases through US claims data and a state cancer registry. Cancer causes & control: CCC 2001, 12(3):257-265.
• [6]Izquierdo JN, Schoenbach VJ: The potential and limitations of data from population-based state cancer registries. Am J Public Health 2000, 90(5):695-698.
• [7]Stavrou EP, Pesa N, Pearson SA: Hospital discharge diagnostic and procedure codes for upper gastro-intestinal cancer: how accurate are they? BMC Health Serv Res 2012, 12(1):331. BioMed Central Full Text
• [8]Kuklina EV, Whiteman MK, Hillis SD, Jamieson DJ, Meikle SF, Posner SF, Marchbanks PA: An enhanced method for identifying obstetric deliveries: implications for estimating maternal morbidity. Matern Child Health J 2008, 12(4):469-477.
• [9]Chakravarty EF, Khanna D, Chung L: Pregnancy outcomes in systemic sclerosis, primary pulmonary hypertension, and sickle cell disease. Obstet Gynecol 2008, 111(4):927-934.
• [10]Chakravarty EF, Nelson L, Krishnan E: Obstetric hospitalizations in the United States for women with systemic lupus erythematosus and rheumatoid arthritis. Arthritis Rheum 2006, 54(3):899-907.
• [11]Nattinger AB, Laud PW, Bajorunaite R, Sparapani RA, Freeman JL: An algorithm for the use of Medicare claims data to identify women with incident breast cancer. Health Serv Res 2004, 39(6 Pt 1):1733-1749.
• [12]Rolnick SJ, Hart G, Barton MB, Herrinton L, Flores SK, Paulsen KJ, Husson G, Harris EL, Geiger AM, Elmore JG, et al.: Comparing breast cancer case identification using HMO computerized diagnostic data and SEER data. Am J Manag Care 2004, 10(4):257-262.
• [13]Koroukian SM, Cooper GS, Rimm AA: Ability of Medicaid claims data to identify incident cases of breast cancer in the Ohio Medicaid population. Health Serv Res 2003, 38(3):947-960.
• [14]Cooper GS, Yuan Z, Stange KC, Dennis LK, Amini SB, Rimm AA: The sensitivity of Medicare claims data for case ascertainment of six common cancers. Med Care 1999, 37(5):436-444.
• [15]Warren JL, Feuer E, Potosky AL, Riley GF, Lynch CF: Use of Medicare hospital and physician data to assess breast cancer incidence. Med Care 1999, 37(5):445-456.
• [16]Penberthy L, McClish D, Pugh A, Smith W, Manning C, Retchin S: Using hospital discharge files to enhance cancer surveillance. Am J Epidemiol 2003, 158(1):27-34.
• [17]Meguerditchian AN, Stewart A, Roistacher J, Watroba N, Cropp M, Edge SB: Claims data linked to hospital registry data enhance evaluation of the quality of care of breast cancer. J Surg Oncol 2010, 101(7):593-599.
• [18]Percy C, Van Holten V, Muir C: ICD-O- International Classification of Diseases for Oncology. 3rd edition. Geneva: WHO; 1990.
• [19]Tracey E, Kerr T, Dobrovic A, Currow D: Cancer In NSW: Incidence and Mortality Report 2008. Sydney, NSW: Cancer Institute; 2010.
• [20]Centre for Health Record Linkage. http://www.cherel.org.au webcite
• [21]Open Source ChoiceMaker Technology. http://oscmt.sourceforge.net webcite
• [22]Bentley JP, Ford JB, Taylor LK, Irvine KA, Roberts CL: Investigating linkage rates among probabilistically linked birth and hospitalization records. BMC Med Res Methodol 2012, 12(1):149. BioMed Central Full Text
• [23]The International Statistical Classification of Diseases and Related Health Problems, Australian Modification – Tabular List of Diseases and Alphabetic Index of Diseases. http://nccc.uow.edu.au/icd10am/icd10am/index.htm webcite
• [24]SAS (2010) SAS/STATA. 92nd edition. Cary, NC, USA: SAS International; 2010.
• [25]Baldi I, Vicari P, Di Cuonzo D, Zanetti R, Pagano E, Rosato R, Sacerdote C, Segnan N, Merletti F, Ciccone G: A high positive predictive value algorithm using hospital administrative data identified incident cancer cases. J Clin Epidemiol 2008, 61(4):373-379.
• [26]Henderson T, Shepheard J, Sundararajan V: Quality of diagnosis and procedure coding in ICD-10 administrative data. Med Care 2006, 44(11):1011-1019.
• [27]Badgery-Parker T, Ford JB, Jenkins MG, Morris JM, Roberts CL: Patterns and outcomes of preterm hospital admissions during pregnancy in NSW, 2001–2008. Med J Aust 2012, 196(4):261-265.
• [28]Badgery-Parker T, SA W, Ford JB, Jenkins MG, Morris JM, Roberts CL: Multifetal pregnancies: preterm admissions and outcomes. Aust Health Rev 2012. http://doi.dx.org/10.1071/AH11106 webcite
• [29]Lain SJ, Hadfield RM, Raynes-Greenow C, Ford JB, Mealing NM, Algert CS, Roberts CL: Quality of data in perinatal population health databases: a systematic review. Med Care 2012, 50(4):e7-e20.
• [30]Roberts CL, Bell JC, Ford JB, Hadfield RM, Algert CS, Morris JM: The accuracy of reporting of the hypertensive disorders of pregnancy in population health data. Hypertens Pregnancy 2008, 27(3):285-297.
• [31]Teutsch SM, Thacker SB: Planning a public health surveillance system. Epidemiol Bull 1995, 16(1):1-6.