Internal medicine (Tokyo, Japan) · 2002
Logistic model analysis of neurological findings in Minamata disease and the predicting index.
Сравнительное исследованиеСтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Nakagawa Masanori, Kodama Tomoko, Akiba Suminori, Arimura Kimiyoshi, Wakamiya Junji, Futatsuka Makoto, Kitano Takao, Osame Mitsuhiro
Аннотация
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.
MATERIALS AND 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.
CONCLUSION: Our statistical diagnostic method for MD was useful in distinguishing MD patients from healthy elders based on their neurological findings.
MATERIALS AND 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.
CONCLUSION: Our statistical diagnostic method for MD was useful in distinguishing MD patients from healthy elders based on their neurological findings.