Email updates

Keep up to date with the latest news and content from IJHG and BioMed Central.

Open Access Highly Accessed Research

Modelling typhoid risk in Dhaka Metropolitan Area of Bangladesh: the role of socio-economic and environmental factors

Robert J Corner1*, Ashraf M Dewan12 and Masahiro Hashizume3

Author Affiliations

1 Department of Spatial Sciences, Curtin University, GPO Box U1987, Perth, Western Australia, 6845, Australia

2 Department of Geography & Environment, University of Dhaka, Dhaka, 1000, Bangladesh

3 Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan

For all author emails, please log on.

International Journal of Health Geographics 2013, 12:13  doi:10.1186/1476-072X-12-13

Published: 16 March 2013

Abstract

Background

Developing countries in South Asia, such as Bangladesh, bear a disproportionate burden of diarrhoeal diseases such as Cholera, Typhoid and Paratyphoid. These seem to be aggravated by a number of social and environmental factors such as lack of access to safe drinking water, overcrowdedness and poor hygiene brought about by poverty. Some socioeconomic data can be obtained from census data whilst others are more difficult to elucidate. This study considers a range of both census data and spatial data from other sources, including remote sensing, as potential predictors of typhoid risk. Typhoid data are aggregated from hospital admission records for the period from 2005 to 2009. The spatial and statistical structures of the data are analysed and Principal Axis Factoring is used to reduce the degree of co-linearity in the data. The resulting factors are combined into a Quality of Life index, which in turn is used in a regression model of typhoid occurrence and risk.

Results

The three Principal Factors used together explain 87% of the variance in the initial candidate predictors, which eminently qualifies them for use as a set of uncorrelated explanatory variables in a linear regression model. Initial regression result using Ordinary Least Squares (OLS) were disappointing, this was explainable by analysis of the spatial autocorrelation inherent in the Principal factors. The use of Geographically Weighted Regression caused a considerable increase in the predictive power of regressions based on these factors. The best prediction, determined by analysis of the Akaike Information Criterion (AIC) was found when the three factors were combined into a quality of life index, using a method previously published by others, and had a coefficient of determination of 73%.

Conclusions

The typhoid occurrence/risk prediction equation was used to develop the first risk map showing areas of Dhaka Metropolitan Area whose inhabitants are at greater or lesser risk of typhoid infection. This, coupled with seasonal information on typhoid incidence also reported in this paper, has the potential to advise public health professionals on developing prevention strategies such as targeted vaccination.