Sur Dipika, Ali Mohammad, von Seidlein Lorenz, Manna Byomkesh, Deen Jacqueline L, Acosta Camilo J, Clemens John D, Bhattacharya Sujit K
1National Institute of Cholera and Enteric Diseases, Kolkata, India.
BMC Public Health. 2007 Oct 12;7:289. doi: 10.1186/1471-2458-7-289.
Exposure of the individual to contaminated food or water correlates closely with the risk for enteric fever. Since public health interventions such as water improvement or vaccination campaigns are implemented for groups of individuals we were interested whether risk factors not only for the individual but for households, neighbourhoods and larger areas can be recognised?
We conducted a large enteric fever surveillance study and analyzed factors which correlate with enteric fever on an individual level and factors associated with high and low risk areas with enteric fever incidence. Individual level data were linked to a population based geographic information systems. Individual and household level variables were fitted in Generalized Estimating Equations (GEE) with the logit link function to take into account the likelihood that household factors correlated within household members.
Over a 12-month period 80 typhoid fever cases and 47 paratyphoid fever cases were detected among 56,946 residents in two bustees (slums) of Kolkata, India. The incidence of paratyphoid fever was lower (0.8/1000/year), and the mean age of paratyphoid patients was older (17.1 years) than for typhoid fever (incidence 1.4/1000/year, mean age 14.7 years). Residents in areas with a high risk for typhoid fever had lower literacy rates and economic status, bigger household size, and resided closer to waterbodies and study treatment centers than residents in low risk areas.
There was a close correlation between the characteristics detected based on individual cases and characteristics associated with high incidence areas. Because the comparison of risk factors of populations living in high versus low risk areas is statistically very powerful this methodology holds promise to detect risk factors associated with diseases using geographic information systems.
个体接触受污染的食物或水与伤寒热风险密切相关。由于诸如改善水质或开展疫苗接种运动等公共卫生干预措施是针对个体群体实施的,我们想知道是否不仅能识别个体的风险因素,还能识别家庭、社区及更大区域的风险因素?
我们开展了一项大型伤寒热监测研究,分析了个体层面与伤寒热相关的因素以及与伤寒热发病率高低风险区域相关的因素。个体层面的数据与基于人群的地理信息系统相链接。个体和家庭层面的变量采用广义估计方程(GEE)并结合logit链接函数进行拟合,以考虑家庭成员间家庭因素相关的可能性。
在印度加尔各答两个棚户区(贫民窟)的56946名居民中,12个月内检测到80例伤寒热病例和47例副伤寒热病例。副伤寒热的发病率较低(0.8/1000/年),副伤寒热患者的平均年龄(17.1岁)高于伤寒热患者(发病率1.4/1000/年,平均年龄14.7岁)。与低风险区域的居民相比,伤寒热高风险区域的居民识字率和经济状况较低,家庭规模较大,居住地点离水体和研究治疗中心更近。
基于个体病例检测到的特征与高发病率区域相关的特征之间存在密切关联。由于对高风险和低风险区域人群的风险因素进行比较在统计学上非常有效,这种方法有望利用地理信息系统检测与疾病相关的风险因素。