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Chinese Journal of Endemiology ; (12): 671-674, 2014.
Artigo em Chinês | WPRIM | ID: wpr-470354

RESUMO

Objective To find out the iodine content in drinking water in Yi County,and to provide a scientific basis for prevention of iodine deficiency disorders.Methods An administrative village was taken as a unit in Yi County,Hebei Province.One drinking water sample was randomly selected from each village with centralized water supply.For villages with decentralized water supply,five drinking water samples were randomly selected according to directions (east,west,south,north and center) if there were more than five water sources in the village ; all the water source samples were taken if there were less than five water sources.Water iodine content of all of the water samples was tested; geomorphological feature,water type and well depth were surveyed.Water iodine content was determined through arsenic cerium catalytic spectrophotometric method.Results The range of water iodine content was 0.00-18.39 μg/L,and water iodine median was 2.08 μg/L in 1 024 water samples in the county.Water samples of 490,505 and 29 copies were collected in the hills,plains and mountains,and water iodine median was 1.74,2.26,3.15 μg/L,respectively.Concerning water iodine medians in drinking water,mountains were less than hills and plains(x2 =37.36,34.25,all P< 0.01),hills were less than (x2=15.27,P < 0.01).Water samples of 742,228,54 copies were collected,respectively,in household well water,tap water and mountain spring water,and water iodine median was 2.08,2.24 and 0.73 μg/L,respectively.There was no significant difference between the water iodine median in tap water and household well water (x2 =2.97,P > 0.05),and the iodine in mountain spring water was less than that of tap water and household well water (x2 =38.23,43.82,P < 0.01).Water samples of 583 and 441 copies were collected,respectively,in the well depth < 40 m and well depth ≥40 m,and water iodine median was 1.81 and 2.24 μg/L,respectively,and there was no significant difference between the water iodine median in the well depth < 40 m and well depth ≥40 m(x2 =2.32,P > 0.05).Conclusions Residents iodine content of drinking water in Yi County is lower; the natural environment is seriously iodine deficiency,and iodine supplementation should be strengthened.

2.
Chinese Journal of Endemiology ; (12): 442-445, 2014.
Artigo em Chinês | WPRIM | ID: wpr-454197

RESUMO

Objective To investigate potential problems and solutions within the data of national surveillance of Keshan disease(KSD), to improve the quality of surveillance data and the reliability of the results. Methods Four key variables (name, sex, age, and KSD diagnosis) in the national surveillance data of KSD in 2009 were cleaned by SPSS 15.0. Cleaning contents included duplicate records, missing values, outliers and logic errors. Name, sex, age, currently residing in townships and currently residing in villages and other variables were combined into different filters to find duplicate records by the command of Identify Duplicate Cases , then the duplicate records were returned to the data reporting agencies, and finally delete or merge. Data with missing values, outliers, or logical errors were found by commands of Frequencies, Descriptives and Select if, then the duplicate records were returned to the data reporting agencies. Data were revised based on not only the feedback , but also by using the relationship between variables, and by consulting KSD clinical experts. Results Four hundred and sixty-four cases of duplicate records were found and cleaned. The number of missing values was 2 047 (specifically, name 0, sex 3, age 32 and KSD diagnosis 2 012). The number of outliers was 1 988 (specifically, name 6, sex 3, age 10 and KSD diagnosis 1 969). The records of 5 kinds of logic errors of KSD diagnosis were 105 in all. Conclusion There are duplicate records, missing values, outliers and logic errors in the national surveillance data of KSD, cleaning work could improve the quality of surveillance data, ensure the authenticity and rliability of the monitoring data.

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