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泰国新冠疫苗接种覆盖率的解释性空间建模:公平分配的政策影响

Explanatory spatial modeling of COVID-19 vaccine coverage in Thailand: policy implications for equitable distribution.

作者信息

Sornlorm Kittipong, Muntaphan Sarayu

机构信息

Faculty of Public Health, Khon Kaen University, Khon Kaen Province, Thailand.

出版信息

BMC Public Health. 2025 Sep 2;25(1):3028. doi: 10.1186/s12889-025-24217-7.

Abstract

BACKGROUND

The COVID-19 pandemic posed significant challenges globally, with vaccine distribution being a critical factor for recovery. Despite achieving vaccination targets, Thailand faced disparities in vaccine coverage across regions. This study aims to explore spatial clustering patterns and examine socioeconomic, demographic, and health service-related determinants of COVID-19 vaccine coverage across Thailand. The findings are intended to inform strategies for equitable vaccine distribution.

METHOD

A cross-sectional ecological study was conducted using secondary data from all 76 provinces in Thailand. Spatial analysis techniques were employed to assess spatial autocorrelation using Moran's I and Local Indicators of Spatial Association (LISA). Additionally, spatial regression models were developed to explain vaccine coverage. Three models were tested and compared: Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM). Model performance was evaluated based on R², Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and residual diagnostics.

RESULT

Moran's I and LISA indicated significant positive spatial autocorrelation in vaccine coverage for both 2021 (0.427) and 2022 (0.467), with high-high clusters primarily concentrated in the Eastern region. The Spatial Error Model demonstrated the best performance and was most suitable for explaining the spatial variation in COVID-19 vaccine coverage. In 2021, vaccine coverage was associated with population density, nighttime light (NTL), public transport, income, health workers, COVID-19 cases and deaths (R² = 0.699). In 2022, key factors were the elderly population, nighttime light, internet access, and health workers (R² = 0.610).

CONCLUSION

We conclude that in 2021, COVID-19 vaccine availability saw limited vaccine quantities distributed unevenly. Areas with substantial economic growth, high COVID-19 incidence rates, and a significant presence of health workers tended to have higher COVID-19 vaccine coverage. In 2022, vaccine availability improved with extensive distribution and community-level services targeting vulnerable groups, supported by online decision-making tools and a reservation system.

摘要

背景

新冠疫情给全球带来了重大挑战,疫苗分配是恢复的关键因素。尽管泰国实现了疫苗接种目标,但各地区的疫苗接种覆盖率仍存在差异。本研究旨在探索空间聚集模式,并考察泰国各地新冠疫苗接种覆盖率的社会经济、人口和卫生服务相关决定因素。研究结果旨在为公平疫苗分配策略提供参考。

方法

利用泰国76个省份的二手数据进行了一项横断面生态研究。采用空间分析技术,使用莫兰指数(Moran's I)和空间关联局部指标(LISA)评估空间自相关性。此外,还建立了空间回归模型来解释疫苗接种覆盖率。测试并比较了三个模型:普通最小二乘法(OLS)、空间滞后模型(SLM)和空间误差模型(SEM)。基于R²、赤池信息准则(AIC)、贝叶斯信息准则(BIC)、对数似然和残差诊断对模型性能进行了评估。

结果

莫兰指数(Moran's I)和LISA表明,2021年(0.427)和2022年(0.467)疫苗接种覆盖率均存在显著的正空间自相关性,高高聚类主要集中在东部地区。空间误差模型表现最佳,最适合解释新冠疫苗接种覆盖率的空间差异。2021年,疫苗接种覆盖率与人口密度、夜间灯光(NTL)、公共交通、收入、卫生工作者、新冠病例和死亡人数相关(R² = 0.699)。2022年,关键因素是老年人口、夜间灯光、互联网接入和卫生工作者(R² = 0.610)。

结论

我们得出结论,2021年,新冠疫苗供应有限,疫苗分配不均衡。经济增长显著、新冠发病率高且卫生工作者大量存在的地区,新冠疫苗接种覆盖率往往较高。2022年,随着广泛的疫苗分发以及针对弱势群体的社区层面服务的开展,疫苗供应情况有所改善,这得到了在线决策工具和预约系统的支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9a1/12406389/7a011b932c84/12889_2025_24217_Fig1_HTML.jpg

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