Pulmonary tuberculosis screening in anti-retroviral treated adults living with HIV in Kenya.

BACKGROUND: People living with HIV (PLHIV) who reside in high tuberculosis burden settings remain at risk for tuberculosis disease despite treatment with anti-retroviral therapy and isoniazid preventive therapy (IPT). The performance of the World Health Organization (WHO) symptom screen for tuberculosis in PLHIV receiving anti-retroviral therapy is sub-optimal and alternative screening strategies are needed. METHODS: We enrolled HIV-positive adults into a prospective study in western Kenya. Individuals who were IPT-naïve or had completed IPT > 6 months prior to enrollment were eligible. We evaluated tuberculosis prevalence overall and by IPT status. We assessed the accuracy of the WHO symptom screen, GeneXpert MTB/RIF (Xpert), and candidate biomarkers including C-reactive protein (CRP), hemoglobin, erythrocyte sedimentation rate (ESR), and monocyte-to-lymphocyte ratio for identifying pulmonary tuberculosis. Some participants were evaluated at 6 months post-enrollment for tuberculosis. RESULTS: The study included 383 PLHIV, of whom > 99% were on antiretrovirals and 88% had received IPT, completed a median of 1.1 years (IQR 0.8-1.55) prior to enrollment. The prevalence of pulmonary tuberculosis at enrollment was 1.3% (n = 5, 95% CI 0.4-3.0%): 4.3% (0.5-14.5%) among IPT-naïve and 0.9% (0.2-2.6%) among IPT-treated participants. The sensitivity of the WHO symptom screen was 0% (0-52%) and specificity 87% (83-90%). Xpert and candidate biomarkers had poor to moderate sensitivity; the most accurate biomarker was CRP ≥ 3.3 mg/L (sensitivity 80% (28-100) and specificity 72% (67-77)). Six months after enrollment, the incidence rate of pulmonary tuberculosis following IPT completion was 0.84 per 100 person-years (95% CI, 0.31-2.23). CONCLUSIONS: In Kenyan PLHIV treated with IPT, tuberculosis prevalence was low at a median of 1.4 years after IPT completion. WHO symptoms screening, Xpert, and candidate biomarkers were insensitive for identifying pulmonary tuberculosis in antiretroviral-treated PLHIV.

The movement of multidrug-resistant tuberculosis across borders in East Africa needs a regional and global solution.

Kevin Cain and colleagues reflect on the cross border movement of people from Somalia with MDR-TB and the implications for MDR-TB programs in East Africa.

Optimizing diagnostic networks to increase patient access to TB diagnostic services: Development of the diagnostic network optimization (DNO) approach and learnings from its application in Kenya, India and the Philippines.

Diagnostic network optimization (DNO) is an analytical approach that enables use of available country data to inform evidence-based decision-making to optimize access to diagnostic services. A DNO methodology was developed using available data sources and a commercial supply chain optimization software. In collaboration with Ministries of Health and partners, the approach was applied in Kenya, India and the Philippines to map TB diagnostic networks, identify misalignments, and determine optimal network design to increase patient access to TB diagnostic services and improve device utilization. The DNO analysis was successfully applied to evaluate and inform TB diagnostic services in Kenya, India and the Philippines as part of national strategic planning for TB. The analysis was tailored to each country's specific objectives and allowed evaluation of factors such as the number and placement of different TB diagnostics, design of sample referral networks and integration of early infant diagnosis for HIV at national and sub-national levels and across public and private sectors. Our work demonstrates the value of DNO as an innovative approach to analysing and modelling diagnostic networks, particularly suited for use in low-resource settings, as an open-access approach that can be applied to optimize networks for any disease.

Tuberculosis control activities in the private and public health sectors of Kenya from 2013 to 2017: how do they compare?

BACKGROUND: Large numbers of tuberculosis (TB) patients seek care from private for-profit providers. This study aimed to assess and compare TB control activities in the private for-profit and public sectors in Kenya between 2013 and 2017. METHODS: We conducted a retrospective cross-sectional study using routinely collected data from the National Tuberculosis, Leprosy and Lung Disease Program. RESULTS: Of 421 409 patients registered and treated between 2013 and 2017, 86 894 (21%) were from the private sector. Data collection was less complete in the private sector for nutritional assessment and follow-up sputum smear examinations (p<0.001). The private sector notified less bacteriologically confirmed TB (43.1% vs 52.6%; p<0.001) and had less malnutrition (body mass index <18.5 kg/m2; 36.4% vs 43.3%; p<0.001) than the public sector. Rates of human immunodeficiency virus (HIV) testing and antiretroviral therapy initiation were >95% and >90%, respectively, in both sectors, but more patients were HIV positive in the private sector (39.6% vs 31.6%; p<0.001). For bacteriologically confirmed pulmonary TB, cure rates were lower in the private sector, especially for HIV-negative patients (p<0.001). The private sector had an overall treatment success of 86.3% as compared with the public sector at 85.7% (p<0.001). CONCLUSIONS: The private sector is performing well in Kenya although there are programmatic challenges that need to be addressed.

Assessing tuberculosis control priorities in high-burden settings: a modelling approach.

BACKGROUND: In the context of WHO's End TB strategy, there is a need to focus future control efforts on those interventions and innovations that would be most effective in accelerating declines in tuberculosis burden. Using a modelling approach to link the tuberculosis care cascade to transmission, we aimed to identify which improvements in the cascade would yield the greatest effect on incidence and mortality. METHODS: We engaged with national tuberculosis programmes in three country settings (India, Kenya, and Moldova) as illustrative examples of settings with a large private sector (India), a high HIV burden (Kenya), and a high burden of multidrug resistance (Moldova). We collated WHO country burden estimates, routine surveillance data, and tuberculosis prevalence surveys from 2011 (for India) and 2016 (for Kenya). Linking the tuberculosis care cascade to tuberculosis transmission using a mathematical model with Bayesian melding in each setting, we examined which cascade shortfalls would have the greatest effect on incidence and mortality, and how the cascade could be used to monitor future control efforts. FINDINGS: Modelling suggests that combined measures to strengthen the care cascade could reduce cumulative tuberculosis incidence by 38% (95% Bayesian credible intervals 27-43) in India, 31% (25-41) in Kenya, and 27% (17-41) in Moldova between 2018 and 2035. For both incidence and mortality, modelling suggests that the most important cascade losses are the proportion of patients visiting the private health-care sector in India, missed diagnosis in health-care settings in Kenya, and drug sensitivity testing in Moldova. In all settings, the most influential delay is the interval before a patient's first presentation for care. In future interventions, the proportion of individuals with tuberculosis who are on high-quality treatment could offer a more robust monitoring tool than routine notifications of tuberculosis. INTERPRETATION: Linked to transmission, the care cascade can be valuable, not only for improving patient outcomes but also in identifying and monitoring programmatic priorities to reduce tuberculosis incidence and mortality. FUNDING: US Agency for International Development, Stop TB Partnership, UK Medical Research Council, and Department for International Development.

Kenya tuberculosis prevalence survey 2016: Challenges and opportunities of ending TB in Kenya.

BACKGROUND: We aimed to determine the prevalence of pulmonary TB amongst the adult population (≥15 years) in 2016 in Kenya. METHOD: A nationwide cross-sectional survey where participants first underwent TB symptom screening and chest x-ray. Subsequently, participants who reported cough >2weeks and/or had a chest x-ray suggestive of TB, submitted sputum specimen for laboratory examination by smear microscopy, culture and Xpert MTB/RIF. RESULT: The survey identified 305 prevalent TB cases translating to a prevalence of 558 [95%CI 455-662] per 100,000 adult population. The highest disease burden was reported among people aged 25-34 years (716 [95% CI 526-906]), males (809 [(95% CI 656-962]) and those who live in urban areas (760 [95% CI 539-981]). Compared to the reported TB notification rate for Kenya in 2016, the prevalence to notification ratio was 2.5:1. The gap between the survey prevalence and notification rates was highest among males, age groups 25-34, and the older age group of 65 years and above. Only 48% of the of the survey prevalent cases reported cough >2weeks. In addition, only 59% of the identified cases had the four cardinal symptoms for TB (cough ≥2 weeks, fever, night sweat and weight loss. However, 88.2% had an abnormal chest x-ray suggestive of TB. The use of Xpert MTB/RIF identified 77.7% of the cases compared to smear microscopy's 46%. Twenty-one percent of the survey participants with respiratory symptoms reported to have sought prior health care at private clinics and chemists. Among the survey prevalent cases who reported TB related symptoms, 64.9% had not sought any health care prior to the survey. CONCLUSION: This survey established that TB prevalence in Kenya is higher than had been estimated, and about half of the those who fall ill with the disease each year are missed.