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Reporting of National Notifiable Infectious Diseases (NNIDs) and Related Characteristics.
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Ye soon Kim, Kee ho Park, Hyo soon Yoo, Jun wook Kwon, Euichul Shin
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Korean J Epidemiol. 2007;29(2):200-210.
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Abstract
communicable diseases. The purposes of the study is to estimate reporting proportion of National Notifiable Infectious Diseases(NNIDs) and investigate characteristics related to reporting using KAP(knowledge, attitude, practice) model.METHOD We surveyed randomly selected 2,185 physicians (speciality: internal medicine, family medicine, pediatrics, dermatology, general physicians) of their knowledge, attitude, and practice of NNIDs reporting through self-administered mail questionnaires. Of them, 231 physicians responded (response proportion: 10.6%).RESULT The reporting proportion was estimated to 27.0%.
Recognition level (knowledge) of NNIDs was relatively high with proportion of 69.4%, and attitude (public health importance) of reporting was 65.8%. Multiple logistic regression analysis showed that knowledge, attitude significantly affected physicians' reporting in a positive direction (O.R. 6.2, 6.2 respectively). Whereas, senior age group, specialty (family medicine, pediatrics, dermatology) showed significantly lower reporting. General (tertiary care) hospital level of care showed significantly higher reporting practice (alpha=0.05).CONCLUSION The NNIDs reporting proportion, 27.0% is similar with those studied recently. Continuous efforts to increase the performance level of communicable diseases surveillance system. Of those, restructuring surveillance systems considering characteristics of notifiable diseases classes must be stressed. Educational approach of physicians needs to be tailored specially to newly-designated diseases such as Group II, Designated Group NNIDs.
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A Forecasting Model for the Epidemic of Nationally Notifiable Communicable Diseases in Korea.
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Yonggyu Park, Hyoung Ah Kim, Kyung Hwan Cho, Euichul Shin, Kwang Ho Meng
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Korean J Epidemiol. 2000;22(2):108-115.
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PURPOSE
S: The authors derived two forecasting models which can be used as objective tools for detecting epidemics and predicting the future frequencies of communicable diseases.METHODS In this study, regression analysis using trigonometric functions, Box and Jenkins's seasonal ARIMA model were applied to the monthly accumulated data of five nationally notifiable communicable diseases from January 1987 to December 1998 in Korea.RESULTS Between two forecasting models, seasonal ARIMA model gives more precise predicted frequencies than regression model in the neighborhood of the current time points and future time, but the regression model is better in overall agreement between the predicted and observed frequencies during 7 years(1992-1998).CONCLUSIONS These forecasting models can be usefully applied in deciding and carrying out a national policy in preventing epidemics in the future, and graphic program is much helpful to understand the present status of disease occurrence.
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Incorrect Disease Coding in Medical Insurance Claims and the Effect of Official Intervention: Based on Medical Insurance Claims of 6 Notifiable Acute Communicable Diseases.
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Yong Mun Park, Kwang Ho Meng, Euichul Shin, Kidong Park, Won Chul Lee, Sukil Kim, Jung Hee Jang
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Korean J Epidemiol. 1999;21(2):142-150.
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BACKGROUND
Because of their large size and excellent computerized records of illness and services rendered, the importance of national insurance program is getting much attentions from the public health researchers and the national and local health authorities. In reality, however, most health records from medical insurance program suffer very much from inaccurate disease coding, and therefore, they are practically in no use.METHODS Pattern of incorrect disease coding of 6 Notifiable Acute Communicable Diseases that believed not to have been occurred in Korea lately was reviewed. The reasons of such incorrect codings in different level of medical institutions were studied. This study also attempted to see how an official intervention asking the medical institutions to correct their coding behavior works by comparing the frequencies of incorrect disease coding before and after the intervention.RESULTS Study results showed that more incorrect disease codings came from clinics than hospitals, and non-physician personnel in clinics and hospitals seemed to be responsible for most of the incorrect disease codings. Most frequent diseases coded incorrectly such as cholera and poliomyelitis were the ones that physicians and non-physician personnel in the clinics and hospitals had been familiar with for a long time period.CONCLUSION Even a simple official intervention asking the clinics and hospitals to correct their coding behavior was very effective : total number of incorrect disease codings before intervention (398 cases from 144 institutions) dramatically decreased (14 cases from 8 institutions) after intervention. Significant decrease in incorrect disease coding was found more in small institutions such as clinics and public health facilities than large institutions.
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