Spatial autocorrelation pattern of COVID-19 vaccine coverage in Thailand 2021 and 2022.

IF 1 4区 医学 Q4 HEALTH CARE SCIENCES & SERVICES
Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-05-13 DOI:10.4081/gh.2025.1368
Sarayu Muntaphan, Kittipong Sornlorm
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引用次数: 0

Abstract

During the COVID-19 pandemic in 2021-2022, vaccination against this infection was crucial for Thailand's recovery. This research aimed to identify spatial patterns of association between the distribution and spread of the COVID-19 pandemic on the one hand and vaccine coverage, health service and socio-economic factors on the other. Univariate analysis using Getis-Ord GI* found strong clustering of the vaccine coverage, mostly in Eastern, Central, and Southern regions (Andaman coast), while bivariate analysis using Moran's I revealed significant positive spatial correlation vaccine coverage with the presence of COVID-19 patients (2021 = 0.273; 2022 = 0.273), Night Time Light (NTL) (2021 = 0.159; 2022 = 0.118) and medical personnel (2021 = 0.174; 2022 = 0.123). In addition, Local Indicators of Spatial Association (LISA) analysis found High-High clusters predominantly in the Eastern and Central regions. Areas with high economic growth (as reflected by high NTL) had greater COVID-19 vaccine coverage, likely due to better access to information and efficient transport systems in areas with stronger financial resources than elsewhere. These factors facilitated access to healthcare ensured presence of adequate personnel and enabled rapid distribution of the vaccine. Additionally, high rates of COVID-19 infections increased public awareness of infection risk leading to better vaccination uptake. Policymakers should prioritise vaccine distribution in high-risk and underserved areas to ensure equitable access. Additionally, increasing health workforce capacity is essential to improving service efficiency and readiness for future outbreaks.

2021年和2022年泰国COVID-19疫苗覆盖率的空间自相关模式
在2021-2022年COVID-19大流行期间,针对这种感染的疫苗接种对泰国的复苏至关重要。本研究旨在确定COVID-19大流行的分布和传播与疫苗覆盖率、卫生服务和社会经济因素之间的空间关联模式。使用Getis-Ord GI*进行的单因素分析发现,疫苗覆盖率具有很强的聚类性,主要在东部、中部和南部地区(安达曼海岸),而使用Moran's I进行的双因素分析显示,疫苗覆盖率与COVID-19患者的存在存在显著的正空间相关性(2021 = 0.273;2022 = 0.273), Night Time Light (NTL) (2021 = 0.159;2022年= 0.118)和医务人员(2021年= 0.174;2022 = 0.123)。此外,空间关联局部指标(LISA)分析发现,高-高集群主要分布在东部和中部地区。经济高增长地区(如高NTL所反映)的COVID-19疫苗覆盖率更高,这可能是由于与其他地区相比,财政资源更雄厚的地区更容易获得信息和高效的运输系统。这些因素有助于获得保健服务,确保有足够的人员在场,并使疫苗能够迅速分发。此外,COVID-19的高感染率提高了公众对感染风险的认识,从而提高了疫苗接种率。决策者应优先在高风险和服务不足地区分发疫苗,以确保公平获取。此外,提高卫生人力能力对于提高服务效率和为未来疫情做好准备至关重要。
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来源期刊
Geospatial Health
Geospatial Health 医学-公共卫生、环境卫生与职业卫生
CiteScore
2.40
自引率
11.80%
发文量
48
审稿时长
12 months
期刊介绍: The focus of the journal is on all aspects of the application of geographical information systems, remote sensing, global positioning systems, spatial statistics and other geospatial tools in human and veterinary health. The journal publishes two issues per year.
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