Valson Joanna Sara, Soman Biju
PhD Scholar, Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India.
Additional Professor, Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India.
Indian J Public Health. 2017 Apr-Jun;61(2):74-80. doi: 10.4103/ijph.IJPH_26_16.
Dengue cases are increasing in Kerala since 2010. Information on clustering of cases across locations and time periods is vital for disease surveillance and timely control.
The objective is to study spatiotemporal clustering of dengue cases and their climatic and physioenvironmental correlates in Thiruvananthapuram district during 2010-2014.
Health department data on reported cases of dengue were obtained from January 2011 to June 2014. Cases were individually geocoded, using Google Earth. Moran's I index was estimated to analyze spatial autocorrelation using GeoDa software. Space-time clustering across 178 geo-divisions within the district was analyzed using SaTScan software. Correlation analysis was done for space-time clustering with climatic variables.
Definite spatial and temporal trends were found on analysis of a total of 8279 dengue cases. Significant spatial autocorrelation (Moran's I = 0.32, P< 0.01) and space-time clusters with very high log-likelihood ratios (P < 0.01) were found across geo-divisions. Pallichal panchayat was the most likely cluster in every year. The monthly incidence of dengue cases showed a significant positive association (P < 0.05) with a 2-month lag of mean minimum temperature (ρ = 0.39), 1-month lag of rainfall (ρ = 0.33), and 1-month lag of humidity (ρ = 0.38). Dengue occurrences showed an inverse association (P < 0.01) with mean maximum temperatures of the respective months (ρ= -0.48).
Spatial analysis using epidemiological tools reveals spatial and temporal clustering of dengue cases within the district and their association with climatic parameters. This information can be used in controlling outbreaks in the future. This work upholds scope and feasibility of geospatial research in public health in India.
自2010年以来，喀拉拉邦的登革热病例不断增加。了解病例在不同地点和时间段的聚集情况对于疾病监测和及时防控至关重要。
研究2010 - 2014年期间特里凡得琅地区登革热病例的时空聚集情况及其与气候和物理环境的相关性。
获取了2011年1月至2014年6月卫生部门报告的登革热病例数据。使用谷歌地球对病例进行了单独的地理编码。使用GeoDa软件估计莫兰指数（Moran's I）以分析空间自相关性。使用SaTScan软件分析了该地区178个地理分区的时空聚集情况。对时空聚集与气候变量进行了相关性分析。
对总共8279例登革热病例进行分析后发现了明确的空间和时间趋势。在各地理分区中发现了显著的空间自相关性（莫兰指数I = 0.32，P < 0.01）以及对数似然比非常高的时空聚集（P < 0.01）。帕利查尔村在每年都是最有可能的聚集区。登革热病例的月发病率与平均最低温度滞后2个月（ρ = 0.39）、降雨量滞后1个月（ρ = 0.33）以及湿度滞后1个月（ρ = 0.38）呈显著正相关（P < 0.05）。登革热发病情况与各月平均最高温度呈负相关（P < 0.01）（ρ = -0.48）。
使用流行病学工具进行的空间分析揭示了该地区登革热病例的时空聚集情况及其与气候参数的关联。这些信息可用于未来的疫情防控。这项工作证明了印度公共卫生领域地理空间研究的范围和可行性。
