Journal of Preventive Medicine and Public Health

Original Article

A Comparison of Green, Delta, and Monte Carlo Methods to Select an Optimal Approach for Calculating the 95% Confidence Interval of the Population-attributable Fraction: Guidance for Epidemiological Research: Sangjun Lee, Sungji Moon, Kyungsik Kim, Soseul Sung, Youjin Hong, Woojin Lim, Sue K. Park; J Prev Med Public Health. 2024;57(5):499-507. Published online September 6, 2024; DOI: https://doi.org/10.3961/jpmph.24.272

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Abstract Summary PDF Supplementary Material

Objectives
This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations.
Methods
A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations.
Results
No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs.
Conclusions
This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Summary

Korean summary
본 연구는 인구 기여 분율(PAF)의 95% 신뢰구간을 추정하는 데 있어 Delta, Greenland, Monte Carlo 방법을 비교하여 최적의 방법을 찾고, 주요 매개변수의 변화가 PAF 계산에 미치는 영향을 분석했음. Delta 방법은 상대적으로 낮은 유병률이나 위험도(RR)가 낮을 때 적합하며, Greenland 방법은 높은 유병률에서 효과적이고, Monte Carlo 방법은 전반적으로 안정적인 결과를 제공하지만, 많은 계산 자원이 필요할 수 있음.

Key Message
This study compared Delta, Greenland, and Monte Carlo methods for calculating the 95% confidence intervals (CIs) of population-attributable fractions (PAFs). While all three methods demonstrated comparable performance under conditions of low prevalence or relative risk (RR), they diverged under other scenarios. The Delta method is effective for low-prevalence or minimal RR, Greenland for high-prevalence scenarios, and Monte Carlo is robust but computationally intensive. This research offers practical guidance for selecting the appropriate method based on study conditions, enhancing the reliability of epidemiological studies in estimating PAFs.

Citations

Citations to this article as recorded by

Effect of PM2.5 and its constituents on hospital admissions for cardiometabolic multimorbidity in Urumqi, China
Di Wu, Cheng Li, Yu Shi, Junjie Han, Yaoqin Lu, Yilipa Yilihamu, Yanling Zheng, Liping Zhang
Scientific Reports.2025;[Epub] CrossRef

Systematic Review

Adjusting for Confounders in Outcome Studies Using the Korea National Health Insurance Claim Database: A Review of Methods and Applications: Seung Jin Han, Kyoung Hoon Kim; J Prev Med Public Health. 2024;57(1):1-7. Published online November 16, 2023; DOI: https://doi.org/10.3961/jpmph.23.250

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Abstract Summary PDF

Objectives
Adjusting for potential confounders is crucial for producing valuable evidence in outcome studies. Although numerous studies have been published using the Korea National Health Insurance Claim Database, no study has critically reviewed the methods used to adjust for confounders. This study aimed to review these studies and suggest methods and applications to adjust for confounders.
Methods
We conducted a literature search of electronic databases, including PubMed and Embase, from January 1, 2021 to December 31, 2022. In total, 278 studies were retrieved. Eligibility criteria were published in English and outcome studies. A literature search and article screening were independently performed by 2 authors and finally, 173 of 278 studies were included.
Results
Thirty-nine studies used matching at the study design stage, and 171 adjusted for confounders using regression analysis or propensity scores at the analysis stage. Of these, 125 conducted regression analyses based on the study questions. Propensity score matching was the most common method involving propensity scores. A total of 171 studies included age and/or sex as confounders. Comorbidities and healthcare utilization, including medications and procedures, were used as confounders in 146 and 82 studies, respectively.
Conclusions
This is the first review to address the methods and applications used to adjust for confounders in recently published studies. Our results indicate that all studies adjusted for confounders with appropriate study designs and statistical methodologies; however, a thorough understanding and careful application of confounding variables are required to avoid erroneous results.

Summary

Korean summary
건강보험청구자료를 사용한 성과연구에서는 교란요인 통제가 중요하다. 최근 발표된 연구들은 연구설계와 통계 분석 과정에 적절하게 교란요인을 통제하였다. 연구의 질을 높이기 위해서는 건강보험청구자료에서 수집 가능한 교란 요인에 대한 이해와 방법론적 가이드라인이 요구된다.

Key Message
All recently published studies using Health Insurance Claims Database adjusted for confounders with appropriate study designs and statistical methodologies. The review suggests the need for careful application of confounding variables and the methodological guidance to improve the quality of outcome studies.

Citations

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Review

Qualitative Research in Healthcare: Data Analysis: Dasom Im, Jeehye Pyo, Haneul Lee, Hyeran Jung, Minsu Ock; J Prev Med Public Health. 2023;56(2):100-110. Published online February 15, 2023; DOI: https://doi.org/10.3961/jpmph.22.471

24,594 View
992 Download
31 Web of Science
39 Crossref

Abstract Summary PDF

Qualitative research methodology has been applied with increasing frequency in various fields, including in healthcare research, where quantitative research methodology has traditionally dominated, with an empirically driven approach involving statistical analysis. Drawing upon artifacts and verbal data collected from in-depth interviews or participatory observations, qualitative research examines the comprehensive experiences of research participants who have experienced salient yet unappreciated phenomena. In this study, we review 6 representative qualitative research methodologies in terms of their characteristics and analysis methods: consensual qualitative research, phenomenological research, qualitative case study, grounded theory, photovoice, and content analysis. We mainly focus on specific aspects of data analysis and the description of results, while also providing a brief overview of each methodology’s philosophical background. Furthermore, since quantitative researchers have criticized qualitative research methodology for its perceived lack of validity, we examine various validation methods of qualitative research. This review article intends to assist researchers in employing an ideal qualitative research methodology and in reviewing and evaluating qualitative research with proper standards and criteria.

Summary

Korean summary
질적연구 방법론은 보건의료 연구에서 연구 참여자가 경험하는 주요 현상을 잘 포착할 수 있도록 해 준다. 이 논문에서는 합의적 질적연구, 현상학적 연구, 질적 사례 연구, 근거이론, 포토보이스, 내용 분석 등 6가지의 대표적인 질적 연구 방법론에 대한 간략한 철학적 배경과 구체적인 데이터 분석 및 해석 방법을 소개한다.

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