Literature DB >> 21187798

Prevalence rates and costs of metabolic syndrome and associated risk factors using employees' integrated laboratory data and health care claims.

Howard G Birnbaum1, Miles E Mattson, Sara Kashima, Todd E Williamson.   

Abstract

OBJECTIVE: This study assessed the relative value of using laboratory, claims, or integrated laboratory-claims data to identify prevalence rates and costs of metabolic syndrome among Chevron Texaco Corporation, San Ramon, California, employees.
METHODS: This study identified five metabolic syndrome risk factors by using three identification methods: (1) health-screening data, applying the National Cholesterol Education Program Adult Treatment Panel III and World Health Organization definitions; (2) employer-based claims data applying proxy International Classification of Diseases, 9th Revision definitions; and (3) integrated laboratory-claims data. Prevalence rates and costs of metabolic syndrome and associated risk factors were estimated.
RESULTS: Laboratory-defined and claims-defined approaches underestimated metabolic syndrome prevalence rates compared with the integrated approach by 22.9% and 87.5%, respectively. Employees with metabolic syndrome had double the costs of those without any risk factors ($4603 vs $1859; P = 0.0384).
CONCLUSIONS: Results suggest that integrating laboratory and claims data is a more balanced approach than either approach alone for identifying metabolic syndrome among Chevron employees.

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Mesh:

Year:  2011        PMID: 21187798     DOI: 10.1097/JOM.0b013e3181ff0594

Source DB:  PubMed          Journal:  J Occup Environ Med        ISSN: 1076-2752            Impact factor:   2.162


  9 in total

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4.  Genetic epidemiology of cardiometabolic risk factors and their clustering patterns in Mexican American children and adolescents: the SAFARI Study.

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Journal:  Hum Genet       Date:  2013-06-05       Impact factor: 4.132

5.  Metabolic syndrome and other cardiovascular risk factors among police officers.

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6.  Increased static and decreased capacity oxidation-reduction potentials in plasma are predictive of metabolic syndrome.

Authors:  Gerd Bobe; Tora J Cobb; Scott W Leonard; Savinda Aponso; Christopher B Bahro; Dipankar Koley; Eunice Mah; Richard S Bruno; Maret G Traber
Journal:  Redox Biol       Date:  2017-02-14       Impact factor: 11.799

7.  Prevalence of overweight and obesity among police officers in Riyadh City and risk factors for cardiovascular disease.

Authors:  Abdullah S Alghamdi; Mohammed A Yahya; Ghedeir M Alshammari; Magdi A Osman
Journal:  Lipids Health Dis       Date:  2017-04-14       Impact factor: 3.876

8.  Association of BMI trajectories with cardiometabolic risk among low-income Mexican American children.

Authors:  Marisol Perez; Laura K Winstone; Juan C Hernández; Sarah G Curci; Daniel McNeish; Linda J Luecken
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9.  Adapting a Prediction Rule for Metabolic Syndrome Risk Assessment Suitable for Developing Countries.

Authors:  Ekram W Abd El-Wahab; Hanan Z Shatat; Fahmy Charl
Journal:  J Prim Care Community Health       Date:  2019 Jan-Dec
  9 in total

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