Additional file 1 of A systematic review of the clinical impact of small colony variants in patients with cystic fibrosis

Additional file 1: Table S1. PubMed search strategy. Table S2. Web of Science search strategy. Table S3. Embase search strategy. Table S4. Scopus search strategy. Table S5. Bias assessment of cohort studies. Table S6. Bias assessment of cross-sectional studies. Table S7. Bias assessment of case series studies. Table S8. Bias assessment for prevalence studies. Figure S1. Flow diagram of search procedure. Figure S2. Funnel plot for prevalence of SCVs. Figure S3. Sensitivity analysis forest plot for prevalence of SCVs. Figure S4. Sensitivity analysis funnel plot of prevalence of SCVs. Figure S5. Funnel plot for mean difference of FEV1% between SCV and NCV participants. Figure S6. Sensitivity analysis forest plot for mean difference of FEV1% between SCV and NCV participants. Figure S7. Sensitivity analysis funnel plot for the mean difference of FEV1% between SCV and NCV participants. Table S9. Specimens used for SCV collection. Table S10. Growth characteristics of SCVs. Table S11. Agar mediums for SCV cultivation. Table S12. Incubation conditions for SCVs. Table S13. Tests used for SCV confirmation

Structure of the SimPaths model.

Top panel = full SimPaths model structure; bottom panel = additional detail on causal mental health module.</p

Estimates of poverty (left panel) and employment (right panel) for modelled UBI scenarios from 2022 to 2026.

Baseline = planned tax/benefit policies for UK. Partial UBI = UBI set at the level of existing benefits. Full UBI = UBI set at the level of MIS. Full+ UBI = MIS plus means-tested benefits for caring, childcare, disability, housing, and limited capability for work. Whiskers = 95% UIs. Note that in the left panel, Full and Full+ UBI lines overlap around zero from 2023 onwards. MIS, Minimum Income Standard; UBI, Universal Basic Income; UI, uncertainty interval; UK, United Kingdom.</p

Estimated prevalence of CMD for modelled UBI policies from 2022 to 2026.

Baseline = planned tax/benefit policies for UK. Partial UBI = UBI set at the level of existing benefits. Full UBI = UBI set at the level of MIS. Full+ UBI = MIS plus means-tested benefits for caring, childcare, disability, housing, and limited capability for work. Whiskers = 95% UIs. CMD, common mental disorder; MIS, Minimum Income Standard; UBI, Universal Basic Income; UI, uncertainty interval; UK, United Kingdom.</p

Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study.

Table A: Key model assumptions of UKMOD and SimPaths. Table B: Effect estimates for use in Step 2 of SimPaths causal mental health module. Table C: All individual benefits retained and/or suspended in each UBI scenario. Table D: Alternative effect estimates for use in Step 2 of SimPaths causal mental health module during sensitivity analyses. Figure A: Internal validation graphs from the SimPaths GUI contrasting predicted outcomes with observed Understanding Society data from 2011–2017 (yo = years old). Figure B: Cumulative mean prevalence of common mental disorder and poverty by number of model iteratio. Figure C: Prevalence of common mental disorder (CMD) in SimPaths versus the Health Survey for England from 2012–2018. Table E: Population-level economic impacts of Universal Basic Income (UBI) policies modelled in UKMOD. Figure D: Gainers and losers by household income decile (before housing costs) ranging from low to high, with Partial UBI compared with baseline tax/benefit policies in 2023 (Scenario 2). Figure E: Gainers and losers by household income decile (before housing costs) ranging from low to high, with Full UBI compared with baseline tax/benefit policies in 2023 (Scenario 3). Table F: Median income, prevalence of poverty, employment rate, and mean hours worked in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Table G: Estimated prevalence of common mental disorders (CMD) and mental health inequalities in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Figure G: Estimated relative (left panel) and slope (right panel) indices of inequality by education for common mental disorder (CMD) in modelled Universal Basic Income (UBI) policies from 2022–2026. Table H: Estimated prevalence of common mental disorders (%) in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 stratified by gender, education, age, and household structure (95% uncertainty intervals. Table I: Structural Sensitivity Analyses—Median income, prevalence of poverty, employment rate, and mean hours worked in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Figure I: Structural Sensitivity Analysis 1, relaxing employment assumptions—Estimated prevalence of common mental disorder (CMD) for modelled Universal Basic Income (UBI) policies from 2022–2026. Figure J: Structural Sensitivity Analysis 2, using economic inactivity effects—Estimated prevalence of common mental disorder (CMD) for modelled Universal Basic Income (UBI) policies from 2022–2026. Table J: Structural Sensitivity Analyses—Estimated prevalence of common mental disorders and mental health inequalities in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Figure K: Structural Sensitivity Analysis 1, relaxing employment assumptions—Estimated relative (left panel) and slope (right panel) indices of inequality by education for common mental disorder (CMD) in modelled Universal Basic Income (UBI) policies from 2022–2026. Figure L: Structural Sensitivity Analysis 2, using economic inactivity effects—Estimated relative (left panel) and slope (right panel) indices of inequality by education for common mental disorder (CMD) in modelled Universal Basic Income (UBI) policies from 2022–2026. Figure M: Structural Sensitivity Analysis 1, relaxing employment assumptions—Estimated prevalence of common mental disorder (CMD) for modelled Universal Basic Income (UBI) policies from 2022 to 2026 with 95% uncertainty intervals, stratified by gender (A), education (B), age (C), and household structure (D). Note different scales used for each stratification. Figure N: Structural Sensitivity Analysis 2, using economic inactivity effects—Estimated prevalence of common mental disorder (CMD) for modelled Universal Basic Income (UBI) policies from 2022 to 2026 stratified by gender (A), education (B), age (C), and household structure (D). Note different scales used for each stratification. Table K: Structural Sensitivity Analyses—Estimated prevalence of common mental disorders in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 stratified by gender, education, age, previous poverty/employment status, and household structure (95% uncertainty intervals). Table L: Analytical Sensitivity Analyses—Median income, prevalence of poverty, and prevalence of unemployment in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Figure O: Analytical Sensitivity Analysis, using alternative estimates from systematic reviews—Estimated prevalence of common mental disorder (CMD) for modelled Universal Basic Income (UBI) policies from 2022–2026. Table M: Analytical Sensitivity Analyses—Prevalence of common mental disorders and mental health inequalities in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Figure P: Analytical Sensitivity Analysis, using alternative estimates from systematic reviews—Estimated relative (left panel) and slope (right panel) indices of inequality by education for common mental disorder (CMD) in modelled Universal Basic Income (UBI) policies from 2022–2026. Table N: Estimated GHQ Likert score in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Table O: Estimated GHQ Likert score in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 stratified by gender, education, age, previous poverty/employment status, and household structure (95% uncertainty intervals). (PDF)</p

Baseline and UBI scenarios modelled.

BackgroundPopulation mental health in the United Kingdom (UK) has deteriorated, alongside worsening socioeconomic conditions, over the last decade. Policies such as Universal Basic Income (UBI) have been suggested as an alternative economic approach to improve population mental health and reduce health inequalities. UBI may improve mental health (MH), but to our knowledge, no studies have trialled or modelled UBI in whole populations. We aimed to estimate the short-term effects of introducing UBI on mental health in the UK working-age population.Methods and findingsAdults aged 25 to 64 years were simulated across a 4-year period from 2022 to 2026 with the SimPaths microsimulation model, which models the effects of UK tax/benefit policies on mental health via income, poverty, and employment transitions. Data from the nationally representative UK Household Longitudinal Study were used to generate the simulated population (n = 25,000) and causal effect estimates. Three counterfactual UBI scenarios were modelled from 2023: “Partial” (value equivalent to existing benefits), “Full” (equivalent to the UK Minimum Income Standard), and “Full+” (retaining means-tested benefits for disability, housing, and childcare). Likely common mental disorder (CMD) was measured using the General Health Questionnaire (GHQ-12, score ≥4). Relative and slope indices of inequality were calculated, and outcomes stratified by gender, age, education, and household structure. Simulations were run 1,000 times to generate 95% uncertainty intervals (UIs). Sensitivity analyses relaxed SimPaths assumptions about reduced employment resulting from Full/Full+ UBI.Partial UBI had little impact on poverty, employment, or mental health. Full UBI scenarios practically eradicated poverty but decreased employment (for Full+ from 78.9% [95% UI 77.9, 79.9] to 74.1% [95% UI 72.6, 75.4]). Full+ UBI increased absolute CMD prevalence by 0.38% (percentage points; 95% UI 0.13, 0.69) in 2023, equivalent to 157,951 additional CMD cases (95% UI 54,036, 286,805); effects were largest for men (0.63% [95% UI 0.31, 1.01]) and those with children (0.64% [95% UI 0.18, 1.14]). In our sensitivity analysis assuming minimal UBI-related employment impacts, CMD prevalence instead fell by 0.27% (95% UI −0.49, −0.05), a reduction of 112,228 cases (95% UI 20,783, 203,673); effects were largest for women (−0.32% [95% UI −0.65, 0.00]), those without children (−0.40% [95% UI −0.68, −0.15]), and those with least education (−0.42% [95% UI −0.97, 0.15]). There was no effect on educational mental health inequalities in any scenario, and effects waned by 2026.The main limitations of our methods are the model’s short time horizon and focus on pathways from UBI to mental health solely via income, poverty, and employment, as well as the inability to integrate macroeconomic consequences of UBI; future iterations of the model will address these limitations.ConclusionsUBI has potential to improve short-term population mental health by reducing poverty, particularly for women, but impacts are highly dependent on whether individuals choose to remain in employment following its introduction. Future research modelling additional causal pathways between UBI and mental health would be beneficial.</div

Estimated prevalence of CMD for modelled UBI policies from 2022 to 2026 with 95% UIs, stratified by gender (A), education (B), age (C), and household structure (D). Note different scales used for each stratification.

y = years. Baseline = planned tax/benefit policies for UK. Full+ UBI = UBI set at the MIS plus means-tested benefits for caring, childcare, disability, housing, and limited capability for work. Ribbons = 95% UIs. Low education = no formal qualifications; medium education = Higher/A-level/GCSE or equivalent; high education = degree or equivalent. CMD, common mental disorder; GCSE, General Certificate of Secondary Education; MIS, Minimum Income Standard; UBI, Universal Basic Income; UI, uncertainty interval; UK, United Kingdom.</p

Difference in prevalence of CMD and CMD inequality measures for Full+ UBI policies versus Baseline policies from 2023 to 2026.

Difference in prevalence of CMD and CMD inequality measures for Full+ UBI policies versus Baseline policies from 2023 to 2026.</p

data_sheet_3_Anomalies in T Cell Function Are Associated With Individuals at Risk of Mycobacterium abscessus Complex Infection.PDF

<p>The increasing global incidence and prevalence of non-tuberculous mycobacteria (NTM) infection is of growing concern. New evidence of person-to-person transmission of multidrug-resistant NTM adds to the global concern. The reason why certain individuals are at risk of NTM infections is unknown. Using high definition flow cytometry, we studied the immune profiles of two groups that are at risk of Mycobacterium abscessus complex infection and matched controls. The first group was cystic fibrosis (CF) patients and the second group was elderly individuals. CF individuals with active M. abscessus complex infection or a history of M. abscessus complex infection exhibited a unique surface T cell phenotype with a marked global deficiency in TNFα production during mitogen stimulation. Importantly, immune-based signatures were identified that appeared to predict at baseline the subset of CF individuals who were at risk of M. abscessus complex infection. In contrast, elderly individuals with M. abscessus complex infection exhibited a separate T cell phenotype underlined by the presence of exhaustion markers and dysregulation in type 1 cytokine release during mitogen stimulation. Collectively, these data suggest an association between T cell signatures and individuals at risk of M. abscessus complex infection, however, validation of these immune anomalies as robust biomarkers will require analysis on larger patient cohorts.</p

data_sheet_2_Anomalies in T Cell Function Are Associated With Individuals at Risk of Mycobacterium abscessus Complex Infection.PDF

The increasing global incidence and prevalence of non-tuberculous mycobacteria (NTM) infection is of growing concern. New evidence of person-to-person transmission of multidrug-resistant NTM adds to the global concern. The reason why certain individuals are at risk of NTM infections is unknown. Using high definition flow cytometry, we studied the immune profiles of two groups that are at risk of Mycobacterium abscessus complex infection and matched controls. The first group was cystic fibrosis (CF) patients and the second group was elderly individuals. CF individuals with active M. abscessus complex infection or a history of M. abscessus complex infection exhibited a unique surface T cell phenotype with a marked global deficiency in TNFα production during mitogen stimulation. Importantly, immune-based signatures were identified that appeared to predict at baseline the subset of CF individuals who were at risk of M. abscessus complex infection. In contrast, elderly individuals with M. abscessus complex infection exhibited a separate T cell phenotype underlined by the presence of exhaustion markers and dysregulation in type 1 cytokine release during mitogen stimulation. Collectively, these data suggest an association between T cell signatures and individuals at risk of M. abscessus complex infection, however, validation of these immune anomalies as robust biomarkers will require analysis on larger patient cohorts.</p