Differences in the Prevalence of Obesity, Smoking and Alcohol in the United States Nationwide Inpatient Sample and the Behavioral Risk Factor Surveillance System

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2015 Nov 5

PMID 26536469

Citations 29

Authors

Elie S Al Kazzi

Brandyn Lau

Tianjing Li

Eric B Schneider

Martin A Makary

Susan Hutfless

Affiliations

Soon will be listed here.

Abstract

Background: The lack of adequate and standardized recording of leading risk factors for morbidity and mortality in medical records have downstream effects on research based on administrative databases. The measurement of healthcare is increasingly based on risk-adjusted outcomes derived from coded comorbidities in these databases. However inaccurate or haphazard assessment of risk factors for morbidity and mortality in medical record codes can have tremendous implications for quality improvement and healthcare reform.

Objective: We aimed to compare the prevalence of obesity, overweight, tobacco use and alcohol abuse of a large administrative database with a direct data collection survey.

Materials And Methods: We used the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for four leading risk factors in the United States Nationwide Inpatient Sample (NIS) to compare them with a direct survey in the Behavioral Risk Factor Surveillance System (BRFSS) in 2011. After confirming normality of the risk factors, we calculated the national and state estimates and Pearson's correlation coefficient for obesity, overweight, tobacco use and alcohol abuse between NIS and BRFSS.

Results: Compared with direct participant questioning in BRFSS, NIS reported substantially lower prevalence of obesity (p<0.01), overweight (p<0.01), and alcohol abuse (p<0.01), but not tobacco use (p = 0.18). The correlation between NIS and BRFSS was 0.27 for obesity (p = 0.06), 0.09 for overweight (p = 0.55), 0.62 for tobacco use (p<0.01) and 0.40 for alcohol abuse (p<0.01).

Conclusions: The prevalence of obesity, overweight, tobacco smoking and alcohol abuse based on codes is not consistent with prevalence based on direct questioning. The accuracy of these important measures of health and morbidity in databases is critical for healthcare reform policies.

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References

. Medicare and Medicaid programs; electronic health record incentive program. Final rule. Fed Regist. 2010; 75(144):44313-588. View

James A, Jamison M, Biswas M, Brancazio L, Swamy G, Myers E . Acute myocardial infarction in pregnancy: a United States population-based study. Circulation. 2006; 113(12):1564-71. DOI: 10.1161/CIRCULATIONAHA.105.576751. View

Blumenthal D, McGinnis J . Measuring Vital Signs: an IOM report on core metrics for health and health care progress. JAMA. 2015; 313(19):1901-2. DOI: 10.1001/jama.2015.4862. View

Gupta T, Kolte D, Khera S, Aronow W, Palaniswamy C, Mujib M . Relation of smoking status to outcomes after cardiopulmonary resuscitation for in-hospital cardiac arrest. Am J Cardiol. 2014; 114(2):169-74. DOI: 10.1016/j.amjcard.2014.04.021. View

Tai B, Wu L, Clark H . Electronic health records: essential tools in integrating substance abuse treatment with primary care. Subst Abuse Rehabil. 2014; 3:1-8. PMC: 3886649. DOI: 10.2147/SAR.S22575. View

Courtney P . Data liquidity in health information systems. Cancer J. 2011; 17(4):219-21. PMC: 3186070. DOI: 10.1097/PPO.0b013e3182270c83. View

Berthelsen C . Evaluation of coding data quality of the HCUP National Inpatient Sample. Top Health Inf Manage. 2001; 21(2):10-23. View

Ogden C, Carroll M, Kit B, Flegal K . Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA. 2014; 311(8):806-14. PMC: 4770258. DOI: 10.1001/jama.2014.732. View

Dixon B, Rosenman M, Xia Y, Grannis S . A vision for the systematic monitoring and improvement of the quality of electronic health data. Stud Health Technol Inform. 2013; 192:884-8. View

10.

Adler-Milstein J, Furukawa M, King J, Jha A . Early results from the hospital Electronic Health Record Incentive Programs. Am J Manag Care. 2013; 19(7):e273-84. View

11.

Danaei G, Ding E, Mozaffarian D, Taylor B, Rehm J, Murray C . The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 2009; 6(4):e1000058. PMC: 2667673. DOI: 10.1371/journal.pmed.1000058. View

12.

Quan H, Parsons G, Ghali W . Validity of procedure codes in International Classification of Diseases, 9th revision, clinical modification administrative data. Med Care. 2004; 42(8):801-9. DOI: 10.1097/01.mlr.0000132391.59713.0d. View

13.

McGinnis J, Williams-Russo P, Knickman J . The case for more active policy attention to health promotion. Health Aff (Millwood). 2002; 21(2):78-93. DOI: 10.1377/hlthaff.21.2.78. View

14.

OMalley K, Cook K, Price M, Wildes K, Hurdle J, Ashton C . Measuring diagnoses: ICD code accuracy. Health Serv Res. 2005; 40(5 Pt 2):1620-39. PMC: 1361216. DOI: 10.1111/j.1475-6773.2005.00444.x. View

15.

Iezzoni L . Risk adjusting rehabilitation outcomes: an overview of methodologic issues. Am J Phys Med Rehabil. 2004; 83(4):316-26. DOI: 10.1097/01.phm.0000118041.17739.bb. View

16.

Bordowitz R, Morland K, Reich D . The use of an electronic medical record to improve documentation and treatment of obesity. Fam Med. 2007; 39(4):274-9. View

17.

McGinnis J, FOEGE W . Actual causes of death in the United States. JAMA. 1993; 270(18):2207-12. View

18.

Charlson M, Pompei P, Ales K, MacKenzie C . A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987; 40(5):373-83. DOI: 10.1016/0021-9681(87)90171-8. View

19.

Belletti D, Zacker C, Daniel Mullins C . Perspectives on electronic medical records adoption: electronic medical records (EMR) in outcomes research. Patient Relat Outcome Meas. 2012; 1:29-37. PMC: 3417895. DOI: 10.2147/prom.s8896. View

20.

Demlo L, Campbell P, Brown S . Reliability of information abstracted from patients' medical records. Med Care. 1978; 16(12):995-1005. DOI: 10.1097/00005650-197812000-00003. View