贵州省2004 — 2016年乙型病毒性肝炎发病时空分布特征

田娟; 罗兴静; 冯军; 杨燕妮; 汪俊华; 陶沁; 蒋琦; 张丽

doi:10.11847/zgggws1123360

贵州省2004 — 2016年乙型病毒性肝炎发病时空分布特征

Spatial-temporal distribution of hepatitis B incidences in Guizhou province, 2004 – 2016

摘要

摘要:
目的探索2004 — 2016年贵州省乙肝发病的时空分布特征及规律，为制定和调整乙肝防控策略提供科学依据。
方法运用ArcGIS 10.2、SatScan 9.6软件对2004 — 2010年、2011 — 2016年乙肝发病率进行空间自相关和时空扫描统计量分析。
结果贵州省2004 — 2016年乙肝报告病例来源于法定报告传染病报告系统，2004 — 2016年贵州省乙肝发病呈空间自相关性，2004 — 2010年、2011 — 2016年全局Moran′s I系数分别为0.148 0和0.443 9（P < 0.05），2004 — 2010年的高值 – 高值聚集区为遵义所辖的3个县（区），2011 — 2016年为遵义、黔南、黔东南所辖的8个县（区）。时空扫描结果显示，2004 — 2010年乙肝可能聚集区发生在2008 — 2009年，主要分布于黔北地区；2011 — 2016年乙肝可能聚集区主要发生在2011年，分布于黔北、黔南、黔东南地区。
结论贵州省乙肝发病总体呈下降趋势，有时空聚集性，其可能聚集区覆盖范围在扩大且呈现从黔北向黔南、黔东南等地区扩展的趋势。

Abstract:
Objective To explore spatio-temporal clustering distribution of hepatitis B (HB) incidences reported in Guizhou province during 2004 – 2016 and to provide evidences for developing and modifying HB prevention and control strategies.
Methods The data on reported HB incidences in 88 municipalities/counties/districts of Guizhou province during 2004 – 2016 were extracted from National Notifiable Disease Reporting System and the data on demographics and geography of the regions were collected simultaneously. ArcGIS 10.2 and SatScan 9.6 software were used in data analysis.
Results Spatial autocorrelation was observed in HB incidences in the province during the 13-year period, with the global Moran′s I values of 0.148 0 and 0.443 9 for the periods of 2004 – 2010 and 2011 – 2016 (P < 0.05). Local spatial autocorrelation analysis revealed high-high clustering of HB incidence at 3 counties in Zunyi region during 2004 – 2010 and at 8 counties mainly located in Zunyi, southern and southeastern region of the province. Space-time scan statistics demonstrated the likely clustering of HB incidence in northern region during 2008 – 2009 and the likely clustering of HB incidence in northern, southern and southeastern region of he province in 2011.
Conclusion The reported HB cidence rate was declining generally and the rate exhibited spatio-temporal clustering, with clustering areas in northern regions expanding to southern and southeastern regions, in Guizhou province from 2004 to 2016.

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