Journal of Preventive Medicine and Public Health

Original Articles

Interactions of Behavioral Changes in Smoking, High-risk Drinking, and Weight Gain in a Population of 7.2 Million in Korea: Yeon-Yong Kim, Hee-Jin Kang, Seongjun Ha, Jong Heon Park; J Prev Med Public Health. 2019;52(4):234-241. Published online July 3, 2019; DOI: https://doi.org/10.3961/jpmph.18.290

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Objectives
To identify simultaneous behavioral changes in alcohol consumption, smoking, and weight using a fixed-effect model and to characterize their associations with disease status.
Methods
This study included 7 000 529 individuals who participated in the national biennial health-screening program every 2 years from 2009 to 2016 and were aged 40 or more. We reconstructed the data into an individual-level panel dataset with 4 waves. We used a fixed-effect model for smoking, heavy alcohol drinking, and overweight. The independent variables were sex, age, lifestyle factors, insurance contribution, employment status, and disease status.
Results
Becoming a high-risk drinker and losing weight were associated with initiation or resumption of smoking. Initiation or resumption of smoking and weight gain were associated with non-high-risk drinkers becoming high-risk drinkers. Smoking cessation and becoming a high-risk drinker were associated with normal-weight participants becoming overweight. Participants with newly acquired diabetes mellitus, ischemic heart disease, stroke, and cancer tended to stop smoking, discontinue high-risk drinking, and return to a normal weight.
Conclusions
These results obtained using a large-scale population-based database documented interactions among lifestyle factors over time.

Summary

Korean summary
이 분석은 흡연, 음주, 체중의 동시적 변화에 대해 패널분석방법론인 고정효과 모형을 이용하여 분석하였으며, 2009년부터 2016년까지 2년 주기로 4차례 모두 건강검진을 수검받은 720만 명을 대상으로 하였다. 흡연, 음주, 체중의 동시적 변화에 대한 방향성을 탐색하여 생활습관 관련 행태가 독자적이 아닌 유기적으로 변화하는 양상을 확인하였다, 또한 당뇨병, 뇌졸중, 암이 신규로 진단되었을 때 행태 변화가 나타나는 것을 확인하였다.

Citations

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Association between Body Mass Index and Risk of Gastric Cancer by Anatomic and Histologic Subtypes in Over 500,000 East and Southeast Asian Cohort Participants
Jieun Jang, Sangjun Lee, Kwang-Pil Ko, Sarah K. Abe, Md. Shafiur Rahman, Eiko Saito, Md. Rashedul Islam, Norie Sawada, Xiao-Ou Shu, Woon-Puay Koh, Atsuko Sadakane, Ichiro Tsuji, Jeongseon Kim, Isao Oze, Chisato Nagata, Shoichiro Tsugane, Hui Cai, Jian-Min
Cancer Epidemiology, Biomarkers & Prevention.2022; 31(9): 1727. CrossRef

Level of Agreement and Factors Associated With Discrepancies Between Nationwide Medical History Questionnaires and Hospital Claims Data: Yeon-Yong Kim, Jong Heon Park, Hee-Jin Kang, Eun Joo Lee, Seongjun Ha, Soon-Ae Shin; J Prev Med Public Health. 2017;50(5):294-302. Published online July 20, 2017; DOI: https://doi.org/10.3961/jpmph.17.024

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Abstract PDF

Objectives
The objectives of this study were to investigate the agreement between medical history questionnaire data and claims data and to identify the factors that were associated with discrepancies between these data types. Methods: Data from self-reported questionnaires that assessed an individual’s history of hypertension, diabetes mellitus, dyslipidemia, stroke, heart disease, and pulmonary tuberculosis were collected from a general health screening database for 2014. Data for these diseases were collected from a healthcare utilization claims database between 2009 and 2014. Overall agreement, sensitivity, specificity, and kappa values were calculated. Multiple logistic regression analysis was performed to identify factors associated with discrepancies and was adjusted for age, gender, insurance type, insurance contribution, residential area, and comorbidities. Results: Agreement was highest between questionnaire data and claims data based on primary codes up to 1 year before the completion of self-reported questionnaires and was lowest for claims data based on primary and secondary codes up to 5 years before the completion of self-reported questionnaires. When comparing data based on primary codes up to 1 year before the completion of self-reported questionnaires, the overall agreement, sensitivity, specificity, and kappa values ranged from 93.2 to 98.8%, 26.2 to 84.3%, 95.7 to 99.6%, and 0.09 to 0.78, respectively. Agreement was excellent for hypertension and diabetes, fair to good for stroke and heart disease, and poor for pulmonary tuberculosis and dyslipidemia. Women, younger individuals, and employed individuals were most likely to under-report disease. Conclusions: Detailed patient characteristics that had an impact on information bias were identified through the differing levels of agreement.

Summary

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Evaluation of Geographic Indices Describing Health Care Utilization: Agnus M. Kim, Jong Heon Park, Sungchan Kang, Yoon Kim; J Prev Med Public Health. 2017;50(1):29-37. Published online December 19, 2016; DOI: https://doi.org/10.3961/jpmph.16.099

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Abstract PDF

Objectives
The accurate measurement of geographic patterns of health care utilization is a prerequisite for the study of geographic variations in health care utilization. While several measures have been developed to measure how accurately geographic units reflect the health care utilization patterns of residents, they have been only applied to hospitalization and need further evaluation. This study aimed to evaluate geographic indices describing health care utilization.
Methods
We measured the utilization rate and four health care utilization indices (localization index, outflow index, inflow index, and net patient flow) for eight major procedures (coronary artery bypass graft surgery, percutaneous transluminal coronary angioplasty, surgery after hip fracture, knee replacement surgery, caesarean sections, hysterectomy, computed tomography scans, and magnetic resonance imaging scans) according to three levels of geographic units in Korea. Data were obtained from the National Health Insurance database in Korea. We evaluated the associations among the health care utilization indices and the utilization rates.
Results
In higher-level geographic units, the localization index tended to be high, while the inflow index and outflow index were lower. The indices showed different patterns depending on the procedure. A strong negative correlation between the localization index and the outflow index was observed for all procedures. Net patient flow showed a moderate positive correlation with the localization index and the inflow index.
Conclusions
Health care utilization indices can be used as a proxy to describe the utilization pattern of a procedure in a geographic unit.

Summary

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Factors Associated with End-Of-Life Health Care Use and Spending in Korea in Comparison with the General Population
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Presence of Thrombectomy-capable Stroke Centers Within Hospital Service Areas Explains Regional Variation in the Case Fatality Rate of Acute Ischemic Stroke in Korea
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The Effect of Geographic Units of Analysis on Measuring Geographic Variation in Medical Services Utilization: Agnus M. Kim, Jong Heon Park, Sungchan Kang, Kyosang Hwang, Taesik Lee, Yoon Kim; J Prev Med Public Health. 2016;49(4):230-239. Published online July 14, 2016; DOI: https://doi.org/10.3961/jpmph.16.034

10,585 View
159 Download
17 Crossref

Abstract PDF Supplementary Material

Objectives
We aimed to evaluate the effect of geographic units of analysis on measuring geographic variation in medical services utilization. For this purpose, we compared geographic variations in the rates of eight major procedures in administrative units (districts) and new areal units organized based on the actual health care use of the population in Korea.
Methods
To compare geographic variation in geographic units of analysis, we calculated the age–sex standardized rates of eight major procedures (coronary artery bypass graft surgery, percutaneous transluminal coronary angioplasty, surgery after hip fracture, knee-replacement surgery, caesarean section, hysterectomy, computed tomography scan, and magnetic resonance imaging scan) from the National Health Insurance database in Korea for the 2013 period. Using the coefficient of variation, the extremal quotient, and the systematic component of variation, we measured geographic variation for these eight procedures in districts and new areal units.
Results
Compared with districts, new areal units showed a reduction in geographic variation. Extremal quotients and inter-decile ratios for the eight procedures were lower in new areal units. While the coefficient of variation was lower for most procedures in new areal units, the pattern of change of the systematic component of variation between districts and new areal units differed among procedures.
Conclusions
Geographic variation in medical service utilization could vary according to the geographic unit of analysis. To determine how geographic characteristics such as population size and number of geographic units affect geographic variation, further studies are needed.

Summary

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