Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity.
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,...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4431757?pdf=render |
| _version_ | 1819156311597645824 |
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| author | Hiroto Narimatsu Yoshinori Nakata Sho Nakamura Hidenori Sato Ri Sho Katsumi Otani Ryo Kawasaki Isao Kubota Yoshiyuki Ueno Takeo Kato Hidetoshi Yamashita Akira Fukao Takamasa Kayama |
| author_facet | Hiroto Narimatsu Yoshinori Nakata Sho Nakamura Hidenori Sato Ri Sho Katsumi Otani Ryo Kawasaki Isao Kubota Yoshiyuki Ueno Takeo Kato Hidetoshi Yamashita Akira Fukao Takamasa Kayama |
| author_sort | Hiroto Narimatsu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T15:50:51Z |
| format | Article |
| id | doaj.art-37952f21fc32437495b7c637be651f7a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T15:50:51Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-37952f21fc32437495b7c637be651f7a2022-12-21T18:20:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01105e012644310.1371/journal.pone.0126443Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity.Hiroto NarimatsuYoshinori NakataSho NakamuraHidenori SatoRi ShoKatsumi OtaniRyo KawasakiIsao KubotaYoshiyuki UenoTakeo KatoHidetoshi YamashitaAkira FukaoTakamasa KayamaData 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.http://europepmc.org/articles/PMC4431757?pdf=render |
| spellingShingle | Hiroto Narimatsu Yoshinori Nakata Sho Nakamura Hidenori Sato Ri Sho Katsumi Otani Ryo Kawasaki Isao Kubota Yoshiyuki Ueno Takeo Kato Hidetoshi Yamashita Akira Fukao Takamasa Kayama Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity. PLoS ONE |
| title | Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity. |
| title_full | Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity. |
| title_fullStr | Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity. |
| title_full_unstemmed | Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity. |
| title_short | Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity. |
| title_sort | applying data envelopment analysis to preventive medicine a novel method for constructing a personalized risk model of obesity |
| url | http://europepmc.org/articles/PMC4431757?pdf=render |
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