Figures
- Association between climate variability and hospital visits for non-cholera diarrhoea in Bangladesh: effects and vulnerable groups

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Figure 1 Seasonal variations in the number of all non-cholera diarrhoea cases per week and meteorological and river-level data in Dhaka, 1996–2002. The river-level data are missing from April 1997 to March 1998

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Figure 2 Relationship between the number of non-cholera diarrhoea cases and average rainfall over lags of (a) 0–8 and (b) 0–16 weeks (shown as a 3 df natural cubic spline) adjusted for seasonal variation, between-year variations, public holidays and temperature. RR represents the relative risk of non-cholera diarrhoea (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

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Figure 3 Percentage change (and 95% CIs) in the number of non-cholera diarrhoea cases for ‘high’ (a; per 10 mm increase above threshold) and ‘low’ rainfall (b; per 10 mm decrease below threshold) at each lag (unconstrained distributed lag models)

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Figure 4 Relationship between the number of non-cholera diarrhoea and the average river level over a lag of 0–4 weeks (shown as a 3 df natural cubic spline), adjusted for seasonal variation, between-year variation, public holiday and temperature. RR is the relative risk of non-cholera diarrhoea (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% CI

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Figure 5 Relationship between the number of non-cholera diarrhoea and the average temperature over a lag of 0–4 weeks (shown as a 3 df natural cubic spline) adjusted for seasonal variation, between-year variation, public holidays and rainfall. RR represents the relative risk of non-cholera diarrhoea (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% CI

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Figure 6 Percentage change (and 95% CIs) in the number of non-cholera diarrhoea cases for a 1°C increase in temperature at each lag (unconstrained distributed lag models)