Reverse Causation and Illness-related Weight Loss in Observational Studies of Body Weight and Mortality
Abstract
In studies of weight and mortality, the construct of reverse causation has come to be used to imply that the exposure-outcome relation is biased by weight loss due to preexisting illness. Observed weight-mortality associations are sometimes thought to result from this bias. Evidence for the occurrence of such bias is weak and inconsistent, suggesting that either the analytical methods used have been inadequate or else illness-related weight loss is not an important source of bias. Deleting participants has been the most frequent approach to control possible bias. As implemented, this can lead to deletion of almost 90% of all deaths in a sample and to deletion of more overweight and obese participants than participants with normal or below normal weight. Because it has not been demonstrated that the procedures used to adjust for reverse causation increase validity or have large or systematic effects on relative risks, it is premature to consider reverse causation as an important cause of bias. Further research would be useful to elucidate the potential effects and importance of reverse causation or illness-related weight loss as a source of bias in the observed associations between weight and mortality in cohort studies.
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Applying extensive exclusions to the analysis of weight and mortality data is a flawed process.
“Flegal et al 16 have emphasized that applying extensive exclusions to the analysis of weight and mortality data is a flawed process.”
From Obesity Paradox in Hypertension: Is This Because Sympathetic Activation in Obesity-Hypertension Takes a Benign Form? (2018) · cites this paper as [16]
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Cited by
Sorted by citations
Chen, Y., Copeland, W. K., Vedanthan, R., Grant, E., Lee, J. E., Gu, D., Gupta, P. C., Ramadas, K., Inoue, M., Tsugane, S., et al. (2013) · BMJ · doi:10.1136/bmj.f5446 · Cited by 299 · see citing works
Revealing the burden of obesity using weight histories
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Leviton, A. (2018) · Nutrients · doi:10.3390/nu10091152 · Cited by 27 · see citing works
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- Authors
- Flegal, K. M., Graubard, B. I., Williamson, D. F., & Cooper, R. S.
- Affiliations
- Centers for Disease Control and Prevention (US)
- Corresponding author
- Flegal, K. M.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-11-08
- Volume
- 173
- Issue
- 1
- Pages
- 1-9
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W2150218756
- Cited by
- 223
- References
- 119
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 8 · 2023: 13 · 2022: 13 · 2021: 18 · 2020: 14 · 2019: 12 · 2018: 16 · 2017: 13 · 2016: 15 · 2015: 22 · 2014: 16 · 2013: 17 · 2012: 10
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Civil War Book Review (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Health Sciences › Medicine › Oncology › Cancer Risks and Factors
- Keywords
- reverse causality, body weight, mortality, bias in observational studies, cohort studies, overweight, obesity, exposure-outcome association
- MeSH terms
- Body Weight, Cause of Death, Humans, Obesity, Risk Factors, Weight Loss, Causality, Survival Rate
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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