Osong Public Health Res Perspect.  2018 Oct;9(5):231-239. 10.24171/j.phrp.2018.9.5.04.

Developing the High-Risk Drinking Scorecard Model in Korea

Affiliations
  • 1Department of Student Aid Policy Research, Korea Student Aid Foundation, Daegu, Korea.
  • 2Department of Health Management, Uiduk University, Gyeongju, Korea. ispark@uu.ac.kr
  • 3Department of Statistics, Yeungnam University, Gyeongsan, Korea.

Abstract


OBJECTIVES
This study aimed to develop a high-risk drinking scorecard using cross-sectional data from the 2014 Korea Community Health Survey.
METHODS
Data were collected from records for 149,592 subjects who had participated in the Korea Community Health Survey conducted from 2014. The scorecard model was developed using data mining, a scorecard and points to double the odds approach for weighted multiple logistic regression.
RESULTS
This study found that there were many major influencing factors for high-risk drinkers which included gender, age, educational level, occupation, whether they received health check-ups, depressive symptoms, over-moderate physical activity, mental stress, smoking status, obese status, and regular breakfast. Men in their thirties to fifties had a high risk of being a drinker and the risks in office workers and sales workers were high. Those individuals who were current smokers had a higher risk of drinking. In the scorecard results, the highest score range was observed for gender, age, educational level, and smoking status, suggesting that these were the most important risk factors.
CONCLUSION
A credit risk scorecard system can be applied to quantify the scoring method, not only to help the medical service provider to understand the meaning, but also to help the general public to understand the danger of high-risk drinking more easily.

Keyword

high-risk drinking; data mining; weighted multiple logistic regression; scorecard; Korea Community Health Survey

MeSH Terms

Breakfast
Commerce
Data Mining
Depression
Drinking*
Health Surveys
Humans
Korea*
Logistic Models
Male
Motor Activity
Occupations
Research Design
Risk Factors
Smoke
Smoking
Smoke
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