BackgroundWe aimed to determine the prevalence of pulmonary TB amongst the adult population (≥15 years) in 2016 in Kenya.MethodA 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.ResultThe 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.ConclusionThis 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.
BackgroundThe prevalence of diseases other than TB detected during chest X-ray (CXR) screening is unknown in sub-Saharan Africa. This represents a missed opportunity for identification and treatment of potentially significant disease. Our aim was to describe and quantify non-TB abnormalities identified by TB-focused CXR screening during the 2016 Kenya National TB Prevalence Survey.MethodsWe reviewed a random sample of 1140 adult (≥15 years) CXRs classified as ‘abnormal, suggestive of TB’ or ‘abnormal other’ during field interpretation from the TB prevalence survey. Each image was read (blinded to field classification and study radiologist read) by two expert radiologists, with images classified into one of four major anatomical categories and primary radiological findings. A third reader resolved discrepancies. Prevalence and 95% CIs of abnormalities diagnosis were estimated.FindingsCardiomegaly was the most common non-TB abnormality at 259 out of 1123 (23.1%, 95% CI 20.6% to 25.6%), while cardiomegaly with features of cardiac failure occurred in 17 out of 1123 (1.5%, 95% CI 0.9% to 2.4%). We also identified chronic pulmonary pathology including suspected COPD in 3.2% (95% CI 2.3% to 4.4%) and non-specific patterns in 4.6% (95% CI 3.5% to 6.0%). Prevalence of active-TB and severe post-TB lung changes was 3.6% (95% CI 2.6% to 4.8%) and 1.4% (95% CI 0.8% to 2.3%), respectively.InterpretationBased on radiological findings, we identified a wide variety of non-TB abnormalities during population-based TB screening. TB prevalence surveys and active case finding activities using mass CXR offer an opportunity to integrate disease screening efforts.FundingNational Institute for Health Research (IMPALA-grant reference 16/136/35).
Introduction Isoniazid preventive therapy (IPT) taken by People Living with HIV (PLHIV) protects against active tuberculosis (TB). Despite its recommendation, data is scarce on the uptake of IPT among PLHIV and factors associated with treatment outcomes. We aimed at determining the proportion of PLHIV initiated on IPT, assessed TB screening practices during and after IPT and IPT treatment outcomes. Methods A retrospective cohort study of a representative sample of PLHIV initiated on IPT between July 2015 and June 2018 in Kenya. For PLHIV initiated on IPT during the study period, we abstracted patient IPT uptake data from the National data warehouse. In contrast, we obtained information on socio-demographic, TB screening practices, IPT initiation, follow up, and outcomes from health facilities' patient record cards, IPT cards, and IPT registers. Further, we assessed baseline characteristics as potential correlates of developing active TB during and after treatment and IPT completion using multivariable logistic regression. Results From the data warehouse, 138,442 PLHIV were enrolled into ART during the study period and initiated 95,431 (68.9%) into IPT. We abstracted 4708 patients’ files initiated on IPT, out of which 3891(82.6%) had IPT treatment outcomes documented, 4356(92.5%) had ever screened for TB at every clinic visit, and 4,243(90.1%) had documentation of TB screening on the IPT tool before IPT initiation. 3712(95.4%) of patients with documented IPT treatment outcomes completed their treatment. 42(0.89%) of the abstracted patients developed active TB,16(38.1%) during, and 26(61.9%) after completing IPT. Follow up for active TB at 6-month post-IPT completion was done for 2729(73.5%) of patients with IPT treatment outcomes. Sex, Viral load suppression, and clinic type were associated with TB development (p<0.05). Levels 4, 5, FBO, and private facilities and IPT prescription practices were associated with IPT completion (p<0.05). Conclusion IPT initiation stands at two-thirds of the PLHIV, with a high completion rate. TB screening practices were better during IPT than after completion. Development of active TB during and after IPT emphasizes the need for a keen follow up.
BACKGROUND: TB is the leading cause of mortality among people living with HIV (PLHIV), for whom isoniazid preventive therapy (IPT) has a proven mortality benefit. Despite WHO recommendations, countries have been slow in scaling up IPT. This study describes processes, challenges, solutions, outcomes and lessons learned during IPT scale‐up in Kenya.METHODS: We conducted a desk review and analyzed aggregated Ministry of Health (MOH) IPT enrollment data from 2014 to 2018 to determine trends and impact of program activities. We further analyzed IPT completion reports for patients initiated from 2015 to 2017 in 745 MOH sites in Nairobi, Central, Eastern and Western Kenya.RESULTS: IPT was scaled up 75‐fold from 2014 to 2018: the number of PLHIV covered increased from 9,981 to 749,890. The highest percentage increases in the cumulative number of PLHIV on IPT were seen in the quarters following IPT pilot projects in 2014 (49%), national launch in 2015 (54%), and HIV treatment acceleration in 2016 (158%). Among 250,069 patients initiating IPT from 2015 to 2017, 97.5% completed treatment, 0.2% died, 0.8% were lost to follow‐up, 1.0% were not evaluated, and 0.6% discontinued treatment.CONCLUSIONS: IPT can be scaled up rapidly and effectively among PLHIV. Deliberate MOH efforts, strong leadership, service delivery integration, continuous mentorship, stakeholder involvement, and accountability are critical to program success.
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BackgroundCommunity-based screening for tuberculosis (TB) could improve detection but is resource intensive. We set out to evaluate the accuracy of computer-aided TB screening using digital chest X-ray (CXR) to determine if this approach met target product profiles (TPP) for community-based screening.MethodsCXR images from participants in the 2016 Kenya National TB Prevalence Survey were evaluated using CAD4TBv6 (Delft Imaging), giving a probabilistic score for pulmonary TB ranging from 0 (low probability) to 99 (high probability). We constructed a Bayesian latent class model to estimate the accuracy of CAD4TBv6 screening compared to bacteriologically-confirmed TB across CAD4TBv6 threshold cut-offs, incorporating data on Clinical Officer CXR interpretation, participant demographics (age, sex, TB symptoms, previous TB history), and sputum results. We compared model-estimated sensitivity and specificity of CAD4TBv6 to optimum and minimum TPPs.ResultsOf 63,050 prevalence survey participants, 61,848 (98%) had analysable CXR images, and 8,966 (14.5%) underwent sputum bacteriological testing; 298 had bacteriologically-confirmed pulmonary TB. Median CAD4TBv6 scores for participants with bacteriologically-confirmed TB were significantly higher (72, IQR: 58-82.75) compared to participants with bacteriologically-negative sputum results (49, IQR: 44-57, p<0.0001). CAD4TBv6 met the optimum TPP; with the threshold set to achieve a mean sensitivity of 95% (optimum TPP), specificity was 83.3%, (95% credible interval [CrI]: 83.0%—83.7%, CAD4TBv6 threshold: 55). There was considerable variation in accuracy by participant characteristics, with older individuals and those with previous TB having lowest specificity.ConclusionsCAD4TBv6 met the optimal TPP for TB community screening. To optimise screening accuracy and efficiency of confirmatory sputum testing, we recommend that an adaptive approach to threshold setting is adopted based on participant characteristics.Take home messageCAD4TBv6 met the optimal WHO target product profile for a community TB screening tool. Specificity was lower in adults with previous TB and those aged 41 years or older; an adaptive approach to setting CAD thresholds will likely be required to optimize use.
SETTING: One hundred high TB burden facilities in nine counties in Kenya.OBJECTIVES: 1) To increase uptake of TB preventive therapy (TPT) among child contacts aged <5 years, and 2) to increase TB diagnosis in children aged <15 years presenting to health facilities for routine care.DESIGN: For objective 1, a clinic-based child contact management strategy incorporating transport/healthcare cost reimbursement, monitoring and evaluation tools, and healthcare worker education was utilized. For objective 2, community health screeners were established in pediatric outpatient departments to perform verbal screening, flagging symptomatic children for further evaluation.RESULTS: Over 15 months, identification of 8,060 individuals diagnosed with bacteriologically confirmed TB led to 2,022 child contacts. Of these, 1,848 (91%) were evaluated; 149 (8%) were diagnosed with TB disease, leaving 1,699 (92%) eligible for TPT; 1,613 (95%) initiated TPT and 1,335 (83%) completed TPT. In outpatient settings, 140,444 children were screened; 54,236 (39%) had at least two TB symptoms; 2,395 (4%) were diagnosed with TB diseaseCONCLUSION: Health system strengthening supporting a clinic-based child contact management program increased the number of children initiating TPT. Systematic screening in outpatient clinics can lead to increased TB case notifications; however, optimal screening tools and clearer diagnostic pathways for the evaluation of these children are needed.
Background: The prevalence of diseases other than tuberculosis (TB) detected during chest X-ray (CXR) screening is unknown in sub-Saharan Africa. This represents a missed opportunity for identification and treatment of potentially significant disease. Our aim was to quantify and characterise non-TB abnormalities identified by TB-focused CXR screening during the 2016 Kenya National TB prevalence survey. Methods: We reviewed a random sample of 1140 adult (≥15 years) CXRs classified as "abnormal, suggestive of TB" or "abnormal other" during field interpretation from the TB Prevalence Survey. Each image was read (blinded to field classification and study radiologist read) by two expert radiologists, with images classified into one of four major anatomical categories and primary radiological diagnosis. A third reader resolved discrepancies. Prevalence and 95% confidence intervals of abnormalities diagnosis were estimated. Findings: Cardiomegaly was the most common non-TB abnormality at 259/1123 (23∙1%, 95% CI 20∙6%-25∙6%), while cardiomegaly with features of cardiac failure occurred in 17/1123 (1∙5 %, 95% CI 0.9%-2∙4%). We also identified chronic pulmonary pathology including suspected chronic obstructive pulmonary disease in 3∙2% (95% CI 2∙3%- 4∙4%) and non-specific patterns in 4∙6% (95% CI 3∙5%- 6∙0%). Prevalence of active-TB and severe post-TB lung changes was 3∙6% (95% CI 2∙6%- 4∙8%) and 1∙4% (95% CI 0∙8%- 2∙3%) respectively. Interpretation: Based on radiological diagnosis, we identified a wide variety of non-TB diagnoses during population-based TB screening. TB prevalence surveys and active case finding activities using mass CXR offer an opportunity to integrate disease screening efforts. Funding National Institute for Health Research (IMPALA-grant reference 16/136/35).
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