Feng Xiyu, Sarma Haribondhu, Bagheri Nasser, Tsheten Tsheten, Seubsman Sam-Ang, Sleigh Adrian, Kelly Matthew
National Centre of Epidemiology and Population Health, the Australian National University, Canberra, Australia.
National Centre of Epidemiology and Population Health, Building 62, Mills Road, Acton 2601, Canberra, Australia.
Arch Public Health. 2025 May 7;83(1):120. doi: 10.1186/s13690-025-01605-4.
This study used Thai Cohort Study (TCS) data to investigate the spatial and sociodemographic determinants of multimorbidity (two or more chronic conditions coexistence on one person) prevalence in Thailand in 2013.
Crude and age-adjusted prevalence were calculated for each province. Hotspot analysis was conducted to identify regions with statistically significant hotspots and cold spots, including areas without significant clustering. Then, ordinal logistic regression was used to identify sociodemographic background variables that predict hotpots.
The highest age-adjusted provincial level prevalence of multimorbidity was in Sing Buri (18.26%). Sak Lek District in Phichit Province also had the highest age-adjusted district level prevalence of multimorbidity at 37.13%. The cold spots region in crude and age-adjusted prevalence of multimorbidity were clustered in Southern Thailand. Forty-eight districts were identified as hotspots in both crude and age-adjusted multimorbidity prevalence, 19 of which are in Bangkok (the capital). Population density (person/km, odd ratio, provincial level: OR:1.00, 95% CI: 1.00-1.01; district level: OR: 1.01, 95% CI: 1.00-1.01), Aging index (provincial level: OR:1.03, 95% CI: 1.01-1.04; district level: OR: 1.01, 95% CI: 1.00-1.01), and average educational years (provincial level: OR:1.92, 95% CI: 1.07-3.48; district level: OR: 1.27, 95% CI: 1.02-2.26) were greater in hot spots areas.
This study shows that the prevalence of multimorbidity in Thailand is positively correlated with the degree of development of the region. Spatial cluster analysis provides new evidence for policymakers to design tailored interventions to target multimorbidity and allocate health resources to areas of unmet need.
本研究使用泰国队列研究（TCS）数据，调查2013年泰国多重疾病（一人同时存在两种或更多慢性疾病）患病率的空间和社会人口学决定因素。
计算每个省份的粗患病率和年龄调整患病率。进行热点分析以识别具有统计学显著热点和冷点的区域，包括无显著聚集的区域。然后，使用有序逻辑回归来识别预测热点的社会人口学背景变量。
年龄调整后多重疾病省级患病率最高的是信武里府（18.26%）。彭世洛府的萨克莱区年龄调整后地区级多重疾病患病率也最高，为37.13%。多重疾病粗患病率和年龄调整患病率的冷点区域集中在泰国南部。48个区被确定为粗患病率和年龄调整患病率的热点，其中19个在首都曼谷。热点地区的人口密度（人/平方公里，比值比，省级：OR：1.00，95%CI：1.00 - 1.01；区级：OR：1.01，95%CI：1.00 - 1.01）、老龄化指数（省级：OR：1.03，95%CI：1.01 - 1.04；区级：OR：1.01，95%CI：1.00 - 1.01）和平均受教育年限（省级：OR：1.92，95%CI：1.07 - 3.48；区级：OR：1.27，95%CI：1.02 - 2.26）更高。
本研究表明，泰国多重疾病患病率与地区发展程度呈正相关。空间聚类分析为政策制定者设计针对多重疾病的定制干预措施以及将卫生资源分配到需求未得到满足的地区提供了新证据。
