A registry-based cohort study of pulmonary tuberculosis treatment outcomes in Istanbul, Turkey

Prediction of pulmonary tuberculosis treatment outcome in a sub-Saharan African context

Failure to treat many pathogens is a concern. Identifying a priori, patients with potential failure treatment outcome of a disease could allow measures to reduce the failure rate. The objectives of this study were to use the Scoring method to identify factors associated with the tuberculosis unsuccessful treatment outcome and to predict the treatment outcome. A total of 1,529 patients with pulmonary tuberculosis were randomly selected in the city of Douala, Cameroon, this sample was randomly split into two parts: one subsample of 1,200 patients (78%) used as the Development sample, and the remaining of 329 patients (22%) used as the Validation sample. Baseline characteristics associated with unsuccessful treatment outcomes were investigated using logistic regression. The optimal score was based on the Youden's index. HIV positive status, active smoker and non-belief in healing were the factors significantly associated with unsuccessful treatment outcomes (P#x003C;0.05). A model used to estimate the risk of unsuccessful treatment outcome was derived. The threshold probability which maximize the area under the ROC curve was 18%. Patients for whom the risk was greater than this threshold were classified as unsuccessful treatment outcome and the others as successful. HIV positive and active smoking status were associated with death; the non-belief in healing, youth and male gender associated with lost-to-follow-up, TB antecedent and not having TB contact associated with therapeutic treatment failure. To increase the tuberculosis treatment success rate, targeted follow-up could be taken during the treatment for TB patients with previous characteristics.

Prevalence and predictive factors of tuberculosis treatment interruption in the Asia region: a systematic review and meta-analysis

INTRODUCTION

Tuberculosis (TB) treatment interruption remains a critical challenge leading to poor treatment outcomes. Two-thirds of global new TB cases are mostly contributed by Asian countries, prompting systematic analysis of predictors for treatment interruption due to the variable findings.

METHODS

Articles published from 2012 to 2021 were searched through seven databases. Studies that established the relationship for risk factors of TB treatment interruption among adult Asian were included. Relevant articles were screened, extracted and appraised using Joanna Briggs Institute's checklists for cohort, case-control and cross-sectional study designs by three reviewers. Meta-analysis was performed using the random effect model in Review Manager software. The pooled prevalence and predictors of treatment interruption were expressed in ORs with 95% CIs; heterogeneity was assessed using the I₂ statistic. The publication bias was visually inspected using the funnel plot.

RESULTS

Fifty eligible studies (658 304 participants) from 17 Asian countries were included. The overall pooled prevalence of treatment interruption was 17% (95% CI 16% to 18%), the highest in Southern Asia (22% (95% CI 16% to 29%)), followed by Eastern Asia (18% (95% CI 16% to 20%)) and South East Asia (16% (95% CI 4% to 28%)). Seven predictors were identified to increase the risk of treatment interruption, namely, male gender (OR 1.38 (95% CI 1.26 to 1.51)), employment (OR 1.43 (95% CI 1.11 to 1.84)), alcohol intake (OR 2.24 (95% CI 1.58 to 3.18)), smoking (OR 2.74 (95% CI 1.98 to 3.78)), HIV-positive (OR 1.50 (95% CI 1.15 to 1.96)), adverse drug reactions (OR 2.01 (95% CI 1.20 to 3.34)) and previously treated cases (OR 1.77 (95% CI 1.39 to 2.26)). All predictors demonstrated substantial heterogeneity except employment and HIV status with no publication bias.

CONCLUSION

The identification of predictors for TB treatment interruption enables strategised planning and collective intervention to be targeted at the high-risk groups to strengthen TB care and control in the Asia region.

FACTORS CONTRIBUTING TO PULMONARY TB TREATMENT LOST TO FOLLOW-UP IN DEVELOPING COUNTRIES: AN OVERVIEW

Background

Despite the available treatment options, pulmonary tuberculosis (TB) remains a leading cause of disease-related deaths worldwide. Treatment non-adherence/lost to follow-up (LTFU), particularly in developing countries, is a continuous concern. LTFU prolongs TB infectiousness and contributes to TB treatment failure, relapse, and death. Furthermore, LTFU also delays global TB eradication by promoting TB spread and drug-resistant TB strain development.[1] The purpose of this paper is to give an overview of the commonly observed risk factors associated with TB treatment LTFU in developing countries.

Materials and Methods

A literature survey was done of studies published in the past decade, which evaluated the risk factors for LTFU in TB patients, specifically in developing countries. Furthermore, some prospective TB treatment adherence initiatives and the feasibility of these initiatives within developing countries were assessed.[3].

Results

Several variables, including socio-demographic, patient-related, TB disease and other health-related-factors, healthcare and system determinants, as well as treatment-related factors, were identified to increase the risk of TB treatment LTFU. More recently applied adherence interventions in developing countries, show potential for implementation on a larger scale.

Conclusion

Successful TB treatment is contingent on treatment adherence, and by addressing these persisting LTFU risk factors, treatment adherence in developing countries may be improved.

ACID-FAST BACILLI CONVERSION OF BEIJING AND NON-BEIJING STRAIN OF PULMONARY TUBERCULOSIS IN SOUTH SULAWESI

Beijing strains are a major part of the Mycobacterium tuberculosis Asian phylogenetic lineage. Beijing strains represent about 50% of all TB strains in East Asia and at least 13% of strains worldwide. Beijing strain of Mycobacterium tuberculosis is presumed as the factor of the increase in bacteria virulence and drug resistance, and the contributor in treatment failure. The aim of this study was to analyze the association between acid-fast bacilli conversion with strain genotipe Beijing and non-Beijing of pulmonary tuberculosis in South Sulawesi. The design of research was observational analytic with prospective approach. The sampling technique used consecutive sampling. Data were taken from active pulmonary tuberculosis patients’ medical record in Balai Besar Kesehatan Paru Masyarakat Makassar (Pulmonary Health Center of Makassar) and Community Health Center in Gowa Regency, South Sulawesi from March to June 2018. Collected sputum samples were screened for AFB and identified as Beijing strain and non Beijing strains using Multiplex PCR in Tropical Disease Institute of Universitas Airlangga. The results is showed that the characteristics of the respondents consisted of 12 respondents (33.3%) aged 56-65 years, 25 respondents (69.4%) men and 28 respondents (77.8%) had low category gradation of AFB smear. Univariate analysis showed 6 respondents (16.7%) with Beijing strains, 30 respondents (83.3%) with non-Beijing strains, 32 respondents (88.9%) conversion sputum AFB and 4 respondents (11.1%) non conversion sputum AFB. Bivariate analysis with Chi-Square statistical test shows that p value 0.022 < 0,05, that means there was association of Beijing strains with BTA conversion. Microscopic examination of BTA can be used to monitor and evaluate the treatment of new pulmonary TB patients undergoing treatment and the Beijing Mycobacterium tuberculosis strain has a significant correlation with the treatment failure of anti-tuberculosis drugs in South Sulawesi.

Risk factors associated with loss to follow-up from tuberculosis treatment in Tajikistan: a case-control study

BACKGROUND

There are very few studies on reasons for loss to follow-up from TB treatment in Central Asia. This study assessed risk factors for LTFU and compared their occurrence with successfully treated (ST) patients in Tajikistan.

METHODS

This study took place in all TB facilities in the 19 districts with at least 5 TB patients registered as loss to follow-up (LTFU) from treatment. With a matched case control design we included all LTFU patients registered in the selected districts in 2011 and 2012 as cases, with ST patients from the same districts being controls. Data were copied from patient records and registers. Conditional logistic regressions were run to analyse associations between collected variables and LTFU as dependent variable.

RESULTS

Three hundred cases were compared to 592 controls. Half of the cases had migrated or moved. In multivariate analysis, risk factors associated with increased LTFU were migration to another country (OR 10.6, 95% CI 6.12-18.4), moving within country (OR 11.0, 95% CI 3.50-34.9), having side effects of treatment (OR 3.67, 95% CI 1.68-8.00) and being previously treated for TB (OR 2.03, 95% CI 1.05-3.93). Medical staff also mentioned patient refusal, stigma and family problems as risk factors.

CONCLUSIONS

LTFU of TB patients in Tajikistan is largely a result of migration, and to a lesser extent associated with side-effects and previous treatment. There is a need to strengthen referral between health facilities within Tajikistan and with neighbouring countries and support patients with side effects and/or previous TB to prevent loss to follow-up from treatment.

Development and validation of a prognostic score during tuberculosis treatment

BACKGROUND

Death under care is a major challenge for tuberculosis (TB) treatment programs. We derived and validated a simple score to predict mortality during tuberculosis treatment in high endemicity areas.

METHODS

We used data for patients aged ≥15 years, diagnosed and treated for tuberculosis at the Yaounde Jamot Hospital between January 2012 and December 2013. Baseline characteristics associated with mortality were investigated using logistic regressions. A simple prognosis score (CABI) was constructed with regression coefficients for predictors in the final model. Internal validation used bootstrap resampling procedures. Models discrimination was assessed using c-statistics and calibration assessed via calibration plots and the Hosmer and Lemeshwow (H-L) statistics. The optimal score was based on the Youden's index.

RESULTS

A total of 2250 patients (men 57.2%) with a mean age of 35.8 years were included; among whom 213 deaths (cumulative incidence 9.5%) were recorded. Clinical form of tuberculosis (C), age (A, years), adjusted body mass index (B, BMI, kg/m²) and status for HIV (Human immunodefiency virus) infection (I) were significant predictors in the final model (p < 0.0001) which was of the form Death risk = 1/(1 + e ^{- (-1.3120 + 0.0474 ∗ age - 0.1866 ∗ BMI + 1.1637 (if smear negative TB) + 0.5418(if extra - pulmonary TB) + 1.3820(if HIV+))}). The c-statistic was 0.812 in the derivation sample and 0.808 after correction for optimism. The calibration was good [H-Lχ² = 6.44 (p = 0.60)]. The optimal absolute risk threshold was 4.8%, corresponding to a sensitivity of 81% and specificity of 67%.

CONCLUSIONS

The preliminary promising findings from this study require confirmation through independent external validation studies. If confirmed, the model derived could facilitate the stratification of TB patients for mortality risk and implementation of additional monitoring and management measures in vulnerable patients.

An investigation into the relationship between region specific quality of life and adverse tuberculosis treatment outcomes in Istanbul, Turkey

BACKGROUND AND OBJECTIVE

Istanbul has the highest incidence of tuberculosis (TB) in Turkey. It is also the largest city, with considerable differences in quality of life across its urban regions. The aim of this study is to investigate the relationship between (i) the diverse quality of life across specific urban regions, (ii) TB incidence rates, inclusive of demographic and clinical characteristics of TB patients, and (iii) adverse treatment outcomes.

METHODS

This retrospective study included 23,845 new TB patients (recorded in the National TB Registry between 2006 and 2010) in Istanbul. Thirty-nine urban districts of Istanbul were ranked into five groups on the basis of an urban quality of life index. Patient data were matched with these groups, and further categorized according to 'age', 'sex', 'country of birth' and 'antibiotics resistance'. Adverse treatment outcomes and TB incidence rates were extracted from official records. Logistic regression, clustered analyses, 95% CI and p values (STATA) were reported to describe the association between variables.

RESULTS

Six per cent of total cases had 'at least one adverse treatment outcome' (default 3.8%, failure 0.5%, death 1.7% in total cases). 'An adverse treatment outcome' was found to be associated with age OR (CI 95%) (1.02 (1.01-1.03)); 'male sex' 1.65 (1.28-2.12); 'other country of birth' 4.82 (3.05-7.62); 100,000 per 'over 60' insidence goups 1.61 (1.32-1.97), the lowest quality of life index 0.65 (0.47-0.83).

CONCLUSIONS

Patients with high tuberculosis risk factors living in high incidence regions need to be closely monitored. Patients living in lower ranking regions are more likely to have 'poor treatment outcomes'.