Literature DB >> 28120359

Misclassification of incident hospitalized and outpatient heart failure in administrative claims data: the Atherosclerosis Risk in Communities (ARIC) study.

Ricky Camplain1, Anna Kucharska-Newton1, Carmen C Cuthbertson1, Jacqueline D Wright2, Alvaro Alonso3, Gerardo Heiss1.   

Abstract

PURPOSE: The aim of this study was to quantify the influence of the length of the look-back period on misclassification of heart failure (HF) incidence in Medicare claims available for participants of a population-based cohort.
METHODS: Atherosclerosis Risk in Communities participants with ≥3 years of continuous fee-for-service Medicare enrollment from 2000 to 2012 was assigned an index date 36 months after enrollment separating the time-in-observation period into the look-back and the incidence periods. Incident HF events were identified using ICD-9-CM code algorithms as the first observed hospitalization claim or the second of two HF outpatient claims occurring within 12 months. Using 36 months as a referent, the look-back period was reduced by 6-month increments. For each look-back period, we calculated the incidence rate, percent of prevalent HF events misclassified as incident, and loss in sample size.
RESULTS: We identified 9568 Atherosclerosis Risk in Communities participants at risk for HF. For hospitalized and outpatient HF, the number of events misclassified as incident increased, and the total number of incident events decreased with increased length of the look-back period. The incident rate (per 1000 person years) decreased with increased length of the look-back period from 6 to 36 months and had a greater impact on outpatient HF; for example, from 11.2 to 10.6 for ICD-9-CM 428.xx hospitalization in the primary position and 10.5 to 7.9 for outpatient HF.
CONCLUSION: Our estimates can be used to optimize trade-offs between the degree of misclassification and number of events in the estimation of incident HF from administrative claims data, as pertinent to different study questions.
Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Entities:  

Keywords:  administrative claims; heart failure; hospitalizations; misclassification; outpatient

Mesh:

Year:  2017        PMID: 28120359      PMCID: PMC5380482          DOI: 10.1002/pds.4162

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  16 in total

1.  A retrospective, observational cohort analysis of a nationwide database to compare heart failure prescriptions and related health care utilization before and after publication of updated treatment guidelines in the United States.

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Journal:  Clin Ther       Date:  2010-08       Impact factor: 3.393

2.  Assessing the burden of hospitalized and community-care heart failure in Canada.

Authors:  Claudia Blais; Sulan Dai; Chris Waters; Cynthia Robitaille; Mark Smith; Lawrence W Svenson; Kim Reimer; Jill Casey; Rolf Puchtinger; Helen Johansen; Yana Gurevich; Lisa M Lix; Hude Quan; Karen Tu
Journal:  Can J Cardiol       Date:  2013-12-20       Impact factor: 5.223

3.  Congestive heart failure in the United States: is there more than meets the I(CD code)? The Corpus Christi Heart Project.

Authors:  D C Goff; D K Pandey; F A Chan; C Ortiz; M Z Nichaman
Journal:  Arch Intern Med       Date:  2000-01-24

4.  Do discharge codes underestimate hospitalisation due to heart failure? Validation study of hospital discharge coding for heart failure.

Authors:  Aleem U Khand; Morag Shaw; Islay Gemmel; John G F Cleland
Journal:  Eur J Heart Fail       Date:  2005-08       Impact factor: 15.534

5.  Trends in the incidence and outcomes of heart failure in Ontario, Canada: 1997 to 2007.

Authors:  Darwin F Yeung; Nicole K Boom; Helen Guo; Douglas S Lee; Susan E Schultz; Jack V Tu
Journal:  CMAJ       Date:  2012-08-20       Impact factor: 8.262

6.  Heart failure-related hospitalization in the U.S., 1979 to 2004.

Authors:  Jing Fang; George A Mensah; Janet B Croft; Nora L Keenan
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7.  Heart failure–associated hospitalizations in the United States.

Authors:  Saul Blecker; Margaret Paul; Glen Taksler; Gbenga Ogedegbe; Stuart Katz
Journal:  J Am Coll Cardiol       Date:  2013-03-26       Impact factor: 24.094

8.  The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators.

Authors: 
Journal:  Am J Epidemiol       Date:  1989-04       Impact factor: 4.897

9.  Heart failure prevalence, incidence, and mortality in the elderly with diabetes.

Authors:  Alain G Bertoni; W Gregory Hundley; Mark W Massing; Denise E Bonds; Gregory L Burke; David C Goff
Journal:  Diabetes Care       Date:  2004-03       Impact factor: 19.112

10.  Misclassification of incident conditions using claims data: impact of varying the period used to exclude pre-existing disease.

Authors:  Robert I Griffiths; Cynthia D O'Malley; Robert J Herbert; Mark D Danese
Journal:  BMC Med Res Methodol       Date:  2013-03-06       Impact factor: 4.615

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  10 in total

1.  Incidence of Heart Failure Observed in Emergency Departments, Ambulatory Clinics, and Hospitals.

Authors:  Ricky Camplain; Anna Kucharska-Newton; Thomas C Keyserling; J Bradley Layton; Laura Loehr; Gerardo Heiss
Journal:  Am J Cardiol       Date:  2018-03-01       Impact factor: 2.778

2.  Socioeconomic status and access to care and the incidence of a heart failure diagnosis in the inpatient and outpatient settings.

Authors:  Carmen C Cuthbertson; Gerardo Heiss; Jacqueline D Wright; Ricky Camplain; Mehul D Patel; Randi E Foraker; Kunihiro Matsushita; Nicole Puccinelli-Ortega; Amil M Shah; Anna M Kucharska-Newton
Journal:  Ann Epidemiol       Date:  2018-04-17       Impact factor: 3.797

3.  Variability in Coronary Artery Disease Testing for Patients With New-Onset Heart Failure.

Authors:  Jimmy Zheng; Paul A Heidenreich; Shun Kohsaka; William F Fearon; Alexander T Sandhu
Journal:  J Am Coll Cardiol       Date:  2022-03-08       Impact factor: 27.203

4.  Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations.

Authors:  Brent A Williams; Stephen Voyce; Stephen Sidney; Véronique L Roger; Timothy B Plante; Sharon Larson; Michael J LaMonte; Darwin R Labarthe; Bailey M DeBarmore; Alexander R Chang; Alanna M Chamberlain; Catherine P Benziger
Journal:  J Am Heart Assoc       Date:  2022-04-12       Impact factor: 6.106

5.  Dyskalemia, its patterns, and prognosis among patients with incident heart failure: A nationwide study of US veterans.

Authors:  Kunihiro Matsushita; Yingying Sang; Chao Yang; Shoshana H Ballew; Morgan E Grams; Josef Coresh; Miklos Z Molnar
Journal:  PLoS One       Date:  2019-08-08       Impact factor: 3.240

6.  The impact of lookback windows on the prevalence and incidence of chronic diseases among people living with HIV: an exploration in administrative health data in Canada.

Authors:  Ni Gusti Ayu Nanditha; Xinzhe Dong; Taylor McLinden; Paul Sereda; Jacek Kopec; Robert S Hogg; Julio S G Montaner; Viviane D Lima
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7.  A nationwide registry study on heart failure in Norway from 2008 to 2018: variations in lookback period affect incidence estimates.

Authors:  Kristina Malene Ødegaard; Sandre Svatun Lirhus; Hans Olav Melberg; Jonas Hallén; Sigrun Halvorsen
Journal:  BMC Cardiovasc Disord       Date:  2022-03-05       Impact factor: 2.298

8.  Trends for Readmission and Mortality After Heart Failure Hospitalisation in Malaysia, 2007 to 2016.

Authors:  Yvonne Mei Fong Lim; Su Miin Ong; Stefan Koudstaal; Wen Yea Hwong; Houng Bang Liew; Jeyamalar Rajadurai; Diederick E Grobbee; Folkert W Asselbergs; Sheamini Sivasampu; Ilonca Vaartjes
Journal:  Glob Heart       Date:  2022-03-08

9.  Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration.

Authors:  Caroline A Presley; Jea Young Min; Jonathan Chipman; Robert A Greevy; Carlos G Grijalva; Marie R Griffin; Christianne L Roumie
Journal:  BMJ Open       Date:  2018-03-25       Impact factor: 2.692

10.  Constructing Epidemiologic Cohorts from Electronic Health Record Data.

Authors:  Brent A Williams
Journal:  Int J Environ Res Public Health       Date:  2021-12-14       Impact factor: 3.390

  10 in total

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