The effect of temperature on mortality in rural Bangladesha population-based time-series study

Methods A generalized linear Poisson regression model was used to regress a time-series of daily mortality for all-cause and selected causes against temperature, controlling for seasonal and interannual variations, day of week and public holidays. A total of 13 270 all-cause deaths excluding external causes for residents under demographic surveillance in Matlab, Bangladesh were available between January 1994 and December 2002. Results There was a marked increase in all-cause deaths and deaths due to cardiovascular, respiratory and perinatal causes at low temperatures over a lag of 013 days. Every 1C decrease in mean temperature was associated with a 3.2 (95 CI 0.95.5) increase in all-cause mortality. However, there was no clear heat effect on all-cause mortality for any of the lags examined. Conclusions This study found that daily mortality increased with low temperatures in the preceding weeks, while there was no association found between high temperatures and daily mortality in rural Bangladesh. Preventive measures during low temperatures should be considered especially for young infants.