Survival model analysis of tuberculosis treatment among patients with human immunodeficiency virus coinfection.

Background: Tuberculosis (TB) with human immunodeficiency virus (HIV) coinfection is the highest clinical epidemiology and public health issue. Despite many programs established to tackle the epidemic, TB target controls have not been reached. One of the many factors attributed to the failure in TB treatment is HIV coinfection. The aim of this study is to assess the survival rate of HIV infection among TB patients and the risk factors of death among the TB patients with HIV coinfection during the retro of directly observed treatment, short-course (DOTS) program. Methods: This study is a retrospective cohort conducted to compare the survivorship between TB/HIV patients for 8 months DOTS. Death among TB patients was considered as failures and those defaulted or survived were censored. The Cox proportional-hazards regression and log-linear model were used to establish the hazard ratio (HR) of death for each variable at baseline and estimate the risk factors effect among TB patients. Results: The findings revealed that 50% of death from TB/HIV patients were from HIV coinfection (advanced HR = 2.01, 95% confidence interval = 1.13-3.17). The risk of death was significantly higher in HIV-positive TB patients (P = 0.000) during the extension care phase. TB/HIV-positive patients on antiretroviral therapy have decreased survival rate (log-rank test = 14.88, df = 2, P = 0.0001). The probability of TB patients surviving is significantly decreased in HIV positive with some factors such as age, weight, smoking, and alcohol found significant. Conclusion: The probability of survival in HIV-positive TB patients was significantly lower during the TB treatment. Weight loss, age, alcohol, smoking, and pregnancy were showed to affect the survival probability of TB/HIV patients' coinfection significantly.

Associated factors with unsuccessful tuberculosis treatment outcomes among tuberculosis/HIV coinfected patients with drug-resistant tuberculosis.

Background: In Sub-Saharan Africa, HIV endemic has substantially contributed to the increasing tuberculosis (TB) incidence. The joint effect of the HIV and TB pestilences has confronted the feeble systems of healthcare in resource-limited countries. Methods: The aim of this study was to determine the pathological attributes, outcomes of TB treatment, and the contributing factors of unsuccessful results among TB/HIV patients. A retrospective cohort study of all confirmed adult TB/HIV coinfected hospitalized patients with drug-resistant TB reported for the treatment in two different hospitals from 2010 to 2016 in Eastern Cape, South Africa. Cox proportional hazard model was used in identifying the predictors of unsuccessful treatment. Results: Unsuccessful treatment outcomes among TB/HIV coinfected patients with treatment category were (95% confidence interval [CI]: 0.988-1.318) age, smoking (1.047; 95% CI: 0.892-1.229), pregnancy (1.940; 95% CI: 0.793-4.743), CD 4+ count (1.163; 95% CI: 0.993-1.361), and patients with comorbidity diseases such as diabetes, liver diseases, renal failure, hepatitis, and silicosis were all significantly associated with unsuccessful treatment. The preantiretroviral treatment (ART) females appear to have significantly better survival than pre-ART males. Conclusion: The study showed that the unsuccessful treatment outcomes among TB/HIV coinfected patients were slightly below the WHO target. The key predictors of unsuccessful TB treatment outcomes among the TB/HIV coinfected patients were associated with pregnancy, productive age group, gender, contraception, and comorbidity diseases.

Seasonal behavior and forecasting trends of tuberculosis incidence in Holy Kerbala, Iraq.

Background: Pulmonary tuberculosis (PTB) remains major public health problem over the world. Cities witnessing religious event throughout of the year like Kerbala/Iraq require great efforts to minimize the incidence of deadly communicable diseases like TB. The aim of this study is to model the monthly incidence rates of PTB cases in Kerbala/Iraq. Methods: This is a retrospective study in which records of confirmed PTB patients whom they referred to the chest and respiratory illnesses center of Holy Kerbala governorate were obtained. Monthly registered new smear-positive PTB cases from January 2010 to December 2016 were analyzed. Seasonal autoregressive integrated moving average (SARIMA), SARIMA-exponential smoothing method (ETS), SARIMA-neural network autoregressive, and SARIMA-adaptive neuro-fuzzy inference system (SARIMA-ANFIS) were used for forecasting monthly incidence rate of TB in Kerbala, Iraq. Mean absolute percentage error, root mean square error, and mean absolute square error were used to compare the models, and Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to selected best model. Results: The trend of PTB incidence showed a seasonal characteristic, with peaks in spring and winter. Predicted estimates using all models proposed to forecast the number of PTB cases from 2016 to 2018 showed that the PTB cases indicated marginal decrease trends and best forecasted in SARIMA-ANFIS model (the lower AIC and BIC values, 712.69 and 731.05, respectively). Conclusion: Seasonal characteristic of PTB incidence was observed with peaks during spring and winter. Forecasting of PTB incidence between the period 2016 and 2018 showed marginal decrease trends, and the best forecasting model was SARIMA-ANFIS model.

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.

BACKGROUND: Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. METHODS: TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. RESULTS: Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. CONCLUSIONS: The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.