Seasonal Variation of Newly Notified Pulmonary Tuberculosis Cases from 2004 to 2013 in Wuhan, China

Yang, Xiufen; Duan, Qionghong; Wang, Jianjie; Zhengbin, Zhang; Jiang, Gaofeng

doi:10.1371/journal.pone.0108369

Cited by 36 publications

(35 citation statements)

References 34 publications

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“…A similar study shows that the summer peak was mainly as a result of enhanced winter transmission of TB due to indoor crowding [21]. Another study in United States suggests that reduced winter exposure may not be a strong contributor to TB risk [11,22].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Seasonal variation is a considerable factor in regard to PTB, but the specific effects it has on the epidemic is not entirely clear since several studies have identified incidence peaks in winter, spring, and summer [9–11]. Furthermore, the interpretation of seasonality in relation to PTB risk is complicated due to the large number of factors to consider such as environmental factors (temperature, humidity, sunlight), social factors (crowding and person-to-person contact), and delays in the diagnosis and treatment of tuberculosis particularly in winter [12,13].…”

Section: Introductionmentioning

confidence: 99%

Environmental Factors Related to Pulmonary Tuberculosis in HIV-Infected Patients in the Combined Antiretroviral Therapy (cART) Era

et al. 2016

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The aim of our study was to evaluate the seasonal variations and whether short-term exposure to environmental risk factors, such as climate and air pollution, is associated with PTB-related hospital admissions in human immunodeficiency virus (HIV)-infected patients in Spain during the era of combined antiretroviral therapy (cART). A retrospective study was carried out using data from the Minimum Basic Data Set (MBDS) and the State Meteorological Agency (AEMET) of Spain. The primary outcome variable was hospital admissions with PTB diagnosis. The environmental risk factors evaluated were season, temperature, humidity, NO2, SO2, O3, PM10, and CO. Overall, HIV-infected patients had a lower frequency of PTB-related hospital admissions in summer (22.8%) and autumn (22.4%), but higher values in winter (26.6%) and spring (28.2%). Using a Bayesian temporal model, PTB-related hospital admissions were less frequent in summer-autumn and more abundant in winter-spring during the first years of follow-up. During the later years of follow-up, the seasonal trends continued resulting in the lowest values in autumn and the highest in spring. When considering short-term exposure to environmental risk factors, lower temperatures at 1 week (odds ratio (OR) = 1.03; p = 0.008), 1.5 weeks (OR = 1.03; p<0.001), 2 weeks (OR = 1.04; p<0.001), and 3 weeks (OR = 1.03; p<0.001) prior to PTB admission. In addition, higher concentration of NO2 at the time of admission were significantly associated with higher likelihoods of PTB-related hospital admission in HIV-infected patients when 1.5 weeks (OR = 1.1; p = 0.044) and 2 weeks (OR = 1.21; p<0.001) were used as controls. Finally, higher concentration of SO2 at 1.5 weeks prior to PTB admission was significantly associated with a higher likelihood of PTB-related hospital admissions (OR = 0.92; p = 0.029). In conclusion, our data suggest an apparent seasonal variation in hospital admissions of HIV-infected patients with a PTB diagnosis (summer/autumn vs. winter/spring), as well as a link to short-term exposure to environmental risk factors, such as temperature and ambient NO2 and SO2.

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“…Studies conducted in various countries in the northern hemisphere have identi ed peak months of TB noti cation in spring (March to May) (4-10), late spring and early summer (April to June) (7,11,12), or summer (June to August) (13)(14)(15)(16). Trough months have been noted in the fall (September to November) (5), late fall and early winter (October to December) (4,6,11,12), or winter (January to February) (15).…”

Section: Introductionmentioning

confidence: 99%

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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Seasonal Variation of Newly Notified Pulmonary Tuberculosis Cases from 2004 to 2013 in Wuhan, China

Cited by 36 publications

References 34 publications

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

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

Environmental Factors Related to Pulmonary Tuberculosis in HIV-Infected Patients in the Combined Antiretroviral Therapy (cART) Era

The Relationship Between Seasonality, Latitude and Tuberculosis Notifications in Pakistan

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