The Prevalence of Drug-Resistant Tuberculosis in Mainland China: An Updated Systematic Review and Meta-Analysis

<div><p>Background</p><p>In recent years, drug resistant tuberculosis (DR-TB) particularly the emergence of multi-drug-resistant tuberculosis (MDR-TB) has become a major public health issue. The most recent study regarding the prevalence of drug-resistant tuberculosis in mainland China was a meta-analysis published in 2011, and the subjects from the included studies were mostly enrolled before 2008, thus making it now obsolete. Current data on the national prevalence of DR-TB is needed. This review aims to provide a comprehensive and up-to-date assessment of the status of DR-TB epidemic in mainland China.</p><p>Methods</p><p>A systematic review and meta-analysis of studies regarding the prevalence of drug-resistant tuberculosis in mainland China was performed. Pubmed/MEDLINE, EMBASE, the Cochrane central database, the Chinese Biomedical Literature Database and the China National Knowledge Infrastructure Database were searched for studies relevant to drug-resistant tuberculosis that were published between January 1, 2012 and May 18, 2015. Comprehensive Meta-Analysis (V2.2, Biostat) software was used to analyse the data.</p><p>Results</p><p>A total of fifty-nine articles, published from 2012 to 2015, were included in our review. The result of this meta-analysis demonstrated that among new cases, the rate of resistance to any drug was 20.1% (18.0%–22.3%; n/N = 7203/34314) and among retreatment cases, the rate was 49.8% (46.0%–53.6%; n/N = 4155/8291). Multi-drug resistance among new and retreatment cases was 4.8% (4.0%–5.7%; n/N = 2300/42946) and 26.3% (23.1%–29.7%; n/N = 3125/11589) respectively. The results were significantly heterogeneous (p<0.001, I² tests). Resistance to isoniazid was the most common resistance observed, and HRSE (H: isoniazid; R: rifampicin; S: streptomycin; E: ethambutol) was the most common form for MDR among both new and retreatment cases. Different drug resistance patterns were found by subgroup analysis according to geographic areas, subject enrolment time, and methods of drug susceptibility test (DST).</p><p>Conclusions</p><p>The prevalence of resistance to any drug evidently dropped for both new and retreatment cases, and multi-drug resistance declined among new cases but became more prevalent among retreatment cases compared to the data before 2008. Therefore, drug-resistant tuberculosis, particularly multi-drug-resistant tuberculosis among retreatment TB cases is a public health issue in China that requires a constant attention in order to prevent increase in MDR-TB cases.</p></div

Flow chart depicting the study selection process.

<p>*The reasons including irrelevant topic, articles from Chinese non-scientific-key journals, review, insufficient data.</p

A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China

<div><p>Backgrounds/Objective</p><p>Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas.</p><p>Methods</p><p>A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model.</p><p>Results</p><p>The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869×10⁻⁴, 0.0029, 0.0419 with a corresponding testing error of 0.9375×10⁻⁴, 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend.</p><p>Conclusion</p><p>The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases.</p></div

Autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of original prevalence series.

<p>A and B. ACF and PACF plots of original schistosomisis prevalence (1956–2008); C and D. ACF and PACF plots after one order of regular differencing (1956–2008); E and F. ACF and PACF plots of original schistosomisis prevalence (1956–2012); G and H. ACF and PACF plots after one order of regular differencing (1956–2012). Dotted lines indicate 95% confidence intervals. Most of the correlations fall around zero within their 95% confidence intervals except for the one at zero lag, which indicate the series achieved stationary.</p

The change trend of prevalence of schistosomiasis from three models.

<p>The comparison of observation and predicted values between the hybrid ARIMA-NARNN model, and the single ARIMA or NARNN model are shown in Figure 6A. On the whole, the red line is closer to the observation curve that indicates the predicted values from the ARIMA-NARNN model are the best fit for the prevalence of schistosomiasis in humans. Figure 6B shows the predicted prevalence of schistosomiasis (1960–2016) from the reconstructed hybrid ARIMA-NARNN model.</p

The optimum networks configuration of different target series.

<p>Note: OS = original prevalence series, RS = residual series, NRS = new residual series</p><p>All MSE values should be multiplied by 10⁻⁴.</p

The configuration of the NARNN.

<p>The final established NARNN (original prevalence series as target series) consisted of one output layer with 1 neuron and one hidden layer with 13 neurons and 4 delays. Figure 1A shows the close loop form and Figure 1B shows the open loop form.</p

The predicted prevalence (%) from three models.

<p>Note: The 2009–2012 values are predicted using modelling data 1956–2008.</p><p>The 2013–2016 values are predicted using modelling data 1956–2012.</p

The time-series response plots of different target series.

<p>The plots indicate which time points are selected for training, testing and validation. Since the outputs were distributed evenly on both sides of the response curve and the errors versus time were small, we determined that we had chosen the appropriate model.</p