Declination of Treatment, Racial and Ethnic Disparity, and Overall Survival in US Patients With Breast Cancer.

Importance: Declining treatment negatively affects health outcomes among patients with cancer. Limited research has investigated national trends of and factors associated with treatment declination or its association with overall survival (OS) among patients with breast cancer. Objectives: To examine trends and racial and ethnic disparities in treatment declination and racial and ethnic OS differences stratified by treatment decision in US patients with breast cancer. Design, Setting, and Participants: This retrospective cross-sectional study used data for patients with breast cancer from the 2004 to 2020 National Cancer Database. Four treatment modalities were assessed: chemotherapy, hormone therapy (HT), radiotherapy, and surgery. The chemotherapy cohort included patients with stage I to IV disease. The HT cohort included patients with stage I to IV hormone receptor-positive disease. The radiotherapy and surgery cohorts included patients with stage I to III disease. Data were analyzed from March to November 2023. Exposure: Race and ethnicity and other sociodemographic and clinicopathologic characteristics. Main Outcomes and Measures: Treatment decision, categorized as received or declined, was modeled using logistic regression. OS was modeled using Cox regression. Models were controlled for year of initial diagnosis, age, sex, health insurance, median household income, facility type, Charlson-Deyo comorbidity score, histology, American Joint Committee on Cancer stage, molecular subtype, and tumor grade. Results: The study included 2 837 446 patients (mean [SD] age, 61.6 [13.4] years; 99.1% female), with 1.7% American Indian, Alaska Native, or other patients; 3.5% Asian or Pacific Islander patients; 11.2% Black patients; 5.6% Hispanic patients; and 78.0% White patients. Of 1 296 488 patients who were offered chemotherapy, 124 721 (9.6%) declined; 99 276 of 1 635 916 patients (6.1%) declined radiotherapy; 94 363 of 1 893 339 patients (5.0%) declined HT; and 15 846 of 2 590 963 patients (0.6%) declined surgery. Compared with White patients, American Indian, Alaska Native, or other patients (adjusted odds ratio [AOR], 1.47; 95% CI, 1.26-1.72), Asian or Pacific Islander patients (AOR, 1.29; 95% CI, 1.15-1.44), and Black patients (AOR, 2.01; 95% CI, 1.89-2.14) were more likely to decline surgery; American Indian, Alaska Native, or other patients (AOR, 1.13; 95% CI, 1.05-1.21) and Asian or Pacific Islander patients (AOR, 1.21; 95% CI, 1.16-1.27) were more likely to decline chemotherapy; and Black patients were more likely to decline radiotherapy (AOR, 1.05; 95% CI, 1.02-1.08). Asian or Pacific Islander patients (AOR, 0.81; 95% CI, 0.77-0.85), Black patients (AOR, 0.86; 95% CI, 0.83-0.89), and Hispanic patients (AOR, 0.66; 95% CI, 0.63-0.69) were less likely to decline HT. Furthermore, Black patients who declined chemotherapy had a higher mortality risk than White patients (adjusted hazard ratio [AHR], 1.07; 95% CI, 1.02-1.13), while there were no OS differences between Black and White patients who declined HT (AHR, 1.05; 95% CI, 0.97-1.13) or radiotherapy (AHR, 0.98; 95% CI, 0.92-1.04). Conclusions and Relevance: This cross-sectional study highlights racial and ethnic disparities in treatment declination and OS, suggesting the need for equity-focused interventions, such as patient education on treatment benefits and improved patient-clinician communication and shared decision-making, to reduce disparities and improve patient survival.

Effects of Social Determinants and Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions.

Context: The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. Objective: Evaluate aggregate contribution of social determinants, comorbidity, and gene-x-drug interactions to moderate 90-day hospital readmission. Study Design and Analysis: Non-concurrent cohort study; Multivariable logistic regression Setting: Hospital/integrated healthcare delivery system in northern Illinois Population Studied: 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel Outcome Measure: 90-day hospital readmission (primary outcome) Results: Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45-10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61-4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77-3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88-2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47-3.07) (p < 0.0001), median household income (OR = 1.63, 1.03-2.58) (p = 0.035), male gender (OR = 1.47, 1.21-1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18-1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08-1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). Conclusions: These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.

Do medications increase the efficacy of digital interventions for smoking cessation? Secondary results from the iCanQuit randomized trial.

BACKGROUND AND AIMS: iCanQuit is a smartphone application (app) proven efficacious for smoking cessation in a Phase III randomized controlled trial (RCT). This study aimed to measure whether medications approved by the US Food and Drug Administration (FDA) for smoking cessation would further enhance the efficacy of iCanQuit, relative to its parent trial comparator-the National Cancer Institute's (NCI's) QuitGuide app. DESIGN: Secondary analysis of the entire parent trial sample of a two-group (iCanQuit and QuitGuide), stratified, doubled-blind RCT. SETTING: United States. PARTICIPANTS: Participants who reported using an FDA-approved cessation medication on their own (n = 619) and those who reported no use of cessation medications (n = 1469). INTERVENTIONS: Participants were randomized to receive iCanQuit app or NCI's QuitGuide app. MEASUREMENTS: Use of FDA-approved medications was measured at 3 months post-randomization. Smoking cessation outcomes were measured at 3, 6 and 12 months. The primary outcome was 12-month self-reported 30-day point prevalence abstinence (PPA). FINDINGS: The data retention rate at the 12-month follow-up was 94.0%. Participants were aged 38.5 years, 71.0% female, 36.6% minority race/ethnicity, 40.6% high school or less education, residing in all 50 US States and smoking 19.2 cigarettes/day. The 29.6% of all participants who used medications were more likely to choose nicotine replacement therapy (NRT; 78.8%) than other cessation medications (i.e. varenicline or bupropion; 18.3 and 10.5%, respectively) and use did not differ by app treatment assignment (all P > 0.05). There was a significant (P = 0.049) interaction between medication use and app treatment assignment on PPA. Specifically, 12-month quit rates were 34% for iCanQuit versus 20% for QuitGuide [odds ratio (OR) = 2.36, 95% confidence interval (CI) = 1.59, 3.49] among participants reporting any medication use, whereas among participants reporting no medication use, quit rates were 28% for iCanQuit versus 22% for QuitGuide (OR = 1.41, 95% CI = 1.09, 1.82). Results were stronger for those using only NRT: 40% quit rates for iCanQuit versus 18% quit rates for QuitGuide (OR = 3.57, 95% CI = 2.20, 5.79). CONCLUSIONS: The iCanQuit smartphone app for smoking cessation was more efficacious than the QuitGuide smartphone app, regardless of whether participants used medications to aid cessation. Smoking cessation medications, especially nicotine replacement therapy, might enhance the efficacy of the iCanQuit app.

Personalized medicine in a community health system: the NorthShore experience.

Genomic and personalized medicine implementation efforts have largely centered on specialty care in tertiary health systems. There are few examples of fully integrated care systems that span the healthcare continuum. In 2014, NorthShore University HealthSystem launched the Center for Personalized Medicine to catalyze the delivery of personalized medicine. Successful implementation required the development of a scalable family history collection tool, the Genetic and Wellness Assessment (GWA) and Breast Health Assessment (BHA) tools; integrated pharmacogenomics programming; educational programming; electronic medical record integration; and robust clinical decision support tools. To date, more than 225,000 patients have been screened for increased hereditary conditions, such as cancer risk, through these tools in primary care. More than 35,000 patients completed clinical genetic testing following GWA or BHA completion. An innovative program trained more than 100 primary care providers in genomic medicine, activated with clinical decision support and access to patient genetic counseling services and digital healthcare tools. The development of a novel bioinformatics platform (FLYPE) enabled the incorporation of genomics data into electronic medical records. To date, over 4,000 patients have been identified to have a pathogenic or likely pathogenic variant in a gene with medical management implications. Over 33,000 patients have clinical pharmacogenomics data incorporated into the electronic health record supported by clinical decision support tools. This manuscript describes the evolution, strategy, and successful multispecialty partnerships aligned with health system leadership that enabled the implementation of a comprehensive personalized medicine program with measurable patient outcomes through a genomics-enabled learning health system model that utilizes implementation science frameworks.

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Cross-sectional online survey of clinicians' knowledge, attitudes and challenges to screening and counselling adolescents and young adults for substance use.

OBJECTIVE: To examine adolescent healthcare clinicians' self-reported screening practices as well as their knowledge, attitudes, comfort level and challenges with screening and counselling adolescents and young adults (AYA) for cigarette, e-cigarette, alcohol, marijuana, hookah and blunt use. DESIGN: A 2016 cross-sectional survey. SETTING: Academic departments and community-based internal medicine, family medicine and paediatrics practices. PARTICIPANTS: Adolescent healthcare clinicians (N=771) from 12 US medical schools and respondents to national surveys. Of the participants, 36% indicated male, 64% female, mean age was 44 years (SD=12.3); 12.3% of participants identified as Asian, 73.7% as white, 4.8% as black, 4.2% as Hispanic and 3.8% as other. PRIMARY AND SECONDARY OUTCOME MEASURES: Survey items queried clinicians about knowledge, attitudes, comfort level, self-efficacy and challenges with screening and counselling AYA patients about marijuana, blunts, cigarettes, e-cigarettes, hookah and alcohol. RESULTS: Participants were asked what percentage of their 10-17 years old patients they screened for substance use. The median number of physicians reported screening 100% of their patients for cigarette (1st, 3rd quartiles; 80, 100) and alcohol use (75, 100) and 99.5% for marijuana use (50,100); for e-cigarettes, participants reported screening half of their patients and 0.0% (0, 50), (0, 75)) reported screening for hookah and blunts, respectively. On average (median), clinicians estimated that 15.0% of all 10-17 years old patients smoked cigarettes, 10.0% used e-cigarettes, 20.0% used marijuana, 25.0% drank alcohol and 5.0% used hookah or blunts, respectively; yet they estimated lower than national rates of use of each product for their own patients. Clinicians reported greater comfort discussing cigarettes and alcohol with patients and less comfort discussing e-cigarettes, hookah, marijuana and blunts. CONCLUSIONS: This study identified low rates of screening and counselling AYA patients for use of e-cigarettes, hookahs and blunts by adolescent healthcare clinicians and points to potential missed opportunities to improve prevention efforts.

The mental health burden of racial and ethnic minorities during the COVID-19 pandemic.

Racial/ethnic minorities have been disproportionately impacted by COVID-19. The effects of COVID-19 on the long-term mental health of minorities remains unclear. To evaluate differences in odds of screening positive for depression and anxiety among various racial and ethnic groups during the latter phase of the COVID-19 pandemic, we performed a cross-sectional analysis of 691,473 participants nested within the prospective smartphone-based COVID Symptom Study in the United States (U.S.) and United Kingdom (U.K). from February 23, 2021 to June 9, 2021. In the U.S. (n=57,187), compared to White participants, the multivariable odds ratios (ORs) for screening positive for depression were 1·16 (95% CI: 1·02 to 1·31) for Black, 1·23 (1·11 to 1·36) for Hispanic, and 1·15 (1·02 to 1·30) for Asian participants, and 1·34 (1·13 to 1·59) for participants reporting more than one race/other even after accounting for personal factors such as prior history of a mental health disorder, COVID-19 infection status, and surrounding lockdown stringency. Rates of screening positive for anxiety were comparable. In the U.K. (n=643,286), racial/ethnic minorities had similarly elevated rates of positive screening for depression and anxiety. These disparities were not fully explained by changes in leisure time activities. Racial/ethnic minorities bore a disproportionate mental health burden during the COVID-19 pandemic. These differences will need to be considered as health care systems transition from prioritizing infection control to mitigating long-term consequences.

Rare genetic variants explain missing heritability in smoking.

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System.

The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45−10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61−4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77−3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88−2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47−3.07) (p < 0.0001), median household income (OR = 1.63, 1.03−2.58) (p = 0.035), male gender (OR = 1.47, 1.21−1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18−1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08−1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.

Symptom prevalence, duration, and risk of hospital admission in individuals infected with SARS-CoV-2 during periods of omicron and delta variant dominance: a prospective observational study from the ZOE COVID Study.

BACKGROUND: The SARS-CoV-2 variant of concern, omicron, appears to be less severe than delta. We aim to quantify the differences in symptom prevalence, risk of hospital admission, and symptom duration among the vaccinated population. METHODS: In this prospective longitudinal observational study, we collected data from participants who were self-reporting test results and symptoms in the ZOE COVID app (previously known as the COVID Symptoms Study App). Eligible participants were aged 16-99 years, based in the UK, with a body-mass index between 15 and 55 kg/m2, had received at least two doses of any SARS-CoV-2 vaccine, were symptomatic, and logged a positive symptomatic PCR or lateral flow result for SARS-CoV-2 during the study period. The primary outcome was the likelihood of developing a given symptom (of the 32 monitored in the app) or hospital admission within 7 days before or after the positive test in participants infected during omicron prevalence compared with those infected during delta prevalence. FINDINGS: Between June 1, 2021, and Jan 17, 2022, we identified 63 002 participants who tested positive for SARS-CoV-2 and reported symptoms in the ZOE app. These patients were matched 1:1 for age, sex, and vaccination dose, across two periods (June 1 to Nov 27, 2021, delta prevalent at >70%; n=4990, and Dec 20, 2021, to Jan 17, 2022, omicron prevalent at >70%; n=4990). Loss of smell was less common in participants infected during omicron prevalence than during delta prevalence (16·7% vs 52·7%, odds ratio [OR] 0·17; 95% CI 0·16-0·19, p<0·001). Sore throat was more common during omicron prevalence than during delta prevalence (70·5% vs 60·8%, 1·55; 1·43-1·69, p<0·001). There was a lower rate of hospital admission during omicron prevalence than during delta prevalence (1·9% vs 2·6%, OR 0·75; 95% CI 0·57-0·98, p=0·03). INTERPRETATION: The prevalence of symptoms that characterise an omicron infection differs from those of the delta SARS-CoV-2 variant, apparently with less involvement of the lower respiratory tract and reduced probability of hospital admission. Our data indicate a shorter period of illness and potentially of infectiousness which should impact work-health policies and public health advice. FUNDING: Wellcome Trust, ZOE, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, and Medical Research Council.

The Contribution of Pharmacogenetic Drug Interactions to 90-Day Hospital Readmissions: Preliminary Results from a Real-World Healthcare System.

Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines exist for many medications commonly prescribed prior to hospital discharge, yet there are limited data regarding the contribution of gene-x-drug interactions to hospital readmissions. The present study evaluated the relationship between prescription of CPIC medications prescribed within 30 days of hospital admission and 90-day hospital readmission from 2010 to 2020 in a study population (N = 10,104) who underwent sequencing with a 14-gene pharmacogenetic panel. The presence of at least one pharmacogenetic indicator for a medication prescribed within 30 days of hospital admission was considered a gene-x-drug interaction. Multivariable logistic regression analyzed the association between one or more gene-x-drug interactions with 90-day readmission. There were 2211/2354 (93.9%) admitted patients who were prescribed at least one CPIC medication. Univariate analyses indicated that the presence of at least one identified gene-x-drug interaction increased the risk of 90-day readmission by more than 40% (OR = 1.42, 95% confidence interval (CI) 1.09-1.84) (p = 0.01). A multivariable model adjusting for age, race, sex, employment status, body mass index, and medical conditions slightly attenuated the effect (OR = 1.32, 95% CI 1.02-1.73) (p = 0.04). Our results suggest that the presence of one or more CPIC gene-x-drug interactions increases the risk of 90-day hospital readmission, even after adjustment for demographic and clinical risk factors.

Pharmacotherapy for smoking cessation: effects by subgroup defined by genetically informed biomarkers.

This review has been withdrawn because it has been found to be in breach of the Cochrane Commercial Sponsorship policy clause 2:  'Individuals who are currently employed or where employed any time in the last three years by a company that has a real or potential financial interest in the outcome of the review (including but not limited to drug companies or medical device manufacturers); or who hold or have applied for a patent related to the review are prohibited from being Cochrane Review authors. In most cases, current or previous employment would be characterized by the affiliation statement made by the author at the title registration, protocol, or review stage of the review'.

Implementing Primary Care Mediated Population Genetic Screening Within an Integrated Health System.

INTRODUCTION: Genetic screenings can have a large impact on enabling personalized preventive care. However, this can be limited by the primary use of medical history-based screenings in determining care. The purpose of this study was to understand the impact of DNA10K, a population-based genetic screening program mediated by primary care physicians within an integrated health system to emphasize its contribution to preventive healthcare. METHODS: Construction of the patient experience as part of DNA10K shaped the context for PCP engagement within the program. A cross-sectional analysis of patient consents, orders, tests, and results of nearly 10,000 patients within the primary care specialties of family medicine, internal medicine or obstetrics/gynecology between April 1, 2019 and January 22, 2020 was conducted. RESULTS: Across all specialties, a median number of 7.5 cancer and cardiovascular disease variants per PCP was found. The average age of the study population was 49.6 years. Over 8% of these patients had at least one actionable genetic risk variant and almost 2% of patients had at least one CDC Tier 1 variant. The median numbers of patients per PCP with either hereditary breast and ovarian cancer, Lynch Syndrome, or Familial Hypercholesterolemia was 1 (Interquartile Range 0-2). DISCUSSION: The analysis of test results and the engagement of an integrated healthcare system in the implementation of a genetic screening program suggests that it can have a large impact on population health outcomes and minimal referral burden to PCPs if identified risks can lead to preventive care.

Target specific serologic analysis of COVID-19 convalescent plasma.

This study compared the performance of four serology assays for Coronavirus Disease 2019 (COVID-19) and investigated whether COVID-19 disease history correlates with assay performance. Samples were tested at Northshore using the Elecsys Anti-SARS-CoV-2 (Roche Diagnostics), Access SARS-CoV-2 IgG anti-RBD (Beckman Coulter), and LIAISON SARS-CoV-2 S1/S2 IgG (DiaSorin) as well as at Genalyte using Maverick Multi-Antigen Serology Panel. The study included one hundred clinical samples collected before December 2019 and ninety-seven samples collected from convalescent plasma donors originally diagnosed with COVID-19 by PCR. COVID-19 disease history was self-reported by the plasma donors. There was no difference in specificity between the assays tested. Clinical sensitivity of these four tests was 98% (Genalyte), 96% (Roche), 92% (DiaSorin), and 87% (Beckman). The only statistically significant differences in clinical sensitivity was between the Beckman assay and both Genalyte and Roche assays. Convalescent plasma donor characteristics and disease symptoms did not correlate with false negative results from the Beckman and DiaSorin assays. All four tests showed high specificity (100%) and varying sensitivities (89-98%). No correlations between disease history and serology results were observed. The Genalyte Multiplex assay showed as good or better sensitivity to three other previously validated assays with FDA Emergency Use Authorizations.

Whom Should We Test for COVID-19? Performance of a Symptom and Risk Factor Questionnaire on COVID-19 Test Results and Patient Outcomes in an Immediate Care Setting.

INTRODUCTION: The CDC and Illinois Department of Public Health disseminated risk factor criteria for COVID-19 testing early in the pandemic. The objective of this study is to assess the effectiveness of risk stratifying patients for COVID-19 testing and to identify which risk factors and which other clinical variables were associated with SARS-CoV-2 PCR test positivity. METHODS: We conducted an observational cohort study on a sample of symptomatic patients evaluated at an immediate care setting. A risk assessment questionnaire was administered to every patient before clinician evaluation. High-risk patients received SARS-CoV-2 test and low-risk patients were evaluated by a clinician and selectively tested based on clinician judgment. Multivariate analyses tested whether risk factors and additional variables were associated with test positivity. RESULTS: The adjusted odds ratio of testing positive was associated with COVID-19-positive or suspect close contact (aOR 1.56, 95% CI 1.15-2.10), large gathering attendance with a COVID-19-positive individual (aOR 1.92, 95% CI 1.10-3.34), and, with the largest effect size, decreased taste/smell (aOR 2.83, 95% CI 2.01-3.99). Testing positive was associated with ages 45-64 and ≥65 (aOR 1.75, 95% CI 1.25-2.44, and aOR 2.78, 95% CI 1.49-5.16), systolic blood pressures ≤120 (aOR 1.64, 95% CI 1.20-2.24), and, with the largest effect size, temperatures ≥99.0°F (aOR 3.06, 95% CI 2.23-4.20). The rate of positive SARS-CoV-2 test was similar between high-risk and low risk patients (225 [22.2%] vs 50 [19.8%]; P = .41). DISCUSSION: The risk assessment questionnaire was not effective at stratifying patients for testing. Although individual risk factors were associated with SARS-CoV-2 test positivity, the low-risk group had similar positivity rates to the high-risk group. Our observations underscore the need for clinicians to develop clinical experience and share best practices and for systems and payors to support policies, funding, and resources to test all symptomatic patients.

Variant Interpretation in Current Pharmacogenetic Testing.

In the current marketplace, there are now more than a dozen commercial companies providing pharmacogenetic tests. Each company varies in the panel of genes they test and the variants they are able to screen for. The reports generated by these companies provide phenotypic interpretations of pharmacogenes and clinically actionable gene-drug interactions based on internally curated data and proprietary algorithms. The freedom to choose the types of evidence to include versus exclude in interpreting genomics has created reporting discrepancies in the industry. The case report presented here reveals the discordant phenotype analysis provided by two pharmacogenetic testing companies. The uncertainty and unnecessary distress experienced by the patient highlights the need for consensus in phenotype reporting within the industry.

The association between cigarette smoking, cancer screening, and cancer stage: a prospective study of the women's health initiative observational cohort.

OBJECTIVE: To assess the dose-dependent relationship between smoking history and cancer screening rates or staging of cancer diagnoses. DESIGN: Prospective, population-based cohort study. SETTING: Questionnaire responses from the Women's Health Initiative (WHI) Observational Study. PARTICIPANTS: 89 058 postmenopausal women. OUTCOME MEASURES: Logistic regression models were used to assess the odds of obtaining breast, cervical, and colorectal cancer screening as stratified by smoking status. The odds of late-stage cancer diagnoses among patients with adequate vs inadequate screening as stratified by smoking status were also calculated. RESULTS: Of the 89 058 women who participated, 52.8% were never smokers, 40.8% were former smokers, and 6.37% were current smokers. Over an average of 8.8 years of follow-up, current smokers had lower odds of obtaining breast (OR 0.55; 95% CI 0.51 to 0.59), cervical (OR 0.53; 95% CI 0.47 to 0.59), and colorectal cancer (OR 0.71; 95% CI 0.66 to 0.76) screening compared with never smokers. Former smokers were more likely than never smokers to receive regular screening services. Failure to adhere to screening guidelines resulted in diagnoses at higher cancer stages among current smokers for breast cancer (OR 2.78; 95% CI 1.64 to 4.70) and colorectal cancer (OR 2.26; 95% CI 1.01 to 5.05). CONCLUSIONS: Active smoking is strongly associated with decreased use of cancer screening services and more advanced cancer stage at the time of diagnosis. Clinicians should emphasise the promotion of both smoking cessation and cancer screening for this high-risk group.

Perspectives on Precision Health Among Racial/Ethnic Minority Communities and the Physicians That Serve Them.

Background: In order for precision health to address health disparities, engagement of diverse racial/ethnic minority communities and the physicians that serve them is critical. Methods: A community-based participatory research approach with mixed methods was employed to gain a deeper understanding of precision health research and practice among American Indian, African American, Latino, Chinese, and Vietnamese groups and physicians that serve these communities. A survey assessed demographics and opinions of precision health, genetic testing, and precision health research. Focus groups (n=12) with each racial/ethnic minority group and physicians further explored attitudes about these topics. Results: One hundred community members (American Indian [n=17], African American [n=13], Chinese [n=17], Latino [n=27], and Vietnamese [n=26]) and 14 physicians completed the survey and participated in the focus groups. Familiarity with precision health was low among community members and high among physicians. Most groups were enthusiastic about the approach, especially if it considered influences on health in addition to genes (eg, environmental, behavioral, social factors). Significant concerns were expressed by African American and American Indian participants about precision health practice and research based on past abuses in biomedical research. In addition, physician and community members shared concerns such as security and confidentiality of genetic information, cost and affordability of genetic tests and precision medicine, discrimination and disparities, distrust of medical and research and pharmaceutical institutions, language barriers, and physician's specialty. Conclusions: Engagement of racial/ethnic minority communities and the providers who serve them is important for advancing a precision health approach to addressing health disparities.

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci.

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.