Summary statistics for the estimation sample are listed in Supplementary Online Appendix Table A. For the sample as a whole, 84 % of respondents incur some medical care costs, and the average annual expenditures among those who incur any is $US3,372 (year 2010 values). The average BMI in the sample, which is based on self-reported values, is 28.1 kg/m2.
The Impact of Body Mass Index (BMI) on Medical Expenditures
Figure 1 describes the relationship between BMI and total annual medical care expenditures estimated by the IV model for all respondents utilizing MEPS 2000–2010 data (year 2010 values).Footnote 9 For values of BMI with many observations (the dotted line is high), the estimates will be more precise, with tighter confidence intervals (i.e. the dashed lines will be closer together). Figure 1 can be compared with the previously reported estimates based on 2000–2005 data in figure 1 of Cawley and Meyerhoefer ; the introduction of additional data does not affect the estimated relationship between BMI and medical care expenditures. Expenditures continue to have a J-shape over the BMI range; i.e. expenditures fall with BMI through the underweight and healthy weight categories, are relatively constant with BMI in the overweight category, then rise exponentially with BMI through the obese category, especially at BMI levels >35 kg/m2. The BMI value associated with minimum expenditures continues to be roughly 25 kg/m2, the threshold for overweight. The implications of the nonlinearity are important; for example, it implies that, in the obese range, savings from a given reduction in weight will increase with the starting BMI.
Table 1 presents results for all adults pooled; each table cell lists the reduction in annual medical care costs associated with a given percent reduction in BMI (5, 10, 15, or 20 %), from a given starting BMI (each unit between 30–45 kg/m2). Below that is listed the standard error in parentheses; savings that are statistically significant at the 5 % level are listed in bold. These estimates are based on the IV model.
The nonlinearity of medical expenditures over BMI is such that, for any given reduction in BMI, cost savings are greater among the class 3 obese (BMI ≥40 kg/m2) than among the class 2 obese (35 kg/m2 ≤ BMI < 40 kg/m2), and in turn the savings among the class 2 obese are much greater than those among the class 1 obese (30 kg/m2 ≤ BMI < 35 kg/m2). For example, a 10 % reduction of BMI is associated with annual savings of $US10,992 in medical care costs if the starting BMI is 44 kg/m2, a savings of $US3,402 if the starting BMI is 40 kg/m2, a savings of $US853 if the starting BMI is 35 kg/m2, and no statistically significant change in medical expenditures if the starting BMI is 30 kg/m2. Put another way, the savings associated with a given reduction in weight can be nearly 13 times greater for those with a BMI of 44 kg/m2 than for those with a BMI of 35 kg/m2.
A second observation from Fig. 1 is that the annual medical care costs of the class 1 obese (30 kg/m2 ≤ BMI < 35 kg/m2) are not that much higher than those of the healthy weight or overweight. For individuals with a BMI of 30 kg/m2 (just at the threshold of obesity), reductions in BMI do not imply statistically significant changes in medical expenditures. In fact, the point estimates of the savings associated with a reduction of 15 or 20 % from a BMI of 30 kg/m2 imply negative savings—that is, that medical expenditures would be higher if subjects lost that much weight. These estimates are not statistically different from zero, but illustrate that medical expenditures do not dramatically jump higher at the threshold for obesity (BMI = 30 kg/m2); instead, as Table 1 shows, they rise at first slowly, and then more quickly, within the obese category (BMI ≥30 kg/m2). These patterns of medical care costs over BMI are consistent with the patterns of mortality risk over BMI, which show that those just over the threshold of obesity do not have higher mortality risk than individuals who are not obese, but mortality risk rises sharply with BMI in the morbidly obese range (see, for example, Flegal et al.  and Mehta and Chang ).
A third observation, also based on the nonlinearity of annual medical expenditures over BMI, is that doubling the weight loss does not double the savings. (This is observed by looking across the columns within a given row of a table.) For example, in Table 1, a person with a BMI of 40 kg/m2 who experienced a weight loss of 5 % is expected to experience a reduction in medical care costs of $US2,137, but doubling the weight loss to 10 % does not double the expected savings—it increases only 59.2 %, to $US3,402. Doubling the weight loss yet again to 20 % raises the expected savings by only 35.4 % to $US4,607. Because medical expenditures rise exponentially with BMI in the obese region, the initial 5 % weight loss results in more savings than subsequent additional increments of 5 % weight loss. The largest incremental medical care cost savings are seen with a 5 and 10 % weight loss.
Robustness Checks: Alternate Instrumental Variable (IV) Specifications
The IV model controls for BMI and BMI squared. To investigate the robustness of the results, we also estimated models with different specifications. This section describes the result of four robustness checks.
First, we estimated an IV model that controls only for linear BMI (not its square); restricting the flexibility of the functional form in this way results in an over-estimation of the marginal effects up to BMI values around 36, and an underestimation of the marginal effects for BMI values above that threshold.
Second, we also sought to estimate a model that was more flexible and controlled for BMI, BMI squared, and BMI cubed, but this model did not converge. Previously published estimates from such a model that were estimated using data from 2000 to 2005 confirm the robustness of our finding that medical expenditures are J-shaped over BMI .
Third, we estimated IV models using a different version of the instrument; instead of using the BMI of the oldest child, we used the BMI of the youngest child. We found that the marginal effects of a 5, 10, and 15 % change in BMI were very similar over most of the BMI range, and approximately 15–20 % smaller at the upper end of the BMI range. However, the standard errors of these marginal effects are larger, although most of the marginal effects still have a p value of <0.10. This increase in the standard errors is a result of the instrument being less powerful, reflected in a lower F statistic of the instrument in the first stage of the IV model.
In our IV model, we excluded income from the set of regressors because income is partly affected by weight (see, for example, Cawley ), and our objective was to estimate the total effect of obesity on medical expenditures through all channels, including income. As a fourth robustness check, we estimated models that control for the log of family income and found that it caused the predicted expenditure curve to flatten somewhat. This made little difference along most of the BMI distribution, but at the upper end of the BMI distribution the marginal effects were roughly one-third smaller.
Extension 1: Results for Those With and Without Diabetes
We estimated models for two subpopulations of interest: those without diabetes, and those with diabetes. Figures 2 and 3 plot predicted medical care costs for those without diabetes and those with diabetes, by BMI unit. Because of data limitations, the model we estimated contains only BMI (not BMI squared), and to best fit the nonlinear increase in medical expenditures in the obese range, we excluded the underweight and healthy weight (i.e. those with BMI under 25 kg/m2). For this reason, the estimates for those with and without diabetes should not be compared with those for the full sample, only with each other.
Respondents without diabetes are estimated to have a largely linear relationship between BMI and medical care expenditures, with an uptick in slope occurring at approximately BMI = 35 kg/m2. Respondents with diabetes have a much more pronounced nonlinear relationship, with an exponential increase occurring at approximately BMI = 30 kg/m2. At very high levels of BMI (BMI > 42 kg/m2), the large differences in costs between respondents with and without diabetes are not statistically significant because of the small sample sizes and lack of power. As a result, estimates at higher BMI levels should be interpreted cautiously.
For the samples of those with and without diabetes, as for the sample of all adults, the nonlinearity in the relationship between BMI and total medical care expenditures implies that potential cost reductions from a specific reduction in BMI will vary depending on the starting BMI value. Tables 2 and 3 describe the impact of 5, 10, 15, and 20 % BMI reductions for a range of starting BMI values for respondents without and with diabetes. As can be seen in Table 2, the annual reduction in medical care costs achievable with a 10 % BMI reduction for respondents without diabetes varies fourfold depending on the initial BMI; e.g. a starting BMI = 30 kg/m2 implies a cost saving of $US496, and a starting BMI = 45 kg/m2 implies an annual cost saving of $US1,838. Among respondents with diabetes, the cost savings, as expected, were uniformly larger, as seen in Table 3. In this group, the annual reduction in medical care costs achievable with a 10 % reduction in weight among respondents with initial BMI at 30 kg/m2 is $US1,076 versus $US7,093 for respondents with BMI of 45 kg/m2, which is nearly a sevenfold difference.
As is apparent from a comparison of Tables 2 and 3 or Figs. 2 and 3, the estimated annual medical care costs of respondents with diabetes uniformly exceed those without diabetes; this difference starts small at lower BMI levels and is not statistically significant prior to the range of BMI >30 kg/m2. However, the gap widens in the obese range of BMI. This is shown in Fig. 4.
Extension 2: Results for Prescription Drug Costs Only
Table 4 presents results specific to prescription drug costs for all adults. For an individual with a BMI of 40 kg/m2, a weight loss of 5 % is expected to result in a $US402 reduction in annual prescription drug costs, a 10 % weight loss saves $US679, a 15 % weight loss saves $US869, and a 20 % weight loss saves $US999. Prescription drug costs, like overall medical costs, rise exponentially with BMI in individuals with severe obesity, with the result that doubling the weight loss less than doubles the savings.
Extension 3: Results from Non-IV Models That Do Not Address Endogeneity and Reporting Error
Previous studies of the change in medical care costs with BMI have used the same data (MEPS); the key difference of this analysis is the analytic technique: IVs. In order to determine the impact of alternative estimation techniques, we also estimated models of total medical expenditures that do not use the IV method.
While these models have the disadvantage of suffering an unknown degree of omitted variables bias and reporting error bias, they have the important advantage of a larger sample. This is because they can be estimated using the entire MEPS sample; the sample does not need to be restricted to adults with a biological child in the household, as is required for the IV model. As a result, the non-IV model can be estimated with a sample of 172,066 individuals, compared with the sample of 41,435 for the IV model. That increased sample size enables more precise estimates.
The non-IV results are presented in Appendix Table 1 (see the Electronic Supplementary Material [ESM] for all appendices) for the pooled full sample of adults, Appendix Table 2 for the pooled IV sample of adults (i.e. the non-IV model is estimated using the IV sample for the sake of comparability), Appendix Table 3 for those without diabetes, Appendix Table 4 for those with diabetes, and Appendix Table 5 for prescription drug expenditures. Overall, IV models tend to predict greater savings from weight loss than do non-IV models, and this difference increases with BMI. In other words, the IV models estimate that medical expenditures rise more steeply with BMI than do non-IV models. For example, a 5 % weight loss starting from a BMI of 31 kg/m2 is expected to lower annual medical expenditures by $US131 per year in the IV model (Table 1), which is roughly 11 % higher than the estimate of $US118 from the non-IV model (Appendix Table 1). However, if the starting BMI is 40 kg/m2, that same 5 % decrease in weight implies a $US2,137 annual savings according to the IV model, which is over six times larger than the savings of $US313 implied by the non-IV model. Clearly, addressing endogeneity and measurement error makes a substantial difference at high levels of BMI.
The non-IV model estimates of the difference in medical expenditures by BMI level are greater when the model is estimated using the full sample (Appendix Table 1) than the IV sample (Appendix Table 2). One explanation is that the condition to be included in the IV sample—having biological children—results in the IV sample being healthier than average; we return to this point in the Discussion section. If one wishes to know the correlation (rather than causal effect), then the results from the non-IV model estimated using the full sample (Appendix Table 1) are preferable to those estimated using the IV sample (Appendix Table 2) because the non-IV sample is so much larger (N = 172,066 vs. 41,435) and thus the estimates are more precise.
Extension 4: The Medical Care Costs of Adult Obesity: Per Case and Aggregate for the USA
In order to calculate the effect of obesity on medical care costs, both per obese individual and for the US as a whole, we estimate IV models in which the endogenous regressor is an indicator variable for obesity, rather than BMI and BMI squared. This represents an update of a previous study , which estimated identical models using MEPS data for 2000–2005, whereas this paper uses the longer MEPS panel of 2000–2010.
The results of this modified IV model indicate that obesity raises annual medical care costs by $US3,508 per year (year 2010 values), with a standard error of $US806. Because the regression model uses an indicator variable for obesity, this represents the additional costs of the obese relative to the non-obese (as opposed to only the healthy weight). The results of the IV model imply that, for the US as a whole, adult obesity raised annual medical care costs by $US315.8 billion in 2010.Footnote 10