Patient Diagnostic Rate as Indicator of Tuberculosis Case Detection, South Africa.
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ABSTRACT:
SUBMITTER: Claassens MM
PROVIDER: S-EPMC4766915 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Mathematical models are increasingly being used to compare strategies for tuberculosis (TB) control and inform policy decisions. Models often do not consider financial and other constraints on implementation and may overestimate the impact that can be achieved. We developed a pragmatic approach for incorporating resource constraints into mathematical models of TB. Using a TB transmission model calibrated for South Africa, we estimated the epidemiologic impact and resource requirements (financial, human resource (HR), and diagnostic) of 9 case-finding interventions. We compared the model-estimated resources with scenarios of future resource availability and estimated the impact of interventions under these constraints. Without constraints, symptom screening in public health clinics and among persons receiving care for human immunodeficiency virus infection was predicted to lead to larger reductions in TB incidence (9.5% (2.5th-97.5th percentile range (PR), 8.6-12.2) and 14.5% (2.5th-97.5th PR, 12.2-16.3), respectively) than improved adherence to diagnostic guidelines (2.7%; 2.5th-97.5th PR, 1.6-4.1). However, symptom screening required large increases in resources, exceeding future HR capacity. Even under our most optimistic HR scenario, the reduction in TB incidence from clinic symptom screening was 0.2%-0.9%-less than that of improved adherence to diagnostic guidelines. Ignoring resource constraints may result in incorrect conclusions about an intervention's impact and may lead to suboptimal policy decisions. Models used for decision-making should consider resource constraints.
| S-EPMC6545281 | biostudies-literature
Project description:Time-course expression analysis profiling whole blood samples collected from healthy South African adolescents while monitoring their potential acquisition of a Mycobacterium tuberculosis infection.
2018-08-18 | GSE116014 | GEO
Project description:RationaleTuberculosis case-detection rates are below internationally established targets in high-burden countries. Real-time monitoring and evaluation of adherence to widely endorsed standards of tuberculosis care might facilitate improved case finding.ObjectivesTo monitor and evaluate the quality of tuberculosis case-detection and management services in a low-income country with a high incidence of tuberculosis.MethodsWe prospectively evaluated tuberculosis diagnostic services at five primary health-care facilities in Uganda for 1 year, after introducing a real-time, electronic performance-monitoring system. We collected data on every clinical encounter, and measured quality using indicators derived from the International Standards of Tuberculosis Care.Measurements and main resultsIn 2009, there were 62,909 adult primary-care visits. During the first quarter of 2009, clinicians referred only 21% of patients with cough greater than or equal to 2 weeks for sputum smear microscopy and only 71% of patients with a positive sputum examination for tuberculosis treatment. These proportions increased to 53% and 84%, respectively, in the fourth quarter of 2009. The cumulative probability that a smear-positive patient with cough greater than or equal to 2 weeks would be appropriately evaluated and referred for treatment rose from 11% to 34% (P = 0.005). The quarterly number of tuberculosis cases identified and prescribed treatment also increased four-fold, from 5 to 21.ConclusionsPoor adherence to internationally accepted standards of tuberculosis care improved after introduction of real-time performance monitoring and was associated with increased tuberculosis case detection. Real-time monitoring and evaluation can strengthen health systems in low-income countries and facilitate operational research on the effectiveness and sustainability of interventions to improve tuberculosis case detection.
| S-EPMC3175538 | biostudies-literature
Project description:BackgroundSouth Africa has one of the highest per capita rates of tuberculosis (TB) incidence in the world. In 2012, the South African government produced a National Strategic Plan (NSP) to control the spread of TB with the ambitious aim of zero new TB infections and deaths by 2032, and a halving of the 2012 rates by 2016.MethodsWe used a transmission model to investigate whether the NSP targets could be reached if immediate scale up of control methods had happened in 2014. We explored the potential impact of four intervention portfolios; 1) "NSP" represents the NSP strategy, 2) "WHO" investigates increasing antiretroviral therapy eligibility, 3) "Novel Strategies" considers new isoniazid preventive therapy strategies and HIV "Universal Test and Treat" and 4) "Optimised" contains the most effective interventions.FindingsWe find that even with this scale-up, the NSP targets are unlikely to be achieved. The portfolio that achieved the greatest impact was "Optimised", followed closely by "NSP". The "WHO" and "Novel Strategies" had little impact on TB incidence by 2050. Of the individual interventions explored, the most effective were active case finding and reductions in pre-treatment loss to follow up which would have a large impact on TB burden.ConclusionUse of existing control strategies has the potential to have a large impact on TB disease burden in South Africa. However, our results suggest that the South African TB targets are unlikely to be reached without new technologies. Despite this, TB incidence could be dramatically reduced by finding and starting more TB cases on treatment.
| S-EPMC4388715 | biostudies-literature
Project description:Antimicrobial-resistant (AMR) infections pose a major threat to global public health. Similar to other AMR pathogens, both historical and ongoing drug-resistant tuberculosis (TB) epidemics are characterized by transmission of a limited number of predominant Mycobacterium tuberculosis (Mtb) strains. Understanding how these predominant strains achieve sustained transmission, particularly during the critical period before they are detected via clinical or public health surveillance, can inform strategies for prevention and containment. In this study, we employ whole-genome sequence (WGS) data from TB clinical isolates collected in KwaZulu-Natal, South Africa to examine the pre-detection history of a successful strain of extensively drug-resistant (XDR) TB known as LAM4/KZN, first identified in a widely reported cluster of cases in 2005. We identify marked expansion of this strain concurrent with the onset of the generalized HIV epidemic 12 y prior to 2005, localize its geographic origin to a location in northeastern KwaZulu-Natal ∼400 km away from the site of the 2005 outbreak, and use protein structural modeling to propose a mechanism for how strain-specific rpoB mutations offset fitness costs associated with rifampin resistance in LAM4/KZN. Our findings highlight the importance of HIV coinfection, high preexisting rates of drug-resistant TB, human migration, and pathoadaptive evolution in the emergence and dispersal of this critical public health threat. We propose that integrating whole-genome sequencing into routine public health surveillance can enable the early detection and local containment of AMR pathogens before they achieve widespread dispersal.
| S-EPMC6859317 | biostudies-literature
Project description:Intensive case finding is endorsed for tuberculosis (TB) control in high-risk populations. Novel case-finding strategies are needed in hard-to-reach rural populations with high prevalence of TB and human immunodeficiency virus (HIV).We performed community-based integrated HIV and TB intensive case finding in a rural South African subdistrict from March 2010 to June 2012. We offered TB symptom screening, sputum collection for microbiologic diagnosis, rapid fingerstick HIV testing, and phlebotomy for CD4 cell count. We recorded number of cases detected and calculated population-level rates and number needed to screen (NNS) for drug-susceptible and -resistant TB.Among 5615 persons screened for TB at 322 community sites, 91.2% accepted concurrent HIV testing, identifying 510 (9.9%) HIV-positive individuals with median CD4 count of 382 cells/mm3 (interquartile range = 260-552). Tuberculosis symptoms were reported by 2049 (36.4%), and sputum was provided by 1033 (18.4%). Forty-one (4.0%) cases of microbiologically confirmed TB were detected for an overall case notification rate of 730/100000 (NNS = 137); 11 (28.6%) were multidrug-resistant or extensively drug-resistant TB. Only 5 (12.2%) TB cases were HIV positive compared with an HIV coinfection rate of 64% among contemporaneously registered TB cases (P = .001).Community-based integrated intensive case finding is feasible and is high yield for drug-susceptible and -resistant TB and HIV in rural South Africa. Human immunodeficiency virus-negative tuberculosis predominated in this community sample, suggesting a distinct TB epidemiology compared with cases diagnosed in healthcare facilities. Increasing HIV/TB integrated community-based efforts and other strategies directed at both HIV-positive and HIV-negative tuberculosis may contribute to TB elimination in high TB/HIV burden regions.
| S-EPMC5499582 | biostudies-other
Project description:The aim of this study was to compare the transcriptional response to TB in regions of different incidence / prevalence.
2010-08-11 | GSE19442 | GEO
Project description:BackgroundXpert MTB/RIF (Xpert) is being widely adopted in high TB burden countries. Analysis is needed to guide the placement of devices within health systems to optimize the tuberculosis (TB) case detection rate (CDR).MethodsWe used epidemiological and operational data from Uganda (139 sites serving 87,600 individuals tested for TB) to perform a model-based comparison of the following placement strategies for Xpert devices: 1) Health center level (sites ranked by size from national referral hospitals to health care level III centers), 2) Smear volume (sites ranked from highest to lowest volume of smear microscopy testing), 3) Antiretroviral therapy (ART) volume (sites ranked from greatest to least patients on ART), 4) External equality assessment (EQA) performance (sites ranked from worst to best smear microscopy performance) and 5) TB prevalence (sites ranked from highest to lowest). We compared two clinical algorithms, one where Xpert was used only for smear microscopy negative samples versus another replacing smear microscopy. The primary outcome was TB CDR; secondary outcomes were detection of multi-drug resistant TB, number of sites requiring device placement to achieve specified rollout coverage, and cost.ResultsPlacement strategies that prioritized sites with higher TB prevalence maximized CDR, with an incremental rate of 6.2-12.6% compared to status quo (microscopy alone). Diagnosis of MDR-TB was greatest in the TB Prevalence strategy when Xpert was used in place of smear microscopy. While initial implementation costs were lowest in the Smear Volume strategy, cost per additional TB case detected was lowest in the TB prevalence strategy.ConclusionIn Uganda, placement of Xpert devices in sites with high TB prevalence yielded the highest TB CDR at the lowest cost per additional case diagnosed. These results represent novel use of program level data to inform the optimal placement of new technology in resource-constrained settings.
| S-EPMC4382196 | biostudies-literature
Project description:BackgroundChest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberculosis, there is often a lack of radiological expertise to interpret chest X-ray. Computer-aided detection systems based on artificial intelligence are therefore increasingly used to screen for tuberculosis-related abnormalities on digital chest radiographies. The CAD4TB software has previously been shown to demonstrate high sensitivity for chest X-ray tuberculosis-related abnormalities, but it is not yet calibrated for the detection of non-tuberculosis abnormalities. When screening for tuberculosis, users of computer-aided detection need to be aware that other chest pathologies are likely to be as prevalent as, or more prevalent than, active tuberculosis. However, non--tuberculosis chest X-ray abnormalities detected during chest X-ray screening for tuberculosis remain poorly characterized in the sub-Saharan African setting, with only minimal literature.Case presentationIn this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma.ConclusionsSolely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.
| S-EPMC10464059 | biostudies-literature
Project description:The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.
| S-EPMC9757491 | biostudies-literature