Productivity costs associated with cardiometabolic risk factor clusters in the United States.
OBJECTIVE
Cardiometabolic risk factors such as overweight/obesity, hyperlipidemia, diabetes, and hypertension are prone to cluster together in the same individual and result in an elevated risk of cardiovascular disease and mortality. The purpose of this study was to examine and quantify the impact of cardiometabolic risk factor clusters independent of heart disease on productivity in a nationally representative sample of US adults.


METHODS
The current study estimated the impact of cardiometabolic risk factor clusters on missed work days and bed days, controlling for sociodemographic characteristics, comorbidity, and smoking status in a nationally representative, pooled 2000 and 2002 Medical Expenditure Panel Survey sample. Cardiometabolic risk factor clusters included BMI >or= 25 and two of the following three: diabetes, hyperlipidemia, and/or hypertension. All estimates were expressedin $US 2005. Sensitivity analyses were conducted to examine the impact of varying assumptions on the results.


RESULTS
After controlling for differences in sociodemographics, smoking and comorbidity, individuals with cardiometabolic risk factor clusters missed 179% more work days and spent 147% more days in bed (in addition to lost work days) than those without. Lost work days and bed days resulted in $17.3 billion annually in lost productivity attributable to cardiometabolic risk factor clusters in the United States. Sensitivity analyses resulted in a range of annual lost productivity costs from $3.2 to $23.1 billion.


CONCLUSIONS
Common cardiometabolic risk factor clusters have a significant deleterious impact on the US economy, resulting in $17.3 billion in lost productivity.
Sullivan
Patrick W
PW
University of Colorado at Denver and Health Sciences Center, Pharmaceutical Outcomes Research Program, School of Pharmacy, 4200 East Ninth Avenue, C238, Denver, CO 80262, USA. patrick.sullivan@uchsc.edu
Ghushchyan
Vahram
V
Wyatt
Holly R
HR
Wu
Eric Q
EQ
Hill
James O
JO
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D017281:Cost of Illness; D004526:Efficiency; D004651:Employment / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000191:economics* / Q000453:epidemiology; D008875:Middle Aged; D015999:Multivariate Analysis; D015995:Prevalence; D012044:Regression Analysis; D012307:Risk Factors; D018582:Sick Leave / Q000191:economics*; D014481:United States / Q000453:epidemiology