BMC Public Health | |
Ascertainment of chronic diseases using population health data: a comparison of health administrative data and patient self-report | |
Douglas G Manuel1  Carol Bennett2  Erin Graves2  Elizabeth Muggah3  | |
[1] Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada;Institute for Clinical Evaluative Sciences, Ottawa and Toronto, Ontario, Canada;Department of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada | |
关键词: Population health survey; Chronic diseases; Health administrative data; Chronic disease ascertainment; | |
Others : 1162663 DOI : 10.1186/1471-2458-13-16 |
|
received in 2012-06-26, accepted in 2012-12-27, 发布年份 2013 | |
【 摘 要 】
Background
Health administrative data is increasingly being used for chronic disease surveillance. This study explored agreement between administrative and survey data for ascertainment of seven key chronic diseases, using individually linked data from a large population of individuals in Ontario, Canada.
Methods
All adults who completed any one of three cycles of the Canadian Community Health Survey (2001, 2003 or 2005) and agreed to have their responses linked to provincial health administrative data were included. The sample population included 85,549 persons. Previously validated case definitions for myocardial infarction, asthma, diabetes, chronic lung disease, stroke, hypertension and congestive heart failure based on hospital and physician billing codes were used to identify cases in health administrative data and these were compared with self-report of each disease from the survey. Concordance was measured using the Kappa statistic, percent positive and negative agreement and prevalence estimates.
Results
Agreement using the Kappa statistic was good or very good (kappa range: 0.66-0.80) for diabetes and hypertension, moderate for myocardial infarction and asthma and poor or fair (kappa range: 0.29-0.36) for stroke, congestive heart failure and COPD. Prevalence was higher in health administrative data for all diseases except stroke and myocardial infarction. Health Utilities Index scores were higher for cases identified by health administrative data compared with self-reported data for some chronic diseases (acute myocardial infarction, stroke, heart failure), suggesting that administrative data may pick up less severe cases.
Conclusions
In the general population, discordance between self-report and administrative data was large for many chronic diseases, particularly disease with low prevalence, and differences were not easily explained by individual and disease characteristics.
【 授权许可】
2013 Muggah et al.; licensee BioMed Central Ltd.
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
20150413073916672.pdf | 198KB | download |
【 参考文献 】
- [1]Alwan A, Armstrong T, Bettcher D, Branca F, Chisholm D, Ezzati M, et al.: Global status report on non-communicable diseases 2010. World Health: Organization; 2010.
- [2]Manuel DG, Rosella LC, Stukel TA: Importance of accurately identifying disease in studies using electronic health records. BMJ 2010, 341:c4226.
- [3]Lix LM, Yogendran MS, Leslie WD, Shaw SY, Baumgartner R, Bowman C, et al.: Using multiple data features improved the validity of osteoporosis case ascertainment from administrative databases. J Clin Epidemiol 2008, 61(12):1250-1260.
- [4]Lix LM, Yogendran MS, Shaw SY, Targownick LE, Jones J, Bataineh O: Comparing administrative and survey data for ascertaining cases of irritable bowel syndrome: a population-based investigation. BMC Health Serv Res 2010, 10(1):31. BioMed Central Full Text
- [5]Saczynski JS, Andrade SE, Harrold LR, Tjia T, Cutrona SL, Dodd KS, et al.: A systematic review of validated methods for identifying heart failure using administrative data. Pharmacoepidem Drug Safe 2012, 21:129-140.
- [6]Andrade SE, Harrold LR, Tjia T, Cutrona SL, Saczynski JS, Dodd KS, et al.: A systematic review of validated methods for identifying cerebrovascular accident or transient ischemic attack using administrative data. Pharmacoepidem Drug Safe 2012, 21:100-128.
- [7]Gershon AS, Wang C, Guan J, Vasilevska-Ristovska J, Cicutto L, To T: Identifying individuals with physcian diagnosed COPD in health administrative databases. COPD: J Chron Obstruct Pulmon Dis 2009, 6(5):388-394.
- [8]Tu K, Campbell NRC, Chen ZL, Cauch-Dudek KJ, McAlister FA: Accuracy of administrative databases in identifying patients with hypertension. Open Med 2007, 1(1):e18.
- [9]Singh JA: Accuracy of veterans affairs databases for diagnoses of chronic diseases. Prev Chronic Dis 2009, 6(4):A126. Epub 2009 Sep 15
- [10]Lix LM, Yogendran MS, Shaw SY, Burchill C, Metge C, Bond R: Population-based data sources for chronic disease surveillance. Chronic Dis Can 2008, 29(1):31-38.
- [11]Simpson CF, Boyd CM, Carlson MC, Griswold ME, Guralnik JM, Fried LP: Agreement between self-report of disease diagnoses and medical record validation in disabled older women: factors that modify agreement. J Am Geriatr Soc 2004, 52(1):123-127.
- [12]Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ: Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol 2004, 57(10):1096-1103.
- [13]Feeny D, Torrance G, Furlong W: Health utilities index. In Quality of life and pharmacoeconomics in clinical trials. 2nd edition. Edited by Spilder B. Philadelphia: Lippincott-Raven; 1996.
- [14]Statistics Canada: Population estimates and pojections: population by year, by province and territory, 2007–2011. http://www.statcan.gc.ca webcite 2011 [cited 2012 Apr 25]; Available from: URL: http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/demo02a-eng.htm webcite
- [15]Statistics Canada: Canadian community health survey: public use microdata file. http://www.statcan.gc.ca webcite 2012 [cited 2012 Apr 25]; Available from: URL: http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=82M0013X webcite
- [16]Tu JV, Austin P, Naylor CD, Iron K, Zhang H: Chapter 5 - acute myocardial infarction outcomes in Ontario. In Cardiovascular Health and Services in Ontario: An ICES Atlas. Edited by Naylor CD, Slaughter PM. Toronto: Institute for Clinical Evaluative Sciences; 1999:83-110.
- [17]Hux JE, Ivis F, Flintoft V, Bica A: Diabetes in ontario: determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care 2002, 25(3):512-516.
- [18]Guttmann A, Nakhla M, Henderson M, To T, Daneman D, Cauch-Dudek K, et al.: Validation of a health administrative data algorithm for assessing the epidemiology of diabetes in Canadian children. Pediatr Diabetes 2010, 11(2):122-128.
- [19]Iron K, Lu H, Manuel D, Henry D, Gershon A: Using linked health administrative data to assess the clinical and healthcare system impact of chronic diseases in Ontario. Healthcare quarterly (Toronto, Ont ) 2011, 14(3):23.
- [20]Feeny D, Torrance G, Furlong W: Health utilities index - chapter 26. In Quality of life and pharmacoeconomics in clinical trials. 2nd edition. Edited by Spilder B. Philadelphia: Lippincott-Raven; 1996.
- [21]Cunningham M: More than just the kappa coefficient: a program to fully characterize inter-rater reliability between Two raters. SAS global forum 2009. [cited 2012 Apr 25]; Available from: URL: http://support.sas.com/resources/papers/proceedings09/242-2009.pdf webcite
- [22]Carter K, Barber PA, Shaw C: How does self-reported history of stroke compare to hospitalization data in a population-based survey in New Zealand? Stroke 2010, 41(11):2678-2680.
- [23]Leys D, Lucas C, Devos D, Mounier-Vehier F, Godefroy O, Pruvo JP: Misdiagnoses in 1,250 consecutive patients admitted to an acute stroke unit. Cerebrovasc Dis 1997, 7(Suppl 5):284-288.
- [24]Rosamond WD, Sprafka JM, McGovern PG, Nelson M, Luepker RV: Validation of self-reported history of acute myocardial infarction: experience of the Minnesota heart survey registry. Epidemiology 1996, 6(1):67-69.
- [25]Benchimol EI, Manuel DG, To T, Griffiths AM, Rabeneck L, Guttmann A: Development and use of reporting guidelines for assessing the quality of validation studies of health administrative data. J Clin Epidemiol 2011, 64(8):821-829.
- [26]Chen G, Faris P, Hemmelgarn B, Walker RL, Quan H: Measuring agreement of administrative data with chart data using prevalence unadjusted and adjusted kappa. BMC Med Res Methodol 2009, 1:21(9).