scholarly journals Application of Mendelian Randomization to Investigate the Association of Body Mass Index with Health Care Costs

2020 ◽  
Vol 40 (2) ◽  
pp. 156-169 ◽  
Author(s):  
Christoph F. Kurz ◽  
Michael Laxy
Keyword(s):  
Health Care ◽  
Body Mass Index ◽  
Body Mass ◽  
Health Care Costs ◽  
Instrumental Variables ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Omitted Variables ◽  
Care Costs ◽  
Genetic Instruments

Causal effect estimates for the association of obesity with health care costs can be biased by reversed causation and omitted variables. In this study, we use genetic variants as instrumental variables to overcome these limitations, a method that is often called Mendelian randomization (MR). We describe the assumptions, available methods, and potential pitfalls of using genetic information and how to address them. We estimate the effect of body mass index (BMI) on total health care costs using data from a German observational study and from published large-scale data. In a meta-analysis of several MR approaches, we find that models using genetic instruments identify additional annual costs of €280 for a 1-unit increase in BMI. This is more than 3 times higher than estimates from linear regression without instrumental variables (€75). We found little evidence of a nonlinear relationship between BMI and health care costs. Our results suggest that the use of genetic instruments can be a powerful tool for estimating causal effects in health economic evaluation that might be superior to other types of instruments where there is a strong association with a modifiable risk factor.

scholarly journals Association of Obesity with Health Care Costs: A Mendelian Randomization study

2019 ◽  
Author(s):  
Christoph Kurz ◽  
Michael Laxy
Keyword(s):  
Health Care ◽  
Health Care Costs ◽  
Instrumental Variables ◽  
Causal Effect ◽  
Strong Association ◽  
Health Economic Evaluation ◽  
Omitted Variables ◽  
Unit Increase ◽  
Care Costs ◽  
Genetic Instruments

AbstractCausal effect estimates for the association of obesity with health care costs can be biased by reversed causation and omitted variables. In this study, we use genetic variants as instrumental variables to overcome these limitations. We estimate the effect of body mass index (BMI) and waist-to-hip ratio (WHR) on total health care costs using data from a German observational study. We find that the model using genetic instruments identifies additional annual costs of 189€ for a one unit increase in BMI, and additional 1165€ for a 0.1 unit increase in WHR. This is more than two times higher than estimates from linear regression without instrumental variables. We found little evidence of a non-linear relationship between BMI or WHR and health care costs. Our results imply that the use of genetic instruments can be a powerful tool for estimating causal effects in health economic evaluation that might be superior to other types of instruments where there is a strong association with a modifiable risk factor.

scholarly journals Relationship Between Body Mass Index and Health Care Costs by Place of Service in Employed Adults

2013 ◽  
Vol 16 (7) ◽  
pp. A380
Author(s):  
S. Abouzaid ◽  
N.L. Kleinman ◽  
L. Andersen ◽  
Z. Wang ◽  
A. Powers
Keyword(s):  
Health Care ◽  
Body Mass Index ◽  
Body Mass ◽  
Health Care Costs ◽  
Care Costs

scholarly journals Assessing the Impact of Body Mass Index Information on the Performance of Risk Adjustment Models in Predicting Health Care Costs and Utilization

Medical Care ◽  
2018 ◽  
Vol 56 (12) ◽  
pp. 1042-1050 ◽  
Author(s):  
Hadi Kharrazi ◽  
Hsien-Yen Chang ◽  
Sara E. Heins ◽  
Jonathan P. Weiner ◽  
Kimberly A. Gudzune
Keyword(s):  
Health Care ◽  
Body Mass Index ◽  
Body Mass ◽  
Health Care Costs ◽  
Risk Adjustment ◽  
The Impact ◽  
Care Costs

scholarly journals Association of body mass index with health care expenditures in the United States by age and sex

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247307
Author(s):  
Zachary J. Ward ◽  
Sara N. Bleich ◽  
Michael W. Long ◽  
Steven L. Gortmaker
Keyword(s):  
Health Care ◽  
Body Mass Index ◽  
Body Mass ◽  
Health Care Costs ◽  
Severe Obesity ◽  
Health Care Expenditures ◽  
Medical Expenditures ◽  
Excess Costs ◽  
Using Data ◽  
Care Costs

Background Estimates of health care costs associated with excess weight are needed to inform the development of cost-effective obesity prevention efforts. However, commonly used cost estimates are not sensitive to changes in weight across the entire body mass index (BMI) distribution as they are often based on discrete BMI categories. Methods We estimated continuous BMI-related health care expenditures using data from the Medical Expenditure Panel Survey (MEPS) 2011–2016 for 175,726 respondents. We adjusted BMI for self-report bias using data from the National Health and Nutrition Examination Survey (NHANES) 2011–2016, and controlled for potential confounding between BMI and medical expenditures using a two-part model. Costs are reported in $US 2019. Results We found a J-shaped curve of medical expenditures by BMI, with higher costs for females and the lowest expenditures occurring at a BMI of 20.5 for adult females and 23.5 for adult males. Over 30 units of BMI, each one-unit BMI increase was associated with an additional cost of $253 (95% CI $167-$347) per person. Among adults, obesity was associated with $1,861 (95% CI $1,656-$2,053) excess annual medical costs per person, accounting for $172.74 billion (95% CI $153.70-$190.61) of annual expenditures. Severe obesity was associated with excess costs of $3,097 (95% CI $2,777-$3,413) per adult. Among children, obesity was associated with $116 (95% CI $14-$201) excess costs per person and $1.32 billion (95% CI $0.16-$2.29) of medical spending, with severe obesity associated with $310 (95% CI $124-$474) excess costs per child. Conclusions Higher health care costs are associated with excess body weight across a broad range of ages and BMI levels, and are especially high for people with severe obesity. These findings highlight the importance of promoting a healthy weight for the entire population while also targeting efforts to prevent extreme weight gain over the life course.

scholarly journals Waist Circumference and Body Mass Index as Predictors of Health Care Costs

PLoS ONE ◽  
2008 ◽  
Vol 3 (7) ◽  
pp. e2619 ◽  
Author(s):  
Betina Højgaard ◽  
Dorte Gyrd-Hansen ◽  
Kim Rose Olsen ◽  
Jes Søgaard ◽  
Thorkild I. A. Sørensen
Keyword(s):  
Health Care ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Body Mass ◽  
Health Care Costs ◽  
Care Costs

scholarly journals Evaluating the relationship between Body Mass Index (BMI) of diabetic patients and health care costs

2014 ◽  
Vol 17 (3) ◽  
pp. A184-A185
Author(s):  
A. Adeyemi ◽  
K.L. Rascati ◽  
K.A. Lawson ◽  
J.C. Barner ◽  
J. Wilson ◽  
...  
Keyword(s):  
Health Care ◽  
Body Mass Index ◽  
Body Mass ◽  
Health Care Costs ◽  
Diabetic Patients ◽  
The Relationship ◽  
Care Costs

scholarly journals The effect of body mass index on smoking behaviour and nicotine metabolism: a Mendelian randomization study

2018 ◽  
Author(s):  
Amy E. Taylor ◽  
Rebecca C. Richmond ◽  
Teemu Palviainen ◽  
Anu Loukola ◽  
Jaakko Kaprio ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Dna Methylation ◽  
Body Mass ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Smoking Initiation ◽  
Smoking Behaviour ◽  
Bidirectional Association ◽  
Metabolite Ratio ◽  
Nicotine Metabolite Ratio

AbstractBackgroundGiven clear evidence that smoking lowers weight, it is possible that individuals with higher body mass index (BMI) smoke in order to lose or maintain their weight.Methods and FindingsWe undertook Mendelian randomization analyses using 97 genetic variants associated with BMI. We performed two sample Mendelian randomization analyses of the effects of BMI on smoking behaviour in UK Biobank (N=335,921) and the Tobacco and Genetics consortium genomewide association study (GWAS) (N≤74,035) respectively, and two sample Mendelian randomization analyses of the effects of BMI on cotinine levels (N≤4,548) and nicotine metabolite ratio (N≤1,518) in published GWAS, and smoking-related DNA methylation in the Avon Longitudinal Study of Parents and Children (N≤846).In inverse variance weighted Mendelian randomization analysis, there was evidence that higher BMI was causally associated with smoking initiation (OR for ever vs never smoking per one SD increase in BMI: 1.19, 95% CI: 1.11 to 1.27) and smoking heaviness (1.45 additional cigarettes smoked per day per SD increase in BMI, 95% CI: 1.03 to 1.86), but little evidence for a causal effect with smoking cessation. Results were broadly similar using pleiotropy robust methods (MR-Egger, median and weighted mode regression). These results were supported by evidence for a causal effect of BMI on DNA methylation at the aryl-hydrocarbon receptor repressor (AHRR) locus. There was no strong evidence that BMI was causally associated with cotinine, but suggestive evidence for a causal negative association with the nicotine metabolite ratio.ConclusionsThere is a causal bidirectional association between BMI and smoking, but the relationship is likely to be complex due to opposing effects on behaviour and metabolism. It may be useful to consider BMI and smoking together when designing prevention strategies to minimise the effects of these risk factors on health outcomes.

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