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The influence of obesity and overweight on medical costs: a panel data perspective

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Abstract

This paper estimates the increase of direct medical costs of both severe and moderate obesity and overweight with respect to a normal-weight individual using a two-part generalised linear model and a longitudinal dataset of medical and administrative records of patients in primary and secondary healthcare centres followed up over seven consecutive years (2004–2010) in Spain. Our findings indicate that severe and moderate obesity imposes a substantial burden on the Spanish healthcare system. Specifically, being severely obese is associated with increases in medical costs of 26 % (instrumental variables (IV) estimate, 34 %) compared to a normal-weight individual. The effects of moderate obesity and overweight are more modest, raising medical costs by 16 % (IV estimate, 29 %) and 8.5 % (IV estimate, 23 %), respectively. These changes in costs are slightly higher for those patients below the median age and for the women. Notwithstanding, the effects found in this study are comparatively much lower than that reported for the USA, based basically on a private healthcare system and characterised by a more obese population.

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Notes

  1. Among studies of this type, a number estimate medical costs and obesity based on survey data [3, 39, 45].

  2. This is the first paper to estimate the (causal) impact of obesity on medical costs using the Medical Expenditure Panel Survey (MEPS) 2000–2005 data and applying the aforementioned methods in health econometrics.

  3. In our dataset medical costs are zero for 16 % of the sample and positive medical costs are highly skewed to the right.

  4. Both equations of the 2PM are estimated by RE (the errors are normally distributed and uncorrelated with the regressors) owing to the unfeasibility of estimating GLM models by fixed effects.

  5. We have certainly detected the presence of heteroskedasticity in our dataset by means of the Breusch-Pagan and White tests, produced by several covariates, some of which are continuous (i.e. complex heteroskedasticity).

  6. A finding that emerges from the literature that compares the performance of several models for positive expenditures in terms of consistency and precision [6, 9, 21, 26, 28] is that no one method dominates the other and there are important trade-offs in terms of precision and bias, mainly when different subgroups of population or types of medical costs are analysed [21, 22]. Notwithstanding, Mihaylova et al.’s [29] literature review confirms that 2PM models perform better.

  7. Estimates based on logged models are actually often much more precise and robust than direct analyses of the unlogged original dependent variable [24]. They may also reduce (but not eliminate) heteroskedasticity.

  8. This is not our particular view and for this reason our central analytical framework is a 2PM.

  9. Wooldridge [51] proposed to run a robust probit estimation of not having positive costs for each period t and then save the inverse Mills ratios (IMRs). These were later added to the second equation estimated using an RE GLM model. We bootstrapped these procedures. Statistical significance of almost all these Mills ratios denoted the presence of sample selection bias. Likewise, given that the Mills ratio is not strictly exogenous and causes a problem of multicollinearity, we introduced exclusion restrictions to greatly reduce these inconveniences.

  10. A definition of BMI including patients with three or more measurements was also examined, highlighting a potential trade-off between accuracy of BMI definition and sample selection issues.

  11. In line with Chamberlain [12], one option could be to assume that \( \alpha_{i} = \alpha^{\prime}{\text{BMI}}_{i} + u_{i} \sim {\text{idd }}N(0,\sigma^{2} ) \) where BMIi = (BMIi1,…,BMIiT) are the values of the BMI for every year of the panel, and α = (α 1,…, α T).

  12. The sample contains observations with zero medical costs because there are individuals who contacted—at some point during the analysed period—the health system and incurred positive costs, but in other years have zero costs.

  13. Although we have a sample of users, they are almost the entire population representing 96.52 % of total residents in the specific considered geographic area. The NHS nature of the Spanish healthcare system may explain why we observe this high percentage during a consecutive period of 7 years.

  14. Expenditures not directly related to care (e.g. financial spending, losses due to fixed assets, etc.) were excluded from the analysis.

  15. For instance we considered: (1) laboratory tests (haematology, biochemistry, serology and microbiology), (2) conventional radiology (plain film requests, contrast radiology, ultrasound scans, mammograms and radiographs), (3) complementary tests (endoscopy, electromyography, spirometry, CT, densitometry, perimetry, stress testing, echocardiography, etc.); (4) pharmaceutical prescriptions (acute, chronic or on demand).

  16. Although the BMI is the most widely used measure of obesity, it is not free of problems. For instance, the BMI does not take into consideration body composition (adiposity vs. lean weight) or body fat distribution. This means it may fail to predict obesity among very muscular individuals and the elderly.

  17. Of course, a much more reduced sample is obtained (around 80,000 observations) when individuals with three or more anthropometric measurements are considered.

  18. Interestingly, roughly 40 % of the observations without BMI measurements are immigrants. This particularity may help to explain why they are less measured. As they are younger, have less medical episodes and less severity, medical expenditures in the final sample are relatively larger.

  19. The Pregibon link test gives an estimated value of −0.591 × 10−5 (p value = 0.000) which is practically 0, suggesting the logarithm as the link function. The Park [35] test gives a coefficient \( \upsilon = 1.79 \) (p value = 0.000) which is consistent with a gamma-class distribution.

  20. These results can be provided by the authors upon request.

  21. Although not shown, an even worst fit is obtained when we apply the same method on raw (unlogged) medical costs.

  22. If we instead specify a log cost model for the second part of the sample selection model—following Albouy et al. [2]—and apply FE estimation we obtain a slightly lower significant ME coefficient. Note that this alternative model shows a greater RMSE value.

  23. Given that we linked our dataset to census information we were able to obtain household and parental identifiers.

  24. Note that these results provide an estimate of the local average treatment effect (LATE) of being obese or overweight on medical costs for a sample of individuals with children.

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Acknowledgments

The authors would like to thank S. von Hinke Kessler, H. Gravelle, A. Jones, G. Moscelli, N. Rice and participants at the HEDG Seminar (University of York), Health Economics workshop at FEDEA (Madrid), for their useful comments on an earlier draft and Partha Debb for the Stata codes to perform some calculations. We also thank the anonymous referees for their very helpful comments. The authors also acknowledge Badalona Serveis Assistencials (BSA) for providing us with the core dataset to carry out this research and the computational resources provided by the Centre for Scientific and Academic Services of Catalonia (CESCA). We are also indebted to the Catalan Health Department and IDESCAT (the Catalan Statistics Office) for giving us access to the population census data. Toni Mora and Joan Gil gratefully acknowledge financial support from the Generalitat of Catalonia’s grant programmes 2009-SGR-102 and 2009-SGR-359, respectively.

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Mora, T., Gil, J. & Sicras-Mainar, A. The influence of obesity and overweight on medical costs: a panel data perspective. Eur J Health Econ 16, 161–173 (2015). https://doi.org/10.1007/s10198-014-0562-z

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