Epidemiology and Health

Original Articles

Spatiotemporal analysis of sudden infant death syndrome incidence in Korea, 2013–2023: Seungbae Jeon, Minsu Cha, Sunghyun Yun; Epidemiol Health. 2026;48:e2026027. Published online June 10, 2026; DOI: https://doi.org/10.4178/epih.e2026027

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Abstract Summary PDF Supplementary Material: Abstract
OBJECTIVES
This study examined temporal trends and geographic variation in sudden infant death syndrome (SIDS) incidence in Korea.
METHODS
We analyzed a nationwide cardiac arrest registry to identify SIDS cases from 2013 to 2023. The study period was divided into 3 phases: pre-pandemic (2013–2019), pandemic (2020–2021), and post-pandemic (2022–2023). Incidence rate ratios (IRRs) and 95% confidence intervals (CIs) were estimated using Poisson regression, whereas standardized incidence ratios (SIRs) and 95% credible intervals (CrIs) were derived from Bayesian spatial models across all administrative districts. Spatial clustering was evaluated using Moran’s I.
RESULTS
Overall, 884 eligible cases were identified: 575 in the pre-pandemic period, 172 in the pandemic period, and 137 in the post-pandemic period. SIDS incidence increased significantly from the mid-2010s onward, with an annual increase of approximately 8–9%. Compared with the pre-pandemic period, SIDS incidence was significantly higher during the pandemic period (IRR, 1.52; 95% CI, 1.28 to 1.80) and the post-pandemic period (IRR, 1.34; 95% CI, 1.11 to 1.61). Spatial clustering of SIDS incidence increased progressively across the 3 periods (Moran’s I: 0.359, 0.654, and 0.952, respectively; p<0.001). Pyeongtaek-si, Gyeonggi-do, was the only district with a significantly increased SIR in the pre-pandemic period (posterior mean, 1.602; 95% CrI, 1.007 to 2.424); no district met the significance thresholds in the subsequent periods.
CONCLUSIONS
SIDS incidence in Korea has increased significantly since the mid-2010s, with progressive spatial clustering toward southwestern regions across the 3 study periods. The emergence of high-incidence and low-incidence clusters underscores the need for targeted interventions.; Summary

Korean summary
본 연구는 2013년부터 2023년까지 전국 급성심장정지조사 데이터를 활용하여 국내 영아돌연사증후군 발생률이 2014~2016년 이후 연간 약 8~9%씩 유의하게 증가하고 있으며, 팬데믹 및 팬데믹 이후 시기에 발생률이 유의하게 높았음을 확인하였다. 베이즈 공간 분석 결과, 공간적 군집화가 팬데믹 전·중·후 시기에 걸쳐 점진적으로 강화되어(Moran's I: 0.359→0.654→0.952) 수도권은 저위험, 전라남도를 중심으로 한 남서부 지역은 고위험 군집으로 나타났다. 이러한 결과는 고위험 지역을 대상으로 한 안전 수면 교육, 모유 수유 증진, 지역 감시 체계 강화 등 맞춤형 예방 전략의 필요성을 뒷받침한다.

Key Message
Despite declining global sudden infant death syndrome (SIDS) incidence, recent nationwide trends and spatiotemporal patterns in Korea remain poorly characterized. This study showed significantly higher SIDS incidence during pandemic (2020–2021) and post-pandemic (2022–2023) periods than during pre-pandemic period (2013–2019). Bayesian spatial modeling revealed progressively stronger geographic clustering, with low-risk clusters in the capital region (Seoul and Gyeonggi-do) and high-risk clusters in southwestern regions (Jeollanam-do). These findings support geographically targeted interventions, including preventive education, breastfeeding promotion, and strengthened regional surveillance, to reduce the growing epidemiological disparities in SIDS risk among Korean infants.

Unraveling trends in schistosomiasis: deep learning insights into national control programs in China: Qing Su, Cici Xi Chen Bauer, Robert Bergquist, Zhiguo Cao, Fenghua Gao, Zhijie Zhang, Yi Hu; Epidemiol Health. 2024;46:e2024039. Published online March 13, 2024; DOI: https://doi.org/10.4178/epih.e2024039

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Abstract Summary PDF Supplementary Material: Abstract
OBJECTIVES
To achieve the ambitious goal of eliminating schistosome infections, the Chinese government has implemented diverse control strategies. This study explored the progress of the 2 most recent national schistosomiasis control programs in an endemic area along the Yangtze River in China.
METHODS
We obtained village-level parasitological data from cross-sectional surveys combined with environmental data in Anhui Province, China from 1997 to 2015. A convolutional neural network (CNN) based on a hierarchical integro-difference equation (IDE) framework (i.e., CNN-IDE) was used to model spatio-temporal variations in schistosomiasis. Two traditional models were also constructed for comparison with 2 evaluation indicators: the mean-squared prediction error (MSPE) and continuous ranked probability score (CRPS).
RESULTS
The CNN-IDE model was the optimal model, with the lowest overall average MSPE of 0.04 and the CRPS of 0.19. From 1997 to 2011, the prevalence exhibited a notable trend: it increased steadily until peaking at 1.6 per 1,000 in 2005, then gradually declined, stabilizing at a lower rate of approximately 0.6 per 1,000 in 2006, and approaching zero by 2011. During this period, noticeable geographic disparities in schistosomiasis prevalence were observed; high-risk areas were initially dispersed, followed by contraction. Predictions for the period 2012 to 2015 demonstrated a consistent and uniform decrease.
CONCLUSIONS
The proposed CNN-IDE model captured the intricate and evolving dynamics of schistosomiasis prevalence, offering a promising alternative for future risk modeling of the disease. The comprehensive strategy is expected to help diminish schistosomiasis infection, emphasizing the necessity to continue implementing this strategy.; Summary

Key Message
Our research found that CNN-IDE model effectively captured the complex dynamic process of schistosomiasis prevalence. The comprehensive strategy is expected to help diminish schistosomiasis infection.

Editorial

Spatiotemporal analyses of the epidemiological characteristics of diabetes mellitus: Sang Youl Rhee; Epidemiol Health. 2021;43:e2021102. Published online December 16, 2021; DOI: https://doi.org/10.4178/epih.e2021102

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1 Web of Science
1 Crossref

Abstract Summary PDF

Abstract

Research based on spatiotemporal analysis has been conducted to identify various factors that can affect an individual’s or community’s degree of health and disease. These spatiotemporal studies can effectively illustrate patterns in disease frequency, features, and temporal flow in different parts of a country. Furthermore, identifying these regional characteristics can aid in the development of disease prevention or intervention strategies.

Summary

Korean summary
1. 시공간 분석은 국가 혹은 지역의 질병 빈도, 특징 및 시간 흐름의 패턴을 효과적으로 설명할 수 있다. 2. 시공간 분석은 질병의 예방 또는 중재 전략 개발에 도움이 될 수 있다.

Key Message
1. Spatiotemporal analyses can effectively illustrate patterns in disease frequency, features, and temporal flow in different parts of a country. 2. Spatiotemporal analysis can aid in disease prevention or development of intervention strategies.

Citations

Citations to this article as recorded by

Epidemiological characteristics and spatiotemporal analysis of mumps at township level in Wuhan, China, 2005–2019
Ying Peng, Peng Wang, De-guang Kong, Wen-zhen Li, Dong-ming Wang, Li Cai, Sha Lu, Bin Yu, Bang-hua Chen, Pu-Lin Liu
Epidemiology and Infection.2023;[Epub] CrossRef

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