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. 2022 Jun 7;22(1):525.
doi: 10.1186/s12879-022-07499-9.

Time trend prediction and spatial-temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014-2020

Wang Yun  1 , Chen Huijuan  2 , Liao Long  3 , Lu Xiaolong  3 , Zhang Aihua  1
Affiliations

Time trend prediction and spatial-temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014-2020

Wang Yun et al. BMC Infect Dis. .

Abstract

Background: Guizhou is located in the southwest of China with high multidrug-resistant tuberculosis (MDR-TB) epidemic. To fight this disease, Guizhou provincial authorities have made efforts to establish MDR-TB service system and perform the strategies for active case finding since 2014. The expanded case finding starting from 2019 and COVID-19 pandemic may affect the cases distribution. Thus, this study aims to analyze MDR-TB epidemic status from 2014 to 2020 for the first time in Guizhou in order to guide control strategies.

Methods: Data of notified MDR-TB cases were extracted from the National TB Surveillance System correspond to population information for each county of Guizhou from 2014 to 2020. The percentage change was calculated to quantify the change of cases from 2014 to 2020. Time trend and seasonality of case series were analyzed by a seasonal autoregressive integrated moving average (SARIMA) model. Spatial-temporal distribution at county-level was explored by spatial autocorrelation analysis and spatial-temporal scan statistic.

Results: Guizhou has 9 prefectures and 88 counties. In this study, 1,666 notified MDR-TB cases were included from 2014-2020. The number of cases increased yearly. Between 2014 and 2019, the percentage increase ranged from 6.7 to 21.0%. From 2019 to 2020, the percentage increase was 62.1%. The seasonal trend illustrated that most cases were observed during the autumn with the trough in February. Only in 2020, a peak admission was observed in June. This may be caused by COVID-19 pandemic restrictions being lifted until May 2020. The spatial-temporal heterogeneity revealed that over the years, most MDR-TB cases stably aggregated over four prefectures in the northwest, covering Bijie, Guiyang, Liupanshui and Zunyi. Three prefectures (Anshun, Tongren and Qiandongnan) only exhibited case clusters in 2020.

Conclusion: This study identified the upward trend with seasonality and spatial-temporal clusters of MDR-TB cases in Guizhou from 2014 to 2020. The fast rising of cases and different distribution from the past in 2020 were affected by the expanded case finding from 2019 and COVID-19. The results suggest that control efforts should target at high-risk periods and areas by prioritizing resources allocation to increase cases detection capacity and better access to treatment.

Keywords: MDR-TB; Prediction; SARIMA model; Spatial−temporal analysis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
MDR-TB cases reported disaggregated by age and gender from 2014 to 2020
Fig. 2
Fig. 2
Time series decomposition of MDR-TB cases from January 2014 to December 2020
Fig. 3
Fig. 3
The ACF and PACF graphs for estimating the parameter. a The ACF graph of the raw data (d = 0 and D = 0); b the PACF graph of the raw data (d = 0 and D = 0); c the ACF graph of one-order trend difference data (d = 1 and D = 0); d the PACF graph of one-order trend difference data (d = 1 and D = 0); e the ACF graph of one-order seasonal difference data (d = 1 and D = 1); f the PACF graph of one-order seasonal difference data (d = 1 and D = 1)
Fig. 4
Fig. 4
Comparison of actual and predicted cases of MDR-TB in Guizhou China. the black and green lines represent the observed values and predicted values, respectively; blue line represents 95% confidence intervals; after grey vertical line, the orange and yellow part represent 80% and 95% confidence intervals, respectively
Fig. 5
Fig. 5
The geographical distribution of total MDR-TB cases in Guizhou from 2014 to 2020. a The geographical location of Guizhou in China; b The geographical location of 9 prefectures in Guizhou; c The geographical location of 88 counties in 9 prefectures, and distribution of total MDR-TB cases over seven years in each county
Fig. 6
Fig. 6
Maps of local spatial autocorrelation cluster of MDR-TB for each county in Guizhou province from 2014 to 2020 by ArcMap software. Only those counties whose local Moran’s I have reached the significance level of 0.05 will be present on the map: a MDR-TB clusters in 2014; b MDR-TB clusters in 2015; c MDR-TB clusters in 2016; d MDR-TB clusters in 2017; e MDR-TB clusters in 2018; f MDR-TB clusters in 2019; g MDR-TB clusters in 2020
Fig. 7
Fig. 7
The detected spatial–temporal cluster map of MDR-TB cases in Guizhou (2014–2020)
See this image and copyright information in PMC

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