Trends, seasonality and forecasts of pulmonary tuberculosis in Portugal

Time Series Modeling of Tuberculosis Cases in India from 2017 to 2022 Based on the SARIMA-NNAR Hybrid Model

Tuberculosis (TB) is still one of the severe progressive threats in developing countries. There are some limitations to social and economic development among developing nations. The present study forecasts the notified prevalence of TB based on seasonality and trend by applying the SARIMA-NNAR hybrid model. The NIKSHAY database repository provides monthly informed TB cases (2017 to 2022) in India. A time series model was constructed based on the seasonal autoregressive integrated moving averages (SARIMA), neural network autoregressive (NNAR), and, SARIM-NNAR hybrid models. These models were estimated with the help of the Bayesian information criterion (BIC) and Akaike information criterion (AIC). These models were established to compare the estimation. A total of 12,576,746 notified TB cases were reported over the years whereas the average case was observed as 174,677.02. The evaluating parameters values of RMSE, MAE, and MAPE for the hybrid model were found to be (13738.97), (10369.48), and (06.68). SARIMA model was (19104.38), (14304.15), and (09.45) and the NNAR were (11566.83), (9049.27), and (05.37), respectively. Therefore, the NNAR model performs better with time series data for fitting and forecasting compared to other models such as SARIMA as well as the hybrid model. The NNAR model indicated a suitable model for notified TB incidence forecasting. This model can be a good tool for future prediction. This will assist in devising a policy and strategizing for better prevention and control.

Forecasting New Tuberculosis Cases in Malaysia: A Time-Series Study Using the Autoregressive Integrated Moving Average (ARIMA) Model

Background The application of the Box-Jenkins autoregressive integrated moving average (ARIMA) model has been widely employed in predicting cases of infectious diseases. It has shown a positive impact on public health early warning surveillance due to its capability in producing reliable forecasting values. This study aimed to develop a prediction model for new tuberculosis (TB) cases using time-series data from January 2013 to December 2018 in Malaysia and to forecast monthly new TB cases for 2019. Materials and methods The ARIMA model was executed using data gathered between January 2013 and December 2018 in Malaysia. Subsequently, the well-fitted model was employed to make projections for new TB cases in the year 2019. To assess the efficacy of the model, two key metrics were utilized: the mean absolute percentage error (MAPE) and stationary R-squared. Furthermore, the sufficiency of the model was validated via the Ljung-Box test. Results The results of this study revealed that the ARIMA (2,1,1)(0,1,0)12 model proved to be the most suitable choice, exhibiting the lowest MAPE value of 6.762. The new TB cases showed a clear seasonality with two peaks occurring in March and December. The proportion of variance explained by the model was 55.8% with a p-value (Ljung-Box test) of 0.356. Conclusions The application of the ARIMA model has developed a simple, precise, and low-cost forecasting model that provides a warning six months in advance for monitoring the TB epidemic in Malaysia, which exhibits a seasonal pattern.

[Analysis of the seasonality of tuberculosis in Brazilian capitals and the Federal District from 2001 to 2019]

The literature has few studies on the seasonality of tuberculosis (TB) in the southern hemisphere, entailing the fill of this knowledge gap. This study aims to analyze whether TB incidence in Brazilian capitals and the Federal District is seasonal. This is an ecological study of a time series (2001-2019) of TB cases, conducted with 26 capitals and the Federal District. The Ministry of Health database, with 516,524 TB cases, was used. Capitals and the Federal District were divided into five groups based on social indicators, disease burden, and the Koppen climate classification. The seasonal variation of TB notifications and group amplitude were evaluated. We found TB seasonality in Brazil with a 1% significance in all capital groups (Stability assumption and Krusall-Wallis tests, p < 0.01). In the combined seasonality test, capital groups A, D, and E showed seasonality, whereas groups B and C, its probability. Our findings showed that health service supply and/or demand - rather than climate - may be the most relevant underlying factor in TB seasonality. It is challenging to raise the other seasonal factors underlying TB seasonality in tropical regions in the Southern Hemisphere.

Evolução temporal e distribuição espacial da hanseníase em município de baixa endemicidade no estado de São Paulo

Resumo Objetivo Analisar a distribuição espacial e temporal da hanseníase em cenário de baixa endemicidade no estado de São Paulo, Brasil. Métodos Estudo ecológico, sobre casos de hanseníase notificados no município de Ribeirão Preto, entre 2006 e 2016. A tendência temporal da taxa de detecção de hanseníase foi verificada mediante decomposição de séries temporais, e identificadas as áreas de alta e de baixa ocorrência da doença utilizando-se a técnica Getis-Ord Gi*. Resultados Foram registrados 890 casos, e a taxa de detecção apresentou uma tendência crescente no período analisado, com crescimento médio de 1% ao mês. Identificaram-se áreas de alta ocorrência da doença na região norte do município (99% e 95% de confiança). Conclusão A taxa de detecção de hanseníase apresentou tendência temporal crescente, e a análise espacial permitiu visualizar que a região do município com maior ocorrência da doença se caracteriza por apresentar as maiores desigualdades sociais.

Seasonality in extra-pulmonary tuberculosis notifications in Germany 2004-2014- a time series analysis

BACKGROUND

Seasonality in tuberculosis (TB) has been found in different parts of the world, showing a peak in spring/summer and a trough in autumn/winter. The evidence is less clear which factors drive seasonality. It was our aim to identify and evaluate seasonality in the notifications of TB in Germany, additionally investigating the possible variance of seasonality by disease site, sex and age group.

METHODS

We conducted an integer-valued time series analysis using national surveillance data. We analysed the reported monthly numbers of started treatments between 2004 and 2014 for all notified TB cases and stratified by disease site, sex and age group.

RESULTS

We detected seasonality in the extra-pulmonary TB cases (N = 11,219), with peaks in late spring/summer and troughs in fall/winter. For all TB notifications together (N = 51,090) and for pulmonary TB only (N = 39,714) we did not find a distinct seasonality. Additional stratified analyses did not reveal any clear differences between age groups, the sexes, or between active and passive case finding.

CONCLUSION

We found seasonality in extra-pulmonary TB only, indicating that seasonality of disease onset might be specific to the disease site. This could point towards differences in disease progression between the different clinical disease manifestations. Sex appears not to be an important driver of seasonality, whereas the role of age remains unclear as this could not be sufficiently investigated.

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

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.

Does sunlight drive seasonality of TB in Vietnam? A retrospective environmental ecological study of tuberculosis seasonality in Vietnam from 2010 to 2015

BACKGROUND

Tuberculosis (TB) is a major global health burden, with an estimated quarter of the world's population being infected. The World Health Organization (WHO) launched the "End TB Strategy" in 2014 emphasising knowing the epidemic. WHO ranks Vietnam 12th in the world of high burden countries. TB spatial and temporal patterns have been observed globally with evidence of Vitamin D playing a role in seasonality. We explored the presence of temporal and spatial clustering of TB in Vietnam and their determinants to aid public health measures.

METHODS

Data were collected by the National TB program of Vietnam from 2010 to 2015 and linked to the following datasets: socio-demographic characteristics; climatic variables; influenza-like-illness (ILI) incidence; geospatial data. The TB dataset was aggregated by province and quarter. Descriptive time series analyses using LOESS regression were completed per province to determine seasonality and trend. Harmonic regression was used to determine the amplitude of seasonality by province. A mixed-effect linear model was used with province and year as random effects and all other variables as fixed effects.

RESULTS

There were 610,676 cases of TB notified between 2010 and 2015 in Vietnam. Heat maps of TB incidence per quarter per province showed substantial temporal and geospatial variation. Time series analysis demonstrated seasonality throughout the country, with peaks in spring/summer and troughs in autumn/winter. Incidence was consistently higher in the south, the three provinces with the highest incidence per 100,000 population were Tay Ninh, An Giang and Ho Chi Minh City. However, relative seasonal amplitude was more pronounced in the north. Mixed-effect linear model confirmed that TB incidence was associated with time and latitude. Of the demographic, socio-economic and health related variables, population density, percentage of those under 15 years of age, and HIV infection prevalence per province were associated with TB incidence. Of the climate variables, absolute humidity, average temperature and sunlight were associated with TB incidence.

CONCLUSION

Preventative public health measures should be focused in the south of Viet Nam where incidence is highest. Vitamin D is unlikely to be a strong driver of seasonality but supplementation may play a role in a package of interventions.

The ongoing challenge of Pulmonary Tuberculosis in Southern Tunisia: A review of a 22-year period

BACKGROUND

Despite the wide use of anti-tuberculosis drugs, pulmonary tuberculosis (PTB) remains one of the most important causes of mortality and morbidity, particularly in developing countries. Therefore, combining clinical and epidemiological approach would be of a great benefit. Our study aimed to describe the epidemiological and clinical specificities of PTB and its recent chronological trends.

METHODS

We conducted a retrospective study of all PTB new cases of any age diagnosed between 1995 and 2016 in Southern Tunisia. We applied the direct method of age-standardization using the World Standard Population to compute the age standardized incidence rate (ASIR) and the age standardized mortality rate (ASMR) per 100 000 inhabitants.

RESULTS

We recorded 1121 new cases with PTB among 2771 new cases of tuberculosis (40.5%). The ASIR of PTB was 5.3/100 000 inhabitants/year and didn't change over the study period (rho=0.3; P=0.2). Patients with PTB were mainly aged between 15 and 59 years (n=861; 76.8%) and came from urban areas (n=600; 55%). The median duration of treatment was 7.6 months (IQR=[6-8 months]). Successful outcome was notified in 1075 cases (95.9%). Forty-one patients died yielding an ASMR of 0.18/100 000 inhabitants/year. Factors statistically associated with unsuccessful outcome included age≥60 years (OR=5; P<0.001) and shorter treatment duration (6.15 months vs 7.76 months; P<0.001).

CONCLUSION

In contrast to the decline in the global PTB incidence reported worldwide and in the neighboring countries, our study revealed no significant change in the PTB rates from 1995 to 2016. Therefore, tools and strategies used to manage PTB should be strengthened by a substantial effort in both basic science and epidemiology to have better incidence curves.

Forecasting the impact of population ageing on tuberculosis incidence

Background

Tuberculosis (TB) disease reactivates from distant latent infection or recent (re)infection. Progression risks increase with age. Across the World Health Organisation Western Pacific region, many populations are ageing and have the highest per capita TB incidence rates in older age groups. However, methods for analysing age-specific TB incidence and forecasting epidemic trends while accounting for demographic change remain limited.

Methods

We applied the Lee-Carter models, which were originally developed for mortality modelling, to model the temporal trends in age-specific TB incidence data from 2005 to 2018 in Taiwan. Females and males were modelled separately. We combined our demographic forecasts, and age-specific TB incidence forecasts to project TB incidence until 2035. We compared TB incidence projections with demography fixed in 2018 to projections accounting for demographic change.

Results

Our models quantified increasing incidence rates with age and declining temporal trends. By 2035, the forecast suggests that the TB incidence rate in Taiwan will decrease by 54% (95% Prediction Interval (PI): 45%-59%) compared to 2015, while most age-specific incidence rates will reduce by more than 60%. In 2035, adults aged 65 and above will make up 78% of incident TB cases. Forecast TB incidence in 2035 accounting for demographic change will be 39% (95% PI: 36%-42%) higher than without population ageing.

Conclusions

Age-specific incidence forecasts coupled with demographic forecasts can inform the impact of population ageing on TB epidemics. The TB control programme in Taiwan should develop plans specific to older age groups and their care needs.

Development and validation of a predictive ecological model for TB prevalence

Background

Nationally representative tuberculosis (TB) prevalence surveys provide invaluable empirical measurements of TB burden but are a massive and complex undertaking. Therefore, methods that capitalize on data from these surveys are both attractive and imperative. The aim of this study was to use existing TB prevalence estimates to develop and validate an ecological predictive statistical model to indirectly estimate TB prevalence in low- and middle-income countries without survey data.

Methods

We included national and subnational estimates from 30 nationally representative surveys and 2 district-level surveys in India, resulting in 50 data points for model development (training set). Ecological predictors included TB notification and programmatic data, co-morbidities and socio-environmental factors extracted from online data repositories. A random-effects multivariable binomial regression model was developed using the training set and was used to predict bacteriologically confirmed TB prevalence in 63 low- and middle-income countries across Africa and Asia in 2015.

Results

Out of the 111 ecological predictors considered, 14 were retained for model building (due to incompleteness or collinearity). The final model retained for predictions included five predictors: continent, percentage retreated cases out of all notified, all forms TB notification rates per 100 000 population, population density and proportion of the population under the age of 15. Cross-fold validations in the training set showed very good average fit (R-sq = 0.92).

Conclusion

Predictive ecological modelling is a useful complementary approach to indirectly estimating TB burden and can be considered alongside other methods in countries with limited robust empirical measurements of TB among the general population.

Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China

BACKGROUND

Guangxi is one of the provinces having the highest notification rate of tuberculosis in China. However, spatial and temporal patterns and the association between environmental diversity and tuberculosis notification are still unclear.

OBJECTIVE

To detect the spatiotemporal pattern of tuberculosis notification rates from 2010 to 2016 and its potential association with ecological environmental factors in Guangxi Zhuang autonomous region, China.

METHODS

We performed a spatiotemporal analysis with prediction using time series analysis, Moran's I global and local spatial autocorrelation statistics, and space-time scan statistics to detect temporal and spatial clusters of tuberculosis notifications in Guangxi between 2010 and 2016. Spatial panel models were employed to identify potential associating factors.

RESULTS

The number of reported cases peaked in spring and summer and decreased in autumn and winter. The predicted number of reported cases was 49,946 in 2017. Moran's I global statistics were greater than 0 (0.363-0.536) during the study period. The most significant hot spots were mainly located in the central area. The eastern area exhibited a low-low relation. By the space-time scanning, the clusters identified were similar to those of the local autocorrelation statistics, and were clustered toward the early part of 2016. Duration of sunshine, per capita gross domestic product, the treatment success rate of tuberculosis and participation rate of the new cooperative medical care insurance scheme in rural areas had a significant negative association with tuberculosis notification rates.

CONCLUSION

The notification rate of tuberculosis in Guangxi remains high, with the highest notification cluster located in the central region. The notification rate is associated with economic level, treatment success rate and participation in the new cooperative medical care insurance scheme.

Drivers of Seasonal Variation in Tuberculosis Incidence: Insights from a Systematic Review and Mathematical Model

BACKGROUND

Seasonality in tuberculosis incidence has been widely observed across countries and populations; however, its drivers are poorly understood. We conducted a systematic review of studies reporting seasonal patterns in tuberculosis to identify demographic and ecologic factors associated with timing and magnitude of seasonal variation.

METHODS

We identified studies reporting seasonal variation in tuberculosis incidence through PubMed and EMBASE and extracted incidence data and population metadata. We described key factors relating to seasonality and, when data permitted, quantified seasonal variation and its association with metadata. We developed a dynamic tuberculosis natural history and transmission model incorporating seasonal differences in disease progression and/or transmission rates to examine magnitude of variation required to produce observed seasonality in incidence.

RESULTS

Fifty-seven studies met inclusion criteria. In the majority of studies (n=49), tuberculosis incidence peaked in spring or summer and reached a trough in late fall or winter. A standardized seasonal amplitude was calculated for 34 of the studies, resulting in a mean of 17.1% (range: 2.7-85.5%) after weighting by sample size. Across multiple studies, stronger seasonality was associated with younger patients, extrapulmonary disease, and latitudes farther from the Equator. The mathematical model was generally able to reproduce observed levels of seasonal case variation; however, substantial variation in transmission or disease progression risk was required to replicate several extreme values.

CONCLUSIONS

We observed seasonal variation in tuberculosis, with consistent peaks occurring in spring, across countries with varying tuberculosis burden. Future research is needed to explore and quantify potential gains from strategically conducting mass screening interventions in the spring.

Evaluating the Dynamics of Groundwater Depletion for an Arid Land in the Tarim Basin, China

: Groundwater depletion has become a hotly debated topic, particularly in arid land. In this study, groundwater depletion and its dynamic factors were investigated in the Tarim Basin. The groundwater data were collected randomly from 10 groundwater monitoring wells, from September 2002–December 2014. Piezometric groundwater level decreased with the range from 667.00 cm to 1288.50 cm, and also with a linear decreasing rate of 73.96 cm per year, on average. Significant spatial variation characteristics have been detected. Groundwater depletion was more serious in the northwest than the southeast of the study area. A correlation analysis was conducted to explore the major influence factors. These results showed that the annual irrigated land area was the primary influencing factor. Driving force analysis also suggested that electricity consumption could be an effective and convenient factor to assess groundwater exploitation. This study indicated that human activity was the major impact factor for groundwater decline. The seasonal-trend decomposition analysis supported these findings, as observed from the correlation analysis and the spatial variation. It also provided new insight into the groundwater time-series and enhanced the identification of groundwater-flow characteristics. These findings may be useful for understanding the groundwater fluctuations in high water demand regions and also for developing safety policies for groundwater management.

Seasonal patterns of tuberculosis case notification in the tropics of Africa: A six-year trend analysis in Ethiopia

Objective

Seasonal variations affect the health system’s functioning, including tuberculosis (TB) services, but there is little evidence about seasonal variations in TB case notification in tropical countries, including Ethiopia. This study sought to fill this gap in knowledge using TB data reported from 10 zones, 5 each from Amhara and Oromia regions.

Methods

Notified TB cases for 2010–2016 were analyzed using SPSS version 20. We calculated the quarterly and annual average TB case notification rates and the proportion of seasonal amplitudes. We applied Winters’ multiplicative method of exponential smoothing to break down the original time series into seasonal, trend, and irregular components and to build a suitable model for forecasting.

Results

A total of 205,575 TB cases were identified (47.8% from Amhara, 52.2% from Oromia), with a male-to-female ratio of 1.2:1. The means of 8,200 (24%), 7,992 (23%), 8,849 (26%), and 9,222 (27%) TB cases were reported during July-September, October-December, January-March, and April-June, respectively. The seasonal component of our model indicated a peak in April-June and a trough in October-December. The seasonal amplitude in Amhara region is 10% greater than that of Oromia (p < 0.05).

Conclusions

TB is shown to be a seasonal disease in Ethiopia, with a peak in quarter four and a low in quarter two of the fiscal year. The peak TB case notification rate corresponds with the end of the dry season in the two agrarian regions of Ethiopia. TB prevention and control interventions, such as efforts to increase community TB awareness about TB transmission and contact tracing, should consider seasonal variation. Regional variations in TB seasonality may require consideration of geographic-specific TB case-finding strategies. The mechanisms underlying the seasonal variation of TB are complex, and further study is needed.

Estimating the incidence of tuberculosis cases reported at a tertiary hospital in Ghana: a time series model approach

Background

The incidence of Tuberculosis (TB) differs among countries and contributes to morbidity and mortality especially in the developing countries. Trends and seasonal changes in the number of patients presenting with TB have been studied worldwide including sub-Saharan Africa. However, these changes are unknown at the Korle-Bu Teaching Hospital (KBTH). The aim of this study was to obtain a time series model to estimate the incidence of TB cases at the chest clinic of the Korle-Bu Teaching hospital.

Methods

A time series analysis using a Box-Jenkins approach propounded as an autoregressive moving average (ARIMA) was conducted on the monthly TB cases reported at the KBTH from 2008 to 2017. Various models were stated and compared and the best was found to be based on the Akaike Information Criterion and Bayesian Information Criterion.

Results

There was no evidence of obvious increasing or decreasing trend in the TB data. The log-transformed of the data achieved stationarity with fairly stable variations around the mean of the series. ARIMA (1, 0, 1) or ARMA (1,1) was obtained as the best model. The monthly forecasted values of the best model ranged from 53 to 55 for the year 2018; however, the best model does not always produce the best results with respect to the mean absolute and mean square errors.

Conclusions

Irregular fluctuations were observed in the 10 -year data studied. The model equation to estimate the expected monthly TB cases at KBTH produced an AR coefficient of 0.971 plus an MA coefficient of − 0.826 with a constant value of 4.127. The result is important for developing a hypothesis to explain the dynamics of TB occurrence so as to outline prevention programmes, optimal use of resources and effective service delivery.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-6221-z) contains supplementary material, which is available to authorized users.

Seasonality of tuberculosis in the Republic of Korea, 2006-2016

OBJECTIVES

While the seasonality of notified tuberculosis has been identified in several populations, there is not a descriptive epidemiological study on the seasonality of tuberculosis in Korea. This study aimed to evaluate the seasonality of tuberculosis in Korea from 2006 to 2016.

METHODS

Data regarding notified cases of tuberculosis by year and month was obtained from the Infectious Diseases Surveillance Yearbook, 2017 published by the Korea Centers for Disease Control and Prevention. Seasonal decomposition was conducted using the method of structural model of time series analysis with simple moving averages.

RESULTS

While the trough season was winter from 2006 to 2016, the peak season was summer between 2006 and 2012, but shifted to spring between 2013 and 2016.

CONCLUSIONS

Notified tuberculosis in Korea also showed seasonality. It is necessary to evaluate factors related to the seasonality of tuberculosis for controlling tuberculosis.

Times series analysis of age-specific tuberculosis at a rapid developing region in China, 2011-2016

The city of Shenzhen has recently experienced extraordinary economic growth accompanied by a huge internal migrant influx. We investigated the local dynamics of tuberculosis (TB) epidemiology in the Nanshan District of Shenzhen to provide insights for TB control strategies for this district and other rapidly developing regions in China. We analyzed the age-specific incidence and number of TB cases in the Nanshan District from 2011 to 2016. Over all, the age-standardized incidence of TB decreased at an annual rate of 3.4%. The incidence was lowest amongst the age group 0-14 and showed no increase in this group over the six-year period (P = 0.587). The fastest decreasing incidence was among the 15-24 age group, with a yearly decrease of 13.3% (β = 0.867, P < 0.001). In contrast, the TB incidence increased in the age groups 45-54, 55-54, and especially in those aged ≥65, whose yearly increase was 13.1% (β = 1.131, P < 0.001). The peak time of TB case presentation was in April, May, and June for all age groups, except in August for the 45-54 cohort. In the rapidly developing Nanshan District, TB control policies targeted to those aged 45 years and older should be considered. The presentation of TB cases appears to peak in the spring months.

Time-series analysis of tuberculosis from 2005 to 2017 in China

Seasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA-GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA-GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA-GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.

Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model

Objectives

The aims of this study were to develop a forecasting model for the incidence of tuberculosis (TB) and analyze the seasonality of infections in China; and to provide a useful tool for formulating intervention programs and allocating medical resources.

Methods

Data for the monthly incidence of TB from January 2004 to December 2015 were obtained from the National Scientific Data Sharing Platform for Population and Health (China). The Box–Jenkins method was applied to fit a seasonal auto-regressive integrated moving average (SARIMA) model to forecast the incidence of TB over the subsequent six months.

Results

During the study period of 144 months, 12,321,559 TB cases were reported in China, with an average monthly incidence of 6.4426 per 100,000 of the population. The monthly incidence of TB showed a clear 12-month cycle, and a seasonality with two peaks occurring in January and March and a trough in December. The best-fit model was SARIMA (1,0,0)(0,1,1)₁₂, which demonstrated adequate information extraction (white noise test, p > 0.05). Based on the analysis, the incidence of TB from January to June 2016 were 6.6335, 4.7208, 5.8193, 5.5474, 5.2202 and 4.9156 per 100,000 of the population, respectively.

Conclusions

According to the seasonal pattern of TB incidence in China, the SARIMA model was proposed as a useful tool for monitoring epidemics.

Distribution of Mycobacterium tuberculosis in Korea in the preceding decade

BACKGROUND

Tuberculosis (TB) is an infectious disease caused by the bacillus Mycobacterium tuberculosis (MTB); it is transmitted among people through air. The aim of this study was to assess the prevalence of TB and its clinical trends by collecting and analyzing data on specimens in Korea.

METHODS

All clinical specimens referred to the Dankook University Hospital Laboratory in Cheonan, Korea, from September 2005 to June 2016 were tested to isolate MTB using solid and liquid cultures, acid-fast bacilli (AFB) smears, and polymerase chain reactions (PCR).

RESULTS

In total, 146 150 specimens were collected; the mean TB positivity rate was 7.8%. The highest positivity rate was observed among patients 30-39 years of age (12.6%), followed by those 20-29 years of age (12.2%). The mean positivity rate was highest in 2010 and lowest in 2016 (10.7% and 6.7%, respectively). When comparing 2015-2011, we saw a decrease in the number of TB-positive patients of 3.4%; this represented an annual decrease in 0.9%.

CONCLUSION

Our data revealed a trend for a decrease in TB prevalence over time. Moreover, TB positivity rates were highest among the younger age groups in our study. Therefore, rapid diagnosis and treatment of TB in younger individuals are crucial.

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

OBJECTIVES

Xinjiang 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.

METHODS

Monthly 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.

RESULTS

A 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).

CONCLUSION

TB 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.