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Background <p>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.</p> Method <p>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.</p> Result <p>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).</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月2日

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  • Kittipong Sornlorm,
  • Sarayu Muntaphan

摘要

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.