Literature DB >> 30620612

Incidence and prevalence of the metabolic syndrome using ICD-9 and ICD-10 diagnostic codes, active component, U.S. Armed Forces, 2002-2017.

Valerie F Williams, Gi-Taik Oh, Shauna Stahlman.   

Abstract

This report uses ICD-9 and ICD-10 codes (277.7 and E88.81, respectively) for the metabolic syndrome (MetS) to summarize trends in the incidence and prevalence of this condition among active component members of the U.S. Armed Forces between 2002 and 2017. During this period, the crude overall incidence rate of MetS was 7.5 cases per 100,000 person-years (p-yrs). Compared to their respective counterparts, overall incidence rates were highest among Asian/Pacific Islanders, Air Force members, and warrant officers and were lowest among those of other/unknown race/ethnicity, Marine Corps members, and junior enlisted personnel and officers. During 2002-2017, the annual incidence rates of MetS peaked in 2009 at 11.6 cases per 100,000 p-yrs and decreased to 5.9 cases per 100,000 p-yrs in 2017. Annual prevalence rates of MetS increased steadily during the first 11 years of the surveillance period reaching a high of 38.9 per 100,000 active component service members in 2012, after which rates declined slightly to 31.6 per 100,000 active component service members in 2017. Validation of ICD-9/ICD-10 diagnostic codes for MetS using the National Cholesterol Education Program Adult Treatment Panel III criteria is needed to establish the level of agreement between the two methods for identifying this condition.

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Year:  2018        PMID: 30620612

Source DB:  PubMed          Journal:  MSMR        ISSN: 2152-8217


  3 in total

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Authors:  Sanghoo Lee; Seol-A Kim; Yejin Kim; Juhoon Kim; Gayeon Hong; Jeonghoon Hong; Kyeonghwan Choi; Chun-Sick Eom; Saeyun Baik; Mi-Kyeong Lee; Kyoung-Ryul Lee
Journal:  Genes (Basel)       Date:  2022-08-22       Impact factor: 4.141

2.  Incidence and risk factors of metabolic syndrome among Royal Thai Army personnel.

Authors:  Boonsub Sakboonyarat; Ram Rangsin; Murray A Mittleman
Journal:  Sci Rep       Date:  2022-09-20       Impact factor: 4.996

3.  Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.

Authors:  Johannes Benecke; Cornelius Benecke; Marius Ciutan; Mihnea Dosius; Cristian Vladescu; Victor Olsavszky
Journal:  PLoS Negl Trop Dis       Date:  2021-11-01
  3 in total

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