A cardiovascular disease risk prediction algorithm for use with the Medicare current beneficiary survey.
Identifieur interne : 000045 ( Main/Exploration ); précédent : 000044; suivant : 000046A cardiovascular disease risk prediction algorithm for use with the Medicare current beneficiary survey.
Auteurs : Hassan Fouayzi [États-Unis] ; Arlene S. Ash [États-Unis] ; Amy K. Rosen [États-Unis]Source :
- Health services research [ 1475-6773 ] ; 2020.
Abstract
OBJECTIVE
To develop a cardiovascular disease (CVD) risk score that can be used to quantify CVD risk in the Medicare Current Beneficiary Survey (MCBS).
DATA SOURCES
We used 1999-2013 MCBS data.
STUDY DESIGN
We used a backward stepwise approach and cox proportional hazards regressions to build and validate a new CVD risk score, similar to the Framingham Risk Score (FRS), using only information available in MCBS. To assess its performance, we calculated C statistics and examined calibration plots.
DATA COLLECTION/EXTRACTION METHODS
We studied 21 968 community-dwelling Medicare beneficiaries aged 65 years or older without pre-existing CVD. We obtained risk factors from both survey and claims data. We used claims data to derive "CVD event within 3 years" following the FRS definition of CVD.
PRINCIPAL FINDINGS
About five percent of MCBS participants developed a CVD event over a mean follow-up period of 348 days. Our final MCBS-based model added morbidity burden, reported general health status, and functional limitation to the traditional FRS predictors of CVD. This model had relatively fair discrimination (C statistic = 0.69; 95% confidence interval [CI], 0.67-0.71) and performed well on validation (C = 0.68; CI, 0.66-0.70). More importantly, the plot of observed CVD outcomes versus predicted ones showed that this model had a good calibration.
CONCLUSIONS
Our new CVD risk score can be calculated using MCBS data, thereby extending the survey's ability to quantify CVD risk in the Medicare population and better inform both health policy and health services research.
DOI: 10.1111/1475-6773.13290
PubMed: 32285938
Affiliations:
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Ash</name><affiliation wicri:level="2"><nlm:affiliation>Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Massachusetts</region></placeName><wicri:cityArea>Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester</wicri:cityArea></affiliation></author><author><name sortKey="Rosen, Amy K" sort="Rosen, Amy K" uniqKey="Rosen A" first="Amy K" last="Rosen">Amy K. Rosen</name><affiliation wicri:level="2"><nlm:affiliation>Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, Massachusetts.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Massachusetts</region></placeName><wicri:cityArea>Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:affiliation>Department of Surgery, School of Medicine, Boston University, Boston, Massachusetts.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Massachusetts</region></placeName><wicri:cityArea>Department of Surgery, School of Medicine, Boston University, Boston</wicri:cityArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32285938</idno><idno type="pmid">32285938</idno><idno type="doi">10.1111/1475-6773.13290</idno><idno type="wicri:Area/Main/Corpus">000015</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000015</idno><idno type="wicri:Area/Main/Curation">000015</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000015</idno><idno type="wicri:Area/Main/Exploration">000015</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A cardiovascular disease risk prediction algorithm for use with the Medicare current beneficiary survey.</title><author><name sortKey="Fouayzi, Hassan" sort="Fouayzi, Hassan" uniqKey="Fouayzi H" first="Hassan" last="Fouayzi">Hassan Fouayzi</name><affiliation wicri:level="2"><nlm:affiliation>Meyers Primary Care Institute (A Joint Endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health), Worcester, Massachusetts.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Massachusetts</region></placeName><wicri:cityArea>Meyers Primary Care Institute (A Joint Endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health), Worcester</wicri:cityArea></affiliation></author><author><name sortKey="Ash, Arlene S" sort="Ash, Arlene S" uniqKey="Ash A" first="Arlene S" last="Ash">Arlene S. Ash</name><affiliation wicri:level="2"><nlm:affiliation>Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Massachusetts</region></placeName><wicri:cityArea>Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester</wicri:cityArea></affiliation></author><author><name sortKey="Rosen, Amy K" sort="Rosen, Amy K" uniqKey="Rosen A" first="Amy K" last="Rosen">Amy K. Rosen</name><affiliation wicri:level="2"><nlm:affiliation>Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, Massachusetts.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Massachusetts</region></placeName><wicri:cityArea>Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:affiliation>Department of Surgery, School of Medicine, Boston University, Boston, Massachusetts.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Massachusetts</region></placeName><wicri:cityArea>Department of Surgery, School of Medicine, Boston University, Boston</wicri:cityArea></affiliation></author></analytic><series><title level="j">Health services research</title><idno type="eISSN">1475-6773</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b></p><p>To develop a cardiovascular disease (CVD) risk score that can be used to quantify CVD risk in the Medicare Current Beneficiary Survey (MCBS).</p></div><div type="abstract" xml:lang="en"><p><b>DATA SOURCES</b></p><p>We used 1999-2013 MCBS data.</p></div><div type="abstract" xml:lang="en"><p><b>STUDY DESIGN</b></p><p>We used a backward stepwise approach and cox proportional hazards regressions to build and validate a new CVD risk score, similar to the Framingham Risk Score (FRS), using only information available in MCBS. To assess its performance, we calculated C statistics and examined calibration plots.</p></div><div type="abstract" xml:lang="en"><p><b>DATA COLLECTION/EXTRACTION METHODS</b></p><p>We studied 21 968 community-dwelling Medicare beneficiaries aged 65 years or older without pre-existing CVD. We obtained risk factors from both survey and claims data. We used claims data to derive "CVD event within 3 years" following the FRS definition of CVD.</p></div><div type="abstract" xml:lang="en"><p><b>PRINCIPAL FINDINGS</b></p><p>About five percent of MCBS participants developed a CVD event over a mean follow-up period of 348 days. Our final MCBS-based model added morbidity burden, reported general health status, and functional limitation to the traditional FRS predictors of CVD. This model had relatively fair discrimination (C statistic = 0.69; 95% confidence interval [CI], 0.67-0.71) and performed well on validation (C = 0.68; CI, 0.66-0.70). More importantly, the plot of observed CVD outcomes versus predicted ones showed that this model had a good calibration.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>Our new CVD risk score can be calculated using MCBS data, thereby extending the survey's ability to quantify CVD risk in the Medicare population and better inform both health policy and health services research.</p></div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32285938</PMID><DateRevised><Year>2020</Year><Month>04</Month><Day>14</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1475-6773</ISSN><JournalIssue CitedMedium="Internet"><PubDate><Year>2020</Year><Month>Apr</Month><Day>14</Day></PubDate></JournalIssue><Title>Health services research</Title><ISOAbbreviation>Health Serv Res</ISOAbbreviation></Journal><ArticleTitle>A cardiovascular disease risk prediction algorithm for use with the Medicare current beneficiary survey.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.1111/1475-6773.13290</ELocationID><Abstract><AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">To develop a cardiovascular disease (CVD) risk score that can be used to quantify CVD risk in the Medicare Current Beneficiary Survey (MCBS).</AbstractText><AbstractText Label="DATA SOURCES" NlmCategory="METHODS">We used 1999-2013 MCBS data.</AbstractText><AbstractText Label="STUDY DESIGN" NlmCategory="METHODS">We used a backward stepwise approach and cox proportional hazards regressions to build and validate a new CVD risk score, similar to the Framingham Risk Score (FRS), using only information available in MCBS. To assess its performance, we calculated C statistics and examined calibration plots.</AbstractText><AbstractText Label="DATA COLLECTION/EXTRACTION METHODS" NlmCategory="METHODS">We studied 21 968 community-dwelling Medicare beneficiaries aged 65 years or older without pre-existing CVD. We obtained risk factors from both survey and claims data. We used claims data to derive "CVD event within 3 years" following the FRS definition of CVD.</AbstractText><AbstractText Label="PRINCIPAL FINDINGS" NlmCategory="RESULTS">About five percent of MCBS participants developed a CVD event over a mean follow-up period of 348 days. Our final MCBS-based model added morbidity burden, reported general health status, and functional limitation to the traditional FRS predictors of CVD. This model had relatively fair discrimination (C statistic = 0.69; 95% confidence interval [CI], 0.67-0.71) and performed well on validation (C = 0.68; CI, 0.66-0.70). More importantly, the plot of observed CVD outcomes versus predicted ones showed that this model had a good calibration.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Our new CVD risk score can be calculated using MCBS data, thereby extending the survey's ability to quantify CVD risk in the Medicare population and better inform both health policy and health services research.</AbstractText><CopyrightInformation>© Health Research and Educational Trust.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Fouayzi</LastName><ForeName>Hassan</ForeName><Initials>H</Initials><Identifier Source="ORCID">https://orcid.org/0000-0002-4624-0320</Identifier><AffiliationInfo><Affiliation>Meyers Primary Care Institute (A Joint Endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health), Worcester, Massachusetts.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ash</LastName><ForeName>Arlene S</ForeName><Initials>AS</Initials><AffiliationInfo><Affiliation>Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rosen</LastName><ForeName>Amy K</ForeName><Initials>AK</Initials><Identifier Source="ORCID">https://orcid.org/0000-0002-7539-7749</Identifier><AffiliationInfo><Affiliation>Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, Massachusetts.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Surgery, School of Medicine, Boston University, Boston, Massachusetts.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>UL1RR031982</GrantID><Acronym>NH</Acronym><Agency>NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>04</Month><Day>14</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Health Serv Res</MedlineTA><NlmUniqueID>0053006</NlmUniqueID><ISSNLinking>0017-9124</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">cardiovascular diseases</Keyword><Keyword MajorTopicYN="N">health policy</Keyword><Keyword MajorTopicYN="N">health risk assessment</Keyword><Keyword MajorTopicYN="N">proportional hazards models</Keyword><Keyword MajorTopicYN="N">survey methods</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>4</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>4</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>4</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32285938</ArticleId><ArticleId IdType="doi">10.1111/1475-6773.13290</ArticleId></ArticleIdList><ReferenceList><Title>REFERENCES</Title><Reference><Citation>Benjamin EJ, Blaha MJ, Chiuve SE, et al. Heart Disease and Stroke Statistics’2017 update: a report from the American Heart Association. Circulation. 2017;135(10), e146-e603.</Citation></Reference><Reference><Citation>Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics-2011 update: a report from the American Heart Association. Circulation. 2011;123(4):e18-e209.</Citation></Reference><Reference><Citation>D’Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation. 2008;117(6):743-753.</Citation></Reference><Reference><Citation>Davidoff A, Lopert R, Stuart B, Shaffer T, Lloyd JT, Shoemaker JS. Simulated value-based insurance design applied to statin use by Medicare beneficiaries with diabetes. Value Heal. 2012;15(3):404-411.</Citation></Reference><Reference><Citation>Adler GS. A profile of the Medicare Current Beneficiary Survey. Health Care Financ Rev. 1994;15(4):153-163.</Citation></Reference><Reference><Citation>Sacks NC, Ash AS, Ghosh K, Rosen AK, Wong JB, Rosen AB. Trends in acute myocardial infarction hospitalizations: are we seeing the whole picture? Am Heart J. 2015;170(6):1211-1219.</Citation></Reference><Reference><Citation>Matlock DD, Groeneveld PW, Sidney S, et al. Geographic variation in cardiovascular procedure use among Medicare fee-for-service vs medicare advantage beneficiaries. JAMA. 2013;310(2):155.</Citation></Reference><Reference><Citation>Rowe VL, Weaver FA, Lane JS, Etzioni DA. Racial and ethnic differences in patterns of treatment for acute peripheral arterial disease in the United States, 1998-2006. J Vasc Surg. 2010;51(4):S21-S26.</Citation></Reference><Reference><Citation>Chen J, Normand SLT, Wang Y, Krumholz HM. National and regional trends in heart failure hospitalization and mortality rates for medicare beneficiaries, 1998-2008. JAMA. 2011;306(15):1669-1678.</Citation></Reference><Reference><Citation>McDonald RJ, Kallmes DF, Cloft HJ. Comparison of hospitalization costs and medicare payments for carotid endarterectomy and carotid stenting in asymptomatic patients. Am J Neuroradiol. 2012;33(3):420-425.</Citation></Reference><Reference><Citation>Andrade SE, Harrold LR, Tjia J, et al. A systematic review of validated methods for identifying cerebrovascular accident or transient ischemic attack using administrative data. Pharmacoepidemiol Drug Saf. 2012;21:100-128.</Citation></Reference><Reference><Citation>Shea DG, Terza JV, Stuart BC, Briesacher B. Estimating the effects of prescription drug coverage for medicare beneficiaries. Health Serv Res. 2007;42:933-949.</Citation></Reference><Reference><Citation>Stuart B, Shea D, Briesacher B. Dynamics in drug coverage of Medicare beneficiaries: finders, losers, switchers. Health Aff. 2001;20(2):86-99.</Citation></Reference><Reference><Citation>Naci H, Soumerai SB, Ross-Degnan D, et al. Persistent medication affordability problems among disabled medicare beneficiaries after Part D, 2006-2011. Med Care. 2014;52(11):951-956.</Citation></Reference><Reference><Citation>Madden JM, Graves AJ, Zhang F, et al. Cost-related medication nonadherence and spending on basic needs following implementation of medicare part D. JAMA. 2008;299(16):1922.</Citation></Reference><Reference><Citation>Nagi SZ. An epidemiology of disability among adults in the United States. Milbank Mem Fund Q Health Soc. 1976;54(4):439-467.</Citation></Reference><Reference><Citation>Centers for Medicare & Medicaid Services. https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Risk-Adjustors.html. Accessed April 1, 2019.</Citation></Reference><Reference><Citation>Ash AS, Posner MA, Speckman J, Franco S, Yacht AC, Bramwell L. Using claims data to examine mortality trends following hospitalization for heart attack in Medicare. Health Serv Res. 2003;38(5):1253-1262.</Citation></Reference><Reference><Citation>Briesacher BA, Andrade SE, Fouayzi H, Chan KA. Medication adherence and use of generic drug therapies. Am J Manag Care. 2009;15:450-456.</Citation></Reference><Reference><Citation>Briesacher BA, Quittner AL, Saiman L, Sacco P, Fouayzi H, Quittell LM. Adherence with tobramycin inhaled solution and health care utilization. BMC Pulm Med. 2011;11(1):5.</Citation></Reference><Reference><Citation>Briesacher BA, Andrade SE, Fouayzi H, Chan KA. Comparison of drug adherence rates among patients with seven different medical conditions. Pharmacotherapy. 2008;28(4):437-443.</Citation></Reference><Reference><Citation>Gould MK, Ananth L, Barnett PG. A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest. 2007;131(2):383-388.</Citation></Reference><Reference><Citation>Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Boca Raton, FL: Chapman and Hall; 1993.</Citation></Reference><Reference><Citation>Little RJA, Rubin DB. Statistical analysis with missing data. New York, NY: John Wiley & Sons; 2002.</Citation></Reference><Reference><Citation>Raghunathan T, Berglund PA, Solenberger P. Multiple Imputation in Practice with Examples Using IVEware. New York, NY: Chapman and Hall/CRC; 2018.</Citation></Reference><Reference><Citation>Muntner P, Colantonio LD, Cushman M, et al. Validation of the atherosclerotic cardiovascular disease Pooled Cohort risk equations. JAMA. 2014;311(14):1406.</Citation></Reference><Reference><Citation>Gaziano TA, Young CR, Fitzmaurice G, Atwood S, Gaziano JM. Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort. Lancet. 2008;371(9616):923-931.</Citation></Reference><Reference><Citation>Vaidya D, Yanek LR, Moy TF, Pearson TA, Becker LC, Becker DM. Incidence of coronary artery disease in siblings of patients with premature coronary artery disease: 10 years of follow-up. Am J Cardiol. 2007;100(9):1410-1415.</Citation></Reference><Reference><Citation>Ho PM, Bryson CL, Rumsfeld JS. Medication adherence: Its importance in cardiovascular outcomes. Circulation. 2009;119(23):3028-3035.</Citation></Reference><Reference><Citation>Miyasaka Y, Barnes ME, Gersh BJ, et al. Coronary ischemic events after first atrial fibrillation: risk and survival. Am J Med. 2007;120(4):357-363.e1.</Citation></Reference><Reference><Citation>Extermann M, Overcash J, Lyman GH, Parr J, Balducci L. Comorbidity and functional status are independent in older cancer patients. J Clin Oncol. 1998;16(4):1582-1587.</Citation></Reference><Reference><Citation>Parmelee PA, Thuras PD, Katz IR, Lawton MP. Validation of the cumulative illness rating scale in a geriatric residential population. J Am Geriatr Soc. 1995;43(2):130-137.</Citation></Reference><Reference><Citation>Waldman E, Potter JF. A prospective evaluation of the cumulative illness rating scale. Aging Clin Exp Res. 1992;4(2):171-178.</Citation></Reference><Reference><Citation>Keller BK, Potter JF. Predictors of mortality in outpatient Geriatric Evaluation and Management clinic patients. J Gerontol. 1994;49(6):M246-M251.</Citation></Reference><Reference><Citation>Eppig FJ, Chulis GS. Matching MCBS (Medicare Current Beneficiary Survey) and Medicare data: the best of both worlds. Health Care Financ Rev. 1997;18:211-229.</Citation></Reference><Reference><Citation>Osterberg L, Blaschke T. Drug therapy: adherence to medication. N Engl J Med. 2005;353:487-497.</Citation></Reference><Reference><Citation>Cramer JA. A systematic review of adherence with medications for diabetes. Diabetes Care. 2004;27(5):1218-1224.</Citation></Reference><Reference><Citation>Zeng F, An JJ, Scully R, Barrington C, Patel BV, Nichol MB. The impact of value-based benefit design on adherence to diabetes medications: a propensity score-weighted difference in difference evaluation. Value Heal. 2010;13(6):846-852.</Citation></Reference><Reference><Citation>Choudhry NK, Fischer MA, Avorn J, et al. At pitney bowes, value-based insurance design cut copayments and increased drug adherence. Health Aff. 2010;29(11):1995-2001.</Citation></Reference><Reference><Citation>Maciejewski ML, Farley JF, Parker J, Wansink D. Copayment reductions generate greater medication adherence in targeted patients. Health Aff. 2010;29(11):2002-2008.</Citation></Reference><Reference><Citation>Mahoney JJ. Reducing patient drug acquisition costs can lower diabetes health claims. Am J Manag Care. 2005;11:S170-S176.</Citation></Reference><Reference><Citation>Rosen AB, Hamel MB, Weinstein MC, Cutler DM, Fendrick AM, Vijan S. Cost-effectiveness of full Medicare coverage of angiotensin-converting enzyme inhibitors for beneficiaries with diabetes. Ann Intern Med. 2005;143(2):19.</Citation></Reference><Reference><Citation>Guzder RN, Gatling W, Mullee MA, Mehta RL, Byrne CD. Prognostic value of the Framingham cardiovascular risk equation and the UKPDS risk engine for coronary heart disease in newly diagnosed Type 2 diabetes: Results from a United Kingdom study. Diabet Med. 2005;22(5):554-562.</Citation></Reference><Reference><Citation>McEwan P, Williams JE, Griffiths JD, et al. Evaluating the performance of the Framingham risk equations in a population with diabetes. Diabet Med. 2004;21(4):318-323.</Citation></Reference><Reference><Citation>Stevens RJ, Coleman RL, Holman RR. Framingham risk equations underestimate coronary heart disease risk in diabetes [4]. Diabet Med. 2005;. https://doi.org/10.1111/j.1464-5491.2005.01387.x</Citation></Reference><Reference><Citation>D’Agostino RB Sr, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores. JAMA. 2001;286(2):180.</Citation></Reference><Reference><Citation>Sacco RL, Khatri M, Rundek T, et al. Improving global vascular risk prediction with behavioral and anthropometric factors. The Multiethnic NOMAS (Northern Manhattan Cohort Study). J Am Coll Cardiol. 2009;54(24):2303-2311.</Citation></Reference><Reference><Citation>Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016;9:211-217.</Citation></Reference><Reference><Citation>Burton B, Jesilow P. How healthcare studies use claims data. Open Heal Serv Policy J. 2011;4(1):26-29.</Citation></Reference><Reference><Citation>Duncan I. Mining Health Claims Data for Assessing Patient Risk. In: Holmes DE, Jain L, eds. Data Mining: Foundations and Intelligent Paradigms. Berlin, Germany: Springer; 2012.</Citation></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Massachusetts</li></region></list><tree><country name="États-Unis"><region name="Massachusetts"><name sortKey="Fouayzi, Hassan" sort="Fouayzi, Hassan" uniqKey="Fouayzi H" first="Hassan" last="Fouayzi">Hassan Fouayzi</name></region><name sortKey="Ash, Arlene S" sort="Ash, Arlene S" uniqKey="Ash A" first="Arlene S" last="Ash">Arlene S. Ash</name><name sortKey="Rosen, Amy K" sort="Rosen, Amy K" uniqKey="Rosen A" first="Amy K" last="Rosen">Amy K. Rosen</name><name sortKey="Rosen, Amy K" sort="Rosen, Amy K" uniqKey="Rosen A" first="Amy K" last="Rosen">Amy K. Rosen</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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