Characterizing the effect of temperature fluctuation on the incidence of malaria: an epidemiological study in south-west China using the varying coefficient distributed lag non-linear model

Abstract

Background: Malaria transmission is strongly determined by the environmental temperature and the environment is rarely constant. Therefore, mosquitoes and parasites are not only exposed to the mean temperature, but also to daily temperature variation. Recently, both theoretical and laboratory work has shown, in addition to mean temperatures, daily fluctuations in temperature can affect essential mosquito and parasite traits that determine malaria transmission intensity. However, so far there is no epidemiological evidence at the population level to this problem.

Methods: Thirty counties in southwest China were selected, and corresponding weekly malaria cases and weekly meteorological variables were collected from 2004 to 2009. Particularly, maximum, mean and minimum temperatures were collected. The daily temperature fluctuation was measured by the diurnal temperature range (DTR), the difference between the maximum and minimum temperature. The distributed lag non-linear model (MDLNM) was used to study the correlation between weekly malaria incidences and weekly mean temperatures, and the correlation pattern was allowed to vary over different levels of daily temperature fluctuations.

Results: The overall non-linear patterns for mean temperatures are distinct across different levels of DTR. When under cooler temperature conditions, the larger mean temperature effect on malaria incidences is found in the groups of higher DTR, suggesting that large daily temperature fluctuations act to speed up the malaria incidence in cooler environmental conditions. In contrast, high daily fluctuations under warmer conditions will lead to slow down the mean temperature effect. Furthermore, in the group of highest DTR, 24-25°C or 21-23°C are detected as the optimal temperature for the malaria transmission.

Conclusion: The environment is rarely constant, and the result highlights the need to consider temperature fluctuations as well as mean temperatures, when trying to understand or predict malaria transmission. This work may be the first epidemiological study confirming that the effect of the mean temperature depends on temperature fluctuations, resulting in relevant evidence at the population level.

Figure 1

Box plot comparison of meteorological variables between four diurnal temperature range levels. The dark line in the middle of the boxes is the median value; the bottom and top of the boxes indicates the 25th and 75th percentile, respectively; whiskers represents 1.5 times the height of the box; and dots with numbers represent value of outlier cases.

Figure 2

The estimates of non-linear patterns between mean temperatures and malaria incidences, with three to ten weeks being the lag range of temperatures. The Y-axis represents the logarithm value of the relative risk ratio compared to the reference temperature 0°C. The solid line is the estimated non-linear curve, with dashed lines indicating its 95% confidence interval. On the one hand, A, B, C, D show the scenario for the fourth week lag; E, F, G, H show the scenario for the sixth week lag; and I, J, K, L show the scenario for the eighth week lag. On the other hand, A, E, I are at the first (lowest) DTR level; B, F, J are at the second DTR level; C, G, K are at the third DTR level; and D, H, L are at the fourth (highest) DTR level. The range of X-axis depends on the corresponding range of mean temperatures.