Seasonality of tuberculosis in delhi, India: a time series analysis

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.

The impact of the COVID-19 pandemic and associated suppression measures on the burden of tuberculosis in India

Background

Tuberculosis (TB) is a major cause of death globally. India carries the highest share of the global TB burden. The COVID-19 pandemic has severely impacted diagnosis of TB in India, yet there is limited data on how TB case reporting has changed since the pandemic began and which factors determine differences in case notification.

Methods

We utilized publicly available data on TB case reporting through the Indian Central TB Division from January 2017 through April of 2021 (prior to the first COVID-19 related lockdown). Using a Poisson model, we estimated seasonal and yearly patterns in TB case notification in India from January 2017 through February 2020 and extended this estimate as the counterfactual expected TB cases notified from March 2020 through April 2021. We characterized the differences in case notification observed and those expected in the absence of the pandemic by State and Territory. We then performed a linear regression to examine the relationship between the logit ratio of reported TB to counterfactual cases and mask use, mobility, daily hospitalizations/100,000 population, and public/total TB case reporting.

Results

We found 1,320,203 expected cases of TB (95% uncertainty interval (UI) 1,309,612 to 1,330,693) were not reported during the period from March 2020 through April 2021. This represents a 63.3% difference (95% UI 62.8 to 63.8) in reporting. We found that mobility data and average hospital admissions per month per population were correlated with differences in TB case notification, compared to the counterfactual in the absence of the pandemic (p > 0.001).

Conclusion

There was a large difference between reported TB cases in India and those expected in the absence of the pandemic. This information can help inform the Indian TB program as they consider interventions to accelerate case finding and notification once the pandemic related TB service disruptions improve. Mobility data and hospital admissions are surrogate measures that correlate with a greater difference in reported/expected TB cases and may correlate with a disruption in TB diagnostic services. However, further research is needed to clarify this association and identify other key contributors to gaps in TB case notifications in India.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07078-y.

Trends in incidence and mortality of tuberculosis in India over past three decades: a joinpoint and age-period-cohort analysis

Background

Tuberculosis, as a communicable disease, is an ongoing global epidemic that accounts for high burden of global mortality and morbidity. Globally, with an estimated 10 million new cases and around 1.4 million deaths, TB has emerged as one of the top 10 causes of morbidity and mortality in 2019. Worst hit 8 countries account for two thirds of the new TB cases in 2019, with India leading the count. Despite India's engagement in various TB control activities since its first recognition through the resolution passed in the All-India Sanitary Conference in 1912 and launch of first National Tuberculosis Control Programme in 1962, it has remained a major public health challenge to overcome. To accelerate progress towards the goal of ending TB by 2025, 5 years ahead of the global SDG target, it is imperative to outline the incidence and mortality trends of tuberculosis in India. This study aims to provide deep insights into the recent trends of TB incidence and mortality in India from 1990 to 2019.

Methods

This is an observational study based on the most recent data from the Global Burden of Disease (GBD) Study 2019. We extracted numbers, age-specific and age-standardized incidence and mortality rates of Tuberculosis for the period 1990–2019 from the Global Health Data Exchange. The average annual percent change (AAPC) along with 95% Confidence Interval (CI) in incidence and mortality were derived by joinpoint regression analysis; the net age, period, and cohort effects on the incidence and mortality rates were estimated by using Age–Period–Cohort model.

Results

During the study period, age-standardized incidence and mortality rates of TB in India declines from 390.22 to 223.01 and from 121.72 to 36.11 per 100,000 population respectively. The Joinpoint regression analysis showed a significant decreasing pattern in incidence rates in India between 1990 and 2019 for both male and female; but larger decline was observed in case of females (AAPC: − 2.21; 95% CI: − 2.29 to − 2.12; p < 0.001) as compared to males (AAPC: − 1.63; 95% CI: − 1.71 to − 1.54; p < 0.001). Similar pattern was observed for mortality where the declining trend was sharper for females (AAPC: − 4.35; 95% CI: − 5.12 to − 3.57; p < 0.001) as compared to males (AAPC: − 3.88; 95% CI: − 4.63 to − 3.11; p < 0.001). For age-specific rates, incidence and mortality rates of TB decreased for both male and female across all ages during this period. The age effect showed that both incidence and mortality significantly increased with advancing age; period effect showed that both incidence and mortality decreased with advancing time period; cohort effect on TB incidence and mortality also decreased from earlier birth cohorts to more recent birth cohorts.

Conclusion

Mortality and Incidence of TB decreased across all age groups for both male and female over the period 1990–2019. The incidence as well as mortality was higher among males as compared to females. The net age effect showed an unfavourable trend while the net period effect and cohort effect presented a favourable trend. Aging was likely to drive a continued increase in the mortality of TB. Though the incidence and mortality of tuberculosis significantly decreased from 1990 to 2019, the annual rate of reduction is not sufficient enough to achieve the aim of India’s National Strategic plan 2017–2025. Approximately six decades since the launch of the National Tuberculosis Control Programme, TB still remains a major public health problem in India. Government needs to strengthen four strategic pillars “Detect–Treat–Prevent–Build” (DTPB) in order to achieve TB free India as envisaged in the National Tuberculosis Elimination Programme (2020).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01740-y.

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.

Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China

Background

Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB.

Methods

We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018.

Results

Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively.

Conclusions

Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.

The characteristics of spatial-temporal distribution and cluster of tuberculosis in Yunnan Province, China, 2005-2018

BACKGROUND

Tuberculosis (TB) makes a big challenge to public health, especially in high TB burden counties of China and Greater Mekong Subregion (GMS). The aim of this study was to identify the spatial-temporal dynamic process and high-risk region of notified pulmonary tuberculosis (PTB), sputum smear-positive tuberculosis (SSP-TB) and sputum smear-negative tuberculosis (SSN-TB) cases in Yunnan, the south-western of China between years of 2005 to 2018. Meanwhile, to evaluate the similarity of prevalence pattern for TB among GMS.

METHODS

Data for notified PTB were extracted from the China Information System for Disease Control and Prevention (CISDCP) correspond to population information in 129 counties of Yunnan between 2005 to 2018. Seasonally adjusted time series defined the trend cycle and seasonality of PTB prevalence. Kulldorff's space-time scan statistics was applied to identify temporal, spatial and spatial-temporal PTB prevalence clusters at county-level of Yunnan. Pearson correlation coefficient and hierarchical clustering were applied to define the similarity of TB prevalence among borders with GMS.

RESULT

There were a total of 381,855 notified PTB cases in Yunnan, and the average prevalence was 59.1 per 100,000 population between 2005 to 2018. A declined long-term trend with seasonality of a peak in spring and a trough in winter for PTB was observed. Spatial-temporal scan statistics detected the significant clusters of PTB prevalence, the most likely cluster concentrated in the northeastern angle of Yunnan between 2011 to 2015 (RR = 2.6, P < 0.01), though the most recent cluster for PTB and spatial cluster for SSP-TB was in borders with GMS. There were six potential TB prevalence patterns among GMS.

CONCLUSION

This study detected aggregated time interval and regions for PTB, SSP-TB, and SSN-TB at county-level of Yunnan province. Similarity prevalence pattern was found in borders and GMS. The localized prevention strategy should focus on cross-boundary transmission and SSN-TB control.

Trend Analysis And Seasonality Of Tuberculosis Among Patients At The Hiwot Fana Specialized University Hospital, Eastern Ethiopia: A Retrospective Study

Purpose

Tuberculosis (TB) is one of the top 10 leading killer diseases in developing countries, particularly in Sub-Saharan Africa, including Ethiopia. Thus, this study aimed to assess the trend analysis and seasonality of TB at Hiwot Fana Specialized University Hospital, Eastern Ethiopia.

Methods and patients

A hospital-based retrospective study was conducted on 8,001 patients by reviewing all available patients’ data from January 1, 2015 to April 30, 2019, at the Hiwot Fana Specialized University Hospital, Eastern Ethiopia. Socio-demographic characteristics and results of the GeneXpert assay were taken from the registration book. The data were entered into EpiData 3.1 and analyzed by using the statistical Package for Social Sciences (SPSS) version 20.

Results

From a total of 8,001 samples tested using Genexpert, the overall prevalence of Mycobacterium tuberculosis and rifampicin resistance was found to be 1,254 (15.7%) and 53 (4.1%), respectively. A decreasing trend of TB prevalence was observed, and decreased from 19.3% in 2015, 18.6% in 2016, to 18.4% in 2017, 13.5% in 2018 and down to 13.0% in 2019 (P-value<0.001). The maximum number of TB cases were reported during autumn (454, 17.1%) and summer (310, 17.2%) compared to other seasons of all the study period. Being between the ages of 15–29 years (adjusted odds ratio (AOR)=1.7, 95% confidence interval (CI)=1.41–1.98), of male gender (AOR=0.84, 95% CI=0.75–0.96), experiencing a relapse of TB (AOR=0.51, 95% CI=0.35–0.78), and being HIV positive (AOR=0.51, 95% CI=0.3–0.86) were found to be factors associated with high proportion of tuberculosis.

Conclusion

Prevalence of TB has decreased year to year between January 2015 and April 2019. However, a high percentage of patients are still testing positive for TB with different seasonal variations. Thus, understanding and managing TB in seasonal variation, controlling relapse of TB, and screening of all HIV positive patients are recommended steps to reduce the transmission of tuberculosis in Ethiopia.

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.

Seasonality of childhood tuberculosis cases in Kampala, Uganda, 2010-2015

BACKGROUND

Seasonality in tuberculosis (TB) has been described, especially in children. However, few studies have assessed seasonality of TB in the equatorial region, and none in children.

OBJECTIVES

To assess for seasonality of childhood TB cases in Kampala, Uganda, and determine the role of temperature, rainfall patterns, and influenza cases on TB diagnoses.

METHODS

We retrospectively analyzed demographic and clinical data of children (under 15 years) diagnosed with TB at a pediatric TB clinic in Kampala, Uganda from 2010 to 2015. We performed decomposition analysis of the monthly case time series to assess seasonality. We compared monthly mean plots and performed Poisson regression to assess any association between TB diagnoses and temperature, rainfall, and influenza.

RESULTS

Of the 713 childhood TB cases diagnosed at the clinic, 609 (85%) were clinically diagnosed and 492 (69%) were pulmonary cases. There were minimal monthly variations in TB cases, with a trough in December and peaks in July and October, but there was no significant seasonality. Temperature variations did not show a clear pattern with TB diagnoses. Rainfall alternated with TB diagnoses in the first half of the year, but then overlapped in the second half and was significantly associated with TB diagnoses. Influenza cases were significantly related to TB diagnoses with (β = 0.05, 95% CI 0.01 to 0.09, p = 0.01) or without (β = 0.06, 95% CI 0.01 to 0.1, p = 0.01) rainfall, and had particular overlap with pulmonary TB cases.

CONCLUSIONS

Seasonal variations in childhood TB diagnoses were non-significant. Temperature did not have a clear pattern with TB diagnoses, but rainfall and influenza cases correlated with the primarily clinically diagnosed childhood TB cases.

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.

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.

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.

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.

Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis

BACKGROUND

Tuberculosis (TB) is the notifiable infectious disease with the second highest incidence in the Qinghai province, a province with poor primary health care infrastructure. Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective strategies to control and further eliminate TB.

METHODS

Our TB incidence data and meteorological data were extracted from the China Information System of Disease Control and Prevention and statistical yearbooks, respectively. We calculated the global and local Moran's I by using spatial autocorrelation analysis to detect the spatial clustering of TB incidence each year. A spatial panel data model was applied to examine the associations of meteorological factors with TB incidence after adjustment of spatial individual effects and spatial autocorrelation.

RESULTS

The Local Moran's I method detected 11 counties with a significantly high-high spatial clustering (average annual incidence: 294/100 000) and 17 counties with a significantly low-low spatial clustering (average annual incidence: 68/100 000) of TB annual incidence within the examined five-year period; the global Moran's I values ranged from 0.40 to 0.58 (all P-values < 0.05). The TB incidence was positively associated with the temperature, precipitation, and wind speed (all P-values < 0.05), which were confirmed by the spatial panel data model. Each 10 °C, 2 cm, and 1 m/s increase in temperature, precipitation, and wind speed associated with 9 % and 3 % decrements and a 7 % increment in the TB incidence, respectively.

CONCLUSIONS

High TB incidence areas were mainly concentrated in south-western Qinghai, while low TB incidence areas clustered in eastern and north-western Qinghai. Areas with low temperature and precipitation and with strong wind speeds tended to have higher TB incidences.

Tuberculosis Case Finding in Benin, 2000-2014 and Beyond: A Retrospective Cohort and Time Series Study

Objective. To determine any changes in tuberculosis epidemiology in the last 15 years in Benin, seasonal variations, and forecasted numbers of tuberculosis cases in the next five years. Materials and Methods. Retrospective cohort and time series study of all tuberculosis cases notified between 2000 and 2014. The “R” software version 3.2.1 (Institute for Statistics and Mathematics Vienna Austria) and the Box-Jenkins 1976 modeling approach were used for time series analysis. Results. Of 246943 presumptive cases, 54303 (22%) were diagnosed with tuberculosis. Annual notified case numbers increased, with the highest reported in 2011. New pulmonary bacteriologically confirmed tuberculosis (NPBCT) represented 78%  ± SD 2%. Retreatment cases decreased from 10% to 6% and new pulmonary clinically diagnosed cases increased from 2% to 8%. NPBCT notification rates decreased in males from 2012, in young people aged 15–34 years and in Borgou-Alibori region. There was a seasonal pattern in tuberculosis cases. Over 90% of NPBCT were HIV-tested with a stable HIV prevalence of 13%. The ARIMA best fit model predicted a decrease in tuberculosis cases finding in the next five years. Conclusion. Tuberculosis case notifications are predicted to decrease in the next five years if current passive case finding is used. Additional strategies are needed in the country.

Seasonality and trend analysis of tuberculosis in Lahore, Pakistan from 2006 to 2013

Tuberculosis (TB) is a respiratory infectious disease which shows seasonality. Seasonal variation in TB notifications has been reported in different regions, suggesting that various geographic and demographic factors are involved in seasonality. The study was designed to find out the temporal and seasonal pattern of TB incidence in Lahore, Pakistan from 2006 to 2013 in newly diagnosed pulmonary TB cases. SPSS version 21 software was used for correlation to determine the temporal relationship and time series analysis for seasonal variation. Temperature was found to be significantly associated with TB incidence at the 0.01 level with p = 0.006 and r = 0.477. Autocorrelation function and partial autocorrelation function showed a significant peak at lag 4 suggesting a seasonal component of the TB series. Seasonal adjusted factor showed peak seasonal variation in the second quarter (April–June). The expert modeler predicted the Holt–Winter’s additive model as the best fit model for the time series, which exhibits a linear trend with constant (additive) seasonal variations, and the stationary R² value was found to be 0.693. The forecast shows a declining trend with seasonality. A significant temporal relation with a seasonal pattern and declining trend with variable amplitudes of fluctuation was observed in the incidence of TB.

Analyzing Seasonal Variations in Suicide With Fourier Poisson Time-Series Regression: A Registry-Based Study From Norway, 1969-2007

Seasonal variation in the number of suicides has long been acknowledged. It has been suggested that this seasonality has declined in recent years, but studies have generally used statistical methods incapable of confirming this. We examined all suicides occurring in Norway during 1969-2007 (more than 20,000 suicides in total) to establish whether seasonality decreased over time. Fitting of additive Fourier Poisson time-series regression models allowed for formal testing of a possible linear decrease in seasonality, or a reduction at a specific point in time, while adjusting for a possible smooth nonlinear long-term change without having to categorize time into discrete yearly units. The models were compared using Akaike's Information Criterion and analysis of variance. A model with a seasonal pattern was significantly superior to a model without one. There was a reduction in seasonality during the period. Both the model assuming a linear decrease in seasonality and the model assuming a change at a specific point in time were both superior to a model assuming constant seasonality, thus confirming by formal statistical testing that the magnitude of the seasonality in suicides has diminished. The additive Fourier Poisson time-series regression model would also be useful for studying other temporal phenomena with seasonal components.

Analyzing seasonality of tuberculosis across Indian states and union territories

A significant seasonal variation in tuberculosis (TB) is observed in north India during 2006-2011, particularly in states like Himachal Pradesh, Haryana and Rajasthan. To quantify the seasonal variation, we measure average amplitude (peak to trough distance) across seasons in smear positive cases of TB and observe that it is maximum for Himachal Pradesh (40.01%) and minimum for Maharashtra (3.87%). In north India, smear positive cases peak in second quarter (April-June) and reach a trough in fourth quarter (October-December), however low seasonal variation is observed in southern region of the country. The significant correlations as 0.64 (p-value<0.001), 0.54 (p-value<0.01) and 0.42 (p-value<0.05) are observed between minimum temperature and seasonality of TB at lag-1 in north, central and northeast India respectively. However, in south India, this correlation is not significant.

Time series analysis of demographic and temporal trends of tuberculosis in Singapore

Background

Singapore is an intermediate tuberculosis (TB) incidence country, with a recent rise in TB incidence from 2008, after a fall in incidence since 1998. This study identified population characteristics that were associated with the recent increase in TB cases, and built a predictive model of TB risk in Singapore.

Methods

Retrospective time series analysis was used to study TB notification data collected from 1995 to 2011 from the Singapore Tuberculosis Elimination Program (STEP) registry. A predictive model was developed based on the data collected from 1995 to 2010 and validated using the data collected in 2011.

Results

There was a significant difference in demographic characteristics between resident and non-resident TB cases. TB risk was higher in non-residents than in residents throughout the period. We found no significant association between demographic and macro-economic factors and annual incidence of TB with or without adjusting for the population-at-risk. Despite growing non-resident population, there was a significant decrease in the non-resident TB risk (p < 0.0001). However, there was no evidence of trend in the resident TB risk over this time period, though differences between different demographic groups were apparent with ethnic minorities experiencing higher incidence rates.

Conclusion

The study found that despite an increasing size of non-resident population, TB risk among non-residents was decreasing at a rate of about 3% per year. There was an apparent seasonality in the TB reporting.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2458-14-1121) contains supplementary material, which is available to authorized users.