1998 Fiscal Year Final Research Report Summary
Epidemiological isk assessment based on structural equations with latent variables
| Project/Area Number |
09470119
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Public health/Health science
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| Research Institution | St. Marianna University School of Medicine |
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Principal Investigator |
YOSHIDA Katsumi St.Marianna Univ.Dept of Preventive Med., 医学部, 教授 (80158435)
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| Co-Investigator(Kenkyū-buntansha) |
SUGIMORI Hiroki St.Marianna Univ.Dept of Preventive Med., 医学部, 助手 (20276554)
IZUNO Takashi Toho Univ.Dept of Hygiene, 医学部, 講師 (20213019)
TAKAHASHI Eiko St.Marianna Univ.Dept of Preventive Med., 医学部, 講師 (70271369)
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| Project Period (FY) |
1997 – 1998
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| Keywords | structural equation model / logistic model / neural network / database / risk assessment |
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| Research Abstract |
Risk assessment has attracted much interest according to the importance of primary health care. Risk assessment is usually based on the knowledge acquisition from epidemiological database. Statistical analysis has been standard method for getting knowledge from database. However, this kind of analysis seems to be not enough appropriate to evaluate the non-linear conditions. This project aims to introduce the structural equations with latent variables to epidemiological risk assessment in the non-linear conditions.
The epidemiological data was gathered from the TOYAMA study(cohort study of children in Toyama Prefecture). The risk was calculated for the occurrence of the obese child in the elementary school from 3 y.o. normal body composition. The results were compared between structural equations with latent variables, logistic model and neural network model. The structural equation model showed the advantage of effective analysis by using the latent variables. The models were evaluated based on goodness of fit index, adjusted goodness of fit index, Akaike's information criterion. The results indicated the latent variables should be incorporated into the equations as the products of observed variables. Structural equations should be also evaluated from the viewpoints of medical causal relationships.
This structural equations showed enough abilities to express the non-linear relationships and the expertise. However, estimation of individual risk was difficuit in the structural equations compared with logistic and neural network models.
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