Gronniger, J. T. (2006). A Semiparametric Analysis of the Relationship of Body Mass Index to Mortality. American Journal of Public Health, 96(1), 173–178 This article review is on the above cited work. The purpose of Gronniger’s work was to check the adequacy of conventional Body Mass Index (BMI) categories for planning public health programs to reduce mortality. Body Mass Index (BMI) is a measure of an adult’s weight in relation to height, and it is calculated metrically as weight divided by height squared (kg/m2)” (Foster). This work is timely and important because of the wide publicity given to the contribution of surplus body weight to mortality and morbidity, with numerous accounts showing that obesity causes hundreds of thousands of excess deaths and billions of dollars in excess medical spending each year.
Obesity has been put on par with smoking as a growing health threat and has become the focal point of many policy initiatives. The studies cited by Gronniger on the subject showed that individuals with BMIs of 20 to 25 kg/m2 were regarded as the reference population and compared their health outcomes with those among overweight (BMIs of 25 to 30 kg/m2) and obese (BMIs of 30 kg/m2 and above) individuals.
While Gronniger agrees that obesity is associated with clear increases in risk of mortality, and that overweight is a risk factor for obesity and thus should best be avoided, he states that “relying on broad categories such as overweight and obesity could provide misleading estimates of BMI’s association with mortality if that association is heterogeneous or not monotonic within categories”.
He further stated that the definition of these categories “grew out of a consensus among various health bodies (including the World Health Organization, the National Institutes of Health, and the Centers for Disease Control and Prevention) that health risks increase with increasing body weight above a BMI of 25 and become serious near a BMI of 30”. This he showed in his study. Summary
For the study, the author used the 1987 Cancer Control and 1989 Diabetes supplements of the National Health Interview Survey (NHIS) to obtain baseline personal and biometric information on the survey respondents which were linked to the NHIS Multiple Cause of Death File, where mortality follow-up information was obtained. Information from this source was available for a total of 33,558 individuals, of whom 1,109 were dead or presumed dead. He then constructed nonlinear estimates of the association between BMI and mortality using a semiparametric regression technique.
The results showed that the mortality risk among “normal” weight men (i. . , those in the BMI range of 20 to 25 kg/m2) was as high as that among men in the mild obesity category (BMIs of 30–35 kg/m2), with a minimum risk observed at a BMI of approximately 26 kg/m2. Among women, the mortality risk was smallest at approximately 23 to 24 kg/m2, with the risk increasing steadily with BMIs above 27 kg/m2. The results also suggested negligible risk differences with minor differences in weight for much of the population. This is contrary to predictions of high mortality risks among overweight individuals as the “optimum” BMI appeared to be 26 to 27 overall, 23 to 24 for women, and 26 to 27 for men.
Interestingly, mortality did not increase sharply with BMI until the range of about 27 or above (33–35 for men), which is well into the range of overweight and obesity. The semiparametric mortality estimates also showed that in US adults the mortality among clinically underweight individuals is quite high, although estimates near the tails of the BMI distribution are imprecise as a result of small local sample sizes. Critique The semiparametric approach used here provides a clearer picture of individual mortality risks because restrictive categories were eliminated and the data were allowed to shape the functional form.
Therefore the present results can better be used to consider broad trends over at least several BMI units and to contrast such trends with findings derived from categorical studies. Also the author in his work used information from a valid source from which a complete smoking data was not available. The respondents were only group as “current” or “not current” smokers. This would affect the result considering the effect of smoking habits on mortality. Another limitation in this study was the arbitrary character of the intercept estimates derived using the semiparametric approach which would result in complication in interpretation.
This however does not cause bias in the results. The present study was also unable to solve the heterogeneity problem, as the BMI can be tied to manifold variables that influence mortality. Many of these omitted risk factors might be correlated with BMI, leading to misestimation and gross error in the calculation of the risk of increasing BMI itself. Therefore one can not actually identify the mortality-minimizing or “optimal” BMI from this study. Furthermore, this study involves single-point-in-time measures of BMI.
Therefore there is no guarantee that losing weight will bring the mortality of a severely obese person’s to the optimal level. Therefore the optimal BMI is only based on the current weight. Finally because of the absence of standard errors, the semiparametric estimates presented here cannot be used in hypothesis testing. Thus the expected mortality at a BMI of 29. 99 cannot be statistically compared with the expected mortality at a BMI of 30. 01. Recommendations To give a more comprehensive result, the actual smoking habits of the sample group must be obtained and considered in the study because of its effect on mortality.
Also there is little information about the underweight group in the study. Therefore a more thorough consideration of this group would be appropriate. Conclusion Notwithstanding the limitations in the study, it is a valid research as the results raise questions about whether overweight and mildly obese individuals are classified correctly under current health guidelines. Health professionals are therefore to consider the large number of people involved in the modest mortality differences between BMI units in drafting health guidelines and planning public health programs.
Courtney from Study Moose
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