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.

INTRODUCTION:Tuberculosis (TB) remains a major cause of morbidity and mortality, especially in sub-Saharan Africa. We qualitatively evaluated the implementation of an Evidence-Based Multiple Focus Integrated Intensified TB Screening package (EXIT-TB) in the East African region, aimed at increasing TB case detection and number of patients receiving care.OBJECTIVE:We present the accounts of participants from Tanzania, Kenya, Uganda, and Ethiopia regarding the implementation of EXIT-TB, and suggestions for scaling up.METHODS:A qualitative descriptive design was used to gather insights from purposefully selected healthcare workers, community health workers, and other stakeholders. A total of 27, 13, 14, and 19 in-depth interviews were conducted in Tanzania, Kenya, Uganda, and Ethiopia respectively. Data were transcribed and translated simultaneously and then thematically analysed.RESULTS:The EXIT-TB project was described to contribute to increased TB case detection, improved detection of Multidrug-resistant TB patients, reduced delays and waiting time for diagnosis, raised the index of TB suspicion, and improved decision-making among HCWs. The attributes of TB case detection were: (i) free X-ray screening services; (ii) integrating TB case-finding activities in other clinics such as Reproductive and Child Health clinics (RCH), and diabetic clinics; (iii), engagement of CHWs, policymakers, and ministry level program managers; (iv) enhanced community awareness and linkage of clients; (v) cooperation between HCWs and CHWs, (vi) improved screening infrastructure, (vii) the adoption of the new simplified screening criteria and (viii) training of implementers. The supply-side challenges encountered ranged from disorganized care, limited space, the COVID-19 pandemic, inadequate human resources, inadequate knowledge and expertise, stock out of supplies, delayed maintenance of equipment, to absence of X-ray and GeneXpert machines in some facilities. The demand side challenges ranged from delayed care seeking, inadequate awareness, negative beliefs, fears towards screening, to financial challenges. Suggestions for scaling up ranged from improving service delivery, access to diagnostic equipment and supplies, and infrastructure, to addressing client fears and stigma.CONCLUSION:The EXIT-TB package appears to have contributed towards increasing TB case detection and reducing delays in TB treatment in the study settings. Addressing the challenges identified is needed to maximize the impact of the EXIT-TB intervention.

Introduction: East Africa countries (Tanzania, Kenya, and Uganda) are among tuberculosis high burdened countries globally. As we race to accelerate progress towards a world free of tuberculosis by 2035, gaps related to screening and diagnosis in the cascade care need to be addressed. Methods: We conducted a three-year (2015-2017) retrospective study using routine program data in 21 health facilities from East Africa. Data abstraction were done at tuberculosis clinics, outpatient departments (OPD), human immunodeficiency virus (HIV) and diabetic clinics, and then complemented with structured interviews with healthcare providers to identify possible gaps related to integration, screening, and diagnosis of tuberculosis. Data were analyzed using STATA (TM) Version 14.1. Results: We extracted information from 49,454 presumptive TB patients who were registered in the 21 facilities between January 2015 and December 2017. A total of 9,565 tuberculosis cases were notified; 46.5% (4,450) were bacteriologically confirmed and 31.5% (3,013) were HIV-infected. Prevalence of tuberculosis among presumptive pulmonary tuberculosis cases was 17.4%. The outcomes observed were as follows: 79.8% (7,646) cured or completed treatment, 6.6% (634) died, 13.3% (1,270) lost to follow-up or undocumented and 0.4% (34) treatment failure. In all countries, tuberculosis screening was largely integrated at OPD and HIV clinics. High patient load, weak laboratory specimen referral system, shortage of trained personnel, and frequent interruption of laboratory supplies were the major cited challenges in screening and diagnosis of tuberculosis. Conclusion: Screening and diagnostic activities were frequently affected by scarcity of human and financial resources. Tuberculosis screening was mainly integrated at OPD and HIV clinics, with less emphasis on the other health facility clinics. Closing gaps related to TB case finding and diagnosis in developing countries requires sustainable investment for both human and financial resources and strengthen the integration of TB activities within the health system.

Tuberculosis is the deadliest infection of our time. In contrast, about 11,000 people died of Ebola between 2014 and 2016. Despite this manifest difference in mortality, there is now a vaccine licensed in the United States and by the European Medicines Agency, with up to 100% efficacy against Ebola. The developments that led to the trialing of the Ebola vaccine were historic and unprecedented. The single licensed TB vaccine (BCG) has limited efficacy. There is a dire need for a more efficacious TB vaccine. To deploy such vaccines, trials are needed in sites that combine high disease incidence and research infrastructure. We describe our twelve-year experience building a TB vaccine trial site in contrast to the process in the recent Ebola outbreak. There are additional differences. Relative to the Ebola pipeline, TB vaccines have fewer trials and a paucity of government and industry led trials. While pathogens have varying levels of difficulty in the development of new vaccine candidates, there yet appears to be greater interest in funding and coordinating Ebola interventions. TB is a global threat that requires similar concerted effort for elimination.

OBJECTIVE To evaluate the utility of a broad and non-specific symptom screen for identifying people with undiagnosed HIV infection. DESIGN Secondary analysis of operational data collected during implementation of a cluster-randomized trial for tuberculosis case detection. METHODS As part of the trial, adults reporting cough, fever, night sweats, weight loss, or difficulty breathing of any duration in the past month were identified in health facilities and community-based mobile screening units in western Kenya. Adults reporting any symptom were offered HIV testing. We analysed the HIV testing data from this study, using modified Poisson regression to identify predictors of new HIV diagnoses among adults with symptoms and initially unknown HIV status. RESULTS We identified 3,818 symptomatic adults, referred 1424 (37%) for testing, of whom 1065 (75%) accepted, and 107 (10%) were newly diagnosed with HIV. The prevalence of new HIV diagnoses was 21% (95% CI: 17-25%) among those tested in health facilities and 5% (95% CI 4-7%) among those tested in mobile units. More men were diagnosed with HIV than women despite fewer men being screened. People who reported 4-5 symptoms were over twice as likely to be diagnosed with HIV compared to those reporting 1-3 symptoms (adjusted prevalence ratio [aPR] in health facilities = 2.58, 95% CI, 1.65-4.05; aPR in mobile units = 2.63, 95% CI, 1.37-5.03). CONCLUSION We observed a high yield of new HIV diagnoses among adults identified by active application of a broad symptom screen. Integrated tuberculosis and HIV screening using could help close the detection gap for both conditions.

SETTING:Although Kenya has a high burden of tuberculosis (TB), only 46% of cases were diagnosed in 2016.OBJECTIVE:To identify strategies for increasing attendance at community-based mobile screening units.DESIGN:We analysed operational data from a cluster-randomised trial, which included community-based mobile screening implemented during February 2015-April 2016. Community health volunteers (CHVs) recruited individuals with symptoms from the community, who were offered testing for human immunodeficiency virus (HIV) and sputum collection for Xpert® MTB/RIF testing. We compared attendance across different mobile unit sites using Wilcoxon rank-sum test.RESULTS:A total of 1424 adults with symptoms were screened at 25 mobile unit sites. The median total attendance among sites was 54 (range 6-134, interquartile range [IQR] 24-84). The median yields of TB diagnoses and new HIV diagnoses were respectively 2.4% (range 0.0-16.7, IQR 0.0-5.3) and 2.5% (range 0.0-33.3, IQR 1.2-4.2). Attendance at urban sites was variable; attendance at rural sites where CHVs were paid a daily minimum wage was significantly higher than at rural sites where CHVs were paid a nominal monthly stipend (P < 0.001).CONCLUSION:Mobile units were most effective and efficient when implemented as a single event with community health workers who are paid a daily wage.

SETTING: Efficient tuberculosis (TB) active case-finding strategies are important in settings with high TB burdens and limited resources, such as those in western Kenya.OBJECTIVE: To guide efforts to optimize screening efficiency, we identified the predictors of TB among people screened in health facilities and communities.DESIGN: During February 2015-June 2016, adults aged ≥15 years reporting any TB symptom were identified in health facilities and community mobile screening units, and evaluated for TB. We assessed the predictors of TB using a modified Poisson regression with generalized estimating equations to account for clustering according to screening site.RESULTS: TB was diagnosed in 484 (20.3%) of 2394 symptomatic adults in health facilities and 39 (3.4%) of 1424 in communities. In health facilities, >10% of symptomatic adults in all demographic groups had TB, and no predictors were associated with a ≥2-fold increased risk. In communities, the independent predictors of TB were male sex (adjusted prevalence ratio [aPR] = 4.26, 95%CI 2.43-7.45), HIV infection (aPR 2.37, 95%CI 1.18-4.77), and household TB contact in the last 2 years (aPR 2.84, 95%CI 1.62-4.96).CONCLUSION: Our findings support the notion of general TB screening in health facilities and evaluation of the adult household contacts of TB patients.

Leveraging an existing community health strategy, a contact tracing intervention was piloted under routine programmatic conditions at three facilities in Kisumu County, Kenya. Data collected during a 6-month period were compared to existing programmatic data. After implementation of the intervention, we found enhanced programmatic contact tracing practices, noting an increase in the proportions of index cases traced, symptomatic contacts referred, referred contacts presenting to a facility for tuberculosis screening, and eligible contacts started on isoniazid preventive therapy. As contact tracing is scaled up, health ministries should consider the adoption of similar contact tracing interventions to improve contact tracing practices.