Risk Factors for the Development of Tuberculosis Among HIV-Positive Adults Under Highly Active Antiretroviral Therapy at Government Hospitals in Amhara Region, Ethiopia

Prevalence, treatment outcomes and determinants of TB/HIV coinfection: A 4-year retrospective review of national tuberculosis registry in a country in a MENA region

Background

The co-occurrence of tuberculosis (TB) and AIDS (HIV) has emerged as a significant public health challenge. This study investigated the epidemiological factors and treatment outcomes of TB in individuals based on their HIV status in Iran.

Methods

The current study was a descriptive-analytical cross-sectional study that focused on new patients diagnosed with TB in Iran between 2018 and 2021. Patients' data were sourced from the National Tuberculosis Registry database of Iran. A multiple logistic regression model was used to investigate the relationship between the most important influencing factors and TB/HIV coinfection.

Results

Over a 4-year period, a study was conducted on 25,011 new TB patients out of 30,762 registered in the national database. TB and HIV were coinfected in 672 cases (2.68%). The highest number of coinfection cases were found in patients with smear-negative pulmonary tuberculosis (249 patients, 37.05%) and extrapulmonary tuberculosis (123 patients, 18.19%). TB patients with coinfection had a median TB treatment duration of three months longer than others. The success rate of TB treatment was lower in patients with coinfection (437 patients, 65.02%) than in non-coinfection patients (20,302 patients, 83.41%). Treatment success probability in smear-positive pulmonary tuberculosis patients with and without coinfection was lower than other types of TB. Logistic regression analysis showed that having a TB risk factor was the strongest predictor of coinfection, with an odds ratio of 29.73 (95% CI: 22.05-40.07), followed by having an HIV risk factor with an odds ratio of 17.52 (95% CI: 13.68-22.45).

Conclusions

The findings of this research offer significant insights into the potential causes of HIV coinfection in individuals with TB, which could be used to inform the development of policies and strategies aimed at enhancing the identification and treatment of TB patients who are at risk of TB/HIV coinfection and to promote optimal health status for patients with TB.

Comparison of logistic regression with regularized machine learning methods for the prediction of tuberculosis disease in people living with HIV: cross-sectional hospital-based study in Kisumu County, Kenya

Background

Tuberculosis (TB) is a major public health concern, particularly among people living with the Human immunodeficiency Virus (PLWH). Accurate prediction of TB disease in this population is crucial for early diagnosis and effective treatment. Logistic regression and regularized machine learning methods have been used to predict TB, but their comparative performance in HIV patients remains unclear. The study aims to compare the predictive performance of logistic regression with that of regularized machine learning methods for TB disease in HIV patients.

Methods

Retrospective analysis of data from HIV patients diagnosed with TB in three hospitals in Kisumu County (JOOTRH, Kisumu sub-county hospital, Lumumba health center) between [dates]. Logistic regression, Lasso, Ridge, Elastic net regression were used to develop predictive models for TB disease. Model performance was evaluated using accuracy, and area under the receiver operating characteristic curve (AUC-ROC).

Results

Of the 927 PLWH included in the study, 107 (12.6%) were diagnosed with TB. Being in WHO disease stage III/IV (aOR: 7.13; 95%CI: 3.86-13.33) and having a cough in the last 4 weeks (aOR: 2.34;95%CI: 1.43-3.89) were significant associated with the TB. Logistic regression achieved accuracy of 0.868, and AUC-ROC of 0.744. Elastic net regression also showed good predictive performance with accuracy, and AUC-ROC values of 0.874 and 0.762, respectively.

Conclusions

Our results suggest that logistic regression, Lasso, Ridge regression, and Elastic net can all be effective methods for predicting TB disease in HIV patients. These findings may have important implications for the development of accurate and reliable models for TB prediction in HIV patients.

The incidence rate of tuberculosis and its associated factors among HIV-positive persons in Sub-Saharan Africa: a systematic review and meta-analysis

BACKGROUND

Tuberculosis, along with HIV, is the leading cause of mortality and morbidity globally. Despite the fact that several primary studies have been conducted on the incidence rate of tuberculosis in HIV-infected people in Sub-Saharan Africa, the regional-level tuberculosis incidence rate remains unknown. The objective of this study is to determine the tuberculosis incidence rate and its associated factors in HIV-infected people in Sub-Saharan Africa.

METHODS

A systematic review and meta-analysis were conducted by searching four databases for studies published in English between January 1, 2000, and November 25, 2022. The study was carried out using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. To assess the quality of the studies, the Joanna Briggs Institute critical appraisal checklist was used. A random-effects model meta-analysis was used to determine the pooled incidence of tuberculosis using STATA version 15. The I2 heterogeneity test was used to assess heterogeneity. Subgroup and sensitivity analyses were performed. Funnel plots and Egger's regression tests were used to investigate publication bias. The pooled estimate predictors of tuberculosis incidence rate with a 95% confidence interval were also determined using the hazard ratio of each factor (HR).

RESULTS

Out of a total of 3339 studies, 43 were included in the analysis. The overall pooled incidence rate of tuberculosis in HIV-infected people was 3.49 per 100 person-years (95% CI: 2.88-4.17). In the subgroup analysis, the pooled incidence rate of tuberculosis in HIV-infected children was 3.42 per 100 person-years (95% CI: 1.78, 5.57), and it was 3.79 per 100 person-years (95% CI: 2.63, 5.15) in adults. A meta-analysis revealed that underweight (AHR = 1.79, 95% CI: 1.61-1.96), low CD4 count (AHR = 1.23, 95% CI: 1.13-1.35), male gender (AHR = 1.43, 95% CI: 1.22-1.64), advanced WHO clinical stages (AHR = 2.29, 95% CI: 1.34-3.23), anemia (AHR = 1.73, 95% CI: 1.34-2.13), bedridden or ambulatory (AHR = 1.87, 95%), lack of isoniazid preventive therapy (AHR = 3.32, 95% CI: 1.08-2.28), and lack of cotrimoxazole (AHR = 1.68, 95% CI: 1.08-2.28) were risk factors for tuberculosis incidence. HIV patients who received antiretroviral therapy had a 0.53 times higher risk of acquiring tuberculosis than HIV patients who did not receive antiretroviral therapy (AHR = 0.53; 95% CI: 0.3-0.77).

CONCLUSION

In this systematic review and meta-analysis study, the incidence rate of tuberculosis among HIV-positive people was higher than the WHO 2022 Africa regional estimated report. To reduce the incidence of tuberculosis among HIV patients, HIV patients should take isoniazid prevention therapy (IPT), cotrimoxazole prophylaxis, and antiretroviral therapy (ART) without interruption, as well as increase the frequency and diversity of their nutritional intake. Active tuberculosis screening should be increased among HIV-infected people.

Development and Validation of the Tuberculosis Risk Score for Smokers (TBRSS)

Tuberculosis (TB) remains a significant public health issue worldwide. However, the effectiveness of TB screening programmes among smokers is still questionable. There is a need for a simple, reliable, and validated screening system for this risk population. This study aimed to develop and validate the tuberculosis risk score for smokers (TBRSS) in Kelantan, Malaysia. A case–control study was conducted on 159 patients (smokers with and without TB) between January and July 2020. Simple and multiple logistic regressions were applied to determine the variables to be included in the risk score. The cut-off points to determine a score indicating low or high risk for TB disease were obtained based on the receiver operating characteristics curve. Content validation was carried out through interviews with eight experts to measure each variable′s relevancy. The face validation was conducted among 20 health clinic staff. Seven variables were selected for inclusion in the risk score. The chosen cut-off point was 16 (out of 43), with 91% and 78% sensitivity and specificity, respectively. The scale-level content validity index was 0.83, while the face validity index scores for each element ranged between 0.85 and 1.00. The TBRSS can be considered a validated screening tool for use in screening TB disease risk among smokers, which potentially may lead to an increased detection of TB disease in the community.