The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004-2018

Abstract

Evidence on the long-term influence of climatic variables on pertussis is limited. This study aims to explore the long-term quantitative relationship between weather variability and pertussis. Data on the monthly number of pertussis cases and weather parameters in Chongqing in the period of 2004-2018 were collected. Then, we used a negative binomial multivariable regression model and cointegration testing to examine the association of variations in monthly meteorological parameters and pertussis. Descriptive statistics exhibited that the pertussis incidence rose from 0.251 per 100,000 people in 2004 to 3.661 per 100,000 persons in 2018, and pertussis was a seasonal illness, peaked in spring and summer. The results from the regression model that allowed for the long-term trends, seasonality, autoregression, and delayed effects after correcting for overdispersion showed that a 1 hPa increment in the delayed one-month air pressure contributed to a 3.559% (95% CI 0.746-6.293%) reduction in the monthly number of pertussis cases; a 10 mm increment in the monthly aggregate precipitation, a 1 °C increment in the monthly average temperature, and a 1 m/s increment in the monthly average wind velocity resulted in 3.641% (95% CI 0.960-6.330%), 19.496% (95% CI 2.368-39.490%), and 3.812 (95% CI 1.243-11.690)-fold increases in the monthly number of pertussis cases, respectively. The roles of the mentioned weather parameters in the transmission of pertussis were also evidenced by a sensitivity analysis. The cointegration testing suggested a significant value among variables. Climatic factors, particularly monthly temperature, precipitation, air pressure, and wind velocity, play a role in the transmission of pertussis. This finding will be of great help in understanding the epidemic trends of pertussis in the future, and weather variability should be taken into account in the prevention and control of pertussis.

Figure 1

Time series plot displaying the monthly pertussis incidence and six climatic variables after standardized transformation. (A) Aggregate precipitation; (B) Aggregate sunshine hours; (C) Average wind velocity; (D) Average temperature; (E) Average relative humidity; (F) Average air pressure.

Figure 2

Negative binomial regression results of climatic variables correlated with the transmission of pertussis. (A) Aggregate precipitation; (B) Average temperature; (C) Aggregate sunshine hours; (D) Average relative humidity; (E) Average air pressure; (F) Average wind velocity.

Figure 3

Partial autocorrelation function (PACF) plot for the seasonally differenced series. It was seen that there were two local maximum values at lag 1–2 months. So the autoregressive orders were considered to be 2.

Figure 4

Comparison chart between the observed values and the fitted values based on the climatic variables.