Connection between trajectory of primary cancer monitoring indicators and mortality after cancer in South Korea

Sep 23, 2026

Cancer remains a leading global cause of mortality, with nearly 10 million deaths in 2020, and ongoing demographic pressures in South Korea increase the cancer burden. This study examines how lifestyle-related risk factors relate to cancer case fatality, using a regional prevention index to capture their combined effects. Group-based trajectory modeling identifies patterns across incidence and prevention indicators, and Cox proportional hazards models estimate hazard ratios adjusting for key covariates.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusions

Background

Cancer burden and demographics

Preventability and risk factors

Kim etal. BMC Medicine (2025) 23:320 [https://doi.org/10.1186/s12916-025-04121-y](https://doi.org/10.1186/s12916-025-04121-y) BMC Medicine RESEARCH Open Access Connection between trajectory of primary cancer monitoring indicators and mortality after cancer in South Korea Jung Hyun Kim1*†, Haedong Kim2*†, Man S. Kim3, Mison Chun4 and Jaeyong Shin5,6* Abstract Background Cancer remains a leading global cause of mortality, responsible for nearly 10 million deaths in 2020. Given the country’s low birth rate and aging population, the escalating cancer burden poses significant challenges to its healthcare systems. This study aimed to investigate the relationship between lifestyle risk factors and cancer case fatality, emphasizing the collective impact of these factors through a prevention index at the regional level. Methods The study focused on ten cancer types, categorizing counties into three levels of cancer incidence rates using group-based trajectory modeling to identify disparities in patterns and levels among groups. Additionally, we segmented the proportions of obesity prevalence, average daily per capita smoking amount, prevalence of smoking, prevalence of high-risk alcohol consumption, prevalence of hypertension diagnosis, prevalence of diabetes diagnosis, and cancer case fatality into three groups through group-based trajectory modeling. Cox proportional hazard models were employed to evaluate the hazard ratios (HR) for cancer case fatality, adjusting for age, sex, income level, and cancer stage. Results The study population comprised 294,070 cancer patients, with thyroid, stomach, colorectal, breast, and lung cancers being the most common. The prevention index (PI) levels, calculated from six primary prevention indicators, were categorized into High, Medium, and Low grades. Counties with higher PI levels (H) exhibited significantly lower cancer case fatality among cancer patients compared to those with lower PI levels (L) . Across all cancer types, females had lower cancer case fatality compared to males, higher age was linked to higher cancer case fatality, advanced stage cases had the highest cancer case fatality, and the highest income quintile consistently showed the lowest cancer case fatality. Conclusions The study highlights the significant inverse relationship between primary prevention indicator levels and cancer case fatality. Higher scores on primary prevention indicators are associated with lower cancer mortality among cancer patients for various cancer types, underscoring the importance of comprehensive, community-based prevention strategies in mitigating cancer risk and improving public health outcomes in South Korea. Keywords Cancer prevention, Cancer case fatality, Prevention indicators, SDG3, Good health and well-being †Junghyun Kim and Haedong Kim are contributed equally to this work. *Correspondence: Jung Hyun Kim Haedong Kim Jaeyong Shin [drshin@yuhs.ac](drshin@yuhs.ac) Full list of author information is available at the end of the article © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/.)[.](http://creativecommons.org/li

FAQs

What was the study designed to investigate?

The study examined the relationship between primary prevention indicator levels and cancer case fatality, emphasizing the combined effect of lifestyle-related risk factors at a regional level in South Korea.

How did the researchers model cancer incidence and prevention indicators?

Counties were categorized into incidence-rate levels using group-based trajectory modeling. Proportions related to obesity, smoking amount and prevalence, high-risk alcohol use, hypertension diagnosis, diabetes diagnosis, and cancer case fatality were also segmented into groups using group-based trajectory modeling.

What were the main findings regarding prevention indicator levels?

Counties with higher prevention index levels showed significantly lower cancer case fatality among cancer patients compared with counties with lower prevention index levels, across multiple cancer types.

Sep 23, 2026

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