title: Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity.
abstract: Data envelopment analysis (DEA) is a method of operations research that has not yet been applied in the field of obesity research. However, DEA might be used to evaluate individuals' susceptibility to obesity, which could help establish effective risk models for the onset of obesity. Therefore, we conducted this study to evaluate the feasibility of applying DEA to predict obesity, by calculating efficiency scores and evaluating the usefulness of risk models. In this study, we evaluated data from the Takahata study, which was a population-based cohort study (with a follow-up study) of Japanese people who are >40 years old. For our analysis, we used the input-oriented Charnes-Cooper-Rhodes model of DEA, and defined the decision-making units (DMUs) as individual subjects. The inputs were defined as (1) exercise (measured as calories expended) and (2) the inverse of food intake (measured as calories ingested). The output was defined as the inverse of body mass index (BMI). Using the β coefficients for the participants' single nucleotide polymorphisms, we then calculated their genetic predisposition score (GPS). Both efficiency scores and GPS were available for 1,620 participants from the baseline survey, and for 708 participants from the follow-up survey. To compare the strengths of the associations, we used models of multiple linear regressions. To evaluate the effects of genetic factors and efficiency score on body mass index (BMI), we used multiple linear regression analysis, with BMI as the dependent variable, GPS and efficiency scores as the explanatory variables, and several demographic controls, including age and sex. Our results indicated that all factors were statistically significant (p < 0.05), with an adjusted R2 value of 0.66. Therefore, it is possible to use DEA to predict environmentally driven obesity, and thus to establish a well-fitted model for risk of obesity.
authors.affiliation: Department of Public Health, Yamagata University Graduate School of Medicine, Yamagata, Japan.
authors.forename: Hiroto
authors.initials: H
authors.lastname: Narimatsu
authors.affiliation: Department of Healthcare Management, Teikyo University Graduate School of Public Health, Tokyo, Japan.
authors.forename: Yoshinori
authors.initials: Y
authors.lastname: Nakata
authors.affiliation: Department of Clinical Oncology, Yamagata University Faculty of Medicine, Yamagata, Japan.
authors.forename: Sho
authors.initials: S
authors.lastname: Nakamura
authors.affiliation: Genome Informatics Unit, Institute for Promotion of Medical Science Research, Yamagata University Faculty of Medicine, Yamagata, Japan.
authors.forename: Hidenori
authors.initials: H
authors.lastname: Sato
authors.affiliation: Department of Public Health, Yamagata University Graduate School of Medicine, Yamagata, Japan.
authors.forename: Ri
authors.initials: R
authors.lastname: Sho
authors.affiliation: Department of Public Health, Yamagata University Graduate School of Medicine, Yamagata, Japan.
authors.forename: Katsumi
authors.initials: K
authors.lastname: Otani
authors.affiliation: Department of Public Health, Yamagata University Graduate School of Medicine, Yamagata, Japan.
authors.forename: Ryo
authors.initials: R
authors.lastname: Kawasaki
authors.affiliation: First Department of Internal Medicine, Yamagata University Faculty of Medicine, Yamagata, Japan.
authors.forename: Isao
authors.initials: I
authors.lastname: Kubota
authors.affiliation: Second Department of Internal Medicine, Yamagata University Faculty of Medicine, Yamagata, Japan.
authors.forename: Yoshiyuki
authors.initials: Y
authors.lastname: Ueno
authors.affiliation: Third Department of Internal Medicine, Yamagata University Faculty of Medicine, Yamagata, Japan.
authors.forename: Takeo
authors.initials: T
authors.lastname: Kato
authors.affiliation: Department of Ophthalmology and Visual Sciences, Yamagata University Faculty of Medicine, Yamagata, Japan.
authors.forename: Hidetoshi
authors.initials: H
authors.lastname: Yamashita
authors.affiliation: Department of Public Health, Yamagata University Graduate School of Medicine, Yamagata, Japan.
authors.forename: Akira
authors.initials: A
authors.lastname: Fukao
authors.affiliation: Department of Neurosurgery, Yamagata University Faculty of Medicine, Yamagata, Japan.
authors.forename: Takamasa
authors.initials: T
authors.lastname: Kayama
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015992:Body Mass Index; D004435:Eating; D015444:Exercise; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009765:Obesity / Q000453:epidemiology* / Q000235:genetics* / Q000517:prevention & control; D020641:Polymorphism, Single Nucleotide; D057285:Precision Medicine; D012307:Risk Factors