Evidence map›Paper›PMID 30402378›Full record

ArticleOsong public health and research perspectives2018

Developing the High-Risk Drinking Scorecard Model in Korea.

Jun-Tae Han, Il-Su Park, Suk-Bok Kang, Byeong-Gyu Seo

Abstract read
In one paragraph

Article in Osong public health and research perspectives, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jun-Tae HanDepartment of Student Aid Policy Research, Korea Student Aid Foundation, Daegu, Korea.
Il-Su ParkDepartment of Health Management, Uiduk University, Gyeongju, Korea.
Suk-Bok KangDepartment of Statistics, Yeungnam University, Gyeongsan, Korea.
Byeong-Gyu SeoDepartment of Statistics, Yeungnam University, Gyeongsan, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to develop a high-risk drinking scorecard using cross-sectional data from the 2014 Korea Community Health Survey.

methodsData 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.

resultsThis 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.

conclusionA 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.

Indexed as

data mininghigh-risk drinkingKorea Community Health Surveyscorecardweighted multiple logistic regression

Identifiers

PMID30402378
PMCPMC6202019

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.