Using multiple data features improved the validity of osteoporosis case ascertainment from administrative databases

Journal of Clinical Epidemiology

Volumn 61, Issue 12, 2008, Pages 1250-1260

  Lix, Lisa M ^a,b   Yogendran, Marina S ^a   Leslie, William D ^b   Shaw, Souradet Y ^b   Baumgartner, Richard ^c   Bowman, Christopher ^b,c   Metge, Colleen ^a,b   Gumel, Abba ^b   Hux, Janet ^d   James, Robert C ^e  

^a UNIVERSITY OF MANITOBA   (Canada)

^b UNIVERSITY OF MANITOBA   (Canada)

^c NATIONAL RESEARCH COUNCIL CANADA   (Canada)

^d INSTITUTE FOR CLINICAL EVALUATIVE SCIENCES   (Canada)

^e Salt Spring Island   (Canada)

Author keywords

Classification trees; Logistic regression; Neural networks; Osteoporosis; Prevalence; Sensitivity; Specificity

Indexed keywords

ADULT; AGED; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BONE DENSITY; CALIBRATION; CLINICAL FEATURE; COMORBIDITY; CONTROLLED STUDY; DATA BASE; HUMAN; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; OSTEOPOROSIS; PREVALENCE; PRIORITY JOURNAL;

AGED; ALGORITHMS; BONE DENSITY; DATABASES, FACTUAL; DRUG PRESCRIPTIONS; DRUG UTILIZATION; EPIDEMIOLOGIC METHODS; FEMALE; FORMS AND RECORDS CONTROL; HUMANS; MANITOBA; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); OSTEOPOROSIS, POSTMENOPAUSAL; SOCIOECONOMIC FACTORS;

EID: 55249113158     PISSN: 08954356     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2008.02.002     Document Type: Article

References (53)

1. Chrischilles E., Shireman T., and Wallace R. Costs and health effects of osteoporotic fractures. Bone 15 (1994) 377-386
2. Sakamoto K., Nakamura T., Hagino H., Endo N., Mori S., Muto Y., et al. Report on the Japanese Orthopaedic Association's 3-year project observing hip fractures at fixed-point hospitals. J Orthop Sci 11 (2006) 127-134
3. Sanders K.M., Seeman E., Ugoni A.M., Pasco J.A., Martin T.J., Skoric B., et al. Age- and gender-specific rate of fractures in Australia: a population-based study. Osteoporos Int 10 (1999) 240-247
4. Burge R.T., King A.B., Balda E., and Worley D. Methodology for estimating current and future burden of osteoporosis in state populations: application to Florida in 2000 through 2025. Value Health 6 (2003) 574-583
5. Jaglal S.B., McIsaac W.J., Hawker G., Jaakkimainen L., Cadarette S.M., and Chan B.T. Patterns of use of the bone mineral density test in Ontario, 1992-1998. CMAJ 163 (2000) 1139-1143
6. Tenenhouse A., Joseph L., Kreiger N., Poliquin S., Murray T.M., Blondeau L., et al. Estimation of the prevalence of low bone density in Canadian women and men using a population-specific DXA reference standard: the Canadian Multicentre Osteoporosis Study (CaMos). Osteoporos Int 11 (2000) 897-904
7. Lu Y., Genant H.K., Shepherd J., Zhao S., Mathur A., Fuerst T.P., et al. Classification of osteoporosis based on bone mineral densities. J Bone Miner.Res 16 (2001) 901-910
8. Vestergaard P., Rejnmark L., and Mosekilde L. Osteoporosis is markedly underdiagnosed: a nationwide study from Denmark. Osteoporos Int 16 (2005) 134-141
9. Yang N.P., Deng C.Y., Chou Y.J., Chen P.Q., Lin C.H., Chou P., et al. Estimated prevalence of osteoporosis from a Nationwide Health Insurance database in Taiwan. Health Policy 75 (2006) 329-337
10. Goettsch W.G., de Jong R.B., Kramarz P., and Herings R.M. Developments of the incidence of osteoporosis in The Netherlands: a PHARMO study. Pharmacoepidemiol Drug Saf 16 (2007) 166-172
11. Lippuner K., Golder M., and Greiner R. Epidemiology and direct medical costs of osteoporotic fractures in men and women in Switzerland. Osteoporos Int 16 Suppl 2 (2005) S8-S17
12. Bernstein C.N., Blanchard J.F., Rawsthorne P., and Wajda A. Epidemiology of Crohn's disease and ulcerative colitis in a central Canadian province: a population-based study. Am J Epidemiol 149 (1999) 916-924
13. Blanchard J.F., Ludwig S., Wajda A., Dean H., Anderson K., Kendall O., et al. Incidence and prevalence of diabetes in Manitoba, 1986-1991. Diabetes Care 19 (1996) 807-811
14. Fowles J.B., Fowler E.J., and Craft C. Validation of claims diagnoses and self-reported conditions compared with medical records for selected chronic diseases. J Ambul Care Manage 21 (1998) 24-34
15. Hux J.E., Ivis F., Flintoft V., and Bica A. Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care 25 (2002) 512-516
16. Rector T.S., Wickstrom S.L., Shah M., Thomas Greeenlee N., Rheault P., Rogowski J., et al. Specificity and sensitivity of claims-based algorithms for identifying members of Medicare+Choice health plans that have chronic medical conditions. Health Serv Res 39 (2004) 1839-1857
17. Robinson J.R., Young T.K., Roos L.L., and Gelskey D.E. Estimating the burden of disease. Comparing administrative data and self-reports. Med Care 35 (1997) 932-947
18. Saydah S.H., Geiss L.S., Tierney E., Benjamin S.M., Engelgau M., and Brancati F. Review of the performance of methods to identify diabetes cases among vital statistics, administrative, and survey data. Ann Epidemiol 14 (2004) 507-516
19. Wilchesky M., Tamblyn R.M., and Huang A. Validation of diagnostic codes within medical services claims. J Clin Epidemiol 57 (2004) 131-141
20. Tu K., Campbell N.R.C., Chen Z.-L., Cauch-Dudek K.J., and McAlister F.A. Accuracy of administrative databases in identifying patients with hypertension. Open Med 1 (2007) E3-E5
21. Maio V., Yuen E., Rabinowitz C., Louis D., Jimbo M., Donatini A., Mall S., and Taroni F. Using pharmacy data to identify those with chronic conditions in Emilia Romagna. Italy. J Health Serv Res Policy 10 (2005) 232-238
22. Bartfay E., Mackillop W.J., and Pater J.L. Comparing the predictive value of neural network models to logistic regression models on the risk of death for small-cell lung cancer patients. Eur J Cancer Care (Engl) 15 (2006) 115-124
23. Burke H.B., Goodman P.H., Rosen D.B., Henson D.E., Weinstein J.N., Harrell Jr. F.E., Marks J.R., Winchester D.P., and Bostwick D.G. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 79 (1997) 857-862
24. Datta S., and DePadilla L.M. Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples. Stat Methodol 3 (2006) 79-92
25. French B.M., Dawson M.R., and Dobbs A.R. Classification and staging of dementia of the Alzheimer type: a comparison between neural networks and linear discriminant analysis. Arch Neurol 54 (1997) 1001-1009
26. Harrison R.F., and Kennedy R.L. Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation. Ann Emerg Med 46 (2005) 431-439
27. Lemon S.C., Roy J., Clark M.A., Friedmann P.D., and Rakowski W. Classification and regression tree analysis in public health: methodological review and comparison with logistic regression. Ann Behav Med 26 (2003) 172-181
28. Statistics Canada. Community Profiles. Statistics Canada catalogue #93F0053X. Available at (2001). http://www12.statcan.ca/english/profil101/CP01/Index.cfm?Lang=E Accessed January 26, 2007
29. Watson D.E., Katz A., Reid R.J., Bogdanovic B., Roos N., and Heppner P. Family physician workloads and access to care in Winnipeg: 1991 to 2001. CMAJ 171 (2004) 339-342
30. World Health Organization. WHO's Collaborating Centre for Drug Statistics Methodology: ATC Classification Index with Guidelines for ATC Classification and DDD Assignment (2006), World Health Organization, Geneva
31. Leslie W.D., Caetano P.A., Macwilliam L.R., and Finlayson G.S. Construction and validation of a population-based bone densitometry database. J Clin Densitom 8 (2005) 25-30
32. Leslie W.D., MacWilliam L., Lix L., Caetano P., and Finlayson G.S. A population-based study of osteoporosis testing and treatment following introduction of a new bone densitometry service. Osteoporos Int 16 (2005) 773-782
33. Leslie W.D., Adler R.A., El-Hajj Fuleihan G., Hodsman A.B., Kendler D.L., McClung M., et al. Application of the 1994 WHO classification to populations other than postmenopausal Caucasian women: the 2005 ISCD Official Positions. J Clin Densitom 9 (2006) 22-30
34. Stone K.L., Seeley D.G., Lui L.Y., Cauley J.A., Ensrud K., Browner W.S., et al. BMD at multiple sites and risk of fracture of multiple types: long-term results from the Study of Osteoporotic Fractures. J Bone Miner Res 18 (2003) 1947-1954
35. Reid R.J., Roos N.P., MacWilliam L., Frohlich N., and Black C. Assessing population health care need using a claims-based ACG morbidity measure: a validation analysis in the Province of Manitoba. Health Serv Res 37 (2002) 1345-1364
36. Leslie W.D., Derksen S., Prior H.S., Lix L.M., Metge C., and O'Neil J. The interaction of ethnicity and chronic disease as risk factors for osteoporotic fractures: A comparison in Canadian Aboriginals and non-Aboriginals. Osteoporos Int 17 (2006) 1358-1368
37. Kanis J.A., Johansson H., Oden A., Johnell O., de Laet C., Melton III L.J., et al. A meta-analysis of prior corticosteroid use and fracture risk. J Bone Miner Res 19 (2004) 893-899
38. Mustard C.A., Derksen S., Berthelot J.M., and Wolfson M. Assessing ecologic proxies for household income: a comparison of household and neighbourhood level income measures in the study of population health status. Health Place 5 (1999) 157-171
39. James K.E., White R.F., and Kraemer H.C. Repeated split sample validation to assess logistic regression and recursive partitioning: an application to the prediction of cognitive impairment. Stat Med 24 (2005) 3019-3035
40. Hirsch S., Shapiro J.L., Turega M.A., Frank T.L., Niven R.M., and Frank P.I. Using a neural network to screen a population for asthma. Ann Epidemiol 11 (2001) 369-376
41. Ennis M., Hinton G., Naylor D., Revow M., and Tibshirani R. A comparison of statistical learning methods on the Gusto database. Stat Med 17 (1998) 2501-2508
42. Terrin N., Schmid C.H., Griffith J.L., D'Agostino R.B., and Selker H.P. External validity of predictive models: a comparison of logistic regression, classification trees, and neural networks. J Clin Epidemiol 56 (2003) 721-729
43. Sargent D.J. Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer 91 (2001) 1636-1642
44. SAS Institute Inc. SAS/STAT user's guide (2004), SAS Institute Inc, Cary, NC
45. Harrell Jr. F.E., Lee K.L., and Mark D.B. Tutorial in biostatistics: multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15 (1996) 361-387
46. Spiegelhalter D.J. Probabilistic prediction in patient management and clinical trials. Stat Med 5 (1986) 421-433
47. Redelmeier D.A., Bloch D.A., and Hickam D.H. Assessing predictive accuracy: how to compare Brier scores. J Clin Epidemiol 44 (1991) 1141-1146
48. Steyerberg E.W., Eijkemans M.J., Harrell Jr. F.E., and Habbema J.D. Prognostic modeling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Stat Med 19 (2000) 1059-1079
49. Akaike H. A new look at the statistical model identification. IEEE Trans Automat Contr AC19 (1974) 716-723
50. Ladouceur M., Rahme E., Pineau C.A., and Joseph L. Robustness of prevalence estimates derived from misclassified data from administrative databases. Biometrics 63 (2007) 272-279
51. Vapnik V.N. Statistical learning theory (1998), Wiley, New York, NY
52. Huang Y., Lisboa P.J., and El-Deredy W. Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection. Stat Med 22 (2003) 147-164
53. Lean C., Doran S., Somorjai R.L., Malycha P., Clarke D., Himmelreich U., et al. Determination of grade and receptor status from the primary breast lesion by magnetic resonance spectroscopy. Technol Cancer Res Treat 3 (2004) 551-556