Seasonality of active tuberculosis notification from 2005 to 2014 in Xinjiang, China

Wubuli, Atikaimu; Li, Yuehua; Feng, Xue; Yao, Xuemei; Upur, Halmurat; Wushouer, Qimanguli

doi:10.1371/journal.pone.0180226

Cited by 44 publications

(61 citation statements)

References 24 publications

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“…Contrary to a study published in India (12), which showed Northern areas to have greater seasonal variation than those in central southern regions, our study shows reduced seasonal variation in regions further from the equator. Our results are consistent with a study looking at the seasonality of TB in Xinjiang, in which greater seasonality was noted in southern Xinjiang (34ºN-42ºN) than eastern and northern regions (43ºN-48ºN); however, the difference was not statistically signi cant (9). In the USA, the amplitude of TB seasonality has not been shown to vary according to latitude (6).…”

Section: Discussionsupporting

confidence: 90%

“…The results of our study are consistent with those of a smaller study looking at the seasonality of TB in a single district, Lahore, Pakistan, in which the investigators showed a peak in pulmonary TB cases in the second quarter of the year (17). Several studies (6,9,12,22) have explored the relationship between distance from the equator and seasonal variation of TB, and it is plausible that the seasonality of TB is more pronounced in areas where UV exposure is reduced and dermal vitamin D synthesis is low (12,22).…”

Section: Discussionsupporting

confidence: 90%

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The Relationship Between Seasonality, Latitude and Tuberculosis Notifications in Pakistan

Butt

Younis

et al. 2020

Preprint

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BackgroundPakistan is amongst the top 20 highest burden tuberculosis (TB) countries in the world. Approximately 369,548 cases of TB (all forms) were notified in 2018, with an estimated incidence of 265 per 100,000 people per year. In other settings, TB has been shown to demonstrate seasonal variation, with higher incidence in the spring/summer months and lower incidence in the autumn/winter; the amplitude of seasonal variation has also been reported to be higher with increasing distance from the equator.MethodsNotifications of newly-diagnosed pulmonary and extrapulmonary TB cases were obtained for 139 districts in Pakistan from 2011–2017. Data were provided by the Pakistan National TB Control Programme, Islamabad, Pakistan. Statistical analyses were performed to determine whether there was seasonal variation in TB notifications in Pakistan; whether the amplitude of seasonal variation in TB notifications varied according to latitude; whether the amplitude of seasonal variation of TB in Pakistan differed between extrapulmonary TB vs. pulmonary TB. To assess the quarterly seasonality of TB, we used the X-13-ARIMA-SEATS seasonal adjustment programme from the United States Census Bureau. The mean difference and corresponding 95% confidence intervals of seasonal amplitudes between different latitudes and type of TB were estimated using linear regression.ResultsTB notifications were highest in quarter 2, and lowest in quarter 4. The mean amplitude of seasonal variation was 25.5% (95% CI 25.0–25.9%). The mean seasonal amplitude of TB notifications from latitude 24.5ºN-<26.5ºN was 29.5% (95% CI 29.3–29.7%) whilst the mean seasonal amplitude of TB notifications from latitude 34.5ºN -<36.5ºN was 21.7% (95% CI 19.6–23.9%). The mean seasonal amplitude of TB notifications across Pakistan between latitudes 24.5ºN to 36.5ºN reached statistically significant difference (p < 0.001). The amplitude of seasonal variation was greater for extrapulmonary TB (mean seasonal amplitude: 21.6%, 95% CI 32.1–33.1%) vs. smear positive pulmonary TB (mean seasonal amplitude 32.6%, 95% CI 21.4–21.8%), p < 0.001.ConclusionTB notifications in Pakistan exhibit seasonal variation with a peak in quarter 2 (April-June) and trough in quarter 4 (October-December). The amplitude of seasonality decreases with increasing latitude, and is more pronounced for extrapulmonary TB than for pulmonary TB.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 90%

The Relationship Between Seasonality, Latitude and Tuberculosis Notifications in Pakistan

Butt

Younis

et al. 2020

Preprint

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“…The reason for the decrease in patients reported in February is that the Chinese Spring Festival usually falls in February, in which people are busy celebrating the lunar New Year, and avoiding to seek medical care. This seasonal pattern is the same as that of a national study [12,13], and studies in Guangxi province [36], Xinjiang Uygur Autonomous Region [37], Zhaotong city of Yunnan province [24,32] and other western regions, but a little different from that of studies in Zhejiang province [23] , Hong Kong [38] and Taiwan [39], which are located in the eastern coastal area.…”

Section: Discussionsupporting

confidence: 73%

Spatial-temporal analysis of Tuberculosis in Chongqing, China 2011-2018

et al. 2020

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Background: China is a country with a high burden of pulmonary tuberculosis (PTB). Chongqing is in the southwest of China, where the notification rate of PTB ranks tenth in China. This study analyzed the temporal and spatial distribution characteristics of PTB in Chongqing in order to improve TB control measures. Methods: A spatial-temporal analysis has been performed based on the data of PTB from 2011 to 2018, which was extracted from the National Surveillance System. The effect of TB control was measured by variation trend of pathogenic positive PTB notification rate and total TB notification rate. Time series, spatial autonomic correlation and spatial-temporal scanning methods were used to identify the temporal trends and spatial patterns at county level. Results: A total of 188,528 cases were included in this study. A downward trend was observed in PTB between 2011 and 2018 in Chongqing. The peak of PTB notification occurred in late winter and early spring annually. By calculating the value of Global Moran's I and Local Getis’s Gi*, we found that PTB was spatially clustered and some significant hot spots were detected in the southeast and northeast of Chongqing. One most likely cluster and three secondary clusters were identified by Kulldorff’s scan spatial-temporal Statistic. Conclusions: This study identified seasonal patterns and spatial-temporal clusters of PTB cases in Chongqing. Priorities should be given to southeast and northeast of Chongqing for better TB control.

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“…A study showed that from 2004 to 2008, April was the peak month for student TB cases in China, followed by May and March [15]. The seasonality of active TB registration was peaked in March in Xinjiang, China [16]. There were also some studies on the seasonality and trend analysis of TB incidence around the world [17][18][19][20].…”

Section: Discussionmentioning

confidence: 99%

Time series analysis of tuberculosis registration rates from 2007 to 2019 and prediction: could tuberculosis control milestones be achieved in 2020 and 2025 in Henan Province, China?

Zhang

Li²,

Jiang

et al. 2020

Preprint

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Background: The World Health Organization (WHO) End TB Strategy meant that compared with 2015 baseline, the reduction in pulmonary tuberculosis(PTB) incidence rate should be 20% and 50% in 2020 and 2025, respectively. The incidence number of PTB in China accounted for 9% of the global total in 2018, which ranked the second high in the world. From 2007 to 2019, 854,672active PTB cases were registered and treated in Henan Province, China. We need to assess whether the WHO milestones could be achieved in Henan Province. Methods: The active PTB numbers in Henan Province from 2007 to2019, registered in Chinese Tuberculosis Information Management System (CTIMS) were analyzed to predict the active PTB registration rates in 2020 and 2025, which is conductive to early response measures to ensure the achievement of the WHO milestones. The time series model was created by monthly active PTB registration rates from 2007 to 2016, and the optimal model was verified by data from 2017 to 2019. Monthly and annual active PTB registration rates and 95% confidence interval (CI) from 2020 to 2025 were predicted. Results: High active PTB registration rates in March, April, May and June showed the seasonal variations. The exponential smoothing winter’s multiplication model was selected as the best-fitting model. The predicted values were approximately consistent with the observed ones from 2017 to 2019. The annual active PTB registration rates were predicted as 49.2 (95% CI: 36.0-62.5) and 34.3 (95% CI: 17.7-50.8) per 100 ,000 population in 2020 and 2025 , respectively. Compared with the active PTB registration rate in 2015, the reduction will reach 23.7% (95% CI: 3.1%-44.2%) and 46.9% (95% CI: 21.3%-72.5%) in 2020 and 2025, respectively. Conclusions: The high active PTB registration rates in spring and early summer indicates that high risk of tuberculosis infection in late autumn and winter in Henan Province. Without regard to the confidence interval, the first milestone of WHO End TB Strategy in 2020 will be achieved. However, the second milestone in 2025 will not be easily achieved unless there are early response measures in Henan Province, China. Trial registration: Not applicable

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Seasonality of active tuberculosis notification from 2005 to 2014 in Xinjiang, China

Cited by 44 publications

References 24 publications

The Relationship Between Seasonality, Latitude and Tuberculosis Notifications in Pakistan

The Relationship Between Seasonality, Latitude and Tuberculosis Notifications in Pakistan

Spatial-temporal analysis of Tuberculosis in Chongqing, China 2011-2018

Time series analysis of tuberculosis registration rates from 2007 to 2019 and prediction: could tuberculosis control milestones be achieved in 2020 and 2025 in Henan Province, China?

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