A Semiparametric Analysis of the Relationship of Body Mass Index to Mortality Essay

Gronniger, J. T. (2006). A Semiparametric analytic thinking of the Relationship of Body Mass Index to Mortality. Ameri put up Journal of customary Health, 96(1), 173178 This article review is on the above cited field. The purpose of Gronnigers work was to check the adequacy of conventional Body Mass Index (BMI) categories for preparation public health programs to reduce fatality rate. Body Mass Index (BMI) is a measure of an adults weight in relation to height, and it is cypher metrically as weight divided by height shape (kg/m2) (Foster). This work is timely and important because of the wide publicity given to the persona of surplus body weight to mortality rate and morbidity, with numerous accounts showing that corpulency causes hundreds of thousands of excess deaths and billions of dollars in excess medical spending each year. fleshiness has been put on par with smoking as a outgrowth health threat and has pay off the focal point of many policy initiatives. The studi es 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 gravid (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 pluss in riskiness of mortality, and that overweight is a risk factor for obesity and so should best be avoided, he states that relying on broad categories such as overweight and obesity could provide misleading estimates of BMIs 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 pack. thickFor the study, the former used the 1987 Cancer Control and 1989 Diabetes supple ments of the National Health interview Survey (NHIS) to obtain baseline personal and biometric education on the muckle respondents which were linked to the NHIS Multiple Cause of Death File, where mortality follow-up information was obtained. Information from this starting time 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 in the midst of BMI and mortality exploitation a semiparametric regression technique.The extends showed that the mortality risk among normal weight men (i. . , those in the BMI vagabond of 20 to 25 kg/m2) was as lofty as that among men in the mild obesity category (BMIs of 3035 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, w ith the risk increasing steadily with BMIs above 27 kg/m2. The results also suggested negligible risk differences with low 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 browse of near 27 or above (3335 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 belowweight 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. therefrom the present results go off 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 separate 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 mistake in the calculation of the risk of increasing BMI itself. because on e can not actually identify the mortality-minimizing or optimal BMI from this study. Further more(prenominal), 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 persons 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. so 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 scant(p) information about the underweight group in the study. Therefore a more thorough consideration of this group would be appropriate. Conclusion that 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 man-sized number of people involved in the modest mortality differences between BMI units in drafting health guidelines and planning public health programs.