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. 2010 Dec 16;5(12):e14341.
doi: 10.1371/journal.pone.0014341.

The role of climate variability in the spread of malaria in Bangladeshi highlands

Affiliations

The role of climate variability in the spread of malaria in Bangladeshi highlands

Ubydul Haque et al. PLoS One. .

Abstract

Background: Malaria is a major public health problem in Bangladesh, frequently occurring as epidemics since the 1990s. Many factors affect increases in malaria cases, including changes in land use, drug resistance, malaria control programs, socioeconomic issues, and climatic factors. No study has examined the relationship between malaria epidemics and climatic factors in Bangladesh. Here, we investigate the relationship between climatic parameters [rainfall, temperature, humidity, sea surface temperature (SST), El Niño-Southern Oscillation (ENSO), the normalized difference vegetation index (NDVI)], and malaria cases over the last 20 years in the malaria endemic district of Chittagong Hill Tracts (CHT).

Methods and principal findings: Monthly malaria case data from January 1989 to December 2008, monthly rainfall, temperature, humidity sea surface temperature in the Bay of Bengal and ENSO index at the Niño Region 3 (NIÑO3) were used. A generalized linear negative binomial regression model was developed using the number of monthly malaria cases and each of the climatic parameters. After adjusting for potential mutual confounding between climatic factors there was no evidence for any association between the number of malaria cases and temperature, rainfall and humidity. Only a low NDVI was associated with an increase in the number of malaria cases. There was no evidence of an association between malaria cases and SST in the Bay of Bengal and NIÑO3.

Conclusion and significance: It seems counterintuitive that a low NDVI, an indicator of low vegetation greenness, is associated with increases in malaria cases, since the primary vectors in Bangladesh, such as An. dirus, are associated with forests. This relationship can be explained by the drying up of rivers and streams creating suitable breeding sites for the vector fauna. Bangladesh has very high vector species diversity and vectors suited to these habitats may be responsible for the observed results.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Spatial distribution of malaria prevalence in Bangladesh.
Figure 2
Figure 2. Time series of the number of all malaria cases per month and meteorological data in Rangamati, 1989–2008.
Figure 3
Figure 3. Relationship between the number of malaria cases per month and (a) average mean temperature, (b) total rainfall, (c) average relative humidity and (d) Normalized difference vegetation index (NDVI) over lags of 0–3 months (shown as a 3 d.f. natural cubic spline) adjusted for seasonal variation and between-year variations.
RR represents the relative risk of malaria (scaled against the mean monthly 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 4
Figure 4. Relationship between the number of malaria cases per month and (a) average mean temperature, (b) total rainfall, (c) average relative humidity and (d) Normalized difference vegetation index (NDVI) over lags of 0–3 months (shown as a 3 d.f. natural cubic spline) adjusted for potential mutual confounding between these 4 variables, seasonal variation and between-year variations.
RR represents the relative risk of malaria (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 5
Figure 5. Relationship between the number of malaria cases per month and (a) average sea surface temperature (SST) of the Bay of Bengal over lags of 0–3 months, (b) average NINO3 over lags of 0–3 months, (c) 4–7 months and (d) 8–11 months (shown as a 3 d.f. natural cubic spline) adjusted for potential mutual confounding between the lags of NINO3, seasonal variation and between-year variations.
RR represents the relative risk of malaria (scaled against the mean monthly 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.

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References

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