Literature DB >> 23560205

The Korea national health and nutrition examination survey as a primary data source.

Hyun Ah Park1.   

Abstract

Entities:  

Year:  2013        PMID: 23560205      PMCID: PMC3611106          DOI: 10.4082/kjfm.2013.34.2.79

Source DB:  PubMed          Journal:  Korean J Fam Med        ISSN: 2005-6443


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The Korea National Health and Nutrition Examination Survey (KNHANES) is a population-based cross-sectional survey designed to assess the health related behavior, health condition, and nutritional state of Koreans (http://knhanes.cdc.go.kr/). It was conducted as a tri-annual survey for the first 3 cycles, implemented in 1998, 2001, and 2005. Beginning with the 4th cycle of 2007, it was converted to an annual survey. The 2011 data was recently opened to the public, and is free for all researchers who want to use it. The KNHANES provides a rich source of data which are easy to access and can be quickly obtained. Its ethical problems are minimal and there are no adverse effects in conducting the study. Using the KNHANES saves time, money, and personnel that would otherwise be spent collecting data, and provides a larger and higher-quality database beyond the capacity of any one individual researcher. Further, because the study participants are representative of the Korean population, the results have external validity. Therefore, this data is helpful to young investigators. In fact, the portion of submitted articles to the Korean Journal of Family Medicine (KJFM) using the KNHANES as the primary source of data is significant. Specifically, one article in 2007, three articles in 2008, four articles in 2009, three articles in 2010, five articles in 2011, and five articles in 2012 used the KNHANES data as their primary data source, totaling 21 articles (6.2%) among 338 original articles during the previous five years. However, there are several points investigators should keep in mind when using the KNHANES data. First, the KNHANES data are open to the public and there is no systematic process to control the research topic assignment. Many researchers might investigate the same topic simultaneously, especially popular topics like obesity and metabolic syndrome. Before deciding on the study topic, a thorough search of the database like Medline, EMBASE, and the abstracts of related conferences are needed. Once the topic is decided upon, the article should be submitted as soon as possible. Otherwise someone else may publish a similar study from the same dataset before you do. Second, investigators must be familiar with the huge and complex data structure and study design of the KNHANES. Non-response and multi-stage probability sampling should be taken into account by using survey statistics to estimate the data of the whole Korean population. Despite this, lots of studies submitted to KJFM using the KNHANES do not apply sampling weight in their analysis. In such a case the estimated association might be biased and revision is required. Third, the KNHANES is a secondary data source like the Korea Youth Risk Behavior Web-based Survey and the Community Health Survey, and has the same limitations that they have.1) The main purpose of the KNHANES is to produce national statistics, not to answer a specific research question. Therefore, the particular information the investigator wants may not be collected. For these reasons, the dataset should be examined carefully to confirm that it includes the necessary data. There also exists a potential for errors or mistakes in the data, even with its rigorous documents control system. Missing data is also a problem in the KNHANES.2)
  2 in total

Review 1.  Secondary analysis of data.

Authors:  Sharon M Coyer; Agatha M Gallo
Journal:  J Pediatr Health Care       Date:  2005 Jan-Feb       Impact factor: 1.812

2.  Rate of Missing Socioeconomic Factors in the 4th KNHANES.

Authors:  Hyun Ah Park
Journal:  Korean J Fam Med       Date:  2012-11-27
  2 in total
  38 in total

1.  Oral health behaviors and bone mineral density in South Korea: the 2008-2010 Korean National Health and Nutrition Examination Survey.

Authors:  Hyun-Jin Kim; Yang-Hyun Kim; Kyung-Hwan Cho; Byoung-Duck Han; Seon-Mee Kim; Youn-Seon Choi; Do-Hoon Kim; Kyung-Do Han; Yong-Joo Lee; Chul-Min Kim
Journal:  J Bone Miner Metab       Date:  2015-06-02       Impact factor: 2.626

2.  Influence of occupation on lumbar spine degeneration in men: the Korean National Health and Nutrition Examination Survey 2010-2013.

Authors:  Seoyon Yang; Won Kim; Kyoung Hyo Choi; You Gyung Yi
Journal:  Int Arch Occup Environ Health       Date:  2016-09-09       Impact factor: 3.015

3.  Scaling of adult regional body mass and body composition as a whole to height: Relevance to body shape and body mass index.

Authors:  John M Schuna; Courtney M Peterson; Diana M Thomas; Moonseong Heo; Sangmo Hong; Woong Choi; Steven B Heymsfield
Journal:  Am J Hum Biol       Date:  2014-11-08       Impact factor: 1.937

4.  Sleep disorders, mental health, and dry eye disease in South Korea.

Authors:  Youngju An; Hyojin Kim
Journal:  Sci Rep       Date:  2022-06-30       Impact factor: 4.996

5.  Upper Normal Alanine Aminotransferase Range and Insulin Resistance in Korean Adolescents: Korean National Health and Nutrition Examination Survey, 2009-2010.

Authors:  Yoon Lee; Kyung-Do Han; Jennifer Jaeeun Jung; Kee-Hyoung Lee; Kyung-Hwan Cho; Yang-Hyun Kim
Journal:  Dig Dis Sci       Date:  2015-12-24       Impact factor: 3.199

6.  Mapping health assessment questionnaire disability index (HAQ-DI) score, pain visual analog scale (VAS), and disease activity score in 28 joints (DAS28) onto the EuroQol-5D (EQ-5D) utility score with the KORean Observational study Network for Arthritis (KORONA) registry data.

Authors:  Hye-Lin Kim; Dam Kim; Eun Jin Jang; Min-Young Lee; Hyun Jin Song; Sun-Young Park; Soo-Kyung Cho; Yoon-Kyoung Sung; Chan-Bum Choi; Soyoung Won; So-Young Bang; Hoon-Suk Cha; Jung-Yoon Choe; Won Tae Chung; Seung-Jae Hong; Jae-Bum Jun; Jinseok Kim; Seong-Kyu Kim; Tae-Hwan Kim; Tae-Jong Kim; Eunmi Koh; Hwajeong Lee; Hye-Soon Lee; Jisoo Lee; Shin-Seok Lee; Sung Won Lee; Sung-Hoon Park; Seung-Cheol Shim; Dae-Hyun Yoo; Bo Young Yoon; Sang-Cheol Bae; Eui-Kyung Lee
Journal:  Rheumatol Int       Date:  2016-02-06       Impact factor: 2.631

7.  Normal range albuminuria and metabolic syndrome in South Korea: the 2011-2012 Korean National Health and Nutrition Examination Survey.

Authors:  Si-Young Park; Yong-Kyu Park; Kyung-Hwan Cho; Hee-Jeong Choi; Jee-Hye Han; Kyung-Do Han; Byung-Duck Han; Yeo-Joon Yoon; Yang-Hyun Kim
Journal:  PLoS One       Date:  2015-05-15       Impact factor: 3.240

8.  A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data.

Authors:  Jungyoon Kim; Jihye Lim
Journal:  Int J Environ Res Public Health       Date:  2021-05-18       Impact factor: 3.390

9.  A Deep Learning Algorithm to Predict Hazardous Drinkers and the Severity of Alcohol-Related Problems Using K-NHANES.

Authors:  Suk-Young Kim; Taesung Park; Kwonyoung Kim; Jihoon Oh; Yoonjae Park; Dai-Jin Kim
Journal:  Front Psychiatry       Date:  2021-07-09       Impact factor: 4.157

10.  Association between Blood Lead Levels and Age-Related Macular Degeneration.

Authors:  Ho Sik Hwang; Seung Bum Lee; Donghyun Jee
Journal:  PLoS One       Date:  2015-08-07       Impact factor: 3.240

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