Typhoid Fever and its association with environmental factors in the Dhaka Metropolitan Area of Bangladesh: a spatial and time-series approach

Abstract

Typhoid fever is a major cause of death worldwide with a major part of the disease burden in developing regions such as the Indian sub-continent. Bangladesh is part of this highly endemic region, yet little is known about the spatial and temporal distribution of the disease at a regional scale. This research used a Geographic Information System to explore, spatially and temporally, the prevalence of typhoid in Dhaka Metropolitan Area (DMA) of Bangladesh over the period 2005-9. This paper provides the first study of the spatio-temporal epidemiology of typhoid for this region. The aims of the study were: (i) to analyse the epidemiology of cases from 2005 to 2009; (ii) to identify spatial patterns of infection based on two spatial hypotheses; and (iii) to determine the hydro-climatological factors associated with typhoid prevalence. Case occurrences data were collected from 11 major hospitals in DMA, geocoded to census tract level, and used in a spatio-temporal analysis with a range of demographic, environmental and meteorological variables. Analyses revealed distinct seasonality as well as age and gender differences, with males and very young children being disproportionately infected. The male-female ratio of typhoid cases was found to be 1.36, and the median age of the cases was 14 years. Typhoid incidence was higher in male population than female (χ(2) = 5.88, p<0.05). The age-specific incidence rate was highest for the 0-4 years age group (277 cases), followed by the 60+ years age group (51 cases), then there were 45 cases for 15-17 years, 37 cases for 18-34 years, 34 cases for 35-39 years and 11 cases for 10-14 years per 100,000 people. Monsoon months had the highest disease occurrences (44.62%) followed by the pre-monsoon (30.54%) and post-monsoon (24.85%) season. The Student's t test revealed that there is no significant difference on the occurrence of typhoid between urban and rural environments (p>0.05). A statistically significant inverse association was found between typhoid incidence and distance to major waterbodies. Spatial pattern analysis showed that there was a significant clustering of typhoid distribution in the study area. Moran's I was highest (0.879; p<0.01) in 2008 and lowest (0.075; p<0.05) in 2009. Incidence rates were found to form three large, multi-centred, spatial clusters with no significant difference between urban and rural rates. Temporally, typhoid incidence was seen to increase with temperature, rainfall and river level at time lags ranging from three to five weeks. For example, for a 0.1 metre rise in river levels, the number of typhoid cases increased by 4.6% (95% CI: 2.4-2.8) above the threshold of 4.0 metres (95% CI: 2.4-4.3). On the other hand, with a 1 °C rise in temperature, the number of typhoid cases could increase by 14.2% (95% CI: 4.4-25.0).

Figure 1. The Dhaka Metropolitan Area (DMA).

Figure 2. Annual typhoid incidence.

Figure 3. Seasonal variations in Typhoid cases.

The number of Typhoid cases per week, with reference to river level, temperature and rainfall data in Dhaka Metropolitan Area, 2005–2009.

Figure 4. Bar graph showing percentage of seasonal typhoid cases for 2005–2009.

Figure 5. Spatial regression between typhoid incidence (per 100,000 people) and distance to water bodies.

A) Shows spatial distribution of the t-value, B) shows the parameter estimates. See Figure S1 for higher resolution version.

Figure 6. Spatial variation in the occurrence of typhoid infection.

This shows the Raw annual incidence rate(A) and EB-smoothed incidence rates (B) from 2005 to 2009 in census districts in DMA: See Figure S2 for high resolution version.

Figure 7. Temporal relationship between the number of typhoid cases and environmental variables.

(A) Average river levels over lags of 0–5 weeks, (B) Rainfall over lags of 0–3 weeks and (C) Temperature over lags of 0–4 weeks (shown as a 3 df natural cubic spline) adjusted for seasonal variation, inter-annual variations and public holidays. RR represents the relative risk of typhoid (scaled against the mean weekly number of cases). The centre line in each graph shows the estimated spline curve, and the upper and lower lines represent the 95% confidence limits.

Figure 8. Spatial clusters (hotspots) of typhoid in DMA during 2005–2009.

See Figure S3 for high resolution version.