There were 12 182 hospital visits (2% sample) due to all non-cholera diarrhoea from 1996 to 2002, in which 41% were under one year olds and 31% were aged between 1 and 14 years. Descriptive statistics for the number of patients and weather variables are displayed in Table 1. A wide variety of pathogens were found in the diarrhoea patients, most common were rotavirus and E. coli, each found in about a quarter of the cases. Total non-cholera diarrhoea had a bimodal seasonality of which timing of the first peak was before the monsoon (high rainfall period) and the second peak was at the end of the monsoon (Figure 1). Seasonality was varied between pathogen-specific diarrhoea, for example, rotavirus was common in winter in addition to a lower peak in the middle of the monsoon. E. coli had a single peak before the monsoon.
Relationship with rainfall
The relationships between the number of non-cholera diarrhoea and rainfall adjusted for season, between-year variations, holidays and temperature are shown in Figure 2. An increase in non-cholera diarrhoea can be seen with high rainfall at lag 0–8 weeks, while increase in the cases with both high and low rainfall is observed at lag 0–16 weeks. The maximum likelihood estimates of the threshold for high rainfall and low rainfall coincided at 52 mm (95% CI: 30–70) for average rainfall over lags 0–8 and 0–16 weeks by using double thresholds model. For a 10 mm increase above the threshold, the number of non-cholera diarrhoea cases increased by 5.1% (95% CI: 3.3–6.8). For a 10 mm decrease below 52 mm of average rainfall over lags 0–16 weeks, the number of cases increased by 3.9% (95% CI: 0.6–7.2).
The ‘high rainfall’ effect was observed at shorter lags with statistical significance at lags 1–5 weeks (Figure 3a). In contrast, the positive effect of low rainfall was observed throughout the longer lags around 10–16 weeks in addition to lag 0 (Figure 3b). Investigations of the modification of the rainfall effects by sex, age, socio-economic status and hygiene and sanitation practices found no differences approaching statistical significance (details are provided in Supplementary Table S1 on the web.).
Relationship with river level
The relationships between the number of non-cholera diarrhoea cases and the river level adjusted for season, between-year variations, holidays and temperature are shown in Figure 4. The pattern shows a positive slope with high river levels and there seems to be a threshold at around 4–5 m. The estimated threshold was 4.8 m (95% CI: 4.5–5.1). For a 1 m increase above the threshold, the number of cases increased by 53.9% (95% CI: 43.2–65.5).
Relationship with rainfall adjusted for river level
The positive slope of the number of cases with high and low rainfall observed at lags 0–8 and 0–16 weeks almost disappeared after adjustment for river level. In the double-thresholds model the estimate of the effect of high rainfall decreased on adjustment for river level to 0.6% (95% CI: –1.6, 2.7), using the same threshold with that for unadjusted for river level. The ‘low rainfall’ effect also decreased to 0.3% (95% CI: –2.9, 3.6).
Relationship with temperature
The relationships between the number of non-cholera diarrhoea cases and temperature adjusted for season, between-year variations, holidays and rainfall are shown in Figure 5. There is linear increase in the number of cases with high temperature. For a one degree increase in average temperature over lags 0–4 weeks, the number of cases increased by 5.6% (95% CI: 3.4–7.8) by using a model that assumes a log-linear increase in risk. The independent effects of temperature at different lags showed that the positive association was observed in the same week and decreased to null at lags 2 and afterwards (Figure 6).
There was evidence for differences in temperature effects between sub-groups examined; the risks for non-cholera diarrhoea were higher for those individuals at a lower educational attainment, those living in the household with non-concrete roof and unsanitary toilet users (
Table 2). There was weak evidence for higher risk of non-cholera diarrhoea for those individuals whose drinking water source was more than 5 m distant from the household. No modification of temperature effects were observed by sex or age.
Repeating analyses excluding rotavirus diarrhoea left patterns of the effects of high and low rainfall and of temperature largely unchanged. However, the high rainfall slope was slightly increased from 5.1% to 7.4% (95% CI: 5.0–9.8) and that of low rainfall was increased from 3.9% to 5.7% (95% CI: 0.5–11.1). The effect of temperature also slightly increased from 5.6% to 6.5% (95% CI: 3.5–9.5).
When in sensitivity analyses the degree of seasonal control was halved (3 harmonics) or doubled (12 harmonics), the estimates of the effect of high and low rainfall and of the temperature changed little.
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