Seasonal variations in notification of active tuberculosis cases in China, 2005-2012

Tuberculosis disease burden in China: a spatio-temporal clustering and prediction study

Introduction

The primary aim of this study is to investigate and predict the prevalence and determinants of tuberculosis disease burden in China. Leveraging high-quality data sources and employing a methodologically rigorous approach, the study endeavors to enhance our understanding of tuberculosis control efforts across different regions of China. First, through nationwide spatio-temporal cluster analysis, we summarized the status of tuberculosis burden in various regions of China and explore the differences, thereby providing a basis for formulating more targeted tuberculosis prevention and control policies in different regions; Subsequently, using a time series-based forecasting model, we conducted the first-ever national tuberculosis burden trend forecast to offer scientific guidance for timely adjustments in planning and resource allocation. This research seeks to contribute significantly to China's existing tuberculosis prevention and control system.

Materials and methods

This research draws upon publicly available pulmonary tuberculosis (PTB) incidence and mortality statistics from 31 provinces and municipalities of mainland China between 2004 and 2018. We organized and classified these data according to province, month, year, and patient age group. Overall, the sample included 14,816,329 new instances of PTB and 42,465 PTB-related fatalities. We used spatiotemporal cluster analysis to record the epidemiological characteristics and incidence patterns of PTB during this period. Additionally, a time series model was constructed to forecast and analyze the incidence and mortality trends of PTB in China.

Results

This study reveals significant regional variations in PTB incidence and mortality in China. Tibet (124.24%) and Xinjiang (114.72%) in western China exhibited the largest percentage change in tuberculosis (TB) incidence, while Zhejiang Province (-50.45%) and Jiangsu Province (-51.33%) in eastern China showed the largest decreases. Regions with significant percentage increases in PTB mortality rates (>100%) included four western regions, six central regions, and five eastern regions. The regions with relatively large percentage decreases in the mortality rate of PTB include Tianjin (-52.25%) and Shanghai (-68.30%). These differences are attributed to two main factors: (1) economic imbalances leading to poor TB control in underdeveloped areas, and (2) differences in TB-related policies among provinces causing uneven distribution of disease risks. Consequently, China may still face challenges in achieving the World Health Organization's 2030 tuberculosis control goals. Nationwide, the mortality rate of PTB in China increased between 2004 and 2018 (percentage change: 105.35%, AAPC: 4.1), while the incidence of PTB showed a downward trend (percentage change: -20.59%, AAPC: -2.1). Among different age groups, the 0-19 age group has the smallest disease burden. While incidence and mortality from TB were primarily found in adults 60 years of age or older, the age group of 0-19 years has the smallest burden of TB, highlighting obvious differences in age characteristics. It is predicted that the mortality rate of TB in China will continue to increase. In summary, the TB epidemic in China has been largely controlled due to the implementation of many public health programs and policies targeting specific groups and geographical areas. Finding and supporting effective health programs will make it possible to achieve the World Health Organization's goal of controlling tuberculosis in China.

Temporal disruption in tuberculosis incidence patterns during COVID-19: a time series analysis in China

Background

Despite extensive knowledge of tuberculosis (TB) and its control, there remains a significant gap in understanding the comprehensive impact of the COVID-19 pandemic on TB incidence patterns. This study aims to explore the impact of COVID-19 on the pattern of pulmonary tuberculosis in China and examine the application of time series models in the analysis of these patterns, providing valuable insights for TB prevention and control.

Methods

We used pre-COVID-19 pulmonary tuberculosis (PTB) data (2007-2018) to fit SARIMA, Prophet, and LSTM models, assessing their ability to predict PTB incidence trends. These models were then applied to compare the predicted PTB incidence patterns with actual reported cases during the COVID-19 pandemic (2020-2023), using deviations between predicted and actual values to reflect the impact of COVID-19 countermeasures on PTB incidence.

Results

Prior to the COVID-19 outbreak, PTB incidence in China exhibited a steady decline with strong seasonal fluctuations, characterized by two annual peaks-one in March and another in December. These seasonal trends persisted until 2019. During the COVID-19 pandemic, there was a significant reduction in PTB cases, with actual reported cases falling below the predicted values. The disruption in PTB incidence appears to be temporary, as 2023 data indicate a gradual return to pre-pandemic trends, though the incidence rate remains slightly lower than pre-COVID levels. Additionally, we compared the fitting and forecasting performance of the SARIMA, Prophet, and LSTM models using RMSE (root mean squared error), MAE (mean absolute error), and MAPE (mean absolute percentage error) indexes prior to the COVID-19 outbreak. We found that the Prophet model had the lowest values for all three indexes, demonstrating the best fitting and prediction performance.

Conclusions

The COVID-19 pandemic has had a temporary but significant impact on PTB incidence in China, leading to a reduction in reported cases during the pandemic. However, as pandemic control measures relax and the healthcare system stabilizes, PTB incidence patterns are expected to return to pre-COVID-19 levels. The Prophet model demonstrated the best predictive performance and proves to be a valuable tool for analyzing PTB trends and guiding public health planning in the post-pandemic era.

Effects of meteorological factors on tuberculosis and potential modifiers in Zhejiang Province, China

Although some studies have explored the role of meteorological factors in the development of tuberculosis (TB), the majority have been confined to single regions, leading to inconsistent findings. Consequently, we conducted a multi-city study not only to determine whether meteorological factors significantly influence the risk of developing TB but also to assess the magnitude of these effects and explore potential modifying factors. Data on daily reported TB cases and meteorological factors were collected from January 1, 2013, to December 31, 2022, across 11 cities in Zhejiang Province. A distributed lag non-linear model using a quasi-Poisson distribution was employed. Multivariate meta-regression was used to obtain overall pooled estimates and assess heterogeneity. From 2013 to 2022, 267,932 TB cases were reported in Zhejiang Province. Notably, a nonlinear relationship was observed between temperature and TB, with the relative risk (RR) peaking at 1.0 °C (RR = 1.882, 95% CI 1.173-3.020). The effect of low temperature was immediate and significant for a 13-day lag period, with the maximum effect at lag0 (RR = 1.014, 95% CI 1.008-1.021). The exposure-response curve between relative humidity (RH) and TB exhibited an M-shape, with the RR peaking at 47.7% (RR = 1.642, 95% CI 1.044-2.582). The lag effect of low RH was significant at lag 25-59, with the highest RR observed at lag 32 (RR = 1.011, 95% CI 1.001-1.022). Gross domestic product (GDP) per person, population density, and latitude demonstrated significant modification effects. Our study showed that low temperature and RH were associated with an increased risk of TB. Additionally, GDP per person, population density, and latitude may play important roles in explaining the association between RH and TB. These findings provide scientific evidence for the development of geographically specific public health policies.

Long- and short-run asymmetric impacts of climate variation on tuberculosis based on a time series study

Distinguishing between long-term and short-term effects allows for the identification of different response mechanisms. This study investigated the long- and short-run asymmetric impacts of climate variation on tuberculosis (TB) and constructed forecasting models using the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL). TB showed a downward trend, peaking in March-May per year. A 1 h increment or decrement in aggregate sunshine hours resulted in an increase of 32 TB cases. A 1 m/s increment and decrement in average wind velocity contributed to a decrement of 3600 and 5021 TB cases, respectively (Wald long-run asymmetry test [WLR] = 13.275, P < 0.001). A 1% increment and decrement in average relative humidity contributed to an increase of 115 and 153 TB cases, respectively. A 1 hPa increment and decrement in average air pressure contributed to a decrease of 318 and 91 TB cases, respectively (WLR = 7.966, P = 0.005). ∆temperature(-), ∆(sunshine hours)( -), ∆(wind velocity)(+) and ∆(wind velocity)(-) at different lags had a meaningful short-run effect on TB. The NARDL outperformed the ARDL in forecasting. Climate variation has significant long- and short-run asymmetric impacts on TB. By incorporating both dimensions of effects into the NARDL, the accuracy of the forecasts and policy recommendations for TB can be enhanced.

Spatiotemporal patterns and socioeconomic determinants of pulmonary tuberculosis in Dongguan city, China, during 2011-2020: an ecological study

OBJECTIVE

Pulmonary tuberculosis (PTB) is a critical challenge worldwide, particularly in China. This study aimed to explore the spatiotemporal transmission patterns and socioeconomic factors of PTB in Dongguan city, China.

METHODS/DESIGN

An ecological study based on the reported new PTB cases between 2011 and 2020 was conducted in Dongguan city, China. The spatiotemporal analysis methods were used to explore the long-term trend, spatiotemporal transmission pattern and socioeconomic factors of PTB.

MAIN OUTCOME MEASURES

The number of new PTB cases.

PARTICIPANTS

We collected 35 756 new PTB cases, including 23 572 males and 12 184 females.

RESULTS

The seasonal-trend decomposition indicated a significant downward trend for PTB with a significant peak in 2017 and 2018, and local spatial autocorrelation showed more and more high-high clusters in the central and north-central towns with high incidence. The multivariate spatial time series analysis revealed that the endemic component had a leading role in driving PTB transmission, with a high total effect value being 189.40 (95% CI: 171.65-207.15). A Bayesian spatiotemporal model revealed that PTB incidence is positively associated with the agricultural population ratio (relative risk (RR) =1.074), gender ratio (RR=1.104) and the number of beds in medical institutions (RR=1.028).

CONCLUSIONS

These findings revealed potential spatiotemporal variability and spatial aggregation of PTB, so targeted preventive strategies should be made in different towns based on spatiotemporal transmission patterns and risk factors.

Predicting the incidence of rifampicin resistant tuberculosis in Yunnan, China: a seasonal time series analysis based on routine surveillance data

BACKGROUND

Rifampicin resistant tuberculosis (RR-TB) poses a growing threat to individuals and communities. This study utilized a seasonal autoregressive integrated moving average (SARIMA) model to quantitatively predict the monthly incidence of RR-TB in Yunnan Province which could guide government health administration departments and the centers for disease control and prevention (CDC) in preventing and controlling the RR-TB epidemic.

METHODS

The study utilized routine surveillance reporting data from the infectious Disease Network Surveillance and Reporting System. Monthly incidence rates of RR-TB were collected from January 2019 to December 2022. A time series SARIMA model was used to predict the number of monthly RR-TB cases in Yunnan Province in 2023, and the model was validated using time series plots, seasonal and non-seasonal differencing, autocorrelation and partial autocorrelation analysis, and white noise tests.

RESULTS

From 2019 to 2022, the incidence of RR-TB decreases as the incidence of all TB decreases (P < 0.05). There was no significant change in the proportion of RR-TB among all TB cases, which remained within 2.5% (P>0.05). The time series decomposition shows that it presented obvious seasonality, periodicity and randomness after being decomposed. Time series analysis was performed on the original series after 1 non-seasonal difference and 1 seasonal difference, the ADF test showed P < 0.05. According to ACF and PACF, the SARIMA (1, 1, 1) (1, 1, 0)12 model was chosen and statistically significant model parameter estimates (P < 0.05). The predicted seasonal trend of RR-TB incidence in 2019 to 2023 was similar to the actual data. The percentage accuracy in the prediction excesses 80% in 2019 to 2022 and is all within 95% CI. However there was a certain gap between the actual incidence and the predicted value in 2023, and the acutual incidence had increased by 12.4% compared to 2022. The percentage of accuracy in the prediction was only 70% in 2023.

CONCLUSIONS

We found the incidence of RR-TB was based on that of all TB in Yunnan. The SARIMA model successfully predicted the seasonal incidence trend of RR-TB in Yunnan Province in 2019 to 2023, but the prediction precision could be influenced by factors such as new infectious disease outbreaks or pandemics, social issues, environmental challenges or other unknown risks. Hence CDCs should pay special attention to the post epidemic effects of new infectious disease outbreaks or pandemics, carry out monitoring and early warning, and better optimize disease prediction models.

Pulmonary Tuberculosis Notification Rate Within Shenzhen, China, 2010-2019: Spatial-Temporal Analysis

BACKGROUND

Pulmonary tuberculosis (PTB) is a chronic communicable disease of major public health and social concern. Although spatial-temporal analysis has been widely used to describe distribution characteristics and transmission patterns, few studies have revealed the changes in the small-scale clustering of PTB at the street level.

OBJECTIVE

The aim of this study was to analyze the temporal and spatial distribution characteristics and clusters of PTB at the street level in the Shenzhen municipality of China to provide a reference for PTB prevention and control.

METHODS

Data of reported PTB cases in Shenzhen from January 2010 to December 2019 were extracted from the China Information System for Disease Control and Prevention to describe the epidemiological characteristics. Time-series, spatial-autocorrelation, and spatial-temporal scanning analyses were performed to identify the spatial and temporal patterns and high-risk areas at the street level.

RESULTS

A total of 58,122 PTB cases from 2010 to 2019 were notified in Shenzhen. The annual notification rate of PTB decreased significantly from 64.97 per 100,000 population in 2010 to 43.43 per 100,000 population in 2019. PTB cases exhibited seasonal variations with peaks in late spring and summer each year. The PTB notification rate was nonrandomly distributed and spatially clustered with a Moran I value of 0.134 (P=.02). One most-likely cluster and 10 secondary clusters were detected, and the most-likely clustering area was centered at Nanshan Street of Nanshan District covering 6 streets, with the clustering time spanning from January 2010 to November 2012.

CONCLUSIONS

This study identified seasonal patterns and spatial-temporal clusters of PTB cases at the street level in the Shenzhen municipality of China. Resources should be prioritized to the identified high-risk areas for PTB prevention and control.

The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China

BACKGROUND

Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB.

METHOD

Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA.

RESULTS

A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup.

CONCLUSION

For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.

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.

Association of air pollutants and meteorological factors with tuberculosis: a national multicenter ecological study in China

The impact of weather variability and air pollutants on tuberculosis (TB) has been a research hotspot. Previous studies have mostly been limited to a certain area or with a small sample size of cases, and multi-scale systematic studies are lacking. In this study, 14,816,329 TB cases were collected from 31 provinces in China between 2004 and 2018 to estimate the association between TB risk and meteorological factors and air pollutants using a two-stage time-series analysis. The impact and lagged time of meteorological factors and air pollutants on TB risk varied greatly in different provinces and regions. Overall cumulative exposure-response summary associations across 31 provinces suggested that high monthly mean relative humidity (RH) (66.8-82.4%, percentile56-100 (P56-100)), rainfall (316.5-331.1 mm, P96-100), PM2.5 exposure concentration (93.3-145.0 μg/m3, P58-100), and low monthly mean wind speed (1.6-2.1 m/s, P0-38) increased the risk of TB incidence, with a relative risk (RR) of 1.10 (95% CI: 1.04-1.16), 1.10 (95% CI: 1.03-1.16), 2.08 (95% CI: 1.18-3.65), and 2.06 (95% CI: 1.27-3.33), and attributable risk percent (AR%) of 9%, 9%, 52%, and 51%, respectively. Conversely, high monthly average wind speed (2.3-2.9 m/s, P54-100) and mean temperature (20.2-25.3 °C, P79-96), and low monthly average rainfall (2.4-25.2 mm, P0-7) and concentration of SO2 (8.1-21.2 μg/m3, P0-16) exposure decreased the risk of TB incidence, with an overall cumulative RR of 0.92 (95% CI: 0.87-0.98), 0.74 (95% CI: 0.59-0.94), 0.87 (95% CI: 0.79-0.95), and 0.72 (95% CI: 0.56-0.93), respectively. Our study provided insights into future planning of public health interventions for TB.

Spatial-temporal analysis of pulmonary tuberculosis in Hubei Province, China, 2011-2021

BACKGROUND

Pulmonary tuberculosis (PTB) is an infectious disease of major public health problem, China is one of the PTB high burden counties in the word. Hubei is one of the provinces having the highest notification rate of tuberculosis in China. This study analyzed the temporal and spatial distribution characteristics of PTB in Hubei province for targeted intervention on TB epidemics.

METHODS

The data on PTB cases were extracted from the National Tuberculosis Information Management System correspond to population in 103 counties of Hubei Province from 2011 to 2021. The effect of PTB control was measured by variation trend of bacteriologically confirmed PTB notification rate and total PTB notification rate. Time series, spatial autonomic correlation and spatial-temporal scanning methods were used to identify the temporal trends and spatial patterns at county level of Hubei.

RESULTS

A total of 436,955 cases were included in this study. The total PTB notification rate decreased significantly from 81.66 per 100,000 population in 2011 to 52.25 per 100,000 population in 2021. The peak of PTB notification occurred in late spring and early summer annually. This disease was spatially clustering with Global Moran's I values ranged from 0.34 to 0.63 (P< 0.01). Local spatial autocorrelation analysis indicated that the hot spots are mainly distributed in the southwest and southeast of Hubei Province. Using the SaTScan 10.0.2 software, results from the staged spatial-temporal analysis identified sixteen clusters.

CONCLUSIONS

This study identified seasonal patterns and spatial-temporal clusters of PTB cases in Hubei province. High-risk areas in southwestern Hubei still exist, and need to focus on and take targeted control and prevention measures.

Predicting the Number of Reported Pulmonary Tuberculosis in Guiyang, China, Based on Time Series Analysis Techniques

Tuberculosis (TB) is one of the world's deadliest infectious disease killers today, and despite China's increasing efforts to prevent and control TB, the TB epidemic is still very serious. In the context of the COVID-19 pandemic, if reliable forecasts of TB epidemic trends can be made, they can help policymakers with early warning and contribute to the prevention and control of TB. In this study, we collected monthly reports of pulmonary tuberculosis (PTB) in Guiyang, China, from January 1, 2010 to December 31, 2020, and monthly meteorological data for the same period, and used LASSO regression to screen four meteorological factors that had an influence on the monthly reports of PTB in Guiyang, including sunshine hours, relative humidity, average atmospheric pressure, and annual highest temperature, of which relative humidity (6-month lag) and average atmospheric pressure (7-month lag) have a lagging effect with the number of TB reports in Guiyang. Based on these data, we constructed ARIMA, Holt-Winters (additive and multiplicative), ARIMAX (with meteorological factors), LSTM, and multivariable LSTM (with meteorological factors). We found that the addition of meteorological factors significantly improved the performance of the time series prediction model, which, after comprehensive consideration, included the ARIMAX (1,1,1) (0,1,2)₁₂ model with a lag of 7 months at the average atmospheric pressure, outperforms the other models in terms of both fit (RMSE = 37.570, MAPE = 10.164%, MAE = 28.511) and forecast sensitivity (RMSE = 20.724, MAPE = 6.901%, MAE = 17.306), so the ARIMAX (1,1,1) (0,1,2)₁₂ model with a lag of 7 months can be used as a predictor tool for predicting the number of monthly reports of PTB in Guiyang, China.

A tandem radiative/evaporative cooler for weather-insensitive and high-performance daytime passive cooling

Radiative cooling and evaporative cooling with low carbon footprint are regarded as promising passive cooling strategies. However, the intrinsic limits of continuous water supply with complex systems for evaporative cooling, and restricted cooling power as well as the strict requirement of weather conditions for radiative cooling, hinder the scale of their practical applications. Here, we propose a tandem passive cooler composed of bilayer polymer that enables dual-functional passive cooling of radiation and evaporation. Specifically, the high reflectivity to sunlight and mid-infrared emissivity of this polymer film allows excellent radiative cooling performance, and its good atmospheric water harvesting property of underlayer ensures self-supply of water and high evaporative cooling power. Consequently, this tandem passive cooler overcomes the fundamental difficulties of radiative cooling and evaporative cooling and shows the applicability under various conditions of weather/climate. It is expected that this design can expand the practical application domain of passive cooling.

Spatio-temporal distribution of tuberculosis and the effects of environmental factors in China

Background

Although the World Health Organization reports that the incidence of tuberculosis in China is decreasing every year, the burden of tuberculosis in China is still very heavy. Understanding the spatial and temporal distribution pattern of tuberculosis in China and its influencing environmental factors will provide effective reference for the prevention and treatment of tuberculosis.

Methods

Data of TB incidence from 2010 to 2017 were collected. Time series and global spatial autocorrelation were used to analyze the temporal and spatial distribution pattern of tuberculosis incidence in China, Geodetector and Geographically Weighted Regression model were used to analyze the environmental factors affecting the TB incidence.

Results

In addition to 2007 and 2008, the TB incidence decreased in general. TB has a strong spatial aggregation. Cities in Northwest China have been showing a trend of high-value aggregation. In recent years, the center of gravity of high-value aggregation area in South China has moved further south. Temperature, humidity, precipitation, PM₁₀, PM_2.5, O₃, NO₂ and SO₂ have impacts on TB incidence, and in different regions, the environmental factors show regional differences.

Conclusions

Residents should pay more attention to the risk of developing TB caused by climate change and air pollutant exposure. Increased efforts should be placed on areas with high-value clustering in future public resource configurations.

Time trend prediction and spatial-temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014-2020

Background

Guizhou is located in the southwest of China with high multidrug-resistant tuberculosis (MDR-TB) epidemic. To fight this disease, Guizhou provincial authorities have made efforts to establish MDR-TB service system and perform the strategies for active case finding since 2014. The expanded case finding starting from 2019 and COVID-19 pandemic may affect the cases distribution. Thus, this study aims to analyze MDR-TB epidemic status from 2014 to 2020 for the first time in Guizhou in order to guide control strategies.

Methods

Data of notified MDR-TB cases were extracted from the National TB Surveillance System correspond to population information for each county of Guizhou from 2014 to 2020. The percentage change was calculated to quantify the change of cases from 2014 to 2020. Time trend and seasonality of case series were analyzed by a seasonal autoregressive integrated moving average (SARIMA) model. Spatial–temporal distribution at county-level was explored by spatial autocorrelation analysis and spatial–temporal scan statistic.

Results

Guizhou has 9 prefectures and 88 counties. In this study, 1,666 notified MDR-TB cases were included from 2014–2020. The number of cases increased yearly. Between 2014 and 2019, the percentage increase ranged from 6.7 to 21.0%. From 2019 to 2020, the percentage increase was 62.1%. The seasonal trend illustrated that most cases were observed during the autumn with the trough in February. Only in 2020, a peak admission was observed in June. This may be caused by COVID-19 pandemic restrictions being lifted until May 2020. The spatial–temporal heterogeneity revealed that over the years, most MDR-TB cases stably aggregated over four prefectures in the northwest, covering Bijie, Guiyang, Liupanshui and Zunyi. Three prefectures (Anshun, Tongren and Qiandongnan) only exhibited case clusters in 2020.

Conclusion

This study identified the upward trend with seasonality and spatial−temporal clusters of MDR-TB cases in Guizhou from 2014 to 2020. The fast rising of cases and different distribution from the past in 2020 were affected by the expanded case finding from 2019 and COVID-19. The results suggest that control efforts should target at high-risk periods and areas by prioritizing resources allocation to increase cases detection capacity and better access to treatment.

Spatiotemporal Epidemiology of Tuberculosis in Thailand from 2011 to 2020

Tuberculosis is a leading cause of infectious disease globally, especially in developing countries. Better knowledge of spatial and temporal patterns of tuberculosis burden is important for effective control programs as well as informing resource and budget allocation. Studies have demonstrated that TB exhibits highly complex dynamics in both spatial and temporal dimensions at different levels. In Thailand, TB research has been primarily focused on surveys and clinical aspects of the disease burden with little attention on spatiotemporal heterogeneity. This study aimed to describe temporal trends and spatial patterns of TB incidence and mortality in Thailand from 2011 to 2020. Monthly TB case and death notification data were aggregated at the provincial level. Age-standardized incidence and mortality were calculated; time series and global and local clustering analyses were performed for the whole country. There was an overall decreasing trend with seasonal peaks in the winter. There was spatial heterogeneity with disease clusters in many regions, especially along international borders, suggesting that population movement and socioeconomic variables might affect the spatiotemporal distribution in Thailand. Understanding the space-time distribution of TB is useful for planning targeted disease control program activities. This is particularly important in low- and middle-income countries including Thailand to help prioritize allocation of limited resources.

Physiological Essence of Magnesium in Plants and Its Widespread Deficiency in the Farming System of China

Magnesium (Mg) is an essential nutrient for a wide array of fundamental physiological and biochemical processes in plants. It largely involves chlorophyll synthesis, production, transportation, and utilization of photoassimilates, enzyme activation, and protein synthesis. As a multifaceted result of the introduction of high-yielding fertilizer-responsive cultivars, intensive cropping without replenishment of Mg, soil acidification, and exchangeable Mg (Ex-Mg) leaching, Mg has become a limiting nutrient for optimum crop production. However, little literature is available to better understand distinct responses of plants to Mg deficiency, the geographical distribution of soil Ex-Mg, and the degree of Mg deficiency. Here, we summarize the current state of knowledge of key plant responses to Mg availability and, as far as possible, highlight spatial Mg distribution and the magnitude of Mg deficiency in different cultivated regions of the world with a special focus on China. In particular, ~55% of arable lands in China are revealed Mg-deficient (< 120 mg kg-1 soil Ex-Mg), and Mg deficiency literally becomes increasingly severe from northern (227-488 mg kg-1) to southern (32-89 mg kg-1) China. Mg deficiency primarily traced back to higher depletion of soil Ex-Mg by fruits, vegetables, sugarcane, tubers, tea, and tobacco cultivated in tropical and subtropical climate zones. Further, each unit decline in soil pH from neutral reduced ~2-fold soil Ex-Mg. This article underscores the physiological importance of Mg, potential risks associated with Mg deficiency, and accordingly, to optimize fertilization strategies for higher crop productivity and better quality.

Prediction of tuberculosis cases based on sociodemographic and environmental factors in gombak, Selangor, Malaysia: A comparative assessment of multiple linear regression and artificial neural network models

Background

Early prediction of tuberculosis (TB) cases is very crucial for its prevention and control. This study aims to predict the number of TB cases in Gombak based on sociodemographic and environmental factors.

Methods

The sociodemographic data of 3325 TB cases from January 2013 to December 2017 in Gombak district were collected from the MyTB web and TB Information System database. Environmental data were obtained from the Department of Environment, Malaysia; Department of Irrigation and Drainage, Malaysia; and Malaysian Metrological Department from July 2012 to December 2017. Multiple linear regression (MLR) and artificial neural network (ANN) were used to develop the prediction model of TB cases. The models that used sociodemographic variables as the input datasets were referred as MLR1 and ANN1, whereas environmental variables were represented as MLR2 and ANN2 and both sociodemographic and environmental variables together were indicated as MLR3 and ANN3.

Results

The ANN was found to be superior to MLR with higher adjusted coefficient of determination (R²) values in predicting TB cases; the ranges were from 0.35 to 0.47 compared to 0.07 to 0.14, respectively. The best TB prediction model, that is, ANN3 was derived from nationality, residency, income status, CO, NO₂, SO₂, PM₁₀, rainfall, temperature, and atmospheric pressure, with the highest adjusted R² value of 0.47, errors below 6, and accuracies above 96%.

Conclusions

It is envisaged that the application of the ANN algorithm based on both sociodemographic and environmental factors may enable a more accurate modeling for predicting TB cases.

Short-term effects of ambient air pollution and meteorological factors on tuberculosis in semi-arid area, northwest China: a case study in Lanzhou

To investigate the short-term effects of ambient air pollution and meteorological factors on daily tuberculosis (TB), semi-parametric generalized additive model was used to assess the impacts of ambient air pollutants and meteorological factors on daily TB case from 2005 to 2010 in Chengguan District, Lanzhou, China. Then a non-stratification parametric model and a stratification parametric model were applied to study the interactive effect of air pollutants and meteorological factors on daily TB. The results show that sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter with aerodynamic diameter less than 10μm (PM₁₀) were positively correlated with daily TB case; the excess risk (ER) and 95% confidence interval (CI) were 1.79% (0.40%, 3.20%), 3.86% (1.81%, 5.96%), and 0.32% (0.02%, 0.62%), respectively. Daily TB case was positively correlated with maximum temperature, minimum temperature, average temperature, vapor pressure, and relative humidity, but negatively correlated with atmospheric pressure, wind speed, and sunshine duration. The association with average temperature was the strongest, whose ER and 95% CI were 4.43% (3.15%, 5.72%). In addition, there were significant interaction effects between air pollutants and meteorological factors on daily TB case.

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.

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran's I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.

Seasonal influence of tuberculosis diagnosis in Rwanda

Background

Tuberculosis (TB) remains a major global health concern. Previous research reveals that TB may have a seasonal peak during the spring and summer seasons in temperate climates; however, few studies have been conducted in tropical climates. This study evaluates the influence of seasonality on laboratory-confirmed TB diagnosis in Rwanda, a tropical country with two rainy and two dry seasons.

Methods

A retrospective chart review was performed at the University Teaching Hospital-Kigali (CHUK). From January 2016 to December 2017, 2717 CHUK patients with TB laboratory data were included. Data abstracted included patient demographics, season, HIV status, and TB laboratory results (microscopy, GeneXpert, culture). Univariate and multivariable logistic regression (adjusted for age, gender, and HIV status) analyses were performed to assess the association between season and laboratory-confirmed TB diagnoses.

Results

Patients presenting during rainy season periods had a lower odds of laboratory-confirmed TB diagnosis compared to the dry season (aOR=0.78, 95% CI 0.63–0.97, p=0.026) when controlling for age group, gender, and HIV status. Males, adults, and people living with HIV were more likely to have laboratory-confirmed TB diagnosis. On average, more people were tested for TB during the rainy season per month compared to the dry season (120.3 vs. 103.3), although this difference was not statistically significant.

Conclusion

In Rwanda, laboratory-confirmed TB case detection shows a seasonal variation with patients having higher odds of TB diagnosis occurring in the dry season. Further research is required to further elucidate this relationship and to delineate the mechanism of season influence on TB diagnosis.

The association between extreme temperature and pulmonary tuberculosis in Shandong Province, China, 2005-2016: a mixed method evaluation

BACKGROUND

The effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016.

METHODS

A generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test.

RESULTS

Among the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817-0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856-0.997) and Weihai (RR = 0.910, 95% CI: 0.843-0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966-0.998). However, extreme low temperature was no relationship with the reported incidence of PTB.

CONCLUSION

Our findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.

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.

Influential factors and spatial-temporal distribution of tuberculosis in mainland China

Tuberculosis (TB) is an infectious disease that threatens human safety. Mainland China is an area with a high incidence of tuberculosis, and the task of tuberculosis prevention and treatment is arduous. This paper aims to study the impact of seven influencing factors and spatial–temporal distribution of the relative risk (RR) of tuberculosis in mainland China using the spatial–temporal distribution model and INLA algorithm. The relative risks and confidence intervals (CI) corresponding to average relative humidity, monthly average precipitation, monthly average sunshine duration and monthly per capita GDP were 1.018 (95% CI 1.001–1.034), 1.014 (95% CI 1.006–1.023), 1.026 (95% CI 1.014–1.039) and 1.025 (95% CI 1.011–1.040). The relative risk for average temperature and pressure were 0.956 (95% CI 0.942–0.969) and 0.767 (95% CI 0.664–0.875). Spatially, the two provinces with the highest relative risks are Xinjiang and Guizhou, and the remaining provinces with higher relative risks were mostly concentrated in the Northwest and South China regions. Temporally, the relative risk decreased year by year from 2013 to 2015. It was higher from February to May each year and was most significant in March. It decreased from June to December. Average relative humidity, monthly average precipitation, monthly average sunshine duration and monthly per capita GDP had positive effects on the relative risk of tuberculosis. The average temperature and pressure had negative effects. The average wind speed had no significant effect. Mainland China should adapt measures to local conditions and develop tuberculosis prevention and control strategies based on the characteristics of different regions and time.

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.

Epidemiological characteristics of pulmonary tuberculosis in Anhui Province, Eastern China from 2013 to 2018

OBJECTIVE

Pulmonary tuberculosis (TB) is a severe infectious respiratory disease, the burden of which remains high in China. To provide scientific evidence for developing more targeted prevention and control strategies, this study aimed to determine the incidence trends and explore the epidemiological characteristics of pulmonary TB in Anhui Province, Eastern China between 2013 and 2018.

METHODS

The retrospective study analyzed information regarding pulmonary TB cases reported by the National Infectious Disease Reporting System and census data collected from the Anhui Provincial Bureau of Statistics.

RESULTS

Overall, 211,892 cases of TB patients were reported in Anhui Province, China between 2013 and 2018, with an average annual reported incidence rate of 57.7 per 100,000 persons. A significant decrease in the incidence rate of pulmonary TB (p < 0.001) was observed during the study period. Men had a higher incidence rate of pulmonary TB than women (p < 0.001). The highest annual average reported incidence rate was 204.2 per 100,000 persons in those aged 70-74 years. The number of farmers with pulmonary TB, i.e., 155,415, accounted for 73.4% of all cases. Moreover, the peak period of reported cases was from January to March. Four cities along the Yangtze River-Anqing, Tongling, Chizhou, and Wuhu-reported significantly higher incidence rates of pulmonary TB than other cities (p < 0.001).

CONCLUSIONS

From 2013 to 2018, there was a significant decline in the incidence rate of pulmonary TB in Anhui Province, with peaks occurring from January to March. Prevention and control strategies targeting men, people aged 70-74 years, farmers, and the four cities along the Yangtze River should be strengthened.

An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China

Purpose

Qinghai province has invariably been under an ongoing threat of tuberculosis (TB), which has not only been an obstacle to local development but also hampers the prevention and control process for ending the TB epidemic. Forecasting for future epidemics will serve as the base for early detection and planning resource requirements. Here, we aim to develop an advanced detection technique driven by the recent TB incidence series, by fusing a seasonal autoregressive integrated moving average (SARIMA) with a neural network nonlinear autoregression (NNNAR).

Methods

We collected the TB incidence data between January 2004 and December 2016. Subsequently, the subsamples from January 2004 to December 2015 were employed to measure the efficiency of the single SARIMA, NNNAR, and hybrid SARIMA-NNNAR approaches, whereas the hold-out subsamples were used to test their predictive performances. We finally selected the best-performing technique by considering minimum metrics including the mean absolute error, root-mean-squared error, mean absolute percentage error and mean error rate .

Results

During 2004–2016, the reported TB cases totaled 71,080 resulting in the morbidity of 97.624 per 100,000 persons annually in Qinghai province and showed notable peak activities in late winter and early spring. Moreover, the TB incidence rate was surging by 5% per year. According to the above-mentioned criteria, the best-fitting basic and hybrid techniques consisted of SARIMA(2,0,2)(1,1,0)₁₂, NNNAR(7,1,4)₁₂ and SARIMA(2,0,2)(1,1,0)₁₂-NNNAR(3,1,7)₁₂, respectively. Amongst them, the hybrid technique showed superiority in both mimic and predictive parts, with the lowest values of the measured metrics in both the parts. The sensitivity analysis indicated the same results.

Conclusion

The best-mimicking SARIMA-NNNAR hybrid model outperforms the best-simulating basic SARIMA and NNNAR models, and has a potential application in forecasting and assessing the TB epidemic trends in Qinghai. Furthermore, faced with the major challenge of the ongoing upsurge in TB incidence in Qinghai, there is an urgent need for formulating specific preventive and control measures.

Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework

Objective

Tuberculosis (TB) is a major public health problem in China, and contriving a long-term forecast is a useful aid for better launching prevention initiatives. Regrettably, such a forecasting method with robust and accurate performance is still lacking. Here, we aim to investigate its potential of the error-trend-seasonal (ETS) framework through a series of comparative experiments to analyze and forecast its secular epidemic seasonality and trends of TB incidence in China.

Methods

We collected the TB incidence data from January 1997 to August 2019, and then partitioning the data into eight different training and testing subsamples. Thereafter, we constructed the ETS and seasonal autoregressive integrated moving average (SARIMA) models based on the training subsamples, and multiple performance indices including the mean absolute deviation, mean absolute percentage error, root-mean-squared error, and mean error rate were adopted to assess their simulation and projection effects.

Results

In the light of the above performance measures, the ETS models provided a pronounced improvement for the long-term seasonality and trend forecasting in TB incidence rate over the SARIMA models, be it in various training or testing subsets apart from the 48-step ahead forecasting. The descriptive results to the data revealed that TB incidence showed notable seasonal characteristics with predominant peaks of spring and early summer and began to be plunging at on average 3.722% per year since 2008. However, this rate reduced to 2.613% per year since 2015 and furthermore such a trend would be predicted to continue in years ahead.

Conclusion

The ETS framework has the ability to conduct long-term forecasting for TB incidence, which may be beneficial for the long-term planning of the TB prevention and control. Additionally, considering the predicted dropping rate of TB morbidity, more particular strategies should be formulated to dramatically accelerate progress towards the goals of the End TB Strategy.

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 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.

Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses

Objective

Forecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources; however, this foresting is often hampered by inappropriate prediction methods. In this study, we performed validation research by comparing the accuracy of the autoregressive integrated moving average (ARIMA) model and the back-propagation neural network (BPNN) model in a southeastern province of China.

Methods

We applied the data from 462,214 notified pulmonary tuberculosis cases registered from January 2005 to December 2015 in Jiangsu Province to modulate and construct the ARIMA and BPNN models. Cases registered in 2016 were used to assess the prediction accuracy of the models. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to evaluate the model fitting and forecasting effect.

Results

During 2005–2015, the annual pulmonary tuberculosis notification rate in Jiangsu Province was 56.35/100,000, ranging from 40.85/100,000 to 79.36/100,000. Through screening and comparison, the ARIMA (0, 1, 2) (0, 1, 1)₁₂ and BPNN (3-9-1) were defined as the optimal fitting models. In the fitting dataset, the RMSE, MAPE, MAE and MER were 0.3901, 6.0498, 0.2740 and 0.0608, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)₁₂ model, 0.3236, 6.0113, 0.2508 and 0.0587, respectively, for the BPNN model. In the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.1758, 4.6041, 0.1368 and 0.0444, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)₁₂ model, and 0.1382, 3.2172, 0.1018 and 0.0330, respectively, for the BPNN model.

Conclusion

Both the ARIMA and BPNN models can be used to predict the seasonality and trend of pulmonary tuberculosis in the Chinese population, but the BPNN model shows better performance. Applying statistical techniques by considering local characteristics may enable more accurate mathematical modeling.

Evidence for heterogeneity in China's progress against pulmonary tuberculosis: uneven reductions in a major center of ongoing transmission, 2005-2017

Background

China contributed 8.9% of all incident cases of tuberculosis globally in 2017, and understanding the spatiotemporal distribution of pulmonary tuberculosis (PTB) in major transmission foci in the country is critical to ongoing efforts to improve population health.

Methods

We estimated annual PTB notification rates and their spatiotemporal distributions in Sichuan province, a major center of ongoing transmission, from 2005 to 2017. Time series decomposition was used to obtain trend components from the monthly incidence rate time series. Spatiotemporal cluster analyses were conducted to detect spatiotemporal clusters of PTB at the county level.

Results

From 2005 to 2017, 976,873 cases of active PTB and 388,739 cases of smear-positive PTB were reported in Sichuan Province, China. During this period, the overall reported incidence rate of active PTB decreased steadily at a rate of decrease (3.77 cases per 100,000 per year, 95% confidence interval (CI): 3.28–4.31) that was slightly faster than the national average rate of decrease (3.14 cases per 100,000 per year, 95% CI: 2.61–3.67). Although reported PTB incidence decreased significantly in most regions of the province, incidence was observed to be increasing in some counties with high HIV incidence and ethnic minority populations. Active and smear-positive PTB case reports exhibited seasonality, peaking in March and April, with apparent links to social dynamics and climatological factors.

Conclusions

While PTB incidence rates decreased strikingly in the study area over the past decade, improvements have not been equally distributed. Additional surveillance and control efforts should be guided by the seasonal-trend and spatiotemporal cluster analyses presented here, focusing on areas with increasing incidence rates, and updated to reflect the latest information from real-time reporting.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-4262-2) contains supplementary material, which is available to authorized users.

Seasonality of newly notified pulmonary tuberculosis in Japan, 2007-2015

BACKGROUND

The seasonality of pulmonary tuberculosis (TB) incidence may indicate season-specific risk factors that could be controlled if they were better understood. The aims of this study were to elucidate how the incidence of TB changes seasonally and to determine the factors influencing TB incidence, to reduce the TB burden in Japan.

METHODS

We assessed the seasonality of newly notified TB cases in Japan using national surveillance data collected between 2007 and 2015. To investigate age and sex differences, seasonal variation was analyzed according to sex for all cases and then by stratified age groups (0-4, 5-14, 15-24, 25-44, 45-64, 65-74, and ≥ 75 years). We used Roger's test to analyze the cyclic monthly trends in seasonal variation of TB incidence.

RESULTS

A total of 199,856 newly notified TB cases (male, 62.2%) were reported over the past 9-year period. Among them, 60.6% involved patients aged ≥65 years. Overall, the peak months of TB incidence occurred from April to October, excluding September. In the analysis stratified by age group, a significant seasonal variation in TB cases was observed for age groups ≥15 years, whereas no seasonal variation was observed for age groups ≤14 years. For female patients aged ≥25 years, the peak TB epidemic period was seen from June to December, excluding November. Male patients in the same age groups exhibited declining TB incidence from September to March.

CONCLUSIONS

TB incidence exhibits seasonality in Japan for people aged > 15 years and peaks in summer to fall. Monthly trends differ according to age and sex. For age groups ≥25 years, cases in women showed longer peaks from June to December whereas cases in men declined from September to December. These results suggest that the seasonality of TB incidence in Japan might be influenced by health checkups in young adults, reactivation of latent TB infection with aging, and lifestyle habits in older adults. These findings can contribute to establishing the potential determinants of TB seasonality in Japan.

Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population

Objective: To investigate suitable forecasting models for tuberculosis (TB) in a Chinese population by comparing the predictive value of the autoregressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) hybrid model. Methods: We used the monthly incidence rate of TB in Lianyungang city from January 2007 through June 2016 to construct a fitting model, and we used the incidence rate from July 2016 to December 2016 to evaluate the forecasting accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to assess the performance of these models in fitting and forecasting the incidence of TB. Results: The ARIMA (10, 1, 0) (0, 1, 1)₁₂ model was selected from plausible ARIMA models, and the optimal spread value of the ARIMA-GRNN hybrid model was 0.23. For the fitting dataset, the RMSE, MAPE, MAE and MER were 0.5594, 11.5000, 0.4202 and 0.1132, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)₁₂ model, and 0.5259, 11.2181, 0.3992 and 0.1075, respectively, for the ARIMA-GRNN hybrid model. For the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.2805, 8.8797, 0.2261 and 0.0851, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)₁₂ model, and 0.2553, 5.7222, 0.1519 and 0.0571, respectively, for the ARIMA-GRNN hybrid model. Conclusions: The ARIMA-GRNN hybrid model was shown to be superior to the single ARIMA model in predicting the short-term TB incidence in the Chinese population, especially in fitting and forecasting the peak and trough incidence.

Epidemiological characteristics of pulmonary tuberculosis in mainland China from 2004 to 2015: a model-based analysis

BACKGROUND

We used data released by the government to analyze the epidemiological distribution of pulmonary tuberculosis in mainland China from 2004 to 2015, in order to provide a deeper understanding of trends in the epidemiology of pulmonary tuberculosis in China and a theoretical basis to assess the effectiveness of government interventions and develop more targeted prevention and control strategies.

METHODS

A discrete dynamic model was designed based on the epidemiological characteristics of pulmonary tuberculosis and fitted to data published by the government to estimate changes in indicators such as adequate contact rate, prevalence of non-treated pulmonary tuberculosis (abbreviated as prevalence), and infection rate. Finally, we performed sensitivity analyses of the effects of parameters on the population infection rate.

RESULTS

The epidemiological features of pulmonary tuberculosis in China include a pattern of seasonal fluctuations, with the highest rates of infection in autumn and winter. The adequate contact rate has increased slightly from an average of 0.12/month in 2010 to an average of 0.21/month in 2015. The prevalence in the population has continued to decrease from 3.4% in early 2004 to 1.7% in late 2015. The Mycobacterium tuberculosis (M. tuberculosis) infection rate in the population decreased gradually from 42.3% at the beginning of 2004 to 36.7% at the end of 2015. The actual number of new infections gradually decreased from 1,300,000/year in 2010 to 1,100,000/year in 2015. The actual number of new patients each year has been relatively stable since 2010 and remains at approximately 2,600,000/year.

CONCLUSIONS

The population prevalence and the M. tuberculosis infection rate have decreased year by year since 2004, indicating that the tuberculosis epidemic in China has been effectively controlled. However, pulmonary tuberculosis has become increasingly contagious since 2010. China should focus on the prevention and control of pulmonary tuberculosis during autumn and winter.

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.

Seasonality and Trend Forecasting of Tuberculosis Incidence in Chongqing, China

Tuberculosis (TB) is a global infectious disease and one of the ten leading causes of death worldwide. As TB incidence is seasonal, a reliable forecasting model that incorporates both seasonal and trend effects would be useful to improve the prevention and control of TB. In this study, the X12 autoregressive integrated moving average (X12-ARIMA) model was constructed by dividing the sequence into season term and trend term to forecast the two terms, respectively. Data regarding the TB report rate from January 2004 to December 2015 were included in the model, and the TB report data from January 2016 to December 2016 were used to validate the results. The X12-ARIMA model was compared with the seasonal autoregressive integrated moving average (SARIMA) model. A total of 383,797 cases were reported from January 2004 to December 2016 in Chongqing, China. The report rate of TB was highest in 2005 (151.06 per 100,000 population) and lowest in 2016 (72.58 per 100,000 population). The final X12-ARIMA model included the ARIMA (3,1,3) model for the trend term and the ARIMA (2,1,3) model for the season term. The SARIMA (1,0,2) * (1,1,1)₁₂ model was selected for the SARIMA model. The mean absolute error (MAE) and mean absolute percentage error (MAPE) of fitting and predicting performance based on the X12-ARIMA model were less than the SARIMA model. In conclusion, the occurrence of TB in Chongqing is controlled, which may be attributed to socioeconomic developments and improved TB prevention and control services. Applying the X12-ARIMA model is an effective method to forecast and analyze the trend and seasonality of TB.

The influence of meteorological factors on tuberculosis incidence in Southwest China from 2006 to 2015

The influence of meteorological determinants on tuberculosis (TB) incidence remains severely under-discussed, especially through the perspective of time series analysis. In the current study, we used a distributed lag nonlinear model (DLNM) to analyze a 10-year series of consecutive surveillance data. We found that, after effectively controlling for autocorrelation, the changes in meteorological factors related to temperature, humidity, wind and sunshine were significantly associated with subsequent fluctuations in TB incidence: average temperature was inversely associated with TB incidence at a lag period of 2 months; total precipitation and minimum relative humidity were also inversely associated with TB incidence at lag periods of 3 and 4 months, respectively; average wind velocity and total sunshine hours exhibited an instant rather than lagged influence on TB incidence. Our study results suggest that preceding meteorological factors may have a noticeable effect on future TB incidence; informed prevention and preparedness measures for TB can therefore be constructed on the basis of meteorological variations.

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.

Using the Kulldorff's scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009-2016

BACKGROUND

Although the incidence of tuberculosis (TB) in most parts of China are well under control now, in less developed areas such as Qinghai, TB still remains a major public health problem. This study aims to reveal the spatio-temporal patterns of TB in the Qinghai province, which could be helpful in the planning and implementing key preventative measures.

METHODS

We extracted data of reported TB cases in the Qinghai province from the China Information System for Disease Control and Prevention (CISDCP) during January 2009 to December 2016. The Kulldorff's retrospective space-time scan statistics, calculated by using the discrete Poisson probability model, was used to identify the temporal, spatial, and spatio-temporal clusters of TB at the county level in Qinghai.

RESULTS

A total of 48,274 TB cases were reported from 2009 to 2016 in Qinghai. Results of the Kulldorff's scan revealed that the TB cases in Qinghai were significantly clustered in spatial, temporal, and spatio-temporal distribution. The most likely spatio-temporal cluster (LLR = 2547.64, RR = 4.21, P < 0.001) was mainly concentrated in the southwest of Qinghai, covering seven counties and clustered in the time frame from September 2014 to December 2016.

CONCLUSION

This study identified eight significant space-time clusters of TB in Qinghai from 2009 to 2016, which could be helpful in prioritizing resource assignment in high-risk areas for TB control and elimination in the future.

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.

Relationship between climatic factors and air quality with tuberculosis in the Federal District, Brazil, 2003-2012

INTRODUCTION

Despite the high rate of tuberculosis indicators in Brazil, the Federal District shows a low prevalence of the disease.

OBJECTIVE

To analyze the relationship between climatic factors and air quality with tuberculosis in the Brazilian Federal District.

METHODOLOGY

This was an ecological and descriptive study comparing 3927 new cases of Tuberculosis registered at the Federal District Tuberculosis Control Program with data from the National Institute of Meteorology, Brazilian Institute of Geography and Statistics, Brazilian Agricultural Research Institute, Brasilia Environmental Institute, and the Federal District Planning Company.

RESULTS

From 2003 to 2012, there has been a higher incidence of Tuberculosis (27.0%) in male patients in the winter (27.2%). Patients under 15 years of age (28.6%) and older than 64 years (27.1%) were more affected in the fall. For youth and adults (15-64 years), the highest number of cases was reported during winter (44.3%). The disease was prevalent with ultraviolet radiation over 17MJ/m2 (67.8%; p=<0.001); relative humidity between 31.0% and 69.0% (95.8% of cases; p=<0.00); 12h of daily sunlight or more (40.6%; p=0.001); and temperatures between 20°C and 23°C (72.4%; p=<0.001). In the city of Taguatinga and surrounding area, pollution levels dropped to 15.2% between 2003 and 2012. Smoke levels decreased to 31.9%. In the Sobradinho region, particulate matter dropped to 13.1% and smoke to 19.3%, coinciding with the reduction of Tuberculosis incidence rates during the same period.

CONCLUSION

The results should guide surveillance actions for Tuberculosis control and elimination and indicate the need to expand observation time to new climate indicators and air quality.

Effect of vitamin D deficiency in developed countries

INTRODUCTION

Vitamin D deficiency is common worldwide with adverse effects on skeletal health. In recent years, there has been great interest in non-classical actions of vitamin D. Basic research has uncovered actions in a range of non-skeletal tissues, and observational studies have identified inverse relationships with risk of a number of disease states including sarcopenia, obesity, the metabolic syndrome, cardiovascular disease, cancer and autoimmune diseases.

SOURCES OF DATA

PubMed, Medline and Cochrane Systematic Reviews.

AREAS OF AGREEMENT

Current evidence supports the use of vitamin D supplementation in deficiency to improve skeletal outcomes such as falls/fracture risk and bone mineral density.

AREAS OF CONTROVERSY

There is debate reflected in guidelines on optimal thresholds for circulating levels of vitamin D. Further studies are required to refine dosing regimens and treatment target levels of vitamin D.

GROWING POINTS

A number of studies have investigated the extra-skeletal effects of vitamin D deficiency but causality in humans has yet to be confirmed.

AREAS TIMELY FOR DEVELOPING RESEARCH

Large-scale randomized controlled trials incorporating data on vitamin D status at baseline and follow up, adverse events, and comparison of dosing regimens are required. It is imperative that studies are carried out with a diverse range of participants (age, gender and ethnicity), and settings to allow for a more individualized approach. In addition, we would advocate incorporating cutting-edge research tools into human studies to advance our understanding of the mechanisms of vitamin D action in extra-skeletal disease. This may involve multi-metabolite analysis of vitamin D metabolites, or unbiased approaches to assess regulation of gene/protein expression in tissues of interest.