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Open Access Research article

Comparisons of predictors for typhoid and paratyphoid fever in Kolkata, India

Dipika Sur1, Mohammad Ali2*, Lorenz von Seidlein23, Byomkesh Manna1, Jacqueline L Deen2, Camilo J Acosta2, John D Clemens2 and Sujit K Bhattacharya1

Author Affiliations

1 National Institute of Cholera and Enteric Diseases, Kolkata, India

2 International Vaccine Institute, Seoul, Korea

3 London School of Hygiene and Tropical Medicine, London, UK

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BMC Public Health 2007, 7:289  doi:10.1186/1471-2458-7-289

Published: 12 October 2007

Abstract

Background:

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?

Methods:

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.

Results:

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.

Conclusion:

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.