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细菌性痢疾是常见的由痢疾杆菌引起的肠道传染病。主要有志贺痢疾杆菌、福氏痢疾杆菌、鲍氏痢疾杆菌以及宋氏痢疾杆菌组成[1-2]。这种疾病主要通过粪-口途径传播,会通过接触受污染的水,食物进行传播,以及人与人之间的接触也是主要的传播方式[3-4]。典型的症状包括腹泻、发烧、粉刺和黏液血[5]。
迄今为止,细菌性痢疾仍然是发达国家和发展中国家共同面临的全球性公共卫生问题[6—7]。每年,全世界报告的病例数超过1.6亿,死亡人数超过110万[8]。在中国,过去的几十年中,该病每年造成约300人死亡,2015年其被认为五大传染病之一[9]。由于目前还不完全清楚该疾病的发病机制,随时可能引起大规模流行。因此,针对该疾病发病风险的时空异质性的研究,量化不同地区气象因子和社会经济因子对细菌性痢疾发病率的解释力,将为制定因地制宜的细菌性痢疾风险控制和疾病预防政策提供建议。
许多研究普遍认为,气象因素对细菌性痢疾的传播具有重要影响。例如,有研究表明,北京细菌性痢疾发病率最高是在6月至9月[10],济南细菌性痢疾的发病风险在夏季和秋季达到高峰[11],印度细菌性痢疾的高峰期大多发生在炎热的夏季[12]。在亚洲南部城市达卡,从9月到12月,细菌性痢疾发病率一直处于很高的状态[13]。同样,Liu 等利用主成分分析和分类回归树方法研究中国的细菌性痢疾发病率与天气变量之间的关系,结果表明当最低温度处于较高水平时,如果相对湿度或降水处于较高水平,则痢疾的发病率很高[14]。Liu等利用广义相加模型研究济南市气象因素和细菌性痢疾的关系,其结果表明温度超过阈值后,温度每升高5 °C,滞后0时细菌性痢疾病例数就会增加19% [15]。Li等利用广义相加模型在北京的研究表明温度和相对湿度对细菌性痢疾的影响存在阈值,其建议在制定预防和控制措施中能予以考虑[16]。Yan等利用带有解释变量的自回归综合移动平均模型量化北京地区气象变量和细菌性痢疾病例之间的关系,其发现北京地区滞后2个月的温度和滞后12个月的降雨与细菌性痢疾病例数呈正相关[17]。这些研究论证了单个气象因素对细菌性痢疾发病率的时空异质性有线性关系[18],但对气象因子对细菌性痢疾的非线性关系及其两两交互作用对细菌性痢疾影响的研究较少。
同时,细菌性痢疾的发病率也呈现明显的空间不均匀性。一些研究表明,它与社会经济因素密切相关,比如过于拥挤的生活或工作环境,较差的卫生条件以及不完备的社会基础设施都会在细菌性痢疾的传播方面起到一定的促进作用。例如,Kotloff等指出,中国的细菌性痢疾发病率高于许多发达国家,如美国、澳洲、英国和法国[1]。Chang等指出,该病在中国西北(包括西藏、宁夏和新疆)和中国北方(包括北京和天津)的发病率高于中国其他地区[19]。同样,Xu等指出,北京和天津这两个发达城市的细菌性痢疾风险在中国排名前两位[10]。
目前,很少有研究针对同一省份的不同区域,从时空视角分别量化细菌性痢疾的异质性,同时揭示在不同区域的主导因子及其不同因子两两结合之后对该疾病的影响。本研究是基于2012—2013年山东省细菌性痢疾数据展开的相关研究,空间尺度为区县,时间尺度为周,数据详细。目的是:1)利用BSTHM探讨山东省区县级细菌性痢疾风险的时空异质性;2)利用GeoDetector方法量化气象、社会经济因素及其交互影响对细菌性痢疾的解释力;3)识别细菌性痢疾发病率的热点(发病风险高的地区)、冷点区域(发病风险低的地区)及其机制的解释。
基于贝叶斯时空层次模型(BSTHM)和地理探测器法(GeoDetector)对细菌性痢疾的环境风险评估
Environmental risk assessment of bacillary dysentery based on BSTHM and GeoDetector
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摘要: 在全世界范围,细菌性痢疾是一个得到广泛关注的健康问题,具有显著的时空异质性,但大多数研究只是在某一区域单独实施了时间或空间层面的分析,其在不同地区的时空关联尚不清楚。考虑到山东省社会经济发展不平衡,东、中、西部差异明显,因此本研究首先利用贝叶斯时空层次模型(BSTHM)呈现山东省细菌性痢疾的时空异质性,其次利用地理探测器法(GeoDetector)分别从不同的区域(东、中、西部)来研究气象要素对此疾病的影响,且针对热点地区,量化社会经济要素及其两两的交互作用对其的解释力。结果表明,时间上,发病高峰出现在夏季。空间上,高风险地区主要分布在山东省东部和北部区县,且气象因子在不同地区对该病的影响也存在显著差异。山东省东部沿海地区最重要的两个主导因子是风速和日照时数,其解释力分别为0.28和0.22。山东省西部内陆地区前两个主导因子为平均温度和降水量,其解释力分别为0.47和0.32。山东省中部地区前两个主导因子为平均温度和风速,其解释力分别为0.66和0.48。本研究通过对山东省各个地区细菌性痢疾发病情况的时空异质性以及影响因素的分析发现,湿热的环境有利于细菌性痢疾的传播。另外,在热点地区,起主导作用的社会经济因子是第三产业比重,表明社会经济状况对细菌性痢疾的发病具有一定的影响作用,为本地区细菌性痢疾的控制和预防政策的制定提供理论依据,针对不同地区需要更具体的方案来预防、控制和分配医疗资源,以提高其疾病应对能力,减少该疾病造成的潜在损失。Abstract: Bacillary dysentery remains a worldwide public health problem, which has been found to have spatial–temporal heterogeneity, however most studies have only focused on the disease from either a time or space perspective, the spatial–temporal association in different regions between them has been still unclear. Considering the heterogeneity of socioeconomic conditions in Shandong, in this study, the Bayesian space–time hierarchy model (BSTHM) was used to identify the spatial-temporal heterogeneity of this disease in Shandong province, China. And then GeoDetector was used to quantify the determinant power of meteorological factors and their interactive effect among different regions in Shandong. Moreover, as for hot spots, the association between socioeconomic factors and bacillary dysentery was also qualified. The results indicated that, temporally, the incidence peaked in summer. Geographically, the hot spots were distributed discretely among three regions, among which the effect of meteorological factors on this disease exist significant discrepancy. The most important two dominant factors of eastern coastal region were wind speed and sun hour, with determinant powers of 0.28 and 0.22, respectively. The first two dominant factors of western inland region were average temperature and precipitation, with determinant powers of 0.47 and 0.32, respectively. The first two dominant factors of middle region were average temperature and wind speed, with determinant powers of 0.66 and 0.48, respectively, and the dominant socioeconomic factor was proportion of the tertiary industry. These findings suggest that in a hot and humid environment and socioeconomic conditions would boost the transmission of bacillary dysentery, which can be served as a suggestion and basis for the surveillance and will be helpful for this disease control and implementing disease-prevention policies.
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表 1 山东省中部(q1)、西部(q2)和东部地区(q3)各气象因子的解释力
Table 1. The q statistic values of all meteorological factors in central, western, and eastern regions, Shandong
气象要素 Meteorological factors q1 q2 q3 平均温度/°C 0.66** 0.47* 0.25 相对湿度/% 0.12 0.30 0.14 降水/mm 0.22 0.32 0.20* 风速/ (m·s-1) 0.48* 0.32* 0.28* 日照时数/h 0.17 0.29 0.22* Note: **显著水平 = 0.01; *显著水平 = 0.05. 表 2 热点区域地区各社会经济因子的q统计值
Table 2. The q statistic values of all socioeconomic factors in central, western, and eastern regions, Shandong
社会经济要素
Socioeconomic factors人口密度
Population density农村人口比例
Proportion of rural population第二产业比重
Proportion of Secondary industry第三产业比重
Proportion of tertiary industry人口密度 0.16 农村人口比例 0.66 0.42 第二产业比重 0.35 0.78 0.28 第三产业比重 0.63 0.62 0.61 0.48 -
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