Seasonality of tuberculosis in an Eastern-Asian country with an extreme continental climate

Naranbat, N; Nymadawa, Pagbajabyn; Schopfer, K; Rieder, H. L.

doi:10.1183/09031936.00035309

Cited by 68 publications

(67 citation statements)

References 12 publications

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“…The spring peak month was similar to the results from many other studies conducted in the northern hemisphere including those in India [10], Spain [11], Mongolia [12], China [13], the USA [14], Portugal [15], and the Netherlands [16]. The peak TB notification in South Africa [17] and Australia [18] was in October, which is the month of local spring in the southern hemisphere.…”

Section: Discussionsupporting

confidence: 88%

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

Wubuli¹,

Li²,

Feng³

et al. 2017

PLoS ONE

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ObjectivesXinjiang is one of the highest TB-burdened provinces of China. A time-series analysis was conducted to evaluate the trend, seasonality of active TB in Xinjiang, and explore the underlying mechanism of TB seasonality by comparing the seasonal variations of different subgroups.MethodsMonthly active TB cases from 2005 to 2014 in Xinjiang were analyzed by the X-12-ARIMA seasonal adjustment program. Seasonal amplitude (SA) was calculated and compared within the subgroups.ResultsA total of 277,300 confirmed active TB cases were notified from 2005 to 2014 in Xinjiang, China, with a monthly average of 2311±577. The seasonality of active TB notification was peaked in March and troughed in October, with a decreasing SA trend. The annual 77.31% SA indicated an annual mean of additional TB cases diagnosed in March as compared to October. The 0–14-year-old group had significantly higher SA than 15–44-year-old group (P<0.05). Students had the highest SA, followed by herder and migrant workers (P<0.05). The pleural TB cases had significantly higher SA than the pulmonary cases (P <0.05). Significant associations were not observed between SA and sex, ethnic group, regions, the result of sputum smear microcopy, and treatment history (P>0.05).ConclusionTB notification in Xinjiang shows an apparent seasonal variation with a peak in March and trough in October. For the underlying mechanism of TB seasonality, our results hypothesize that winter indoor crowding increases the risk of TB transmission, and seasonality was mainly influenced by the recent exogenous infection rather than the endogenous reactivation.

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Section: Discussionsupporting

confidence: 88%

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

Wubuli¹,

Li²,

Feng³

et al. 2017

PLoS ONE

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“…Similar to Wuhan (Fig. 7a,b), spring peaks of SSP and SSN pulmonary TB epidemics have been observed in other countries and regions, including North India [4], Spain [5], Ciskei of Central Africa [6], Kuwait [7], Japan [8], South Africa [9] and Mongolia [10]. Regarding the cause of the spring peak of TB epidemics in Wuhan (Fig.…”

Section: Discussionmentioning

confidence: 92%

“…Previous studies have found that the seasonality of TB epidemics is highest in spring in some locations [4][5][6][7][8][9][10]. One dominant hypothesis for the cause of a spring peak in TB epidemics is that an increase in the active mass in poorly ventilated and humid rooms facilitates TB transmission [11].…”

Section: Introductionmentioning

confidence: 98%

Seasonality of reported tuberculosis cases from 2006 to 2010 in Wuhan, China

et al. 2013

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We investigated the seasonality of tuberculosis (TB) in Wuhan, China, to evaluate the increased risk of disease transmission during each season and to develop an effective TB control strategy. We applied spectral analysis to the weekly prevalence data of sputum smear positive (SSP) and sputum smear negative (SSN) pulmonary TB reported from 2006 to 2010. Cases of both SSP and SSN feature 1·0- and 0·5-year periodic modes. The least squares method was used to fit curves to the two periodic modes for SSP and SSN data. The curves demonstrated dominant peaks in spring similar to cases reported previously for other locations. Notably for SSP, dominant peaks were also observed in summer. The spring peaks of SSP and SSN were explained in terms of poorly ventilated and humid rooms and vitamin D deficiency. For the summer peaks of SSP, summer influenza epidemics in Wuhan may contribute to the increase in TB prevalence.

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“…There were some retrospective studies on the seasonality and trend analysis of TB data to describe the trends of TB incidence [9,10,11,12]. In many countries, various models have been used to forecast TB in order to figure out the trends and predict the root cause of the TB incidence epidemic [13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model

Azeez

Obaromi

Odeyemi

et al. 2016

IJERPH

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Background: Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. Methods: TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Results: Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. Conclusions: The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.

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Seasonality of tuberculosis in an Eastern-Asian country with an extreme continental climate

Cited by 68 publications

References 12 publications

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

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

Seasonality of reported tuberculosis cases from 2006 to 2010 in Wuhan, China

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model

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