Are patients with pulmonary tuberculosis who are identified through active case finding in the community different than those identified in healthcare facilities?

Iterative evaluation of mobile computer-assisted digital chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria

Wellness on Wheels (WoW) is a model of mobile systematic tuberculosis (TB) screening of high-risk populations combining digital chest radiography with computer-aided automated detection (CAD) and chronic cough screening to identify presumptive TB clients in communities, health facilities, and prisons in Nigeria. The model evolves to address technical, political, and sustainability challenges. Screening methods were iteratively refined to balance TB yield and feasibility across heterogeneous populations. Performance metrics were compared over time. Screening volumes, risk mix, number needed to screen (NNS), number needed to test (NNT), sample loss, TB treatment initiation and outcomes. Efforts to mitigate losses along the diagnostic cascade were tracked. Persons with high CAD4TB score (≥80), who tested negative on a single spot GeneXpert were followed-up to assess TB status at six months. An experimental calibration method achieved a viable CAD threshold for testing. High risk groups and key stakeholders were engaged. Operations evolved in real time to fix problems. Incremental improvements in mean client volumes (128 to 140/day), target group inclusion (92% to 93%), on-site testing (84% to 86%), TB treatment initiation (87% to 91%), and TB treatment success (71% to 85%) were recorded. Attention to those as highest risk boosted efficiency (the NNT declined from 8.2 ± SD8.2 to 7.6 ± SD7.7). Clinical diagnosis was added after follow-up among those with ≥ 80 CAD scores and initially spot -sputum negative found 11 additional TB cases (6.3%) after 121 person-years of follow-up. Iterative adaptation in response to performance metrics foster feasible, acceptable, and efficient TB case-finding in Nigeria. High CAD scores can identify subclinical TB and those at risk of progression to bacteriologically-confirmed TB disease in the near term.

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.

Pooling sputum for Xpert MTB/RIF and Xpert Ultra testing during the Covid-19 pandemic in Lao People's Democratic Republic

The global Covid-19 pandemic has limited access to molecular TB diagnostics and National Programmes are struggling to maintain essential services. The pooling method (testing several samples together) could reduce the number of cartridges and staff time needed for TB diagnosis but has not been tested within the pandemic. We conducted two independent cross-sectional surveys. Pools composed of four sputum samples were tested using either Xpert-MTB/RIF or Xpert-Ultra. Pooled and individual results were compared to determine the level of agreement. Each survey included 840 participants and 210 pools. In the Xpert MTB/RIF survey, 77/81 (sensitivity 95.1%, 95%CI 87.8%-98.6%) pools containing ≥1 positive sample tested MTB-positive and 4/81 (4.9%, 95%CI 1.4%-12.2%) tested MTB-negative. All 129/129 pools containing MTB-negative samples tested MTB-negative (specificity 100%, 95%CI 97.2%-100%), with 98.1% agreement (Kappa: 0.959). In the Xpert-Ultra survey, 70/70 (sensitivity 100%, 95%CI 94.9%-100%) pools containing ≥ 1 MTB-positive sample tested MTB-positive and 140/140 (specificity 100%, 95%CI 97.4%-100%) pools containing only MTB-negative samples tested MTB-negative, with 100% agreement (Kappa: 1). Pooled testing with Xpert-MTB/RIF and Xpert-Ultra saved 38.3% and 41.7% (322/840 and 350/840, respectively) in cartridge costs alone. The pooling method with Xpert-MTB/RIF and Xpert-Ultra has similar performance to individual testing and can reduce the number of cartridges needed. These efficiencies can facilitate maintenance of stocks and sustain essential services as countries face difficulties for laboratory procurement during the pandemic and will provide cost and time savings post-pandemic.

Does tuberculosis screening improve individual outcomes? A systematic review

BACKGROUND

To determine if tuberculosis (TB) screening improves patient outcomes, we conducted two systematic reviews to investigate the effect of TB screening on diagnosis, treatment outcomes, deaths (clinical review assessing 23 outcome indicators); and patient costs (economic review).

METHODS

Pubmed, EMBASE, Scopus and the Cochrane Library were searched between 1/1/1980-13/4/2020 (clinical review) and 1/1/2010-14/8/2020 (economic review). As studies were heterogeneous, data synthesis was narrative.

FINDINGS

Clinical review: of 27,270 articles, 18 (n=3 trials) were eligible. Nine involved general populations. Compared to passive case finding (PCF), studies showed lower smear grade (n=2/3) and time to diagnosis (n=2/3); higher pre-treatment losses to follow-up (screened 23% and 29% vs PCF 15% and 14%; n=2/2); and similar treatment success (range 68-81%; n=4) and case fatality (range 3-11%; n=5) in the screened group. Nine reported on risk groups. Compared to PCF, studies showed lower smear positivity among those culture-confirmed (n=3/4) and time to diagnosis (n=2/2); and similar (range 80-90%; n=2/2) treatment success in the screened group. Case fatality was lower in n=2/3 observational studies; both reported on established screening programmes. A neonatal trial and post-hoc analysis of a household contacts trial found screening was associated with lower all-cause mortality. Economic review: From 2841 articles, six observational studies were eligible. Total costs (n=6) and catastrophic cost prevalence (n=4; range screened 9-45% vs PCF 12-61%) was lower among those screened.

INTERPRETATION

We found very limited patient outcome data. Collecting and reporting this data must be prioritised to inform policy and practice.

FUNDING

WHO and EDCTP.

How to reduce household costs for people with tuberculosis: a longitudinal costing survey in Nepal

The aim of this study was to compare costs and socio-economic impact of tuberculosis (TB) for patients diagnosed through active (ACF) and passive case finding (PCF) in Nepal. A longitudinal costing survey was conducted in four districts of Nepal from April 2018 to October 2019. Costs were collected using the WHO TB Patient Costs Survey at three time points: intensive phase of treatment, continuation phase of treatment and at treatment completion. Direct and indirect costs and socio-economic impact (poverty headcount, employment status and coping strategies) were evaluated throughout the treatment. Prevalence of catastrophic costs was estimated using the WHO threshold. Logistic regression and generalized estimating equation were used to evaluate risk of incurring high costs, catastrophic costs and socio-economic impact of TB over time. A total of 111 ACF and 110 PCF patients were included. ACF patients were more likely to have no education (75% vs 57%, P = 0.006) and informal employment (42% vs 24%, P = 0.005) Compared with the PCF group, ACF patients incurred lower costs during the pretreatment period (mean total cost: US$55 vs US$87, P < 0.001) and during the pretreatment plus treatment periods (mean total direct costs: US$72 vs US$101, P < 0.001). Socio-economic impact was severe for both groups throughout the whole treatment, with 32% of households incurring catastrophic costs. Catastrophic costs were associated with 'no education' status [odds ratio = 2.53(95% confidence interval = 1.16-5.50)]. There is a severe and sustained socio-economic impact of TB on affected households in Nepal. The community-based ACF approach mitigated costs and reached the most vulnerable patients. Alongside ACF, social protection policies must be extended to achieve the zero catastrophic costs milestone of the End TB strategy.

Interventions to increase tuberculosis case detection at primary healthcare or community-level services

BACKGROUND

Pulmonary tuberculosis is usually diagnosed when symptomatic individuals seek care at healthcare facilities, and healthcare workers have a minimal role in promoting the health-seeking behaviour. However, some policy specialists believe the healthcare system could be more active in tuberculosis diagnosis to increase tuberculosis case detection.

OBJECTIVES

To evaluate the effectiveness of different strategies to increase tuberculosis case detection through improving access (geographical, financial, educational) to tuberculosis diagnosis at primary healthcare or community-level services.

SEARCH METHODS

We searched the following databases for relevant studies up to 19 December 2016: the Cochrane Infectious Disease Group Specialized Register; the Cochrane Central Register of Controlled Trials (CENTRAL), published in the Cochrane Library, Issue 12, 2016; MEDLINE; Embase; Science Citation Index Expanded, Social Sciences Citation Index; BIOSIS Previews; and Scopus. We also searched the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), ClinicalTrials.gov, and the metaRegister of Controlled Trials (mRCT) for ongoing trials.

SELECTION CRITERIA

Randomized and non-randomized controlled studies comparing any intervention that aims to improve access to a tuberculosis diagnosis, with no intervention or an alternative intervention.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed trials for eligibility and risk of bias, and extracted data. We compared interventions using risk ratios (RR) and 95% confidence intervals (CI). We assessed the certainty of the evidence using the GRADE approach.

MAIN RESULTS

We included nine cluster-randomized trials, one individual randomized trial, and seven non-randomized controlled studies. Nine studies were conducted in sub-Saharan Africa (Ethiopia, Nigeria, South Africa, Zambia, and Zimbabwe), six in Asia (Bangladesh, Cambodia, India, Nepal, and Pakistan), and two in South America (Brazil and Colombia); which are all high tuberculosis prevalence areas.Tuberculosis outreach screening, using house-to-house visits, sometimes combined with printed information about going to clinic, may increase tuberculosis case detection (RR 1.24, 95% CI 0.86 to 1.79; 4 trials, 6,458,591 participants in 297 clusters, low-certainty evidence); and probably increases case detection in areas with tuberculosis prevalence of 5% or more (RR 1.52, 95% CI 1.10 to 2.09; 3 trials, 155,918 participants, moderate-certainty evidence; prespecified stratified analysis). These interventions may lower the early default (prior to starting treatment) or default during treatment (RR 0.67, 95% CI 0.47 to 0.96; 3 trials, 849 participants, low-certainty evidence). However, this intervention may have may have little or no effect on treatment success (RR 1.07, 95% CI 1.00 to 1.15; 3 trials, 849 participants, low-certainty evidence), and we do not know if there is an effect on treatment failure or mortality. One study investigated long-term prevalence in the community, but with no clear effect due to imprecision and differences in care between the two groups (RR 1.14, 95% CI 0.65 to 2.00; 1 trial, 556,836 participants, very low-certainty evidence).Four studies examined health promotion activities to encourage people to attend for screening, including mass media strategies and more locally organized activities. There was some increase, but this could have been related to temporal trends, with no corresponding increase in case notifications, and no evidence of an effect on long-term tuberculosis prevalence. Two studies examined the effects of two to six nurse practitioner educational sessions in tuberculosis diagnosis, with no clear effect on tuberculosis cases detected. One trial compared mobile clinics every five days with house-to-house screening every six months, and showed an increase in tuberculosis cases.There was also insufficient evidence to determine if sustained improvements in case detection impact on long-term tuberculosis prevalence; this was evaluated in one study, which indicated little or no effect after four years of either contact tracing, extensive health promotion activities, or both (RR 1.31, 95% CI 0.75 to 2.30; 1 study, 405,788 participants in 12 clusters, very low-certainty evidence).

AUTHORS' CONCLUSIONS

The available evidence demonstrates that when used in appropriate settings, active case-finding approaches may result in increase in tuberculosis case detection in the short term. The effect of active case finding on treatment outcome needs to be further evaluated in sufficiently powered studies.

Discovery and Validation of a Six-Marker Serum Protein Signature for the Diagnosis of Active Pulmonary Tuberculosis

New non-sputum biomarker tests for active tuberculosis (TB) diagnostics are of the highest priority for global TB control. We performed in-depth proteomic analysis using the 4,000-plex SOMAscan assay on 1,470 serum samples from seven countries where TB is endemic. All samples were from patients with symptoms and signs suggestive of active pulmonary TB that were systematically confirmed or ruled out for TB by culture and clinical follow-up. HIV coinfection was present in 34% of samples, and 25% were sputum smear negative. Serum protein biomarkers were identified by stability selection using L1-regularized logistic regression and by Kolmogorov-Smirnov (KS) statistics. A naive Bayes classifier using six host response markers (HR6 model), including SYWC, kallistatin, complement C9, gelsolin, testican-2, and aldolase C, performed well in a training set (area under the sensitivity-specificity curve [AUC] of 0.94) and in a blinded verification set (AUC of 0.92) to distinguish TB and non-TB samples. Differential expression was also highly significant (P < 10⁻²⁰) for previously described TB markers, such as IP-10, LBP, FCG3B, and TSP4, and for many novel proteins not previously associated with TB. Proteins with the largest median fold changes were SAA (serum amyloid protein A), NPS-PLA2 (secreted phospholipase A2), and CA6 (carbonic anhydrase 6). Target product profiles (TPPs) for a non-sputum biomarker test to diagnose active TB for treatment initiation (TPP#1) and for a community-based triage or referral test (TPP#2) have been published by the WHO. With 90% sensitivity and 80% specificity, the HR6 model fell short of TPP#1 but reached TPP#2 performance criteria. In conclusion, we identified and validated a six-marker signature for active TB that warrants diagnostic development on a patient-near platform.