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Re: Prevalence of Metabolic Syndrome in an Employed Population as Determined by Analysis of Three Data Sources

Goetzel, Ron Z. PhD

Journal of Occupational and Environmental Medicine: May 2017 - Volume 59 - Issue 5 - p e101–e102
doi: 10.1097/JOM.0000000000001032
LETTERS TO THE EDITOR
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Johns Hopkins University and IBM Watson Health, Bethesda, MD.

Address correspondence to: Ron Z. Goetzel, PhD, Johns Hopkins University and IBM Watson Health, Bethesda, MD (rgoetzel@us.ibm.com).

The authors report no conflicts of interest.

Readers are invited to submit letters for publication in this department. Submit letters online at http://joem.edmgr.com. Choose “Submit New Manuscript.” A signed copyright assignment and financial disclosure form must be submitted with the letter. Form available at http://www.joem.org under Author and Reviewer information.

To the Editor:

We appreciate the comments and perspectives offered by Drs Schultz, Edington, Chen, and Burton in their letter to the editor. It is an honor to be challenged by such well-regarded researchers. We agree that our article would have benefited from an expanded literature review that would have included these researchers’ previous work related to metabolic syndrome (MetS). We encourage readers to review the studies listed in the letter to the editor for added insights regarding employer efforts to identify and manage MetS in the workplace.

Below, we clarify the intended goal for our manuscript and address the points made by the researchers.

The main purpose of our article was to emphasize the importance of identifying risk factors for MetS, individually and in combination, as each of the risk factors represents a potential for degraded health status over time. We all agree that the best opportunity for intervention is during the early stages of MetS, and success is more likely achieved when individuals have fewer of the identified risk factors for the condition. Our intent was to compare three data sources that may be leveraged in identifying at-risk populations and determine which of the three is most valid and reliable for early identification purposes.

Drs Schultz et al correctly point to differences in the definition of MetS based on professional organization. For example, they note that the AHA/NHLBI definition for MetS must include a waist circumference at least 102 cm in men and at least 88 cm in women, which is equivalent to 40 and 35 inches, respectively. However, the World Health Organization and National Health and Medical Research Council note that waist sizes more than 94 cm and 80 cm indicate a state of increased health risk associated with obesity (Kassi, Pervanidou, Kaltsas & Chrousos, 2011).1–3 We, in collaboration with Lockheed Martin Corporation (LMC), chose to use a more liberal definition of obesity to identify workers who would benefit most from early intervention. Similarly, in terms of high lipid values, we identified patients who had low high-density lipoprotein (HDL), high triglycerides, or elevated total cholesterol values as being at high risk, based on available data for any given individual. Because not all measures were collected for any given employee, we applied a high-risk flag to a person based on the availability of biometric data for that individual in any of the three markers of high risk.

We did not consider medication use when independently analyzing the health risk assessment (HRA) data because such medication-based questions were not part of the existing survey. However, as Dr Schultz et al point out, we were able to use the HRA data to identify employees who self-reported that they had been told by a doctor or a nurse they have certain conditions representative of the MetS risk factors. And as noted in Table 1, we did consider medication use in our analysis of claims data.

The researchers correctly note that “the major reason employers utilize the HRA and biometric screening is to identify health risks and conditions which are previously unknown to the employee.” To emphasize that point, we analyzed claims data as an independent data source to highlight the shortcomings of only relying upon these administrative data to identify patients with MetS. As shown in our analysis, claims data were found to be a poor source for targeting patients with MetS, and our advice to employers was to use biometric screenings and HRAs to uncover potential cases of the condition.

Due to the greater precision available when using claims data with diagnosis codes, we specifically chose to restrict the claims data criteria, as shown in Table 1, to the primary risk factors of obesity, hypertension, hyperlipidemia, and high glucose. Thus, as the researchers point out, we used the diagnosis code 790.29 to flag someone on a claim as having high glucose; however, we did not include a diabetes diagnosis code because we wanted to examine MetS as a possible predictor of diabetes. As the researchers also note, in the first paragraph on page 163, we incorrectly stated that we included anti-hyperglycemic and anti-diabetic drugs as a flag for high glucose in claims data. This was not the case and was stated in the text in error.

We recognize that Table 4 would have been clearer if we had included 0% instead of dashes in the HRA column and if we were explicit in explaining that the data in the table were applicable for the full study sample. The full sample included active employees aged 18 to 64 years who were continuously enrolled in the Company's self-insured health care benefits in 2014 and participated in the biometric screening program. However, we did not require individuals to have completed the HRA.

Finally, the use of the term “gold standard” was intended to refer to the collection and analysis of biometric data (as opposed to HRA or claims data) to identify MetS patients. We did not intend to change or offer any new operational definitions for the condition based on our analysis.

In sum, our intent was to compare the ability of three available data sources to identify individuals at risk for development of MetS. We reasoned that some employers would only have access to HRA or claims, but not biometric data. As stated in the Discussion section, our prevalence estimates in this study using biometric data were like those reported elsewhere and therefore recommend that of the three approaches used to identify individuals at risk, biometric screening is preferred. However, we also cautioned against overtesting and inappropriate screenings for individuals at low risk for MetS and recommend that employers follow USPSTF guidelines on the frequency of screenings based on individuals’ demographics and prior health problems.

We thank Drs Schultz et al for their careful review of our study and acknowledge that additional work is needed to carefully identify MetS among workers and offer follow-up services to those at high risk for the condition.

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REFERENCES

1. Kassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med 2011; 9:48.
2. Lear S, James P, Ko G, Kumanyika S. Appropriateness of waist circumference and waist-to-hip ratio cutoffs for different ethnic groups. Eur J Clin Nutr [serial online] 2010; 64:42–61.
3. Zhang C, Rexrode KM, van Dam RM, Li TY, Hu FB. Abdominal obesity and the risk of all-cause, cardiovascular, and cancer mortality: sixteen years of follow-up in US women. Circulation 2008; 117:1658–1667.
Copyright © 2017 by the American College of Occupational and Environmental Medicine