title: Logistic model analysis of neurological findings in Minamata disease and the predicting index.
abstract: OBJECTIVE
  To establish a statistical diagnostic method to identify patients with Minamata disease (MD) considering factors of aging and sex, we analyzed the neurological findings in MD patients, inhabitants in a methylmercury polluted (MP) area, and inhabitants in a non-MP area.
  
  
  METHODS
  We compared the neurological findings in MD patients and inhabitants aged more than 40 years in the non-MP area. Based on the different frequencies of the neurological signs in the two groups, we devised the following formula to calculate the predicting index for MD: predicting index = 1/(1+e(-x)) x 100 (The value of x was calculated using the regression coefficients of each neurological finding obtained from logistic analysis. The index 100 indicated MD, and 0, non-MD).
  
  
  RESULTS
  Using this method, we found that 100% of male and 98% of female patients with MD (95 cases) gave predicting indices higher than 95. Five percent of the aged inhabitants in the MP area (598 inhabitants) and 0.2% of those in the non-MP area (558 inhabitants) gave predicting indices of 50 or higher.
  
  
  CONCLUSIONS
  Our statistical diagnostic method for MD was useful in distinguishing MD patients from healthy elders based on their neurological findings.
authors.affiliation: Third Department of Internal Medicine, Kagoshima University Faculty of Medicine.
authors.forename: Masanori
authors.initials: M
authors.lastname: Nakagawa
authors.forename: Tomoko
authors.initials: T
authors.lastname: Kodama
authors.forename: Suminori
authors.initials: S
authors.lastname: Akiba
authors.forename: Kimiyoshi
authors.initials: K
authors.lastname: Arimura
authors.forename: Junji
authors.initials: J
authors.lastname: Wakamiya
authors.forename: Makoto
authors.initials: M
authors.lastname: Futatsuka
authors.forename: Takao
authors.initials: T
authors.lastname: Kitano
authors.forename: Mitsuhiro
authors.initials: M
authors.lastname: Osame
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D007564:Japan / Q000453:epidemiology; D016015:Logistic Models; D008297:Male; D020262:Mercury Poisoning, Nervous System / Q000175:diagnosis* / Q000453:epidemiology / Q000503:physiopathology*; D008767:Methylmercury Compounds / Q000506:poisoning*; D008875:Middle Aged; D015999:Multivariate Analysis; D009460:Neurologic Examination; D011237:Predictive Value of Tests; D015995:Prevalence; D012720:Severity of Illness Index
chemical_list: D008767:Methylmercury Compounds