泰国慢性肾病与社会经济因素之间的空间关联。

IF 1 4区 医学 Q4 HEALTH CARE SCIENCES & SERVICES Geospatial Health Pub Date : 2024-01-30 DOI:10.4081/gh.2024.1246
Juree Sansuk, Kittipong Sornlorm
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引用次数: 0

摘要

慢性肾脏病(CKD)是一种持续性、渐进性疾病,其特点是肾功能逐渐衰退,导致一系列健康问题。本研究使用了泰国公共卫生部的最新数据,并应用空间回归和地方空间关联指标(LISA)研究了夜间光线、互联网接入和当地每人口卫生人员数量的空间关联。泰国各府慢性阻塞性肺病的单变量莫兰 I 散点图显示出显著的正空间自相关性,其值为 0.393。高-高(HH)CKD 群体主要分布在北部,低-低(LL)CKD 群体主要分布在南部。通过 LISA 分析,发现 1 个 HH 和 1 个 LL 与互联网接入有关,15 个 HH 和 5 个 LL 群组与夜间光线有关,8 个 HH 和 5 个 LL 群组与该地区的卫生人员数量有关。空间回归揭示了泰国各种因素与慢性肾脏病之间重要而有意义的联系。在空间误差模型(SEM)和空间滞后模型(SLM)中,夜间光线与慢性肾脏病呈正相关,系数分别为 3.356 和 2.999。与此相反,互联网接入与慢性阻塞性肺病呈负相关,其 SEM 系数为-0.035,SLM 系数为-0.039。同样,医务人员/人口比例也与 SEM 和 SLM 呈负相关,系数分别为-0.033 和-0.068。SEM 是最合适的空间回归模型,R2 为 54.8%。此外,阿凯克信息准则(AIC)检验表明,该模型的性能更佳,SEM 和 SLM 的检验结果分别为 697.148 和 698.198。这些发现强调了导致泰国慢性肾脏病患病率的各种因素之间复杂的相互联系,并表明社会经济和医疗服务因素是重要的致病因素。要解决这一问题,就必须集中力量提高医疗服务的可及性,尤其是在经济快速增长的城市地区。
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Spatial associations between chronic kidney disease and socio-economic factors in Thailand.

Chronic kidney disease (CKD) is a persistent, progressive condition characterized by gradual decline of kidney functions leading to a range of health issues. This research used recent data from the Ministry of Public Health in Thailand and applied spatial regression and local indicators of spatial association (LISA) to examine the spatial associations with night-time light, Internet access and the local number of health personnel per population. Univariate Moran's I scatter plot for CKD in Thailand's provinces revealed a significant positive spatial autocorrelation with a value of 0.393. High-High (HH) CKD clusters were found to be predominantly located in the North, with Low-Low (LL) ones in the South. The LISA analysis identified one HH and one LL with regard to Internet access, 15 HH and five LL clusters related to night-time light and eight HH and five LL clusters associated with the number of health personnel in the area. Spatial regression unveiled significant and meaningful connections between various factors and CKD in Thailand. Night-time light displayed a positive association with CKD in both the spatial error model (SEM) and the spatial lag model (SLM), with coefficients of 3.356 and 2.999, respectively. Conversely, Internet access exhibited corresponding negative CKD associations with a SEM coefficient of - 0.035 and a SLM one of -0.039. Similarly, the health staff/population ratio also demonstrated negative associations with SEM and SLM, with coefficients of -0.033 and -0.068, respectively. SEM emerged as the most suitable spatial regression model with 54.8% according to R2. Also, the Akaike information criterion (AIC) test indicated a better performance for this model, resulting in 697.148 and 698.198 for SEM and SLM, respectively. These findings emphasize the complex interconnection between factors contributing to the prevalence of CKD in Thailand and suggest that socioeconomic and health service factors are significant contributing factors. Addressing this issue will necessitate concentrated efforts to enhance access to health services, especially in urban areas experiencing rapid economic growth.

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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.
期刊最新文献
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