The risk and benefit profiles of US eligible lung cancer screening attendees vs. non-attendees.

BACKGROUND: The United States Preventive Services Task Force (USPSTF) recommend lung-cancer screening for individuals aged 50-80 with ≥20 pack-years and ≤15 quit-years, but uptake is low. The risk and benefit profiles of screening attendees are unknown; consequently, the impact and lost opportunity of ongoing lung-cancer screening in the US remains unclear. METHODS: We estimated lung-cancer death risk (using the Lung Cancer Death Risk Assessment Tool) and life gained from screening (using the LYFS-CT model) for individuals 50-79 who ever-smoked in the US-representative 2022 Behavioral Risk Factor Surveillance System. We compared lung-cancer death risk and life-gained among USPSTF-eligible individuals by screening status (self-reported screened vs not screened in past year), and estimated the number of lung-cancer deaths averted and life-years gained under current screening levels and if everyone eligible was screened. RESULTS: USPSTF-eligibility was 33.7% (95%CI:33.1-34.4%), of whom 17.9% (95%CI : 17.0-18.8%) self-reported screening. Screening uptake increased with increasing lung-cancer death risk quintile (Q1 = 5.2% (95%CI : 3.0%-8.8%); Q5 = 21.8% (95%CI : 20.3%-23.3%)) and life-gain from screening quintile (Q1 = 6.2% (95%CI : 3.8%-9.9%); Q5 = 20.8% (95%CI : 19.5%-22.2%)). Screened individuals had higher lung-cancer death risk (Risk Ratio [RR]=1.35, 95%CI : 1.26-1.46) and life-years gained (RR = 1.19, 95%CI : 1.12-1.25) than unscreened individuals. Currently screening averts 19,306 lung-cancer deaths and gains 237,564 life-years; screening everyone eligible would additionally avert 56,956 lung-cancer deaths and gain 751,850 life-years. Two-thirds of USPSTF-lung-eligible women were up-to-date with breast-cancer screening, but only 17.3% attended lung screening in the past year. CONCLUSIONS: Eligible screening attendees had higher lung-cancer death risk and benefit from screening. Higher rates of screening could substantially increase the number of lung-cancer deaths prevented.

Preterm Birth Following Active Surveillance vs Loop Excision for Cervical Intraepithelial Neoplasia Grade 2.

Importance: Active surveillance for cervical intraepithelial neoplasia grade 2 (CIN2) is being implemented in many high-income countries due to the association of excisional treatment with preterm birth. However, it is unknown whether active surveillance results in lower risk of preterm birth given that cervical dysplasia itself is associated with higher risk of preterm birth. Objective: To compare the preterm birth risk between women with CIN2 undergoing active surveillance or immediate loop electrosurgical excision procedure (LEEP). Design, Setting, and Participants: This historical population-based cohort study included women with a first-time diagnosis of CIN2 and a subsequent singleton birth from 1998 to 2018 in Denmark. Women with prior CIN grade 3 or greater or LEEP were excluded. Data were collected from 4 Danish health care registries. Analyses were conducted from October 2022 to June 2023. Exposure: Women were categorized into active surveillance (cervical biopsy and/or cytology) or immediate LEEP based on their first cervical sample after CIN2 diagnosis. The active surveillance group was further subdivided based on whether a delayed LEEP was performed within 28 months from CIN2 diagnosis. Main Outcomes and Measures: Risk of preterm birth (<37 + 0 weeks) was assessed and relative risks (RRs) were calculated using modified Poisson regression. Analyses used inverse probability treatment weighting of the propensity scores to adjust for age, parity, calendar year, index cytology, and smoking. Results: A total of 10 537 women with CIN2 and a singleton birth were identified; 4430 (42%) underwent active surveillance and 6107 (58%) were treated with immediate LEEP. For both groups, most were aged 23 to 29 years at CIN2 diagnosis (3125 [70%] and 3907 [64%], respectively). Overall, 869 births (8.2%) were preterm. The risk of preterm birth was comparable between active surveillance and immediate LEEP (RR, 1.03; 95% CI, 0.90-1.18). However, for women undergoing delayed LEEP after active surveillance (1539 of the active surveillance group [35%]), the risk of preterm birth was higher than for women treated with immediate LEEP (RR, 1.29; 95% CI, 1.08-1.55). Conclusions and relevance: In this cohort study of women with CIN2, the risk of preterm birth was comparable between active surveillance and immediate LEEP. However, delayed LEEP was associated with 30% higher risk of preterm birth than immediate LEEP. Thus, risk stratification at CIN2 diagnosis is important to identify women with increased risk of delayed LEEP.

Risk of COVID-19 death for people with a pre-existing cancer diagnosis prior to COVID-19-vaccination: A systematic review and meta-analysis.

While previous reviews found a positive association between pre-existing cancer diagnosis and COVID-19-related death, most early studies did not distinguish long-term cancer survivors from those recently diagnosed/treated, nor adjust for important confounders including age. We aimed to consolidate higher-quality evidence on risk of COVID-19-related death for people with recent/active cancer (compared to people without) in the pre-COVID-19-vaccination period. We searched the WHO COVID-19 Global Research Database (20 December 2021), and Medline and Embase (10 May 2023). We included studies adjusting for age and sex, and providing details of cancer status. Risk-of-bias assessment was based on the Newcastle-Ottawa Scale. Pooled adjusted odds or risk ratios (aORs, aRRs) or hazard ratios (aHRs) and 95% confidence intervals (95% CIs) were calculated using generic inverse-variance random-effects models. Random-effects meta-regressions were used to assess associations between effect estimates and time since cancer diagnosis/treatment. Of 23 773 unique title/abstract records, 39 studies were eligible for inclusion (2 low, 17 moderate, 20 high risk of bias). Risk of COVID-19-related death was higher for people with active or recently diagnosed/treated cancer (general population: aOR = 1.48, 95% CI: 1.36-1.61, I2 = 0; people with COVID-19: aOR = 1.58, 95% CI: 1.41-1.77, I2 = 0.58; inpatients with COVID-19: aOR = 1.66, 95% CI: 1.34-2.06, I2 = 0.98). Risks were more elevated for lung (general population: aOR = 3.4, 95% CI: 2.4-4.7) and hematological cancers (general population: aOR = 2.13, 95% CI: 1.68-2.68, I2 = 0.43), and for metastatic cancers. Meta-regression suggested risk of COVID-19-related death decreased with time since diagnosis/treatment, for example, for any/solid cancers, fitted aOR = 1.55 (95% CI: 1.37-1.75) at 1 year and aOR = 0.98 (95% CI: 0.80-1.20) at 5 years post-cancer diagnosis/treatment. In conclusion, before COVID-19-vaccination, risk of COVID-19-related death was higher for people with recent cancer, with risk depending on cancer type and time since diagnosis/treatment.

Absolute lung cancer risk increases among individuals with >15 quit-years: Analyses to inform the update of the American Cancer Society lung cancer screening guidelines.

BACKGROUND: This report quantifies counteracting effects of quit-years and concomitant aging on lung cancer risk, especially on exceeding 15 quit-years, when the US Preventive Services Task Force (USPSTF) recommends curtailing lung-cancer screening. METHODS: Cox models were fitted to estimate absolute lung cancer risk among Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and National Lung Screening Trial (NLST) participants who ever smoked. Absolute lung cancer risk and gainable years of life from screening for individuals aged 50 to 80 in the US-representative National Health Interview Survey (NHIS) 2015-2018 who ever smoked were projected. Relaxing USPSTF recommendations to 20/25/30 quit-years versus augmenting USPSTF criteria with individuals whose estimated gain in life expectancy from screening exceeded 16.2 days according to the Life Years From Screening-CT (LYFS-CT) prediction model was compared. RESULTS: Absolute lung cancer risk increased by 8.7%/year (95% CI, 7.7%-9.7%; p < .001) as individuals aged beyond 15 quit-years in the PLCO, with similar results in NHIS and NLST. For example, mean 5-year lung cancer risk for those aged 65 years with 15 quit-years = 1.47% (95% CI, 1.35%-1.59%) versus 1.76% (95% CI, 1.62%-1.90%) for those aged 70 years with 20 quit-years in the PLCO. Removing the quit-year criterion would make 4.9 million more people eligible and increase the proportion of preventable lung cancer deaths prevented (sensitivity) from 63.7% to 74.2%. Alternatively, augmentation using LYFS-CT would make 1.7 million more people eligible while increasing the lung cancer death sensitivity to 74.0%. CONCLUSIONS: Because of aging, absolute lung cancer risk increases beyond 15 quit-years, which does not support exemption from screening or curtailing screening once it has been initiated. Compared with relaxing the USPSTF quit-year criterion, augmentation using LYFS-CT could prevent most of the deaths at substantially superior efficiency, while also preventing deaths among individuals who currently smoke with low intensity or long duration.

Methods for Using Race and Ethnicity in Prediction Models for Lung Cancer Screening Eligibility.

Importance: Using race and ethnicity in clinical prediction models can reduce or inadvertently increase racial and ethnic disparities in medical decisions. Objective: To compare eligibility for lung cancer screening in a contemporary representative US population by refitting the life-years gained from screening-computed tomography (LYFS-CT) model to exclude race and ethnicity vs a counterfactual eligibility approach that recalculates life expectancy for racial and ethnic minority individuals using the same covariates but substitutes White race and uses the higher predicted life expectancy, ensuring that historically underserved groups are not penalized. Design, Setting, and Participants: The 2 submodels composing LYFS-CT NoRace were refit and externally validated without race and ethnicity: the lung cancer death submodel in participants of a large clinical trial (recruited 1993-2001; followed up until December 31, 2009) who ever smoked (n = 39 180) and the all-cause mortality submodel in the National Health Interview Survey (NHIS) 1997-2001 participants aged 40 to 80 years who ever smoked (n = 74 842, followed up until December 31, 2006). Screening eligibility was examined in NHIS 2015-2018 participants aged 50 to 80 years who ever smoked. Data were analyzed from June 2021 to September 2022. Exposure: Including and removing race and ethnicity (African American, Asian American, Hispanic American, White) in each LYFS-CT submodel. Main Outcomes and Measures: By race and ethnicity: calibration of the LYFS-CT NoRace model and the counterfactual approach (ratio of expected to observed [E/O] outcomes), US individuals eligible for screening, predicted days of life gained from screening by LYFS-CT. Results: The NHIS 2015-2018 included 25 601 individuals aged 50 to 80 years who ever smoked (2769 African American, 649 Asian American, 1855 Hispanic American, and 20 328 White individuals). Removing race and ethnicity from the submodels underestimated lung cancer death risk (expected/observed [E/O], 0.72; 95% CI, 0.52-1.00) and all-cause mortality (E/O, 0.90; 95% CI, 0.86-0.94) in African American individuals. It also overestimated mortality in Hispanic American (E/O, 1.08, 95% CI, 1.00-1.16) and Asian American individuals (E/O, 1.14, 95% CI, 1.01-1.30). Consequently, the LYFS-CT NoRace model increased Hispanic American and Asian American eligibility by 108% and 73%, respectively, while reducing African American eligibility by 39%. Using LYFS-CT with the counterfactual all-cause mortality model better maintained calibration across groups and increased African American eligibility by 13% without reducing eligibility for Hispanic American and Asian American individuals. Conclusions and Relevance: In this study, removing race and ethnicity miscalibrated LYFS-CT submodels and substantially reduced African American eligibility for lung cancer screening. Under counterfactual eligibility, no one became ineligible, and African American eligibility increased, demonstrating the potential for maintaining model accuracy while reducing disparities.

Recalibration of a Deep Learning Model for Low-Dose Computed Tomographic Images to Inform Lung Cancer Screening Intervals.

Importance: Annual low-dose computed tomographic (LDCT) screening reduces lung cancer mortality, but harms could be reduced and cost-effectiveness improved by reusing the LDCT image in conjunction with deep learning or statistical models to identify low-risk individuals for biennial screening. Objective: To identify low-risk individuals in the National Lung Screening Trial (NLST) and estimate, had they been assigned a biennial screening, how many lung cancers would have been delayed 1 year in diagnosis. Design, Setting, and Participants: This diagnostic study included participants with a presumed nonmalignant lung nodule in the NLST between January 1, 2002, and December 31, 2004, with follow-up completed on December 31, 2009. Data were analyzed for this study from September 11, 2019, to March 15, 2022. Exposures: An externally validated deep learning algorithm that predicts malignancy in current lung nodules using LDCT images (Lung Cancer Prediction Convolutional Neural Network [LCP-CNN]; Optellum Ltd) was recalibrated to predict 1-year lung cancer detection by LDCT for presumed nonmalignant nodules. Individuals with presumed nonmalignant lung nodules were hypothetically assigned annual vs biennial screening based on the recalibrated LCP-CNN model, Lung Cancer Risk Assessment Tool (LCRAT + CT [a statistical model combining individual risk factors and LDCT image features]), and the American College of Radiology recommendations for lung nodules, version 1.1 (Lung-RADS). Main Outcomes and Measures: Primary outcomes included model prediction performance, the absolute risk of a 1-year delay in cancer diagnosis, and the proportion of people without lung cancer assigned a biennial screening interval vs the proportion of cancer diagnoses delayed. Results: The study included 10 831 LDCT images from patients with presumed nonmalignant lung nodules (58.7% men; mean [SD] age, 61.9 [5.0] years), of whom 195 were diagnosed with lung cancer from the subsequent screen. The recalibrated LCP-CNN had substantially higher area under the curve (0.87) than LCRAT + CT (0.79) or Lung-RADS (0.69) to predict 1-year lung cancer risk (P < .001). If 66% of screens with nodules were assigned to biennial screening, the absolute risk of a 1-year delay in cancer diagnosis would have been lower for recalibrated LCP-CNN (0.28%) than LCRAT + CT (0.60%; P = .001) or Lung-RADS (0.97%; P < .001). To delay only 10% of cancer diagnoses at 1 year, more people would have been safely assigned biennial screening under LCP-CNN than LCRAT + CT (66.4% vs 40.3%; P < .001). Conclusions and Relevance: In this diagnostic study evaluating models of lung cancer risk, a recalibrated deep learning algorithm was most predictive of 1-year lung cancer risk and had least risk of 1-year delay in cancer diagnosis among people assigned biennial screening. Deep learning algorithms could prioritize people for workup of suspicious nodules and decrease screening intensity for people with low-risk nodules, which may be vital for implementation in health care systems.

Predictors of the experience of a Cytosponge test: analysis of patient survey data from the BEST3 trial.

BACKGROUND: The Cytosponge is a cell-collection device, which, coupled with a test for trefoil factor 3 (TFF3), can be used to diagnose Barrett's oesophagus, a precursor condition to oesophageal adenocarcinoma. BEST3, a large pragmatic, randomised, controlled trial, investigated whether offering the Cytosponge-TFF3 test would increase detection of Barrett's. Overall, participants reported mostly positive experiences. This study reports the factors associated with the least positive experience. METHODS: Patient experience was assessed using the Inventory to Assess Patient Satisfaction (IAPS), a 22-item questionnaire, completed 7-14 days after the Cytosponge test. STUDY COHORT: All BEST3 participants who answered ≥ 15 items of the IAPS (N = 1458). STATISTICAL ANALYSIS: A mean IAPS score between 1 and 5 (5 indicates most negative experience) was calculated for each individual. 'Least positive' experience was defined according to the 90th percentile. 167 (11.4%) individuals with a mean IAPS score of ≥ 2.32 were included in the 'least positive' category and compared with the rest of the cohort. Eleven patient characteristics and one procedure-specific factor were assessed as potential predictors of the least positive experience. Multivariable logistic regression analysis using backwards selection was conducted to identify factors independently associated with the least positive experience and with failed swallow at first attempt, one of the strongest predictors of least positive experience. RESULTS: The majority of responders had a positive experience, with an overall median IAPS score of 1.7 (IQR 1.5-2.1). High (OR = 3.01, 95% CI 2.03-4.46, p < 0.001) or very high (OR = 4.56, 95% CI 2.71-7.66, p < 0.001) anxiety (relative to low/normal anxiety) and a failed swallow at the first attempt (OR = 3.37, 95% CI 2.14-5.30, p < 0.001) were highly significant predictors of the least positive patient experience in multivariable analyses. Additionally, sex (p = 0.036), height (p = 0.032), alcohol intake (p = 0.011) and education level (p = 0.036) were identified as statistically significant predictors. CONCLUSION: We have identified factors which predict patient experience. Identifying anxiety ahead of the procedure and discussing particular concerns with patients or giving them tips to help with swallowing the capsule might help improve their experience. Trial registration ISRCTN68382401.

Upper age limits for US male human papillomavirus vaccination for oropharyngeal cancer prevention: a microsimulation-based modeling study.

BACKGROUND: Human papillomavirus (HVP)-positive oropharyngeal cancer is the most common HPV-associated cancer in the United States. The age at acquisition of oral HPV infections that cause oropharyngeal cancer (causal infections) is unknown; consequently, the benefit of vaccination of US men aged 27-45 years remains uncertain. METHODS: We developed a microsimulation-based, individual-level, state-transition model of oral HPV16 and HPV16-positive oropharyngeal cancer among heterosexual US men aged 15-84 years, calibrated to population-level data. We estimated the benefit of vaccination of men aged 27-45 years for prevention of oropharyngeal cancer, accounting for direct- and indirect effects (ie, herd effects) of male and female vaccination. RESULTS: In the absence of vaccination, most (70%) causal oral HPV16 infections are acquired by age 26 years, and 29% are acquired between ages 27 and 45 years. Among men aged 15-45 years in 2021 (1976-2006 birth cohorts), status quo vaccination of men through age 26 years is estimated to prevent 95% of 153 450 vaccine-preventable cancers. Assuming 100% vaccination in 2021, extending the upper age limit to 30, 35, 40, or 45 years for men aged 27-45 years (1976-1994 cohorts) is estimated to yield small benefits (3.0%, 4.2%, 5.1%, and 5.6% additional cancers prevented, respectively). Importantly, status quo vaccination of men through age 26 years is predicted to result in notable declines in HPV16-positive oropharyngeal cancer incidence in young men by 2035 (51% and 24% declines at ages 40-44 years and 45-49 years, respectively) and noticeable declines (12%) overall by 2045. CONCLUSION: Most causal oral HPV16 infections in US men are acquired by age 26 years, underscoring limited benefit from vaccination of men aged 27-45 years for prevention of HPV16-positive oropharyngeal cancers.

Variation in Human Papillomavirus Vaccination Effectiveness in the US by Age at Vaccination.

This survey study assesses the status and timing of HPV vaccination as self-reported by female participants in the National Health and Nutrition Examination Survey from 2011 to 2018.

Explaining differences in the frequency of lung cancer detection between the National Lung Screening Trial and community-based screening in Manchester, UK.

BACKGROUND: The frequency of lung cancer detection in the Manchester Lung Health Checks (MLHCs), a community-based screening service, was higher than in the National Lung Screening Trial (NLST) over two screening rounds. We aimed to identify the potential reasons for this difference. METHODS: We analyzed individual-level data from NLST and MLHCs, restricting to MLHCs participants who met NLST eligibility criteria. We calculated 'detection ratios' comparing the frequency of lung cancer detection in MLHCs vs NLST, first after excluding NLST participants ineligible by MLHC eligibility criteria (6-year lung cancer risk ≥ 1.51 %), and then after standardization to remove the influence of different distributions of baseline lung cancer risk. RESULTS: Among the 1,079 MLHCs participants who met NLST eligibility criteria, 4.7% were diagnosed with lung cancer over two screening rounds compared with 1.7% in NLST, giving an initial detection ratio of 2.6 (95%CI 2.2-3.0). This was reduced to 2.2 (95%CI 1.3-2.3) after imposing the MLHCs eligibility criterion on NLST, and further to 1.6 (95%CI 1.2-2.1) after removing the influence of different risk distributions. In stratified analyses, the standardized detection ratio was particularly elevated in individuals who were older, living in areas of high socioeconomic disadvantage, or had an FEV/FVC ratio less than 60. CONCLUSIONS: The 2.6-fold higher lung cancer detection in the community-based MLHCs vs NLST is partly explained by differences in eligibility criteria and baseline risk distributions. The residual 60% increase may relate to higher detection in certain risk groups, including older participants, those with more obstructive lung disease, and those living in areas of socioeconomic disadvantage.

Non-speculum clinician-taken samples for human papillomavirus testing: a cross-sectional study in older women.

BACKGROUND: Cervical cancer incidence and mortality are high in women aged ≥65 years, despite the disease being preventable by screening. Speculum-based screening can become more uncomfortable after the menopause. AIM: To examine test performance and acceptability of human papillomavirus (HPV) testing on clinician-collected vaginal samples without a speculum (non-speculum). DESIGN AND SETTING: Cross-sectional study in 11 GP practices and four colposcopy clinics in London, UK, between August 2017 and January 2019. METHOD: Non-speculum and conventional (speculum) samples were collected from women aged ≥50 years attending for a colposcopy (following a speculum HPV-positive screening result) or women aged ≥35 years (with confirmed cervical intraepithelial neoplasia (CIN) 2+), and women aged 50-64 years attending routine screening. Sensitivity to CIN2+ was assessed among women with confirmed CIN2+ (colposcopy). Specificity to HPV relative to speculum sampling and overall concordance was assessed among women with negative cytology (routine screening). RESULTS: The sensitivity of non-speculum sampling for detecting CIN2+ was 83.3% (95% confidence interval [CI] = 60.8 to 94.2) (n = 15/18). There was complete concordance among women with positive CIN2+ who had a speculum sample ≤91 days prior to the non-speculum sample (n = 12). Among 204 women with negative cytology, the specificity to HPV was 96.4% (95% CI = 92.7 to 98.5), with 96.6% concordant results (κ 72.4%). Seventy-one percent (n = 120/170) of women preferred a non-speculum sample for their next screen. CONCLUSION: HPV testing on non-speculum clinician-taken samples is a viable approach that warrants further exploration in larger studies. Overall test performance was broadly comparable with that of self-sampling.

Trends and predictors of hysterectomy prevalence among women in the United States.

BACKGROUND: Hysterectomy is the most common nonobstetrical medical procedure performed in US women. Evaluating hysterectomy prevalence trends and determinants is important for estimating gynecologic cancer rates and management of uterine conditions. OBJECTIVE: This study aimed to assess hysterectomy prevalence trends and determinants using the Behavioral Risk Factor Surveillance System (2006-2016). STUDY DESIGN: We estimated crude hysterectomy prevalences and multivariable-adjusted odds ratios and 95% confidence intervals for associations of race or ethnicity, age group (5-year), body mass index (categorical), smoking status, education, insurance, income, and US region with hysterectomy. Missing data were imputed. The number of women in each survey year ranged from 220,302 in 2006 to 275,631 in 2016. RESULTS: Although overall hysterectomy prevalence changed little between 2006 and 2016 (21.4% and 21.1%, respectively), hysterectomy prevalence was lower in 2016 than in 2006 among women aged ≥40 years, particularly among non-Hispanic Black and Hispanic women. Current smoking (odds ratio, 1.38; 95% confidence interval, 1.35-1.41), increasing age (odds ratio, 1.40; 95% confidence interval, 1.39-1.40), living in the South compared with the Midwest (odds ratio, 1.36; 95% confidence interval, 1.34-1.39), higher body mass index (odds ratio, 1.26; 95% confidence interval, 1.25-1.27), Black race compared with White (odds ratio, 1.10; 95% confidence interval, 1.07-1.13), and having insurance compared with being uninsured (odds ratio, 1.26; 95% confidence interval, 1.22-1.30) were most strongly associated with increased prevalence. Hispanic ethnicity and living in the Northeast were most strongly associated with decreased prevalence (odds ratio, 0.73; 95% confidence interval, 0.70-0.76; odds ratio, 0.67; 95% confidence interval, 0.65-0.69). CONCLUSION: Nationwide hysterectomy prevalence decreased among women aged ≥40 years from 2006 to 2016, particularly among non-Hispanic Black and Hispanic women. Age, non-Hispanic Black race, having insurance, current smoking, and living in the South were associated with increased odds of hysterectomy, even after accounting for possible explanatory factors. Further research is needed to better understand associations of race and ethnicity and region with hysterectomy prevalence.

Non-speculum sampling approaches for cervical screening in older women: randomised controlled trial.

BACKGROUND: Cervical cancer disproportionately affects women ≥65 years, especially those not screened regularly. Speculum use is a key barrier. AIM: To assess if offering non-speculum clinician-taken sampling and self-sampling increases uptake for lapsed attenders aged 50-64 years. DESIGN AND SETTING: Pragmatic randomised control trial conducted at 10 general practices in East London, UK. METHOD: Participants were 784 women aged 50-64 years, last screened 6-15 years before randomisation. Intervention participants received a letter offering the choice of non-speculum clinician- or self-sampling. Control participants received usual care. The main outcome measure was uptake within 4 months. RESULTS: Screening uptake 4 months after randomisation was significantly higher in the intervention arm: 20.4% (n = 80/393) versus 4.9% in the control arm (n = 19/391, absolute difference 15.5%, 95% confidence interval [CI] = 11.0% to 20.0%, P<0.001). This was maintained at 12 months: intervention 30.5% (n = 120/393) versus control 13.6% (n = 53/391) (absolute difference 17.0%, 95% CI = 11.3% to 22.7%, P<0.001). Conventional screening attendance within 12 months was very similar for both intervention 12.7% (n = 50/393) and control 13.6% (n = 53/391) arms. Ethnic differences were seen in screening modality preference. More White women opted for self-sampling (50.7%, n = 38/75), whereas most Asian and Black women and those from other ethnic backgrounds opted for conventional screening. CONCLUSION: Offering non-speculum clinician-taken sampling and self-sampling substantially increases uptake in older lapsed attendee women. Non-speculum clinician sampling appeals to women who dislike the speculum but still prefer a clinician to take their sample. Providing a choice of screening modality may be important for optimising cervical screening uptake.

Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom.

BACKGROUND: The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK. METHODS: We analysed current and former smokers aged 40-80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC). RESULTS: Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81-0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79-0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79-0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14-1.27) to 2.16 for LLPv2 (95% CI = 2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%). CONCLUSION: In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.

Using Prediction Models to Reduce Persistent Racial and Ethnic Disparities in the Draft 2020 USPSTF Lung Cancer Screening Guidelines.

We examined whether draft 2020 United States Preventive Services Task Force (USPSTF) lung cancer screening recommendations "partially ameliorate racial disparities in screening eligibility" compared with the 2013 guidelines, as claimed. Using data from the 2015 National Health Interview Survey, USPSTF-2020 increased eligibility by similar proportions for minorities (97.1%) and Whites (78.3%). Contrary to the intent of USPSTF-2020, the relative disparity (differences in percentages of model-estimated gainable life-years from National Lung Screening Trial-like screening by eligible Whites vs minorities) actually increased from USPSTF-2013 to USPSTF-2020 (African Americans: 48.3%-33.4% = 15.0% to 64.5%-48.5% = 16.0%; Asian Americans: 48.3%-35.6% = 12.7% to 64.5%-45.2% = 19.3%; Hispanic Americans: 48.3%-24.8% = 23.5% to 64.5%-37.0% = 27.5%). However, augmenting USPSTF-2020 with high-benefit individuals selected by the Life-Years From Screening with Computed Tomography (LYFS-CT) model nearly eliminated disparities for African Americans (76.8%-75.5% = 1.2%) and improved screening efficiency for Asian and Hispanic Americans, although disparities were reduced only slightly (Hispanic Americans) or unchanged (Asian Americans). The draft USPSTF-2020 guidelines increased the number of eligible minorities vs USPSTF-2013 but may inadvertently increase racial and ethnic disparities. LYFS-CT could reduce disparities in screening eligibility by identifying ineligible people with high predicted benefit regardless of race and ethnicity.