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Number needed to screen for TB in clinical, structural or occupational risk groups.

ABSTRACT: BACKGROUND: Screening for active TB using active case-finding (ACF) may reduce TB incidence, prevalence, and mortality; however, yield of ACF interventions varies substantially across populations. We systematically reviewed studies reporting on ACF to calculate the number needed to screen (NNS) for groups at high risk for TB.METHODS: We conducted a literature search for studies reporting ACF for adults published between November 2010 and February 2020. We determined active TB prevalence detected through various screening strategies and calculated crude NNS for - TB confirmed using culture or Xpert^® MTB/RIF, and weighted mean NNS stratified by screening strategy, risk group, and country-level TB incidence.RESULTS: We screened 27,223 abstracts; 90 studies were included (41 in low/moderate and 49 in medium/high TB incidence settings). High-risk groups included inpatients, outpatients, people living with diabetes (PLWD), migrants, prison inmates, persons experiencing homelessness (PEH), healthcare workers, and miners. Screening strategies included symptom-based screening, chest X-ray and Xpert testing. NNS varied widely across and within incidence settings based on risk groups and screening methods. Screening tools with higher sensitivity (e.g., Xpert, CXR) were associated with lower NNS estimates.CONCLUSIONS: NNS for ACF strategies varies substantially between adult risk groups. Specific interventions should be tailored based on local epidemiology and costs.

SUBMITTER: Naufal F

PROVIDER: S-EPMC9202999 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Number needed to screen for TB in clinical, structural or occupational risk groups.

Naufal F F Chaisson L H LH Robsky K O KO Delgado-Barroso P P Alvarez-Manzo H S HS Miller C R CR Shapiro A E AE Golub J E JE

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease 20220601 6

<b>BACKGROUND:</b> Screening for active TB using active case-finding (ACF) may reduce TB incidence, prevalence, and mortality; however, yield of ACF interventions varies substantially across populations. We systematically reviewed studies reporting on ACF to calculate the number needed to screen (NNS) for groups at high risk for TB.<b>METHODS:</b> We conducted a literature search for studies reporting ACF for adults published between November 2010 and February 2020. We determined active TB preva ...[more]

PMID: 35650693

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Project description:BackgroundHealth inequalities, worse health associated with social and economic disadvantage, are reported by a minority of research articles. Locating these studies when conducting an equity-focused systematic review is challenging due to a deficit in standardised terminology, indexing, and lack of validated search filters. Current reporting guidelines recommend not applying filters, meaning that increased resources are needed at the screening stage.MethodsWe aimed to design and test search filters to locate studies that reported outcomes by a social determinant of health. We developed and expanded a 'specific terms strategy' using keywords and subject headings compiled from recent systematic reviews that applied an equity filter. A 'non-specific strategy' was compiled from phrases used to describe equity analyses that were reported in titles and abstracts, and related subject headings. Gold standard evaluation and validation sets were compiled. The filters were developed in MEDLINE, adapted for Embase and tested in both. We set a target of 0.90 sensitivity (95% CI; 0.84, 0.94) in retrieving 150 gold standard validation papers. We noted the reduction in the number needed to screen in a proposed equity-focused systematic review and the proportion of equity-focused reviews we assessed in the project that applied an equity filter to their search strategy.ResultsThe specific terms strategy filtered out 93-95% of all records, and retrieved a validation set of articles with a sensitivity of 0.84 in MEDLINE (0.77, 0.89), and 0.87 (0.81, 0.92) in Embase. When combined (Boolean 'OR') with the non-specific strategy sensitivity was 0.92 (0.86, 0.96) in MEDLINE (Embase 0.94; 0.89, 0.97). The number needed to screen was reduced by 77% by applying the specific terms strategy, and by 59.7% (MEDLINE) and 63.5% (Embase) by applying the combined strategy. Eighty-one per cent of systematic reviews filtered studies by equity.ConclusionsA combined approach of using specific and non-specific terms is recommended if systematic reviewers wish to filter studies for reporting outcomes by social determinants. Future research should concentrate on the indexing standardisation for equity studies and further development and testing of both specific and non-specific terms for accurate study retrieval.

Project description:BackgroundThe precise age distribution and calculated stroke risk of screen-detected atrial fibrillation (AF) is not known. Therefore, it is not possible to determine the number needed to screen (NNS) to identify one treatable new AF case (NNS-Rx) (i.e., Class-1 oral anticoagulation [OAC] treatment recommendation) in each age stratum. If the NNS-Rx is known for each age stratum, precise cost-effectiveness and sensitivity simulations can be performed based on the age distribution of the population/region to be screened. Such calculations are required by national authorities and organisations responsible for health system budgets to determine the best age cutoffs for screening programs and decide whether programs of screening should be funded. Therefore, we aimed to determine the exact yield and calculated stroke-risk profile of screen-detected AF and NNS-Rx in 5-year age strata.Methods and findingsA systematic review of Medline, Pubmed, and Embase was performed (January 2007 to February 2018), and AF-SCREEN international collaboration members were contacted to identify additional studies. Twenty-four eligible studies were identified that performed a single time point screen for AF in a general ambulant population, including people ≥65 years. Authors from eligible studies were invited to collaborate and share patient-level data. Statistical analysis was performed using random effects logistic regression for AF detection rate, and Poisson regression modelling for CHA2DS2-VASc scores. Nineteen studies (14 countries from a mix of low- to middle- and high-income countries) collaborated, with 141,220 participants screened and 1,539 new AF cases. Pooled yield of screening was greater in males across all age strata. The age/sex-adjusted detection rate for screen-detected AF in ≥65-year-olds was 1.44% (95% CI, 1.13%-1.82%) and 0.41% (95% CI, 0.31%-0.53%) for <65-year-olds. New AF detection rate increased progressively with age from 0.34% (<60 years) to 2.73% (≥85 years). Neither the choice of screening methodology or device, the geographical region, nor the screening setting influenced the detection rate of AF. Mean CHA2DS2-VASc scores (n = 1,369) increased with age from 1.1 (<60 years) to 3.9 (≥85 years); 72% of ≥65 years had ≥1 additional stroke risk factor other than age/sex. All new AF ≥75 years and 66% between 65 and 74 years had a Class-1 OAC recommendation. The NNS-Rx is 83 for ≥65 years, 926 for 60-64 years; and 1,089 for <60 years. The main limitation of this study is there are insufficient data on sociodemographic variables of the populations and possible ascertainment biases to explain the variance in the samples.ConclusionsPeople with screen-detected AF are at elevated calculated stroke risk: above age 65, the majority have a Class-1 OAC recommendation for stroke prevention, and >70% have ≥1 additional stroke risk factor other than age/sex. Our data, based on the largest number of screen-detected AF collected to date, show the precise relationship between yield and estimated stroke risk profile with age, and strong dependence for NNS-RX on the age distribution of the population to be screened: essential information for precise cost-effectiveness calculations.

Project description:IntroductionSubcutaneous galcanezumab was an effective, well-tolerated preventive treatment for adults with episodic (EM) or chronic migraine (CM) in 4 phase 3 randomized controlled trials: EVOLVE-1, EVOLVE-2, REGAIN, and CONQUER. Number needed to treat (NNT) and to harm (NNH) are metrics of effect size used to evaluate benefit-risk profiles. This study evaluated NNT, NNH, and benefit-risk profiles (measured as likelihood to be helped or harmed, LHH) of galcanezumab 120 mg versus placebo in patients with EM or CM.MethodsPrimary efficacy outcomes were responses defined as ≥ 30%, ≥ 50%, and ≥ 75% reductions from baseline in number of monthly migraine headache days in patients with EM (EVOLVE-1; EVOLVE-2; CONQUER) and CM (REGAIN; CONQUER); corresponding NNTs to achieve respective responses; and corresponding NNHs for discontinuations due to adverse events (DCAEs) among the safety population. Secondary efficacy outcomes were responses for patients with ≥ 2 failed prior preventive treatments due to lack of efficacy and/or for tolerability reasons. All LHHs were based on ≥ 50% response and DCAEs.ResultsDuring double-blind treatment periods with galcanezumab 120 mg, NNT to achieve ≥ 30% and ≥ 50% responses ranged from 4 to 10 and NNT to achieve ≥ 75% responses ranged from 5 to 23 in individual trials. NNH ranged from 93 to 1000, while LHH ranged from 18.6 to 104.6. NNTs were generally more robust among patients with EM than with CM; however, in patients with failure of ≥ 2 prior preventive treatments, NNTs to achieve ≥ 30% and ≥ 50% responses were similar between patients with CM and EM. NNHs were imputed as 1000 for both migraine types. Resulting LHHs were 178.8 (EM) and 127 (CM).ConclusionAcross 4 trials, galcanezumab 120 mg demonstrated a favorable benefit-risk profile versus placebo, based on low NNTs to achieve response and high NNHs associated with DCAEs. LHH values consistently far exceeded 1.Trial registration numbersEVOLVE-1: ClinicalTrials.gov identifier, NCT02614183; EVOLVE-2: ClinicalTrials.gov identifier, NCT02614196; REGAIN: ClinicalTrials.gov identifier, NCT02614261; CONQUER: ClinicalTrials.gov identifier, NCT03559257.

Dataset Information

Number needed to screen for TB in clinical, structural or occupational risk groups.

Publications

Number needed to screen for TB in clinical, structural or occupational risk groups.

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