Cardiovascular Prevention and Pharmacotherapy

Original Article

Changes in cardiovascular-related health behaviors during the COVID-19 pandemic: Eunji Kim, Chan-Hee Jung, Dae Jung Kim, Seung-Hyun Ko, Hae-Young Lee, Kyung-Yul Lee, Dae Ryong Kang, Sung Kee Ryu, Won-Young Lee, Eun-Jung Rhee, Hyeon Chang Kim; Cardiovasc Prev Pharmacother. 2023;5(1):15-23. Published online January 27, 2023; DOI: https://doi.org/10.36011/cpp.2023.5.e2

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Background
The COVID-19 pandemic has been the most pressing health challenge in recent years. Meanwhile, prevention for other diseases, such as cardiovascular disease (CVD) has been less prioritized during the pandemic. COVID-19, a novel infectious disease, both had a direct impact on public health and provoked changes in health-related behaviors, including those for CVD prevention. This study sought to examine changes in CVD-related health behaviors during the COVID-19 pandemic and related sociodemographic factors.
Methods
We used data from the Cardiovascular Disease Prevention Awareness Survey conducted in Korea in June 2022. A total of 2,000 adults across Korea’s 17 provinces completed a structured questionnaire online or on a mobile device. Self-reported changes in CVD-related health behaviors were investigated. We used unadjusted and adjusted logistic regression models to explore the associations between negative changes and sociodemographic factors.
Results
In smoking, drinking, and healthcare service use, the proportion of those with positive changes surpassed the proportion of respondents who reported negative changes. In contrast, negative changes predominated for diet, exercise, and stress. Most individuals (52.6%) reported a deterioration of psychological distress. These negative changes were significantly associated with age, sex, marital status, and the presence of cardiometabolic disease.
Conclusions
The COVID-19 pandemic has affected CVD-related health behaviors. Based on these changes, CVD prevention should be encouraged with appropriate and prioritized strategies.

Citations

Citations to this article as recorded by

Cardiovascular-related health behavior changes: lessons from the COVID-19 pandemic and post-pandemic challenges
Inha Jung, Won-Young Lee
Cardiovascular Prevention and Pharmacotherapy.2023; 5(4): 99. CrossRef

Special Articles

From Traditional Statistical Methods to Machine and Deep Learning for Prediction Models: Jun Hyeok Lee, Dae Ryong Kang; Cardiovasc Prev Pharmacother. 2020;2(2):50-55. Published online April 30, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e6

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Abstract PDF: Traditional statistical methods have low accuracy and predictability in the analysis of large amounts of data. In this method, non-linear models cannot be developed. Moreover, methods used to analyze data for a single time point exhibit lower performance than those used to analyze data for multiple time points, and the difference in performance increases as the amount of data increases. Using deep learning, it is possible to build a model that reflects all information on repeated measures. A recurrent neural network can be built to develop a predictive model using repeated measures. However, there are long-term dependencies and vanishing gradient problems. Meanwhile, long short-term memory method can be applied to solve problems with long-term dependency and vanishing gradient by assigning a fixed weight inside the cell state. Unlike traditional statistical methods, deep learning methods allow researchers to build non-linear models with high accuracy and predictability, using information from multiple time points. However, deep learning models cannot be interpreted; although, recently, many methods have been developed to do so by weighting time points and variables using attention algorithms, such as ReversE Time AttentIoN (RETAIN). In the future, deep learning methods, as well as traditional statistical methods, will become essential methods for big data analysis.

Basic Concepts of a Mendelian Randomization Approach: Tae-Hwa Go, Dae Ryong Kang; Cardiovasc Prev Pharmacother. 2020;2(1):24-30. Published online January 31, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e3

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Abstract PDF: The Mendelian Randomization (MR) approach is a method that enables causal inference in observational studies. There are 3 assumptions that must be satisfied to obtain suitable results: 1) The genetic variant is strongly associated with the exposure, 2) The genetic variant is independent of the outcome, given the exposure and all confounders (measured and unmeasured) of the exposure-outcome association, 3) The genetic variant is independent of factors (measured and unmeasured) that confound the exposure-outcome relationship. This analysis has been used increasingly since 2011, but many researchers still do not know how to perform MR. Here, we introduce the basic concepts, assumptions, and methods of MR analysis to enable better understanding of this approach.

Improving Causal Inference in Observational Studies: Propensity Score Matching: Min Heui Yu, Dae Ryong Kang; Cardiovasc Prev Pharmacother. 2019;1(2):57-62. Published online October 31, 2019; DOI: https://doi.org/10.36011/cpp.2019.1.e6

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Abstract PDF

Propensity score matching (PSM) is a useful statistical methods to improve causal inference in observational studies. It guarantees comparability between 2 comparison groups are required. PSM is based on a “counterfactual” framework, where a causal effect on study participants (factual) and assumed participants (counterfactual) are compared. All participants are divided into 2 groups with the same covariates matched as much as possible. Propensity score is used for matching, and it reflects the conditional probabilities that individuals will be included in the experimental group when covariates are controlled for all subjects. The counterfactuals for the experimental group are matched between groups with characteristics as similar as possible. In this article, we introduce the concept of PSM, PSM methods, limitations, and statistical tools.

Citations

Citations to this article as recorded by

Strengthening Association through Causal Inference
Megan Lane, Nicholas L. Berlin, Kevin C. Chung, Jennifer F. Waljee
Plastic & Reconstructive Surgery.2023; 152(4): 899. CrossRef

Editorial

Welcome to the New Journal Cardiovascular Prevention and Pharmacotherapy: Mi-Jeong Kim, Jang-Whan Bae, Dae Ryong Kang; Cardiovasc Prev Pharmacother. 2019;1(1):1-2. Published online July 31, 2019; DOI: https://doi.org/10.36011/cpp.2019.1.e5

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