Refining hypertension surveillance to account for potentially misclassified cases

PLoS ONE

Volumn 10, Issue 3, 2015, Pages

  Peng, Mingkai ^a   Chen, Guanmin ^a   Lix, Lisa M ^b   McAlister, Finlay A ^c   Tu, Karen ^d   Campbell, Norm R ^a,e   Hemmelgarn, Brenda R ^a,e   Svenson, Lawrence W ^f   Quan, Hude ^a  

^a UNIVERSITY OF CALGARY   (Canada)

^b UNIVERSITY OF MANITOBA   (Canada)

^c UNIVERSITY OF ALBERTA   (Canada)

^d UNIVERSITY OF TORONTO   (Canada)

^e UNIVERSITY OF CALGARY   (Canada)

^f Surveillance and Assessment   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGE; AGED; ARTICLE; BAYES THEOREM; CANADA; COMORBIDITY; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE CLASSIFICATION; DISEASE SURVEILLANCE; FEMALE; HOSPITAL DISCHARGE; HUMAN; HYPERTENSION; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; MEDICAL RECORD REVIEW; PREDICTIVE VALUE; PREVALENCE; SENSITIVITY AND SPECIFICITY; SEX; ALBERTA; CLASSIFICATION; DIAGNOSTIC ERROR; GENERAL PRACTICE; HEALTH SURVEY; INTERNATIONAL CLASSIFICATION OF DISEASES; MEDICAL RECORD; MIDDLE AGED; STATISTICS AND NUMERICAL DATA;

ADULT; ALBERTA; BAYES THEOREM; COMORBIDITY; DIAGNOSTIC ERRORS; FEMALE; GENERAL PRACTICE; HUMANS; HYPERTENSION; INTERNATIONAL CLASSIFICATION OF DISEASES; MALE; MEDICAL RECORDS; MIDDLE AGED; POPULATION SURVEILLANCE; PREVALENCE;

EID: 84926140540     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0119186     Document Type: Article

References (29)

1. Kannel WB. Blood pressure as a cardiovascular risk factor: prevention and treatment. JAMA. 1996;275:1571-1576. PMID: 8622248
2. Levy D, Larson MG, Vasan RS, Kannel WB, Ho KK. The progression from hypertension to congestive heart failure. JAMA. 1996;275:1557-1562. PMID: 8622246
3. Kannel WB, Wolf PA, Verter J, McNamara PM. Epidemiologic assessment of the role of blood pressure in stroke. The Framingham Study. JAMA. 1970;214:301-310. PMID: 5469068
4. Kannel WB, Wolf PA. Framingham Study insights on the hazards of elevated blood pressure. JAMA 2008;300:2545-2547. doi: 10.1001/jama.2008.759 PMID: 19050198
5. Haider AW, Chen L, Larson MG, Evans JC, Chen MH, Levy D. Antecedent hypertension confers increased risk for adverse outcomes after initial myocardial infarction. Hypertension. 1997;30:1020-1024. PMID: 9369249
6. Public Health Agency of Canada. The Canadian chronic disease surveillance system. hypertension in Canada, 2010. Ottawa, Canada: PHAC; 2011
7. Robitaille C, Dai S, Waters C, Loukine L, Bancej C, Quach S, et al. Diagnosed hypertension in Canada: incidence, prevalence and associated mortality. CMAJ. 2012 184:E49-E56. doi: 10.1503/cmaj. 101863 PMID: 22105752
8. Atwood KM, Robitaille C, Reimer K, Dai S, Johansen HL, Smith MJ. Comparison of diagnosed, self-reported, and physically-measured hypertension in Canada. Can J Cardiol. 2013;29:606-612. doi: 10. 1016/j.cjca.2012.11.019 PMID: 23395221
9. Blais C, Rochette L, Hamel D, Poirier P. Prevalence, incidence, awareness and control of hypertension in the province of Quebec: perspective from administrative and survey data. Can J Public Health. 2014;105:e79-e85. PMID: 24735701
10. Lix ML, Yogendran MS, Leslie WD, Shaw SY, Baumgartner R, Bowman C, et al. (2008) Using multiple data features improved the validity of osteoporosis case ascertainment from administrative databases. J Clin Epidemiol. 2008;61:1250-1260. doi: 10.1016/j.jclinepi.2008.02.002 PMID: 18619800
11. Austin PC, Tu JV, Ho JE, Levy D, Lee DS. Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. J Clin Epidemiol. 2013;66:398-407. doi: 10.1016/j.jclinepi.2012.11.008 PMID: 23384592
12. Ladouceur M, Rahme E, Pineau CA, Joseph L. Robustness of prevalence estimates derived from misclassified data from administrative databases. Biometrics. 2007;63:272-279. PMID: 17447953
13. Bernatsky S, Lix L, Hanly JG, Hudson M, Badley M, Peschken C, et al. Surveillance of systemic autoimmune rheumatic diseases using administrative data. Rheumatol Int. 2011;31:549-554. doi: 10.1007/s00296-010-1591-2 PMID: 20665025
14. Joseph L, Gyorkos T, Coupal L. Bayesian estimation of disease prevalence and the prevalence of diagnostic tests in the absence of a gold standard. Am J Epidemiol. 1995;141:263-272. PMID: 7840100
15. Quan H, Khan N, Hemmelgarn BR, Chen G, Campbell NR, Hill MD, et al. Validation of a case definition to define hypertension using administrative data. Hypertension. 2009;54:1423-1428. doi: 10.1161/HYPERTENSIONAHA.109.139279 PMID: 19858407
16. Padwal RS, Hemmelgarn BR, Khan NA, Grover S, McKay DW, Wilson T, et al. The 2009 Canadian Hypertension Education Program recommendations for the management of hypertension: part 1-blood pressure measurement, diagnosis and assessment of risk. Can J Cardiol. 2009;25:279-286. PMID: 19417858
17. Quan H, Chen G, Tu K, Bartlett G, Butt DA, Campbell NR, et al. Outcomes among 3.5 million newly diagnosed hypertensive Canadians. Can J Cardiol. 2013;29:592-597. doi: 10.1016/j.cjca.2012.12.016 PMID: 23465341
18. Tu K, Campbell NR, Chen ZL, Cauch-Dueck KJ, McAlister FA. Accuracy of administrative databases in identifying patients with hypertension. Open Med. 2007;1:e18-e26. PMID: 20101286
19. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: prediction, inference and data mining. 2nd ed. New York: Springer-Verlag; 2009.
20. Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47:1245-1251. PMID: 7722560
21. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36:8-27 PMID: 9431328
22. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130-1139. PMID: 16224307
23. Efron B., Tibshirani R. An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC; 1993.
24. R Core Team. R: A Language and Environment for Statistical Computing., Vienna, Austria: R Foundation for Statistical Computing; 2014.
25. Gelman A, Rubin D. Inference from iterative simulation using multiple sequences. Stat Sci. 1992;7(4):457-511.
26. Tu K, Chen Z, Lipscombe LL. Prevalence and incidence of hypertension from 1996 to 2005: a populationbased study. CMAJ. 2008;178:1429-1435. doi: 10.1503/cmaj.071283 PMID: 18490638
27. So L, Beck CA, Brien S, Kennedy J, Feasby TE, Ghali WA, et al. Chart documentation quality and its relationship to the validity of administrative data discharge records. Health Informatics J. 2010;16:101-113. doi: 10.1177/1460458210364784 PMID: 20573643
28. Hennessy DA, Quan H, Faris P, Beck CA. Do coder characteristics influence validity of ICD-10 hospital discharge data? BMC Health Serv Res. 2010;10:99. doi: 10.1186/1472-6963-10-99 PMID: 20409320
29. Kadhim-Saleh A, Green M, Williamson T, Hunter D, Birtwhistle R. Algorithms for 5 chronic conditions in The Canadian primary care sentinel surveillance network (CPCSSN): A Kingston practice-based research network (PBRN) report. JABFM. 2013;26:159-167. doi: 10.3122/jabfm.2013.02.120183 PMID: 23471929