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. 2020 Dec 28:149:e8.
doi: 10.1017/S0950268820003040.

Temperature and humidity associated with increases in tuberculosis notifications: a time-series study in Hong Kong

M Xu  1   2 , Y Li  3 , B Liu  4 , R Chen  5 , L Sheng  6 , S Yan  7 , H Chen  8 , J Hou  9 , L Yuan  10 , L Ke  4 , M Fan  1 , P Hu  1
Affiliations

Temperature and humidity associated with increases in tuberculosis notifications: a time-series study in Hong Kong

M Xu et al. Epidemiol Infect. .

Abstract

Previous studies have revealed associations of meteorological factors with tuberculosis (TB) cases. However, few studies have examined their lag effects on TB cases. This study was aimed to analyse nonlinear lag effects of meteorological factors on the number of TB notifications in Hong Kong. Using a 22-year consecutive surveillance data in Hong Kong, we examined the association of monthly average temperature and relative humidity with temporal dynamics of the monthly number of TB notifications using a distributed lag nonlinear models combined with a Poisson regression. The relative risks (RRs) of TB notifications were >1.15 as monthly average temperatures were between 16.3 and 17.3 °C at lagged 13-15 months, reaching the peak risk of 1.18 (95% confidence interval (CI) 1.02-1.35) when it was 16.8 °C at lagged 14 months. The RRs of TB notifications were >1.05 as relative humidities of 60.0-63.6% at lagged 9-11 months expanded to 68.0-71.0% at lagged 12-17 months, reaching the highest risk of 1.06 (95% CI 1.01-1.11) when it was 69.0% at lagged 13 months. The nonlinear and delayed effects of average temperature and relative humidity on TB epidemic were identified, which may provide a practical reference for improving the TB warning system.

Keywords: Relative humidity; temperature; tuberculosis notification.

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Conflict of interest statement

None.

Figures

Fig. 1.
Fig. 1.
Annual cycle of the total number of TB notifications, average temperature and relative humidity in Hong Kong from 1997 to 2018. Annual cycle of (A) monthly total number of TB notifications; (B) monthly average temperature (°C) averaged over all the weather stations, and (C) monthly relative humidity (%) averaged over all the weather station.
Fig. 2.
Fig. 2.
Decomposition plots of the time-series in Hong Kong from 1997 to 2018. The decomposition plot of (A) monthly total number of TB notifications; (B) monthly average temperature, and (C) monthly relative humidity. The top layer shows the original time-series observed. The other layers show the decomposed components, denoting the seasonal component, long term trend component, and remainder component, respectively.
Fig. 3.
Fig. 3.
3-D and corresponding contour plots showing the RRs of TB notifications at lagged months along with average temperature and relative humidity in Hong Kong from 1997 to 2018. (A, B) Average temperature (°C) and (C, D) relative humidity (%).
Fig. 4.
Fig. 4.
Estimated RRs (95% CI) of TB notifications with (A) average temperatures and (B) relative humidity at selected lagged months (left), and lagged months with respect to selected (C) average temperatures and (D) relative humidity (right) in Hong Kong from 1997 to 2018. Blue solid line: RRs; grey shaded areas: 95% CIs.
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