Interventions addressing health-related social needs among patients with cancer.

Health-related social needs are prevalent among cancer patients; associated with substantial negative health consequences; and drive pervasive inequities in cancer incidence, severity, treatment choices and decisions, and outcomes. To address the lack of clinical trial evidence to guide health-related social needs interventions among cancer patients, the National Cancer Institute Cancer Care Delivery Research Steering Committee convened experts to participate in a clinical trials planning meeting with the goal of designing studies to screen for and address health-related social needs among cancer patients. In this commentary, we discuss the rationale for, and challenges of, designing and testing health-related social needs interventions in alignment with the National Academy of Sciences, Engineering, and Medicine 5As framework. Evidence for food, housing, utilities, interpersonal safety, and transportation health-related social needs interventions is analyzed. Evidence regarding health-related social needs and delivery of health-related social needs interventions differs in maturity and applicability to cancer context, with transportation problems having the most maturity and interpersonal safety the least. We offer practical recommendations for health-related social needs interventions among cancer patients and the caregivers, families, and friends who support their health-related social needs. Cross-cutting (ie, health-related social needs agnostic) recommendations include leveraging navigation (eg, people, technology) to identify, refer, and deliver health-related social needs interventions; addressing health-related social needs through multilevel interventions; and recognizing that health-related social needs are states, not traits, that fluctuate over time. Health-related social needs-specific interventions are recommended, and pros and cons of addressing more than one health-related social needs concurrently are characterized. Considerations for collaborating with community partners are highlighted. The need for careful planning, strong partners, and funding is stressed. Finally, we outline a future research agenda to address evidence gaps.

Clinical impacts of an integrated electronic health record-based smoking cessation intervention during hospitalisation.

OBJECTIVE: To assess the effects of an electronic health record (EHR) intervention that prompts the clinician to prescribe nicotine replacement therapy (NRT) at hospital admission and discharge in a large integrated health system. DESIGN: Retrospective cohort study using interrupted time series (ITS) analysis leveraging EHR data generated before and after implementation of the 2015 EHR-based intervention. SETTING: Kaiser Permanente Northern California, a large integrated health system with 4.2 million members. PARTICIPANTS: Current smokers aged ≥18 hospitalised for any reason. EXPOSURE: EHR-based clinical decision supports that prompted the clinician to order NRT on hospital admission (implemented February 2015) and discharge (implemented September 2015). MAIN OUTCOMES AND MEASURES: Primary outcomes included the monthly percentage of admitted smokers with NRT orders during admission and at discharge. A secondary outcome assessed patient quit rates within 30 days of hospital discharge as reported during discharge follow-up outpatient visits. RESULTS: The percentage of admissions with NRT orders increased from 29.9% in the year preceding the intervention to 78.1% in the year following (41.8% change, 95% CI 38.6% to 44.9%) after implementation of the admission hard-stop intervention compared with the baseline trend (ITS estimate). The percentage of discharges with NRT orders increased acutely at the time of both interventions (admission intervention ITS estimate 15.5%, 95% CI 11% to 20%; discharge intervention ITS estimate 13.4%, 95% CI 9.1% to 17.7%). Following the implementation of the discharge intervention, there was a small increase in patient-reported quit rates (ITS estimate 5.0%, 95% CI 2.2% to 7.8%). CONCLUSIONS: An EHR-based clinical decision-making support embedded into admission and discharge documentation was associated with an increase in NRT prescriptions and improvement in quit rates. Similar systemic EHR interventions can help improve smoking cessation efforts after hospitalisation.

Disparities in Preoperative Goals of Care Documentation in Veterans.

Importance: Preoperative goals of care discussion and documentation are important for patients undergoing surgery, a major health care stressor that incurs risk. Objective: To assess the association of race, ethnicity, and other factors, including history of mental health disability, with disparities in preoperative goals of care documentation among veterans. Design, Setting, and Participants: This retrospective cross-sectional study assessed data from the Veterans Healthcare Administration (VHA) of 229 737 veterans who underwent surgical procedures between January 1, 2017, and October 18, 2022. Exposures: Patient-level (ie, race, ethnicity, medical comorbidities, history of mental health comorbidity) and system-level (ie, facility complexity level) factors. Main Outcomes and Measures: Preoperative life-sustaining treatment (LST) note documentation or no LST note documentation within 30 days prior to or on day of surgery. The standardized mean differences were calculated to assess the magnitude of differences between groups. Odds ratios (ORs) and 95% CIs were estimated with logistic regression. Results: In this study, 13 408 patients (5.8%) completed preoperative LST from 229 737 VHA patients (209 123 [91.0%] male; 20 614 [9.0%] female; mean [SD] age, 65.5 [11.9] years) who received surgery. Compared with patients who did complete preoperative LST, patients tended to complete preoperative documentation less often if they were female (19 914 [9.2%] vs 700 [5.2%]), Black individuals (42 571 [19.7%] vs 2416 [18.0%]), Hispanic individuals (11 793 [5.5%] vs 631 [4.7%]), or from rural areas (75 637 [35.0%] vs 4273 [31.9%]); had a history of mental health disability (65 974 [30.5%] vs 4053 [30.2%]); or were seen at lowest-complexity (ie, level 3) facilities (7849 [3.6%] vs 78 [0.6%]). Over time, despite the COVID-19 pandemic, patients undergoing surgical procedures completed preoperative LST increasingly more often. Covariate-adjusted estimates of preoperative LST completion demonstrated that patients of racial or ethnic minority background (Black patients: OR, 0.79; 95% CI, 0.77-0.80; P <.001; patients selecting other race: OR, 0.78; 95% CI, 0.74-0.81; P <.001; Hispanic patients: OR, 0.78; 95% CI, 0.76-0.81; P <.001) and patients from rural regions (OR, 0.91; 95% CI, 0.90-0.93; P <.001) had lower likelihoods of completing LST compared with patients who were White or non-Hispanic and patients from urban areas. Patients with any mental health disability history also had lower likelihood of completing preoperative LST than those without a history (OR, 0.93; 95% CI, 0.92-0.94; P = .001). Conclusions and Relevance: In this cross-sectional study, disparities in documentation rates within a VHA cohort persisted based on race, ethnicity, rurality of patient residence, history of mental health disability, and access to high-volume, high-complexity facilities.

Evaluation of approaches to recruitment of racially and ethnically diverse breast cancer patients from an integrated health care setting for collection of observational social network data.

PURPOSE: We compared approaches to recruitment of diverse women with breast cancer in a study designed to collect complex social network data. METHODS: We recruited 440 women from the Kaiser Permanente Northern California population newly diagnosed with breast cancer, either in person at a clinic, by email, or by mailed letter. In clinic and mail recruitment, women completed a brief 3-page paper survey (epidemiologic data only), and women had the option to complete a separate, longer (30-40 min) personal social network survey online. In email recruitment, we administered epidemiologic and personal social network measures together in a single online survey. In email and mail recruitment, we limited the sample of non-Hispanic white (NHW) women to 30% of their total. We used descriptive analysis and multinomial logistic regression to examine odds of recruitment vs. mailed letter. RESULTS: Women responded to the social network surveys on average 3.7 months post-diagnosis. Mean age was 59.3 (median = 61.0). In-person clinic recruitment was superior with a 52.1% success rate of recruitment compared with 35.6% by mail or 17.3% by email (χ2 = 65.9, p < 0.001). Email recruitment produced the highest completion rate (82.1%) of personal network data compared with clinic (36.5%) or mail (28.7%), (χ2 = 114.6, p < 0.001). Despite intentional undersampling of NHW patients, response rates for Asian, Hispanic, and Black women by email were lower. However, we found no significant differences in recruitment rates by race and ethnicity for face-to-face clinic recruitment vs. by letter. Letter recruitment produced the highest overall response. CONCLUSION: Mailed letter was the best approach to representative recruitment of diverse women with breast cancer and collection of social network data, and further yielded the highest absolute response.

Strategies to Bridge Equitable Implementation of Telehealth.

During the COVID-19 pandemic, the rapid scaling of telehealth limited the extent to which proactive planning for equitable implementation was possible. The deployment of telehealth will persist in the postpandemic era, given patient preferences, advances in technologies, growing acceptance of telehealth, and the potential to overcome barriers to serve populations with limited access to high-quality in-person care. However, aspects and unintended consequences of telehealth may leave some groups underserved or unserved, and corrective implementation plans that address equitable access will be needed. The purposes of this paper are to (1) describe equitable implementation in telehealth and (2) integrate an equity lens into actionable equitable implementation.

Glycemic Control Over Multiple Decades and Dementia Risk in People With Type 2 Diabetes.

Importance: The levels of glycemic control associated with the lowest risk of dementia in people with type 2 diabetes are unknown. This knowledge is critical to inform patient-centered glycemic target setting. Objective: To examine the associations between cumulative exposure to various ranges of glycated hemoglobin (HbA1c) concentrations with dementia risk across sex and racial and ethnic groups and the association of current therapeutic glycemic targets with dementia risk. Design, Setting, and Participants: This cohort study included members of the Kaiser Permanente Northern California integrated health care system with type 2 diabetes who were aged 50 years or older during the study period from January 1, 1996, to September 30, 2015. Individuals with fewer than 2 HbA1c measurements during the study period, prevalent dementia at baseline, or less than 3 years of follow-up were excluded. Data were analyzed from February 2020 to January 2023. Exposures: Time-updated cumulative exposure to HbA1c thresholds. At each HbA1c measurement, participants were categorized based on the percentage of their HbA1c measurements that fell into the following categories: less than 6%, 6% to less than 7%, 7% to less than 8%, 8% to less than 9%, 9% to less than 10%, and 10% or more of total hemoglobin (to convert percentage of total hemoglobin to proportion of total hemoglobin, multiply by 0.01). Main Outcomes and Measures: Dementia diagnosis was identified using International Classification of Diseases, Ninth Revision codes from inpatient and outpatient encounters. Cox proportional hazards regression models estimated the association of time-varying cumulative glycemic exposure with dementia, adjusting for age, race and ethnicity, baseline health conditions, and number of HbA1c measurements. Results: A total of 253 211 participants were included. The mean (SD) age of participants was 61.5 (9.4) years, and 53.1% were men. The mean (SD) duration of follow-up was 5.9 (4.5) years. Participants with more than 50% of HbA1c measurements at 9% to less than 10% or 10% or more had greater risk of dementia compared with those who had 50% or less of measurements in those categories (HbA1c 9% to <10%: adjusted hazard ratio [aHR], 1.31 [95% CI, 1.15-1.51]; HbA1c≥10%: aHR, 1.74 [95% CI, 1.62-1.86]). By contrast, participants with more than 50% of HbA1c concentrations less than 6%, 6% to less than 7%, or 7% to less than 8% had lower risk of dementia (HbA1c<6%: aHR, 0.92 [95% CI, 0.88-0.97]; HbA1c 6% to <7%: aHR, 0.79 [95% CI, 0.77-0.81]; HbA1c 7% to <8%: aHR, 0.93 [95% CI, 0.89-0.97]). Conclusions and Relevance: In this study dementia risk was greatest among adults with cumulative HbA1c concentrations of 9% or more. These results support currently recommended relaxed glycemic targets for older people with type 2 diabetes.

Disparities in Hemoglobin A1c Levels in the First Year After Diagnosis Among Youths With Type 1 Diabetes Offered Continuous Glucose Monitoring.

Importance: Continuous glucose monitoring (CGM) is associated with improvements in hemoglobin A1c (HbA1c) in youths with type 1 diabetes (T1D); however, youths from minoritized racial and ethnic groups and those with public insurance face greater barriers to CGM access. Early initiation of and access to CGM may reduce disparities in CGM uptake and improve diabetes outcomes. Objective: To determine whether HbA1c decreases differed by ethnicity and insurance status among a cohort of youths newly diagnosed with T1D and provided CGM. Design, Setting, and Participants: This cohort study used data from the Teamwork, Targets, Technology, and Tight Control (4T) study, a clinical research program that aims to initiate CGM within 1 month of T1D diagnosis. All youths with new-onset T1D diagnosed between July 25, 2018, and June 15, 2020, at Stanford Children's Hospital, a single-site, freestanding children's hospital in California, were approached to enroll in the Pilot-4T study and were followed for 12 months. Data analysis was performed and completed on June 3, 2022. Exposures: All eligible participants were offered CGM within 1 month of diabetes diagnosis. Main Outcomes and Measures: To assess HbA1c change over the study period, analyses were stratified by ethnicity (Hispanic vs non-Hispanic) or insurance status (public vs private) to compare the Pilot-4T cohort with a historical cohort of 272 youths diagnosed with T1D between June 1, 2014, and December 28, 2016. Results: The Pilot-4T cohort comprised 135 youths, with a median age of 9.7 years (IQR, 6.8-12.7 years) at diagnosis. There were 71 boys (52.6%) and 64 girls (47.4%). Based on self-report, participants' race was categorized as Asian or Pacific Islander (19 [14.1%]), White (62 [45.9%]), or other race (39 [28.9%]); race was missing or not reported for 15 participants (11.1%). Participants also self-reported their ethnicity as Hispanic (29 [21.5%]) or non-Hispanic (92 [68.1%]). A total of 104 participants (77.0%) had private insurance and 31 (23.0%) had public insurance. Compared with the historical cohort, similar reductions in HbA1c at 6, 9, and 12 months postdiagnosis were observed for Hispanic individuals (estimated difference, -0.26% [95% CI, -1.05% to 0.43%], -0.60% [-1.46% to 0.21%], and -0.15% [-1.48% to 0.80%]) and non-Hispanic individuals (estimated difference, -0.27% [95% CI, -0.62% to 0.10%], -0.50% [-0.81% to -0.11%], and -0.47% [-0.91% to 0.06%]) in the Pilot-4T cohort. Similar reductions in HbA1c at 6, 9, and 12 months postdiagnosis were also observed for publicly insured individuals (estimated difference, -0.52% [95% CI, -1.22% to 0.15%], -0.38% [-1.26% to 0.33%], and -0.57% [-2.08% to 0.74%]) and privately insured individuals (estimated difference, -0.34% [95% CI, -0.67% to 0.03%], -0.57% [-0.85% to -0.26%], and -0.43% [-0.85% to 0.01%]) in the Pilot-4T cohort. Hispanic youths in the Pilot-4T cohort had higher HbA1c at 6, 9, and 12 months postdiagnosis than non-Hispanic youths (estimated difference, 0.28% [95% CI, -0.46% to 0.86%], 0.63% [0.02% to 1.20%], and 1.39% [0.37% to 1.96%]), as did publicly insured youths compared with privately insured youths (estimated difference, 0.39% [95% CI, -0.23% to 0.99%], 0.95% [0.28% to 1.45%], and 1.16% [-0.09% to 2.13%]). Conclusions and Relevance: The findings of this cohort study suggest that CGM initiation soon after diagnosis is associated with similar improvements in HbA1c for Hispanic and non-Hispanic youths as well as for publicly and privately insured youths. These results further suggest that equitable access to CGM soon after T1D diagnosis may be a first step to improve HbA1c for all youths but is unlikely to eliminate disparities entirely. Trial Registration: ClinicalTrials.gov Identifier: NCT04336969.

Tobacco Smoking and Risk of SARS-CoV-2 Infection and Disease Severity Among Adults in an Integrated Healthcare System in California.

INTRODUCTION: The relationship between tobacco smoking status and SARS-CoV-2 infection and coronavirus disease 2019 (COVID-19) severity is highly debated. We conducted a retrospective cohort study of >2.4 million adults in a large healthcare system to evaluate whether smoking is associated with SARS-CoV-2 infection and disease severity. AIMS AND METHODS: This retrospective cohort study of 2,427,293 adults in KPNC from March 5, 2020 (baseline) to December 31, 2020 (pre-vaccine) included smoking status (current, former, never), socio-demographics, and comorbidities from the electronic health record. SARS-CoV-2 infection (identified by a positive PCR test) and COVID-19 severity (hospitalization, ICU admission or death ≤ 30 days of COVID-19 diagnosis) were estimated in time-to-event analyses using Cox proportional hazard regression models adjusting for covariates. Secondary analyses examined COVID-19 severity among patients with COVID-19 using logistic regression. RESULTS: During the study, 44,270 patients had SARS-CoV-2 infection. Current smoking was associated with lower adjusted rates of SARS-CoV-2 infection (aHR = 0.64 95% CI: 0.61-0.67), COVID-19-related hospitalization (aHR = 0.48 95% CI: 0.40-0.58), ICU admission (aHR = 0.62 95% CI: 0.42-0.87), and death (aHR = 0.52 95% CI: 0.27-0.89) than never-smoking. Former smoking was associated with a lower adjusted rate of SARS-CoV-2 infection (aHR = 0.96 95% CI: 0.94-0.99) and higher adjusted rates of hospitalization (aHR = 1.10 95% CI: 1.03-1.08) and death (aHR = 1.32 95% CI: 1.11-1.56) than never-smoking. Logistic regression analyses among patients with COVID-19 found lower odds of hospitalization for current versus never-smoking and higher odds of hospitalization and death for former versus never-smoking. CONCLUSIONS: In the largest US study to date on smoking and COVID-19, current and former smoking showed lower risk of SARS-CoV-2 infection than never-smoking, while a history of smoking was associated with higher risk of severe COVID-19. IMPLICATIONS: In this cohort study of 2.4 million adults, adjusting for socio-demographics and medical comorbidities, current tobacco smoking was associated with a lower risk of both SARS-CoV-2 infection and severe COVID-19 illness compared to never-smoking. A history of smoking was associated with a slightly lower risk of SARS-CoV-2 infection and a modestly higher risk of severe COVID-19 illness compared to never-smoking. The lower observed COVID-19 risk for current versus never-smoking deserves further investigation. Results support prioritizing individuals with smoking-related comorbidities for vaccine outreach and treatments as they become available.

Advancing clinical trials in nursing homes: A proposed roadmap to success.

An effective clinical research effort in nursing homes to address prevention and treatment of COVID-19 faced overwhelming challenges. Under the Health Care Systems Research Network-Older Americans Independence Centers AGING Initiative, a multidisciplinary Stakeholder Advisory Panel was convened to develop recommendations to improve the capability of the clinical research enterprise in US nursing homes. The Panel considered the nursing home as a setting for clinical trials, reviewed the current state of clinical trials in nursing homes, and ultimately developed recommendations for the establishment of a nursing home clinical trials research network that would be centrally supported and administered. This report summarizes the Panel's recommendations, which were developed in alignment with the following core principles: build on available research infrastructure where appropriate; leverage existing productive partnerships of researchers with groups of nursing homes and nursing home corporations; encompass both efficacy and effectiveness clinical trials; be responsive to a broad range of stakeholders including nursing home residents and their care partners; be relevant to an expansive range of clinical and health care delivery research questions; be able to pivot as necessary to changing research priorities and circumstances; create a pathway for industry-sponsored research as appropriate; invest in strategies to increase diversity in study populations and the research workforce; and foster the development of the next generation of nursing home researchers.

Advancing Clinical Trials in Nursing Homes: A Proposed Roadmap to Success.

An effective clinical research effort in nursing homes to address prevention and treatment of COVID-19 faced overwhelming challenges. Under the Health Care Systems Research Network-Older Americans Independence Centers AGING Initiative, a multidisciplinary Stakeholder Advisory Panel was convened to develop recommendations to improve the capability of the clinical research enterprise in US nursing homes. The Panel considered the nursing home as a setting for clinical trials, reviewed the current state of clinical trials in nursing homes, and ultimately developed recommendations for the establishment of a nursing home clinical trials research network that would be centrally supported and administered. This report summarizes the Panel's recommendations, which were developed in alignment with the following core principles: build on available research infrastructure where appropriate; leverage existing productive partnerships of researchers with groups of nursing homes and nursing home corporations; encompass both efficacy and effectiveness clinical trials; be responsive to a broad range of stakeholders including nursing home residents and their care partners; be relevant to an expansive range of clinical and health care delivery research questions; be able to pivot as necessary to changing research priorities and circumstances; create a pathway for industry-sponsored research as appropriate; invest in strategies to increase diversity in study populations and the research workforce; and foster the development of the next generation of nursing home researchers.

Stakeholders' views on priorities essential for establishing a supportive environment for clinical trials in nursing homes.

BACKGROUND: The U.S. clinical research enterprise in nursing homes was unprepared to mount clinical trials in nursing homes to address urgent questions relevant to prevention and treatment during the COVID-19 pandemic. We identify priorities essential for establishing a supportive environment for future clinical trials in nursing homes. METHODS: Two cross-sectional online questionnaires were administered between January and February 2021. One was administered to nursing home providers, researchers, and policymakers; respondents rated the importance of attributes of researchers, facilities, leaders and staff for conducting clinical trials in nursing homes. Because importance may depend on trial type, respondents rated each attribute for efficacy trials (testing an intervention in ideal circumstances) and effectiveness trials (testing an intervention in "real world" circumstances). We calculated the attribute rating means and standard deviations, and used content analysis to characterize open-ended responses. The second questionnaire for resident family members and advocates included open-ended questions about nursing home research, and factors influencing willingness to participate. RESULTS: The attributes rated as most essential for conducting efficacy and effectiveness trials in nursing homes are research team attributes, that is, that researchers recognize regulatory constraints; understand and adapt to nursing home workflow; and work collaboratively with nursing home leaders to identify priorities. Resident and facility diversity emerged as essential for effectiveness trials; important dimensions included resident race, ethnicity and income, as well as nursing home urban/rural location, quality ratings, geography, staffing ratios, size, and profit status. Caregivers and resident advocates stressed the importance of communication among participants, researchers, and nursing home leadership and staff at all stages of a trial. CONCLUSION: Developing a robust U.S. clinical research enterprise capable of efficiently mounting future clinical trials in nursing homes will require a reimagining of the relationships that exist between researchers, facilities, nursing home leaders, and residents, with a research infrastructure specifically focused on supporting and fostering these connections.

Advancing Clinical Trials in Nursing Homes: A Proposed Roadmap to Success.

An effective clinical research effort in nursing homes to address prevention and treatment of COVID-19 faced overwhelming challenges. Under the Health Care Systems Research Network-Older Americans Independence Centers AGING Initiative, a multidisciplinary Stakeholder Advisory Panel was convened to develop recommendations to improve the capability of the clinical research enterprise in US nursing homes. The Panel considered the nursing home as a setting for clinical trials, reviewed the current state of clinical trials in nursing homes, and ultimately developed recommendations for the establishment of a nursing home clinical trials research network that would be centrally supported and administered. This report summarizes the Panel's recommendations, which were developed in alignment with the following core principles: build on available research infrastructure where appropriate; leverage existing productive partnerships of researchers with groups of nursing homes and nursing home corporations; encompass both efficacy and effectiveness clinical trials; be responsive to a broad range of stakeholders including nursing home residents and their care partners; be relevant to an expansive range of clinical and health care delivery research questions; be able to pivot as necessary to changing research priorities and circumstances; create a pathway for industry-sponsored research as appropriate; invest in strategies to increase diversity in study populations and the research workforce; and foster the development of the next generation of nursing home researchers.

Factors associated with persistent high health care utilization in managed Medicaid.

OBJECTIVES: Prior studies have had difficulty predicting which patients will have persistent high utilization past 1 year within the Medicaid population. The objective of this study was to examine the medical diagnoses at the time of enrollment of patients with persistent high health care utilization over 24 months following enrollment in Medicaid managed care in a large integrated care setting. STUDY DESIGN: Retrospective cohort study in a large integrated managed health care system. METHODS: We identified a cohort of high utilizers (top 5% of health care costs in 2014) and extracted their electronic health record data (2014-2016). Differences in baseline characteristics of high utilizers and the general Medicaid population were determined using bivariate analysis. We used multivariable regression to determine the independent association between medical comorbidities and demographics with persistent high health care utilization over the 2 years following enrollment. RESULTS: Compared with the general Medicaid managed care enrollee population, schizophrenia was the only mental health diagnosis at the time of enrollment associated with persistent high health care utilization (risk ratio [RR], 1.50; 95% CI, 1.20-1.86). Additional characteristics associated with persistent high utilization included age between 31 and 50 years (RR, 1.20; 95% CI, 1.02-1.41), dual enrollment in Medicaid and Medicare (RR, 1.26; 95% CI, 1.09-1.45), chronic pain diagnoses (RR, 1.26; 95% CI, 1.04-1.53), and multimorbidity (RR, 1.43; 95% CI, 1.25-1.63). CONCLUSIONS: Among adults newly enrolled in Medicaid managed care, certain diagnoses noted at the time of enrollment into the plan are associated with persistent high health care utilization over the first 2 years, suggesting that targeting early supportive case management to these individuals could optimize care and reduce health care costs.

Racial Disparities in COVID-19 Testing and Outcomes : Retrospective Cohort Study in an Integrated Health System.

BACKGROUND: Racial disparities exist in outcomes after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. OBJECTIVE: To evaluate the contribution of race/ethnicity in SARS-CoV-2 testing, infection, and outcomes. DESIGN: Retrospective cohort study (1 February 2020 to 31 May 2020). SETTING: Integrated health care delivery system in Northern California. PARTICIPANTS: Adult health plan members. MEASUREMENTS: Age, sex, neighborhood deprivation index, comorbid conditions, acute physiology indices, and race/ethnicity; SARS-CoV-2 testing and incidence of positive test results; and hospitalization, illness severity, and mortality. RESULTS: Among 3 481 716 eligible members, 42.0% were White, 6.4% African American, 19.9% Hispanic, and 18.6% Asian; 13.0% were of other or unknown race. Of eligible members, 91 212 (2.6%) were tested for SARS-CoV-2 infection and 3686 had positive results (overall incidence, 105.9 per 100 000 persons; by racial group, White, 55.1; African American, 123.1; Hispanic, 219.6; Asian, 111.7; other/unknown, 79.3). African American persons had the highest unadjusted testing and mortality rates, White persons had the lowest testing rates, and those with other or unknown race had the lowest mortality rates. Compared with White persons, adjusted testing rates among non-White persons were marginally higher, but infection rates were significantly higher; adjusted odds ratios [aORs] for African American persons, Hispanic persons, Asian persons, and persons of other/unknown race were 2.01 (95% CI, 1.75 to 2.31), 3.93 (CI, 3.59 to 4.30), 2.19 (CI, 1.98 to 2.42), and 1.57 (CI, 1.38 to 1.78), respectively. Geographic analyses showed that infections clustered in areas with higher proportions of non-White persons. Compared with White persons, adjusted hospitalization rates for African American persons, Hispanic persons, Asian persons, and persons of other/unknown race were 1.47 (CI, 1.03 to 2.09), 1.42 (CI, 1.11 to 1.82), 1.47 (CI, 1.13 to 1.92), and 1.03 (CI, 0.72 to 1.46), respectively. Adjusted analyses showed no racial differences in inpatient mortality or total mortality during the study period. For testing, comorbid conditions made the greatest relative contribution to model explanatory power (77.9%); race only accounted for 8.1%. Likelihood of infection was largely due to race (80.3%). For other outcomes, age was most important; race only contributed 4.5% for hospitalization, 12.8% for admission illness severity, 2.3% for in-hospital death, and 0.4% for any death. LIMITATION: The study involved an insured population in a highly integrated health system. CONCLUSION: Race was the most important predictor of SARS-CoV-2 infection. After infection, race was associated with increased hospitalization risk but not mortality. PRIMARY FUNDING SOURCE: The Permanente Medical Group, Inc.

Clinical Decision Support to Address Racial Disparities in Hypertension Control in an Integrated Delivery System: Evaluation of a Natural Experiment.

INTRODUCTION: Effective, equity-promoting interventions implemented by health care systems are needed to address health care disparities and population-level health disparities. We evaluated the impact of a clinical decision support tool to improve evidence-based thiazide diuretic prescribing among Black patients to address racial disparities in hypertension control. METHODS: We employed an interrupted time series design and qualitative interviews to evaluate the implementation of the tool. Our primary outcome measure was the monthly rate of thiazide use among eligible patients before and after implementation of the tool (January 2013-December 2016). We modeled month-to-month changes in thiazide use for Black and White patients, overall, and by sex and medical center racial composition. We conducted key informant interviews to identify modifiable facilitators and barriers to implementation of the tool across medical centers. RESULTS: Of the 318,720 patients, 15.5% were Black. We observed no change in thiazide use or blood pressure control following the implementation of the tool in either racial subgroup. There was a slight but statistically significant reduction (2.32 percentage points, p < 0.01) in thiazide use among Black patients following the removal the tool that was not observed among White patients. Factors affecting the tool's implementation included physician and pharmacist resistance to thiazide use and a lack of ongoing promotion of the tool. DISCUSSION: The clinical decision support tool was insufficient to change prescribing practices and improve blood pressure control among Black patients. CONCLUSIONS: Future interventions should consider physician attitudes about thiazide prescribing and the importance of multilevel approaches to address hypertension disparities.

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies.

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR-based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR-based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment-monitoring interventions, due to a large decrease in data support and concerns over finite-sample bias from near-violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.