Dataset Information

Are driver's licenses issued within 3 years of cancer diagnosis a valid source of BMI data?

ABSTRACT:

Purpose

Overweight and obesity are risk factors for several cancers; however, population-based cancer registries do not routinely collect data on body mass index (BMI). This study evaluated the utility of supplementing cancer registry data with BMI data derived from driver's license records.

Methods

We linked self-reported height and weight data from driver's license records to directly measured values, obtained via medical record abstraction, in a sample of 712 adult Iowa residents with cancer diagnosed during 2007-2012. Matched BMI values were subjected to a comprehensive evaluation of quantitative and categorical measures of agreement between data sources.

Results

Driver's license issue dates preceded diagnosis dates in 60.7% of cases, with time lags ranging from 3.0 years pre-diagnosis to 2.9 years post-diagnosis. Statistical analysis of agreement between continuous BMI values and ordinal BMI categories yielded an overall intraclass correlation estimate of 0.79 (95% confidence interval [CI] 0.77, 0.82) and an overall weighted kappa estimate of 0.63 (95% CI 0.59, 0.68), respectively. Subgroup analyses indicated reduced reliability among obesity-related cancers, particularly multiple myeloma, ovarian cancer, and pancreatic cancer. Neither measurement order nor time lag significantly affected agreement between BMI values.

Conclusions

These findings suggest that self-reported driver's license data provide a reasonable approximation of BMI, but are less precise than interview- and questionnaire-based methods. Furthermore, the degree of bias is seemingly unaffected by measurement order and time lag, but appears to become more pronounced as BMI itself increases.

SUBMITTER: Brumm MC

PROVIDER: S-EPMC8095168 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:OBJECTIVE:Body mass index (BMI) is a widely used indicator of obesity status in clinical settings and population health research. However, there are concerns about the validity of BMI as a measure of obesity in postmenopausal women. Unlike BMI, which is an indirect measure of obesity and does not distinguish lean from fat mass, dual-energy x-ray absorptiometry (DXA) provides a direct measure of body fat and is considered a gold standard of adiposity measurement. The goal of this study is to examine the validity of using BMI to identify obesity in postmenopausal women relative to total body fat percent measured by DXA scan. METHODS:Data from 1,329 postmenopausal women participating in the Buffalo OsteoPerio Study were used in this analysis. At baseline, women ranged in age from 53 to 85 years. Obesity was defined as BMI ??30?kg/m and body fat percent (BF%) greater than 35%, 38%, or 40%. We calculated sensitivity, specificity, positive predictive value, and negative predictive value to evaluate the validity of BMI-defined obesity relative BF%. We further explored the validity of BMI relative to BF% using graphical tools, such as scatterplots and receiver-operating characteristic curves. Youden's J index was used to determine the empirical optimal BMI cut-point for each level of BF% defined obesity. RESULTS:The sensitivity of BMI-defined obesity was 32.4% for 35% body fat, 44.6% for 38% body fat, and 55.2% for 40% body fat. Corresponding specificity values were 99.3%, 97.1%, and 94.6%, respectively. The empirical optimal BMI cut-point to define obesity is 24.9?kg/m for 35% BF, 26.49?kg/m for 38% BF, and 27.05?kg/m for 40% BF according to the Youden's index. CONCLUSIONS:Results demonstrate that a BMI cut-point of 30?kg/m does not appear to be an appropriate indicator of true obesity status in postmenopausal women. Empirical estimates of the validity of BMI from this study may be used by other investigators to account for BMI-related misclassification in older women.