Wang Jing, Wang Xiaoya, Li Hairong, Yang Linsheng, Li Yingchun, Kong Chang
Key Laboratory for Geographical Process Analysis & Simulation, Research Institute of Sustainable Development, Central China Normal University, Wuhan, 430079, China.
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
BMC Public Health. 2021 Feb 19;21(1):387. doi: 10.1186/s12889-021-10407-6.
Kashin-Beck disease (KBD) is one of the major endemic diseases in China, which severely impacts the physical health and life quality of people. A better understanding of the spatial distribution of the health loss from KBD and its influencing factors will help to identify areas and populations at high risk so as to plan for targeted interventions.
The data of patients with KBD at village-level were collected to estimate and analyze the spatial pattern of health loss from KBD in Bin County, Shaanxi Province. The years lived with disability (YLDs) index was applied as a measure of health loss from KBD. Spatial autocorrelation methodologies, including Global Moran's I and Local Moran's I, were used to describe and map spatial clusters of the health loss. In addition, basic individual information and environmental samples were collected to explore natural and social determinants of the health loss from KBD.
The estimation of YLDs showed that patients with KBD of grade II and patients over 50 years old contributed most to the health loss of KBD in Bin County. No significant difference was observed between two genders. The spatial patterns of YLDs and YLD rate of KBD were clustered significantly at both global and local scales. Villages in the southwestern and eastern regions revealed higher health loss, while those in the northern regions exhibited lower health loss. This clustering was found to be significantly related to organically bound Se in soil and poverty rate of KBD patients.
Our results suggest that future treatment and prevention of KBD should focus on endemic areas with high organically bound Se in soil and poor economic conditions. The findings can also provide important information for further exploration of the etiology of KBD.
大骨节病（KBD）是中国主要的地方病之一，严重影响人们的身体健康和生活质量。更好地了解大骨节病所致健康损失的空间分布及其影响因素，将有助于识别高风险地区和人群，以便制定有针对性的干预措施。
收集陕西省彬县村级大骨节病患者的数据，以估计和分析大骨节病所致健康损失的空间格局。采用失能调整生命年（YLDs）指数作为衡量大骨节病所致健康损失的指标。运用全局莫兰指数（Global Moran's I）和局部莫兰指数（Local Moran's I）等空间自相关方法来描述和绘制健康损失的空间聚集情况。此外，收集基本的个人信息和环境样本，以探索大骨节病所致健康损失的自然和社会决定因素。
YLDs估计显示，彬县二级大骨节病患者和50岁以上患者对大骨节病的健康损失贡献最大。两性之间未观察到显著差异。大骨节病的YLDs和YLD率在全局和局部尺度上均呈现出显著的聚集性。西南部和东部地区的村庄健康损失较高，而北部地区的村庄健康损失较低。发现这种聚集与土壤中有机结合态硒以及大骨节病患者的贫困率显著相关。
我们的结果表明，未来大骨节病的治疗和预防应聚焦于土壤中有机结合态硒含量高且经济条件差的 endemic areas。这些发现也可为进一步探究大骨节病的病因提供重要信息。
