Background Perinatal quality improvement lacks valid tools to measure adverse hospital experiences disproportionately impacting Black mothers and birthing people. Measuring and mitigating harm requires using a framework that centers the lived experiences of Black birthing people in evaluating inequitable care, namely, obstetric racism. We sought to develop a valid patient‐reported experience measure (PREM) of Obstetric Racism© in hospital‐based intrapartum care designed for, by, and with Black women as patient, community, and content experts. Methods PROMIS© instrument development standards adapted with cultural rigor methodology. Phase 1 included item pool generation, modified Delphi method, and cognitive interviews. Phase 2 evaluated the item pool using factor analysis and item response theory. Results Items were identified or written to cover 7 previously identified theoretical domains. 806 Black mothers and birthing people completed the pilot test. Factor analysis concluded a 3 factor structure with good fit indices (CFI = 0.931‐0.977, RMSEA = 0.087‐0.10, R2 > .3, residual correlation < 0.15). All items in each factor fit the IRT model and were able to be calibrated. Factor 1, “Humanity,” had 31 items measuring experiences of safety and accountability, autonomy, communication, and empathy. A 12‐item short form was created to ease respondent burden. Factor 2, “Racism,” had 12 items measuring experiences of neglect and mistreatment. Factor 3, “Kinship,” had 7 items measuring hospital denial and disruption of relationships between Black mothers and their child or support system. Conclusions The PREM‐OB Scale™ suite is a valid tool to characterize and quantify obstetric racism for use in perinatal improvement initiatives.

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.

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.

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.

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.

Introduction Genetic screenings can have a large impact on enabling personalized preventative 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 preventative 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 preventative care.

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ObjectiveTo 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.DesignA 2016 cross-sectional survey.SettingAcademic departments and community-based internal medicine, family medicine and paediatrics practices.ParticipantsAdolescent 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 measuresSurvey 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.ResultsParticipants 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.ConclusionsThis 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.