Clarifying methodological misunderstandings regarding estimates of excess mortality associated with elevated body weight
Year: 2022
Type: article
Abstract: The rise of obesity in the US has been well-documented,1Flegal KM Kruszon-Moran D Carroll MD Fryar CD Ogden CL. Trends in obesity among adults in the United States, 2005 to 2014.JAMA. 2016; 315: 2284-2291Crossref PubMed Scopus (1997) Google Scholar,2Ward ZJ Bleich SN Cradock AL et al.Projected U.S. state-level prevalence of adult obesity and severe obesity.N Engl J Med. 2019; 381: 2440-2450Crossref PubMed Scopus (574) Google Scholar and a large body of scientific evidence has linked elevated body mass index (BMI) with increased risk of many diseases3Lauby-Secretan B Scoccianti C Loomis D et al.Body fatness and cancer – viewpoint of the IARC working group.N Engl J Med. 2016; 375: 794-798Crossref PubMed Scopus (1565) Google Scholar,4Powell-Wiley TM Poirier P Burke LE et al.Obesity and cardiovascular disease: a scientific statement from the American heart association.Circulation. 2021; 143: e984-e1010Crossref PubMed Scopus (258) Google Scholar and all-cause mortality.5Collaboration Global BMI Mortality E Di Angelantonio Bhupathiraju S et al.Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents.Lancet. 2016; 388: 776-786Summary Full Text Full Text PDF PubMed Scopus (1270) Google Scholar We recently published updated estimates of excess mortality associated with elevated body weight in the US, finding that in 2016, excess weight was responsible for nearly 500,000 deaths and a loss of life expectancy of nearly 2.4 years, with large disparities by state and demographic group.6Ward ZJ Willett WC Hu FB Pacheco LS Long MW Gortmaker SL. Excess mortality associated with elevated body weight in the USA by state and demographic subgroup: a modelling study.EClinicalMedicine. 2022; 48101429Summary Full Text Full Text PDF Scopus (4) Google Scholar However, a letter to the editor from Dr. Flegal asserts that our findings may overestimate BMI-related excess mortality, claiming that our method to adjust for BMI self-report bias “does not reduce systematic error in self-reported BMI”, and that hazard ratios relating BMI to all-cause mortality based on a global pooling dataset may be inappropriate for our analysis.7Flegal KM. Excess mortality estimates may be too high. [Letter submitted to EClinical Medicine].Google Scholar We wish to clarify these points as these comments are inaccurate and misleading. First, Dr. Flegal misrepresents our adjustment method for BMI self-report bias. It is well-known that self-reported BMI substantially underestimates obesity prevalence and needs to be corrected.8Connor Gorber S Tremblay M Moher D Gorber B A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review.Obes Rev. 2007; 8: 307-326Crossref PubMed Scopus (1413) Google Scholar We developed a semi-parametric method to adjust self-reported BMI by quantile, and have shown that this produces adjusted BMI distributions statistically similar to measured BMI in the National Health and Nutrition Examination Survey (NHANES) – the ‘gold standard’ for obesity measurement in the US.2Ward ZJ Bleich SN Cradock AL et al.Projected U.S. state-level prevalence of adult obesity and severe obesity.N Engl J Med. 2019; 381: 2440-2450Crossref PubMed Scopus (574) Google Scholar Furthermore, using cross-validation with NHANES data, we have demonstrated that this method, applied by sex and age group, produces unbiased estimates of mean BMI and obesity prevalence, both overall and by various subgroups.9Ward ZJ Gortmaker SL. A rigorous evaluation of a method to adjust BMI for self-report bias.Obesity (Silver Spring). 2022; 30: 284-285Crossref PubMed Scopus (3) Google Scholar The claim that this method fails to address systematic error has thus been empirically shown to be false. However, no reference is made to this published work in the letter. We are also not aware of any evidence for the speculative assertion that this method “may overestimate the prevalence of the highest BMI category”, and no reference is provided for this statement. Instead, we have previously shown that regression-based approaches to bias-correction underestimate the variance of the BMI distribution, thus underestimating the prevalence of high BMI categories.10Ward ZJ Long MW Resch SC et al.Redrawing the US obesity landscape: bias-corrected estimates of state-specific adult obesity prevalence.PLoS One. 2016; 11e0150735Crossref Scopus (37) Google Scholar In contrast, our method was developed to adjust the entire distribution so that it is statistically similar to measured BMI, thus appropriately capturing the tails of the BMI distribution. It is true that, as with any method, our approach relies on certain assumptions, and we would welcome constructive criticism regarding how this method could potentially be improved. However, the comments from Dr. Flegal provide no such suggestions, nor propose any alternative method of bias-correction. Second, the comments regarding our use of mortality hazard ratios indicate a misunderstanding of our approach. We did not use the estimates from the global pooled dataset5Collaboration Global BMI Mortality E Di Angelantonio Bhupathiraju S et al.Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents.Lancet. 2016; 388: 776-786Summary Full Text Full Text PDF PubMed Scopus (1270) Google Scholar directly in our model, but used them to set prior probability distributions for the general relationship between mortality and BMI categories by age group. These parameters were then calibrated to US-specific data on all-cause mortality rates. This approach is described in the Methods of our manuscript, where we state, “Although we set priors for the hazard ratios using global estimates, we fitted the model parameters to US-specific mortality data by sex, race/ethnicity, age group, and state, allowing us to estimate subgroup-specific hazard ratios which are consistent with empirical mortality rates.”6Ward ZJ Willett WC Hu FB Pacheco LS Long MW Gortmaker SL. Excess mortality associated with elevated body weight in the USA by state and demographic subgroup: a modelling study.EClinicalMedicine. 2022; 48101429Summary Full Text Full Text PDF Scopus (4) Google Scholar Model calibration thus revises these prior estimates so that they produce all-cause mortality estimates consistent with data for over 60,000 demographic groups in the US, accounting for trends in BMI and smoking.6Ward ZJ Willett WC Hu FB Pacheco LS Long MW Gortmaker SL. Excess mortality associated with elevated body weight in the USA by state and demographic subgroup: a modelling study.EClinicalMedicine. 2022; 48101429Summary Full Text Full Text PDF Scopus (4) Google Scholar We also demonstrate that our approach has high predictive accuracy for all-cause mortality rates among a randomly sampled test set of (US-specific) data not used to calibrate the model.6Ward ZJ Willett WC Hu FB Pacheco LS Long MW Gortmaker SL. Excess mortality associated with elevated body weight in the USA by state and demographic subgroup: a modelling study.EClinicalMedicine. 2022; 48101429Summary Full Text Full Text PDF Scopus (4) Google Scholar The claim that hazard ratios from the global dataset were simply ‘combined’ with adjusted BMI values ignores the extensive model calibration process which comprised the majority of our work for this analysis. Our estimates of weight-associated excess mortality are indeed higher than previous estimates, but this was expected given that our study uses more recent data with higher obesity prevalence, and also estimates mortality associated with any excess weight (BMI greater than ‘optimal’ BMI), not just obesity (BMI ≥30). Furthermore, our individual-level model (which accounts for the joint distribution of demographic variables and smoking) uses a continuous approach to model (age-specific) BMI hazard ratios, and models the location of ‘optimal’ BMI as a random variable. Therefore, our results are not sensitive to categorical threshold effects, or the choice of a specific reference group, which can be especially problematic if this group is impacted by reverse causation (i.e., health conditions which both increase mortality risk and decrease BMI). Indeed, as we acknowledge in the manuscript, although we control for smoking and age, our estimates may be influenced by residual impacts of reverse causation and frailty, especially at older ages.6Ward ZJ Willett WC Hu FB Pacheco LS Long MW Gortmaker SL. Excess mortality associated with elevated body weight in the USA by state and demographic subgroup: a modelling study.EClinicalMedicine. 2022; 48101429Summary Full Text Full Text PDF Scopus (4) Google Scholar Therefore, because our estimates of adjusted obesity prevalence are unbiased, and our hazard ratios (fitted to empirical US-specific data) may be underestimated, our estimates of excess weight-related mortality may be conservative. ZJW wrote the original draft. All authors contributed to the review and editing of the manuscript. None. ZJW and SLG received support from The JPB Foundation (Grant No. 1085), the National Institutes of Health (Grant No. R01HL146625), and the Centers for Disease Control and Prevention (CDC) (Grant No. U48DP006376). This work is solely the responsibility of the authors and does not represent official views of the CDC or other agencies. The JPB Foundation, NIH, CDC.
Source: EClinicalMedicine
Institution: Harvard University
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,500
OpenAlex ID: W4283526257