Seasonal variation in notified tuberculosis cases from 2014 to 2018 in eastern China

Abstract

Objective Tuberculosis (TB) incidence shows a seasonal trend. The purpose of this study was to explore seasonal trends in TB cases in Jiangsu Province. Methods TB case data were collected from the TB registration system from 2014 to 2018. The X12-ARIMA model was used to adjust the Jiangsu TB time series. Analysis of variance was used to compare TB seasonal amplitude (SA) between subgroups and identify factors responsible for seasonal variation. Results The TB incidence in Jiangsu showed a seasonal trend. Confirmed active TB peaked in March and reached a minimum in February. The amplitude of the peak-to-bottom difference was 38.15%. The SAs in individuals 7 to 17 years old (80.00%) and students (71.80%) were significantly different than those in other subgroups. Among bacterial culture positive individuals, the SAs among female patients, individuals aged 7 to 17 years and students were significantly different from those in the reference group. Among culture-negative patients, the SA among individuals aged 7 to 17 years was significantly different those in other subgroups. Conclusions The TB incidence in Jiangsu Province displayed a seasonal trend. Factors related to seasonal variation were age and occupation. Our results highlight the importance of controlling Mycobacterium tuberculosis transmission during winter.

Spatio-temporal distribution of tuberculosis and the effects of environmental factors in China

“…The TB incidence of China has been decreasing since 2005, which shows that China’s policy implementation over the years has been very fruitful. The TB incidence has obvious seasonal characteristics in time, which is the same as other regional studies [ 20 , 21 ]. On the monthly scale, due to the difference of regional scale, TB incidence in China was the highest in January, and decreased in February.…”

Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis

“…Such a seasonal pattern showed good consistency with the findings in the preceding published work from across China 33,34 and most of the regions of China (eg, Jiangsu, Guangxi, and Qinghai). 3,30,35 Similar seasonal variation was also reported in the USA, Korea, Mongolia, and Kuwait. 36 An earlier systematic review indicating that a seasonal variation of TB was predominantly found during the spring based on 12 studies performed between 1971 and 2006 from 11 countries/regions worldwide also lends support to our current finding.…”

“…The seasonal variation in the TB incidence has been reported in many countries and regions across the world. [29][30][31][32] Our results also indicated a strong seasonal variation in the TB incidence, a peak in spring and a trough in winter per year, the TB incidence in the remaining months remained a relatively stable fluctuation. Such a seasonal pattern showed good consistency with the findings in the preceding published work from across China 33,34 and most of the regions of China (eg, Jiangsu, Guangxi, and Qinghai).…”

Application of the ARIMA Model in Forecasting the Incidence of Tuberculosis in Anhui During COVID-19 Pandemic from 2021 to 2022

“…32 In this study, ACF and PACF plots were drawn for the differential monthly incidence data of tuberculosis in Anhui Province, and the possible value ranges of each parameter of ARIMA (p,d,q) (P,D, Q) S model were preliminarily determined, and the best fitting model was further determined by the exhaust method. Compared with other similar studies that only selected an optimal model from several alternative models by the size of AIC value, 33 this study use program operation instead of manual selection to ensure the accurate and rapid screening of the best model under the evaluation criteria of AIC. By verifying the prediction effect of the model with monthly tuberculosis incidence data from July to December 2020, the results showed that ARIMA (0,1,1) (0,1,1) 12 model was accurate in predicting the monthly incidence of tuberculosis in Anhui, with an average error rate of only 1.91%.…”