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

Application of ARIMA, and hybrid ARIMA Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya

Tuberculosis (TB) infections among children (below 15 years) is a growing concern, particularly in resource-limited settings. However, the TB burden among children is relatively unknown in Kenya where two-thirds of estimated TB cases are undiagnosed annually. Very few studies have used Autoregressive Integrated Moving Average (ARIMA), and hybrid ARIMA models to model infectious diseases globally. We applied ARIMA, and hybrid ARIMA models to predict and forecast TB incidences among children in Homa Bay and Turkana Counties in Kenya. The ARIMA, and hybrid models were used to predict and forecast monthly TB cases reported in the Treatment Information from Basic Unit (TIBU) system by health facilities in Homa Bay and Turkana Counties between 2012 and 2021. The best parsimonious ARIMA model that minimizes errors was selected based on a rolling window cross-validation procedure. The hybrid ARIMA-ANN model produced better predictive and forecast accuracy compared to the Seasonal ARIMA (0,0,1,1,0,1,12) model. Furthermore, using the Diebold-Mariano (DM) test, the predictive accuracy of ARIMA-ANN versus ARIMA (0,0,1,1,0,1,12) model were significantly different, p<0.001, respectively. The forecasts showed a TB incidence of 175 TB cases per 100,000 (161 to 188 TB incidences per 100,000 population) children in Homa Bay and Turkana Counties in 2022. The hybrid (ARIMA-ANN) model produces better predictive and forecast accuracy compared to the single ARIMA model. The findings show evidence that the incidence of TB among children below 15 years in Homa Bay and Turkana Counties is significantly under-reported and is potentially higher than the national average.

Association between averaged meteorological factors and tuberculosis risk: A systematic review and meta-analysis

Inconsistencies were discovered in the findings regarding the effects of meteorological factors on tuberculosis (TB). This study conducted a systematic review of published studies on the relationship between TB and meteorological factors and used a meta-analysis to investigate the pooled effects in order to provide evidence for future research and policymakers. The literature search was completed by August 3rd, 2021, using three databases: PubMed, Web of Science and Embase. Relative risks (RRs) in included studies were extracted and all effect estimates were combined together using meta-analysis. Subgroup analyses were carried out based on the resolution of exposure time, regional climate, and national income level. A total of eight studies were included after screening for inclusion and exclusion criteria. Our results show that TB risk was positively correlated with precipitation (RR = 1.32, 95% CI: 1.14, 1.51), while temperature (RR = 1.15, 95% CI: 1.00, 1.32), humidity (RR = 1.05, 95% CI: 0.99, 1.10), air pressure (RR = 0.89, 95% CI: 0.69, 1.14) and sunshine duration (RR = 0.95, 95% CI: 0.80, 1.13) all had no statistically significant correlation. Subgroup analysis shows that quarterly measure resolution, low and middle Human Development Index (HDI) level and subtropical climate increase TB risk not only in precipitation, but also in temperature and humidity. Moreover, less heterogeneity was observed in "high and extremely high" HDI areas and subtropical areas than that in other subgroups (I² = 0%). Precipitation, a subtropical climate, and a low HDI level are all positive influence factors to tuberculosis. Therefore, residents and public health managers should take precautionary measures ahead of time, especially in extreme weather conditions.

An influence of dew point temperature on the occurrence of Mycobacterium tuberculosis disease in Chennai, India

Climate factors such as dew point temperature, relative humidity and atmospheric temperature may be crucial for the spread of tuberculosis. This study was conducted for the first time to investigate the relationship of climatic factors with TB occurrence in an Indian setting. Daily tuberculosis notification data during 2008–2015 were generated from the National Treatment Elimination Program, and analogous daily climatic data were obtained from the Regional Meteorological Centre at Chennai city, Tamil Nadu, India. The decomposition method was adopted to split the series into deterministic and non-deterministic components, such as seasonal, non-seasonal, trend and cyclical, and non-deterministic climate factors. A generalized linear model was used to assess the relation independently. TB disease progression from latent stage infection to active was supported by higher dew point temperature and moderate temperature. It had a significant association with TB progression in the summer and monsoon seasons. The relative humidity may be favored in the winter and post-monsoon. The water tiny dew droplets may support the TB bacterium to recuperate in the environment.

Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis

Objective

COVID-19 may have a demonstrable influence on disease patterns. However, it remained unknown how tuberculosis (TB) epidemics are impacted by the COVID-19 outbreak. The purposes of this study are to evaluate the impacts of the COVID-19 outbreak on the decreases in the TB case notifications and to forecast the epidemiological trends in China.

Methods

The monthly TB incidents from January 2005 to December 2020 were taken. Then, we investigated the causal impacts of the COVID-19 pandemic on the TB case reductions using intervention analysis under the Bayesian structural time series (BSTS) method. Next, we split the observed values into different training and testing horizons to validate the forecasting performance of the BSTS method.

Results

The TB incidence was falling during 2005–2020, with an average annual percentage change of −3.186 (95% confidence interval [CI] −4.083 to −2.281), and showed a peak in March–April and a trough in January–February per year. The BSTS method assessed a monthly average reduction of 14% (95% CI 3.8% to 24%) in the TB case notifications from January–December 2020 owing to COVID-19 (probability of causal effect=99.684%, P=0.003), and this method generated a highly accurate forecast for all the testing horizons considering the small forecasting error rates and estimated a continued downward trend from 2021 to 2035 (annual percentage change =−2.869, 95% CI −3.056 to −2.681).

Conclusion

COVID-19 can cause medium- and longer-term consequences for the TB epidemics and the BSTS model has the potential to forecast the epidemiological trends of the TB incidence, which can be recommended as an automated application for public health policymaking in China. Considering the slow downward trend in the TB incidence, additional measures are required to accelerate the progress of the End TB Strategy.

A scoping review on climate change and tuberculosis

Climate change is a global public health challenge. The changes in climatic factors affect the pattern and burden of tuberculosis, which is a worldwide public health problem affecting low and middle-income countries. However, the evidence related to the impact of climate change on tuberculosis is few and far between. This study is a scoping review following a five-stage version of Arksey and O'Malley's method. We searched the literature using the keywords and their combination in Google scholar, and PubMed. Climate change affects tuberculosis through diverse pathways: changes in climatic factors like temperature, humidity, and precipitation influence host response through alterations in vitamin D distribution, ultraviolet radiation, malnutrition, and other risk factors. The rise in extreme climatic events induces population displacement resulting in a greater number of vulnerable and risk populations of tuberculosis. It creates a conducive environment of tuberculosis transmission and development of active tuberculosis and disrupts tuberculosis diagnosis and treatment services. Therefore, it stands to reasons that climate change affects tuberculosis, particularly in highly vulnerable countries and areas. However, further studies and novel methodologies are required to address such a complex relationship and better understand the occurrence of tuberculosis attributable to climate change.

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.