Sex disparities in the incidence of 21 cancer types: Quantification of the contribution of risk factors.

BACKGROUND: Cancer incidence is higher in men than in women at most shared anatomic sites for currently unknown reasons. The authors quantified the extent to which behaviors (smoking and alcohol use), anthropometrics (body mass index and height), lifestyles (physical activity, diet, medications), and medical history collectively explain the male predominance of risk at 21 shared cancer sites. METHODS: Prospective cohort analyses (n = 171,274 male and n = 122,826 female participants; age range, 50-71 years) in the National Institutes of Health-AARP Diet and Health Study (1995-2011). Cancer-specific Cox regression models were used to estimate male-to-female hazard ratios (HRs). The degree to which risk factors explained the observed male-female risk disparity was quantified using the Peters-Belson method. RESULTS: There were 26,693 incident cancers (17,951 in men and 8742 in women). Incidence was significantly lower in men than in women only for thyroid and gallbladder cancers. At most other anatomic sites, the risks were higher in men than in women (adjusted HR range, 1.3-10.8), with the strongest increases for bladder cancer (HR, 3.33; 95% confidence interval [CI], 2.93-3.79), gastric cardia cancer (HR, 3.49; 95% CI, 2.26-5.37), larynx cancer (HR, 3.53; 95% CI, 2.46-5.06), and esophageal adenocarcinoma (HR, 10.80; 95% CI, 7.33-15.90). Risk factors explained a statistically significant (nonzero) proportion of the observed male excess for esophageal adenocarcinoma and cancers of liver, other biliary tract, bladder, skin, colon, rectum, and lung. However, only a modest proportion of the male excess was explained by risk factors (ranging from 50% for lung cancer to 11% for esophageal adenocarcinoma). CONCLUSIONS: Men have a higher risk of cancer than women at most shared anatomic sites. Such male predominance is largely unexplained by risk factors, underscoring a role for sex-related biologic factors.

Approaches to Assessing and Adjusting for Selective Outcome Reporting in Meta-analysis.

BACKGROUND: Selective or non-reporting of study outcomes results in outcome reporting bias. OBJECTIVE: We sought to develop and assess tools for detecting and adjusting for outcome reporting bias. DESIGN: Using data from a previously published systematic review, we abstracted whether outcomes were reported as collected, whether outcomes were statistically significant, and whether statistically significant outcomes were more likely to be reported. We proposed and tested a model to adjust for unreported outcomes and compared our model to three other methods (Copas, Frosi, trim and fill). Our approach assumes that unreported outcomes had a null intervention effect with variance imputed based on the published outcomes. We further compared our approach to these models using simulation, and by varying levels of missing data and study sizes. RESULTS: There were 286 outcomes reported as collected from 47 included trials: 142 (48%) had the data provided and 144 (52%) did not. Reported outcomes were more likely to be statistically significant than those collected but for which data were unreported and for which non-significance was reported (RR, 2.4; 95% CI, 1.9 to 3.0). Our model and the Copas model provided similar decreases in the pooled effect sizes in both the meta-analytic data and simulation studies. The Frosi and trim and fill methods performed poorly. LIMITATIONS: Single intervention of a single disease with only randomized controlled trials; approach may overestimate outcome reporting bias impact. CONCLUSION: There was evidence of selective outcome reporting. Statistically significant outcomes were more likely to be published than non-significant ones. Our simple approach provided a quick estimate of the impact of unreported outcomes on the estimated effect. This approach could be used as a quick assessment of the potential impact of unreported outcomes.

A semiparametric Gumbel regression model for analyzing longitudinal data with non-normal tails.

Abnormal longitudinal values in biomarkers can be a sign of abnormal status or signal development of a disease. Identifying new biomarkers for early and efficient disease detection is crucial for disease prevention. Compared to the majority of the healthy general population, abnormal values are located within the tails of the biomarker distribution. Thus, parametric regression models that accommodate abnormal values in biomarkers can better detect the association between biomarkers and disease. In this article, we propose semiparametric Gumbel regression models for (1) longitudinal continuous biomarker outcomes, (2) flexibly modeling the time-effect on the outcome, and (3) accounting for the measurement error in biomarker measurements. We adopted the EM algorithm in combination with a two-dimensional grid search to estimate regression parameters and a function of time-effect. We proposed an efficient asymptotic variance estimator for regression parameter estimates. The proposed estimator is asymptotically unbiased in both theory and simulation studies. We applied the proposed model and two other models to investigate associations between fasting blood glucose biomarkers and potential risk factors from a diabetes ancillary study to the Atherosclerosis Risk in Communities (ARIC) study. The real data application was illustrated by fitting the proposed regression model and graphically evaluating the goodness-of-fit value.

Contribution of Etiologic Cofactors to CIN3+ Risk Among Women With Human Papillomavirus-Positive Screening Test Results.

OBJECTIVE: The US screening and management guidelines for cervical cancer are based on the absolute risk of precancer estimated from large clinical cohorts and trials. Given the widespread transition toward screening with human papillomavirus (HPV) testing, it is important to assess which additional factors to include in clinical risk assessment to optimize management of HPV-infected women. MATERIALS AND METHODS: We analyzed data from HPV-infected women, ages 30-65 years, in the National Cancer Institute-Kaiser Permanente Northern California Persistence and Progression study. We estimated the influence of HPV risk group, cytology result, and selected cofactors on immediate risk of cervical intraepithelial neoplasia grade 3 or higher (CIN 3+) among 16,094 HPV-positive women. Cofactors considered included, age, race/ethnicity, income, smoking, and hormonal contraceptive use. RESULTS: Human papillomavirus risk group and cytology test result were strongly correlated with CIN 3+ risk. After considering cytology and HPV risk group, other cofactors (age, race/ethnicity, income, smoking, and hormonal contraceptive use) had minimal impact on CIN 3+ risk and did not change recommended management based on accepted risk thresholds. We had insufficient data to assess the impact of long-duration heavy smoking, parity, history of sexually transmitted infection, or immunosuppression. CONCLUSIONS: In our study at the Kaiser Permanente Northern California, the risk of CIN 3+ was determined mainly by HPV risk group and cytology results, with other cofactors having limited impact in adjusted analyses. This supports the use of HPV and cytology results in risk-based management guidelines.

Risks of subsequent primary cancers among breast cancer survivors according to hormone receptor status.

BACKGROUND: This study was aimed at examining the risks of subsequent primary cancers (SPCs) among breast cancer survivors by hormone receptor (HR) status and age at diagnosis. METHODS: Data from 12 Surveillance, Epidemiology, and End Results registries were used to identify 431,222 breast cancer survivors (at least 1 year) diagnosed between the ages of 20 and 84 years from 1992 to 2015. Risks of SPCs were measured as the standardized incidence ratio (SIR) and the excess absolute risk (EAR) per 10,000 person-years. Poisson regression was used to test the difference in SIRs by HR status. RESULTS: In comparison with the general population, the risk of new cancer diagnoses among survivors was 20% higher for those with HR-positive cancers (SIR, 1.20; 95% confidence interval [CI], 1.19-1.21; EAR, 23.3/10,000 person-years) and 44% higher for those with HR-negative cancers (SIR, 1.44; 95% CI, 1.41-1.47; EAR, 45.2/10,000 person-years), with the risk difference between HR statuses statistically significant. The higher risk after HR-negative cancer was driven by acute nonlymphocytic leukemia and breast, ovarian, peritoneal, and lung cancers. By age at diagnosis, the total EAR per 10,000 person-years ranged from 15.8 (95% CI, 14.1-17.5; SIR, 1.11) among late-onset (age, 50-84 years) HR-positive survivors to 69.4 (95% CI, 65.1-73.7; SIR, 2.24) among early-onset (age, 20-49 years) HR-negative survivors, with subsequent breast cancer representing 73% to 80% of the total EAR. After breast cancer, the greatest EARs were for ovarian cancer among early-onset HR-negative survivors, lung cancer among early- and late-onset HR-negative survivors, and uterine corpus cancer among late-onset HR-positive survivors. CONCLUSIONS: Risks of SPCs after breast cancer differ substantially by subtype and age. This suggests that more targeted approaches for cancer prevention and early-detection strategies are needed in survivorship care planning.

BBB07 contributes to, but is not essential for, Borrelia burgdorferi infection in mice.

Borrelia burgdorferi, a causative agent of Lyme disease, encodes a protein BBB07 on the genomic plasmid cp26. BBB07 was identified as a candidate integrin ligand based on the presence of an RGD tripeptide motif, which is present in a number of mammalian ligands for ß1 and ß3 integrins . Previous work demonstrated that BBB07 in recombinant form binds to ß1 integrins and induces inflammatory responses in synovial cells in culture. Several transposon mutants in bbb07 were attenuated in an in vivo screen of the transposon library in mice. We therefore tested individual transposon mutant clones in single-strain infections in mice and found that they were attenuated in terms of ID50 but did not have significantly reduced tissue burdens in mice. Based on data presented here we conclude that BBB07 is not essential for, but does contribute to, B. burgdorferi infectivity in mice.

Sample-weighted semiparametric estimation of cause-specific cumulative risk and incidence using left- or interval-censored data from electronic health records.

Electronic health records (EHRs) can be a cost-effective data source for forming cohorts and developing risk models in the context of disease screening. However, important issues need to be handled: competing outcomes, left-censoring of prevalent disease, interval-censoring of incident disease, and uncertainty of prevalent disease when accurate disease ascertainment is not conducted at baseline. Furthermore, novel tests that are costly and limited in availability can be conducted on stored biospecimens selected as samples from EHRs by using different sampling fractions. We extend sample-weighted semiparametric marginal mixture models to estimating competing risks. For flexible modeling of relative risks, a general transformation of the subdistribution hazard function and regression parameters is used. We propose a numerical algorithm for nonparametrically calculating the maximum likelihood estimates for subdistribution hazard functions and regression parameters. Methods for calculating the consistent confidence intervals for relative and absolute risk estimates are presented. The proposed algorithm and methods show reliable finite sample performance through simulation studies. We apply our methods to a cohort assembled from EHRs at a health maintenance organization where we estimate cumulative risk of cervical precancer/cancer and incidence of infection-clearance by HPV genotype among human papillomavirus (HPV) positive women. There is no significant difference in 3-year HPV-clearance rates across different HPV types, but 3-year cumulative risk of progression-to-precancer/cancer from HPV-16 is relatively higher than the other HPV genotypes.

Association of First Primary Cancer With Risk of Subsequent Primary Cancer Among Survivors of Adult-Onset Cancers in the United States.

Importance: The number of cancer survivors who develop new cancers is projected to increase, but comprehensive data on the risk of subsequent primary cancers (SPCs) among survivors of adult-onset cancers are limited. Objective: To quantify the overall and cancer type-specific risks of SPCs among adult-onset cancer survivors by first primary cancer (FPC) types and sex. Design, Setting, and Participants: A retrospective cohort study from 12 Surveillance, Epidemiology, and End Results registries in the United States, that included 1 537 101 persons aged 20 to 84 years diagnosed with FPCs from 1992-2011 (followed up until December 31, 2017) and who survived at least 5 years. Exposures: First primary cancer. Main Outcomes and Measures: Incidence and mortality of SPCs per 10 000 person-years; standardized incidence ratio (SIR) and standardized mortality ratio (SMR) compared with those expected in the general population. Results: Among 1 537 101 survivors (mean age, 60.4 years; 48.8% women), 156 442 SPC cases and 88 818 SPC deaths occurred during 11 197 890 person-years of follow-up (mean, 7.3 years). Among men, the overall risk of developing any SPCs was statistically significantly higher for 18 of the 30 FPC types, and risk of dying from any SPCs was statistically significantly higher for 27 of 30 FPC types as compared with risks in the general population. Among women, the overall risk of developing any SPCs was statistically significantly higher for 21 of the 31 FPC types, and risk of dying from any SPCs was statistically significantly higher for 28 of 31 FPC types as compared with risks in the general population. The highest overall SIR and SMR were estimated among survivors of laryngeal cancer (SIR, 1.75 [95% CI, 1.68-1.83]; incidence, 373 per 10 000 person-years) and gallbladder cancer (SMR, 3.82 [95% CI, 3.31-4.39]; mortality, 341 per 10 000 person-years) among men, and among survivors of laryngeal cancer (SIR, 2.48 [95% CI, 2.27-2.72]; incidence, 336 per 10 000 person-years; SMR, 4.56 [95% CI, 4.11-5.06]; mortality, 268 per 10 000 person-years) among women. Substantial variation existed in the associations of specific types of FPCs with specific types of SPC risk; however, only a few smoking- or obesity-associated SPCs, such as lung, urinary bladder, oral cavity/pharynx, colorectal, pancreatic, uterine corpus, and liver cancers constituted considerable proportions of the total incidence and mortality, with lung cancer alone accounting for 31% to 33% of mortality from all SPCs. Conclusions and Relevance: Among survivors of adult-onset cancers in the United States, several types of primary cancer were significantly associated with greater risk of developing and dying from an SPC, compared with the general population. Cancers associated with smoking or obesity comprised substantial proportions of overall SPC incidence and mortality among all survivors and highlight the importance of ongoing surveillance and efforts to prevent new cancers among survivors.

Prioritized concordance index for hierarchical survival outcomes.

We propose an extension of Harrell's concordance (C) index to evaluate the prognostic utility of biomarkers for diseases with multiple measurable outcomes that can be prioritized. Our prioritized concordance index measures the probability that, given a random subject pair, the subject with the worst disease status as of a time τ has the higher predicted risk. Our prioritized concordance index uses the same approach as the win ratio, by basing generalized pairwise comparisons on the most severe or clinically important comparable outcome. We use an inverse probability weighting technique to correct for study-specific censoring. Asymptotic properties are derived using U-statistic properties. We apply the prioritized concordance index to two types of disease processes with a rare primary outcome and a more common secondary outcome. Our simulation studies show that when a predictor is predictive of both outcomes, the new concordance index can gain efficiency and power in identifying true prognostic variables compared to using the primary outcome alone. Using the prioritized concordance index, we examine whether novel clinical measures can be useful in predicting risk of type II diabetes in patients with impaired glucose resistance whose disease status can also regress to normal glucose resistance. We also examine the discrimination ability of four published risk models among ever smokers at risk of lung cancer incidence and subsequent death.

Validation of a Human Papillomavirus (HPV) DNA Cervical Screening Test That Provides Expanded HPV Typing.

As cervical cancer screening shifts from cytology to human papillomavirus (HPV) testing, a major question is the clinical value of identifying individual HPV types. We aimed to validate Onclarity (Becton Dickinson Diagnostics, Sparks, MD), a nine-channel HPV test recently approved by the FDA, by assessing (i) the association of Onclarity types/channels with precancer/cancer; (ii) HPV type/channel agreement between the results of Onclarity and cobas (Roche Molecular Systems, Pleasanton, CA), another FDA-approved test; and (iii) Onclarity typing for all types/channels compared to typing results from a research assay (linear array [LA]; Roche). We compared Onclarity to histopathology, cobas, and LA. We tested a stratified random sample (n = 9,701) of discarded routine clinical specimens that had tested positive by Hybrid Capture 2 (HC2; Qiagen, Germantown, MD). A subset had already been tested by cobas and LA (n = 1,965). Cervical histopathology was ascertained from electronic health records. Hierarchical Onclarity channels showed a significant linear association with histological severity. Onclarity and cobas had excellent agreement on partial typing of HPV16, HPV18, and the other 12 types as a pool (sample-weighted kappa value of 0.83); cobas was slightly more sensitive for HPV18 and slightly less sensitive for the pooled high-risk types. Typing by Onclarity showed excellent agreement with types and groups of types identified by LA (kappa values from 0.80 for HPV39/68/35 to 0.97 for HPV16). Onclarity typing results corresponded well to histopathology and to an already validated HPV DNA test and could provide additional clinical typing if such discrimination is determined to be clinically desirable.

Grouping methods for estimating the prevalences of rare traits from complex survey data that preserve confidentiality of respondents.

Originally, 2-stage group testing was developed for efficiently screening individuals for a disease. In response to the HIV/AIDS epidemic, 1-stage group testing was adopted for estimating prevalences of a single or multiple traits from testing groups of size q, so individuals were not tested. This paper extends the methodology of 1-stage group testing to surveys with sample weighted complex multistage-cluster designs. Sample weighted-generalized estimating equations are used to estimate the prevalences of categorical traits while accounting for the error rates inherent in the tests. Two difficulties arise when using group testing in complex samples: (1) How does one weight the results of the test on each group as the sample weights will differ among observations in the same group. Furthermore, if the sample weights are related to positivity of the diagnostic test, then group-level weighting is needed to reduce bias in the prevalence estimation; (2) How does one form groups that will allow accurate estimation of the standard errors of prevalence estimates under multistage-cluster sampling allowing for intracluster correlation of the test results. We study 5 different grouping methods to address the weighting and cluster sampling aspects of complex designed samples. Finite sample properties of the estimators of prevalences, variances, and confidence interval coverage for these grouping methods are studied using simulations. National Health and Nutrition Examination Survey data are used to illustrate the methods.

Challenges in risk estimation using routinely collected clinical data: The example of estimating cervical cancer risks from electronic health-records.

Electronic health-records (EHR) are increasingly used by epidemiologists studying disease following surveillance testing to provide evidence for screening intervals and referral guidelines. Although cost-effective, undiagnosed prevalent disease and interval censoring (in which asymptomatic disease is only observed at the time of testing) raise substantial analytic issues when estimating risk that cannot be addressed using Kaplan-Meier methods. Based on our experience analysing EHR from cervical cancer screening, we previously proposed the logistic-Weibull model to address these issues. Here we demonstrate how the choice of statistical method can impact risk estimates. We use observed data on 41,067 women in the cervical cancer screening program at Kaiser Permanente Northern California, 2003-2013, as well as simulations to evaluate the ability of different methods (Kaplan-Meier, Turnbull, Weibull and logistic-Weibull) to accurately estimate risk within a screening program. Cumulative risk estimates from the statistical methods varied considerably, with the largest differences occurring for prevalent disease risk when baseline disease ascertainment was random but incomplete. Kaplan-Meier underestimated risk at earlier times and overestimated risk at later times in the presence of interval censoring or undiagnosed prevalent disease. Turnbull performed well, though was inefficient and not smooth. The logistic-Weibull model performed well, except when event times didn't follow a Weibull distribution. We have demonstrated that methods for right-censored data, such as Kaplan-Meier, result in biased estimates of disease risks when applied to interval-censored data, such as screening programs using EHR data. The logistic-Weibull model is attractive, but the model fit must be checked against Turnbull non-parametric risk estimates.

Proof-of-principle study of a novel cervical screening and triage strategy: Computer-analyzed cytology to decide which HPV-positive women are likely to have ≥CIN2.

A challenge in implementation of sensitive HPV-based screening is limiting unnecessary referrals to colposcopic biopsy. We combined two commonly recommended triage methods: partial HPV typing and "reflex" cytology, evaluating the possibility of automated cytology. This investigation was based on 1,178 exfoliated cervical specimens collected during the enrollment phase of The Study to Understand Cervical Cancer Early Endpoints and Determinants (SUCCEED, Oklahoma City, OK). We chose a colposcopy clinic population to maximize number of outcomes, for this proof-of-principle cross-sectional study. Residual aliquots of PreservCyt were HPV-typed using Linear Array (LA, Roche Molecular Systems, Pleasanton, CA). High-risk HPV typing data and cytologic results (conventional and automated) were used jointly to predict risk of histologically defined ≥CIN2. We developed a novel computer algorithm that uses the same optical scanning features that are generated by the FocalPoint Slide Profiler (BD, Burlington, NC). We used the Least Absolute Shrinkage and Selection Operator (LASSO) method to build the prediction model based on a training dataset (n = 600). In the validation set (n = 578), for triage of all HPV-positive women, a cytologic threshold of ≥ASC-US had a sensitivity of 0.94, and specificity of 0.30, in this colposcopy clinic setting. When we chose a threshold for the severity score (generated by the computer algorithm) that had an equal specificity of 0.30, the sensitivity was 0.91. Automated cytology also matched ≥ASC-US when partial HPV typing was added to the triage strategy, and when we re-defined cases as ≥CIN3. If this strategy works in a prospective screening setting, a totally automated screening and triage technology might be possible.

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

A cohort study of cervical screening using partial HPV typing and cytology triage.

HPV testing is more sensitive than cytology for cervical screening. However, to incorporate HPV tests into screening, risk-stratification ("triage") of HPV-positive women is needed to avoid excessive colposcopy and overtreatment. We prospectively evaluated combinations of partial HPV typing (Onclarity, BD) and cytology triage, and explored whether management could be simplified, based on grouping combinations yielding similar 3-year or 18-month CIN3+ risks. We typed ∼9,000 archived specimens, taken at enrollment (2007-2011) into the NCI-Kaiser Permanente Northern California (KPNC) HPV Persistence and Progression (PaP) cohort. Stratified sampling, with reweighting in the statistical analysis, permitted risk estimation of HPV/cytology combinations for the 700,000+-woman KPNC screening population. Based on 3-year CIN3+ risks, Onclarity results could be combined into five groups (HPV16, else HPV18/45, else HPV31/33/58/52, else HPV51/35/39/68/56/66/68, else HPV negative); cytology results fell into three risk groups ("high-grade," ASC-US/LSIL, NILM). For the resultant 15 HPV group-cytology combinations, 3-year CIN3+ risks ranged 1,000-fold from 60.6% to 0.06%. To guide management, we compared the risks to established "benchmark" risk/management thresholds in this same population (e.g., LSIL predicted 3-year CIN3+ risk of 5.8% in the screening population, providing the benchmark for colposcopic referral). By benchmarking to 3-year risk thresholds (supplemented by 18-month estimates), the widely varying risk strata could be condensed into four action bands (very high risk of CIN3+ mandating consideration of cone biopsy if colposcopy did not find precancer; moderate risk justifying colposcopy; low risk managed by intensified follow-up to permit HPV "clearance"; and very low risk permitting routine screening.) Overall, the results support primary HPV testing, with management of HPV-positive women using partial HPV typing and cytology.

Smoking status, usual adult occupation, and risk of recurrent urothelial bladder carcinoma: data from The Cancer Genome Atlas (TCGA) Project.

PURPOSE: Tobacco smoking and occupational exposures are the leading risk factors for developing urothelial bladder carcinoma (UBC), yet little is known about the contribution of these two factors to risk of UBC recurrence. We evaluated whether smoking status and usual adult occupation are associated with time to UBC recurrence for 406 patients with muscle-invasive bladder cancer submitted to The Cancer Genome Atlas (TCGA) project. METHODS: Kaplan-Meier and Cox proportional hazard methods were used to assess the association between smoking status, employment in a high-risk occupation for bladder cancer, occupational diesel exhaust exposure, and 2010 Standard Occupational Classification group and time to UBC recurrence. RESULTS: Data on time to recurrence were available for 358 patients over a median follow-up time of 15 months. Of these, 133 (37.2%) experienced a recurrence. Current smokers who smoked for more than 40 pack-years had an increased risk of recurrence compared to never smokers (HR 2.1, 95% CI 1.1, 4.1). Additionally, employment in a high-risk occupation was associated with a shorter time to recurrence (log-rank p = 0.005). We found an increased risk of recurrence for those employed in occupations with probable diesel exhaust exposure (HR 1.8, 95% CI 1.1, 3.0) and for those employed in production occupations (HR 2.0, 95% CI 1.1, 3.6). CONCLUSIONS: These findings suggest smoking status impacts risk of UBC recurrence, although several previous studies provided equivocal evidence regarding this association. In addition to the known causal relationship between occupational exposure and bladder cancer risk, our study suggests that occupation may also be related to increased risk of recurrence.

Mediators of Black-White inequities in cardiovascular mortality among survivors of 18 cancers in the USA.

BACKGROUND: This study aims to quantify Black-White inequities in cardiovascular disease (CVD) mortality among US survivors of 18 adult-onset cancers and the extent to which these inequities are explained by differences in socio-economic and clinical factors. METHODS: Survivors of cancers diagnosed at ages 20-64 years during 2007-16 were identified from 17 Surveillance, Epidemiology and End Results registries. Associations between race and CVD mortality were examined using proportional hazards models. Mediation analyses were performed to quantify the contributions of potential mediators, including socio-economic [health insurance, neighbourhood socio-economic status (nSES), rurality] and clinical (stage, surgery, chemotherapy, radiotherapy) factors. RESULTS: Among 904 995 survivors, 10 701 CVD deaths occurred (median follow-up, 43 months). Black survivors were more likely than White survivors to die from CVD for all 18 cancers with hazard ratios ranging from 1.30 (95% CI = 1.15-1.47) for lung cancer to 4.04 for brain cancer (95% CI = 2.79-5.83). The total percentage mediations (indirect effects) ranged from 24.8% for brain (95% CI=-5.2-59.6%) to 99.8% for lung (95% CI = 61.0-167%) cancers. Neighbourhood SES was identified as the strongest mediator for 14 cancers with percentage mediations varying from 25.0% for kidney cancer (95% CI = 14.1-36.3%) to 63.5% for lung cancer (95% CI = 36.5-108.7%). Insurance ranked second for 12 cancers with percentage mediations ranging from 12.3% for leukaemia (95% CI = 0.7-46.7%) to 31.3% for thyroid cancer (95% CI = 10.4-82.7%). CONCLUSIONS: Insurance and nSES explained substantial proportions of the excess CVD mortality among Black survivors. Mitigating the effects of unequal access to care and differing opportunities for healthy living among neighbourhoods could substantially reduce racial inequities in CVD mortality among cancer survivors.

Offering nurse care management for opioid use disorder in primary care: Impact on emergency and hospital utilization in a cluster-randomized implementation trial.

BACKGROUND: Patients with opioid use disorder (OUD) have increased emergency and hospital utilization. The PROUD trial showed that implementation of office-based addiction treatment (OBAT) increased OUD medication treatment compared to usual care, but did not decrease acute care utilization in patients with OUD documented pre-randomization (clinicaltrials.gov/study/NCT03407638). This paper reports secondary emergency and hospital utilization outcomes in patients with documented OUD in the PROUD trial. METHODS: This cluster-randomized implementation trial was conducted in 12 clinics from 6 diverse health systems (March 2015-February 2020). Patients who visited trial clinics and had an OUD diagnosis within 3 years pre-randomization were included in primary analyses; secondary analyses added patients with OUD who were new to the clinic or with newly-documented OUD post-randomization. Outcomes included days of emergency care and hospital utilization over 2 years post-randomization. Explanatory outcomes included measures of OUD treatment. Patient-level analyses used mixed-effect regression with clinic-specific random intercepts. RESULTS: Among 1988 patients with documented OUD seen pre-randomization (mean age 49, 53 % female), days of emergency care or hospitalization did not differ between intervention and usual care; OUD treatment also did not differ. In secondary analyses among 1347 patients with OUD post-randomization, there remained no difference in emergency or hospital utilization despite intervention patients receiving 32.2 (95 % CI 4.7, 59.7) more days of OUD treatment relative to usual care. CONCLUSIONS: Implementation of OBAT did not reduce emergency or hospital utilization among patients with OUD, even in the sample with OUD first documented post-randomization in whom the intervention increased treatment.

Patient and Provider Factors Associated With Successfully Addressing Medical Needs Using Telehealth: A Cross-Sectional Survey.

Few data exist that highlight areas where telemedicine shines or struggles from the patient perspective. We conducted a retrospective analysis of patient experience data from 19,465 visits using a logistic regression to model the odds a virtual visit addressed a patient's medical needs. Patient age (80 years: OR 0.58; 95% CI, 0.50-0.67 vs 40-64 years), race (Black: 0.68; 95% CI, 0.60-0.76 vs White), and connection (telephone conversion: OR 0.59; 95% CI, 0.53-0.66 vs video success) were associated with a lower likelihood of addressing medical needs; results varied modestly across specialties. These data suggest that while telehealth is generally well accepted by patients, differences are seen among patient factors and specialty.

Nurse Care Management for Opioid Use Disorder Treatment: The PROUD Cluster Randomized Clinical Trial.

Importance: Few primary care (PC) practices treat patients with medications for opioid use disorder (OUD) despite availability of effective treatments. Objective: To assess whether implementation of the Massachusetts model of nurse care management for OUD in PC increases OUD treatment with buprenorphine or extended-release injectable naltrexone and secondarily decreases acute care utilization. Design, Setting, and Participants: The Primary Care Opioid Use Disorders Treatment (PROUD) trial was a mixed-methods, implementation-effectiveness cluster randomized clinical trial conducted in 6 diverse health systems across 5 US states (New York, Florida, Michigan, Texas, and Washington). Two PC clinics in each system were randomized to intervention or usual care (UC) stratified by system (5 systems were notified on February 28, 2018, and 1 system with delayed data use agreement on August 31, 2018). Data were obtained from electronic health records and insurance claims. An implementation monitoring team collected qualitative data. Primary care patients were included if they were 16 to 90 years old and visited a participating clinic from up to 3 years before a system's randomization date through 2 years after. Intervention: The PROUD intervention included 3 components: (1) salary for a full-time OUD nurse care manager; (2) training and technical assistance for nurse care managers; and (3) 3 or more PC clinicians agreeing to prescribe buprenorphine. Main Outcomes and Measures: The primary outcome was a clinic-level measure of patient-years of OUD treatment (buprenorphine or extended-release injectable naltrexone) per 10 000 PC patients during the 2 years postrandomization (follow-up). The secondary outcome, among patients with OUD prerandomization, was a patient-level measure of the number of days of acute care utilization during follow-up. Results: During the baseline period, a total of 130 623 patients were seen in intervention clinics (mean [SD] age, 48.6 [17.7] years; 59.7% female), and 159 459 patients were seen in UC clinics (mean [SD] age, 47.2 [17.5] years; 63.0% female). Intervention clinics provided 8.2 (95% CI, 5.4-∞) more patient-years of OUD treatment per 10 000 PC patients compared with UC clinics (P = .002). Most of the benefit accrued in 2 health systems and in patients new to clinics (5.8 [95% CI, 1.3-∞] more patient-years) or newly treated for OUD postrandomization (8.3 [95% CI, 4.3-∞] more patient-years). Qualitative data indicated that keys to successful implementation included broad commitment to treat OUD in PC from system leaders and PC teams, full financial coverage for OUD treatment, and straightforward pathways for patients to access nurse care managers. Acute care utilization did not differ between intervention and UC clinics (relative rate, 1.16; 95% CI, 0.47-2.92; P = .70). Conclusions and Relevance: The PROUD cluster randomized clinical trial intervention meaningfully increased PC OUD treatment, albeit unevenly across health systems; however, it did not decrease acute care utilization among patients with OUD. Trial Registration: ClinicalTrials.gov Identifier: NCT03407638.