Journal of Preventive Medicine and Public Health

Special Article

Statin Intake and Gastric Cancer Risk: An Updated Subgroup Meta-analysis Considering Immortal Time Bias: Jong-Myon Bae; J Prev Med Public Health. 2022;55(5):424-427. Published online August 18, 2022; DOI: https://doi.org/10.3961/jpmph.22.209

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A retrospective record-linkage study (RLS) based on medical records containing drug prescription histories involves immortal time bias (ITB). Thus, it is necessary to control for this bias in the research planning and analysis stages. Furthermore, a summary of a metaanalysis including RLSs that did not control for ITB showed that specific drugs had a preventive effect on the occurrence of the disease. Previous meta-analytic results of three systematic reviews evaluating the association between statin intake and gastric cancer risk showed that the summary hazard ratio (sHR) of the RLSs was lower than 1 and was statistically significant. We should consider the possibility of ITB in the sHR of RLSs and interpret the results carefully.

Summary

Korean summary
이차자료를 활용하여 구축한 코호트 추적연구는 immortal time bias가 개입될 개연성을 염두에 두어야 한다. 이들 연구결과들을 제외한 메타분석 결과 스타닌 복용에 따른 위암발생 위험 감소는 관련성이 없다는 결과를 도출하였다.

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Sodium-glucose cotransporter-2 inhibitors use and the risk of gout: a systematic review and meta-analysis
Shih-Wei Lai, Bing-Fang Hwang, Yu-Hung Kuo, Chiu-Shong Liu, Kuan-Fu Liao
Frontiers in Endocrinology.2023;[Epub] CrossRef

Original Article

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.

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Deutsches Ärzteblatt international.2024;[Epub] CrossRef
Immeasurable Time Bias in Self-controlled Designs: Case-crossover, Case-time-control, and Case-case-time-control Analyses
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English Abstract

Strengthening Causal Inference in Studies using Non-experimental Data: An Application of Propensity Score and Instrumental Variable Methods.: Myoung Hee Kim, Young Kyung Do; J Prev Med Public Health. 2007;40(6):495-504.; DOI: https://doi.org/10.3961/jpmph.2007.40.6.495

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Abstract PDF

OBJECTIVES
This study attempts to show how studies using non-experimental data can strengthen causal inferences by applying propensity score and instrumental variable methods based on the counterfactual framework. For illustrative purposes, we examine the effect of having private health insurance on the probability of experiencing at least one hospital admission in the previous year. METHODS: Using data from the 4th wave of the Korea Labor and Income Panel Study, we compared the results obtained using propensity score and instrumental variable methods with those from conventional logistic and linear regression models, respectively. RESULTS: While conventional multiple regression analyses fail to identify the effect, the results estimated using propensity score and instrumental variable methods suggest that having private health insurance has positive and statistically significant effects on hospital admission. CONCLUSIONS: This study demonstrates that propensity score and instrumental variable methods provide potentially useful alternatives to conventional regression approaches in making causal inferences using non-experimental data.

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Original Article

A Cohort Study on Risk Factors for Chronic Liver Disease: Analytic Strategies Excluding Potentially Incident Subjects.: Moo Song Lee, Dae Sung Kim, Dong Hyun Kim, Jong Myun Bae, Myung Hee Shin, Yoon Ok Ahn; Korean J Prev Med. 1999;32(4):452-458.

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Abstract PDF: OBJECTIVES
The authors conducted the study to evaluate bias when potentially diseased subjects were included in cohort members while analyzing risk factors of chronic liver diseases. METHODS: Total of 14,529 subjects were followed up for the incidence of liver diseases from January 1993 to June 1997. We have used databases of insurance company with medical records, cancer registry, and death certificate data to identify 102 incident cases. The cohort members were classified into potentially diseased group(n=2,217) when they were HBsAg positive, serum GPT levels higher than 40 units, or had or has liver diseases in baseline surveys. Cox' model were used for potentially diseased group, other members, and total subjects, respectively. RESULTS: The risk factors profiles were similar for total and potentially diseased subjects: HBsAg positivity, history of acute liver disease, and recent quittance of smoking or drinking increased the risk, while intake of pork and coffee decreased it. For the potentially diseased, obesity showed marginally significant protective effect. Analysis of subjects excluding the potentially diseased showed distinct profiles: obesity increased the risk, while quitting smoking or drinking had no association. For these intake of raw liver or processed fish or soybean paste stew increased risk; HBsAg positivity, higher levels of liver enzymes and history of acute liver diseases increased the risk. CONCLUSIONS: The results suggested the potential bias in risk ratio estimates when potentially diseased subjects were included in cohort study on chronic liver diseases, especially for lifestyles possibly modified after disease onset. The analytic strategy excluding potentially diseased subjects was considered appropriate for identifying risk factors for chronic liver diseases.; Summary

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