A Pragmatic Approach to Identifying and Profiling Primary Care Clinicians and Primary Care Practices in the USA.

BACKGROUND: There are no consistent data on US primary care clinicians and primary care practices owing to the lack of standard methods to identify them, hampering efforts in primary care improvement. METHODS: We develop a pragmatic framework that identifies primary care clinicians and practices in the context of the US healthcare system, and applied the framework to the IQVIA OneKey Healthcare Professional database to identify and profile primary care clinicians and practices in the USA. RESULTS: Our framework prescribes sequential steps to identify primary care clinicians by cross-examining clinician specialties and organizational affiliations, and then identify primary care practices based on organization types and presence of primary care clinicians. Applying this framework to the 2021 IQVIA data, we identified 365,751 physicians with a primary specialty in primary care, and after excluding those who further specialized (24%), served as hospitalists (5%), or worked in non-primary care settings (41%), we determined that 179,369 (49%) of them were actually practicing primary care. We identified 287,506 nurse practitioners and 134,083 physician assistants and determined that 88,574 (31%) and 29,781 (22%), respectively, were delivering primary care. We identified 94,489 primary care practices, and found that 45% of them were with one primary care physician, 15% had two physicians, 12% employed nurse practitioners or physician assistants only, and 19% employed both primary care physicians and specialists. CONCLUSIONS: Our approach offers a pragmatic and consistent alternative to the diverse methods currently used to identify and profile primary care workforce and organizations in the USA.

Commentary: Achieving Health Equity - The Role of Learning Health Systems.

Achieving health equity, for decades a domain of high-performing health systems, has been elevated to a priority and recognized as a central objective of health system transformation and quality improvement efforts. By prioritizing health equity; developing, implementing and evaluating models of care that optimize individual and population health; developing strong partnerships with patients and communities; conducting research to generate evidence on the effectiveness of interventions across diverse populations; implementing strategies to integrate clinical care, public health and social care; and participating in multisector collaborations to address social needs, learning health systems can play a pivotal role in eliminating health inequities.

Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care.

Importance: Health care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income. Objective: To provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity. Evidence Review: The Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback. Findings: The panel developed a conceptual framework to apply guiding principles across an algorithm's life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms. Conclusions and Relevance: Multiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.

Promoting Equity In Clinical Decision Making: Dismantling Race-Based Medicine.

As the use of artificial intelligence has spread rapidly throughout the US health care system, concerns have been raised about racial and ethnic biases built into the algorithms that often guide clinical decision making. Race-based medicine, which relies on algorithms that use race as a proxy for biological differences, has led to treatment patterns that are inappropriate, unjust, and harmful to minoritized racial and ethnic groups. These patterns have contributed to persistent disparities in health and health care. To reduce these disparities, we recommend a race-aware approach to clinical decision support that considers social and environmental factors such as structural racism and social determinants of health. Recent policy changes in medical specialty societies and innovations in algorithm development represent progress on the path to dismantling race-based medicine. Success will require continued commitment and sustained efforts among stakeholders in the health care, research, and technology sectors. Increasing the diversity of clinical trial populations, broadening the focus of precision medicine, improving education about the complex factors shaping health outcomes, and developing new guidelines and policies to enable culturally responsive care are important next steps.

Awareness of Racial and Ethnic Bias and Potential Solutions to Address Bias With Use of Health Care Algorithms.

Importance: Algorithms are commonly incorporated into health care decision tools used by health systems and payers and thus affect quality of care, access, and health outcomes. Some algorithms include a patient's race or ethnicity among their inputs and can lead clinicians and decision-makers to make choices that vary by race and potentially affect inequities. Objective: To inform an evidence review on the use of race- and ethnicity-based algorithms in health care by gathering public and stakeholder perspectives about the repercussions of and efforts to address algorithm-related bias. Design, Setting, and Participants: Qualitative methods were used to analyze responses. Responses were initially open coded and then consolidated to create a codebook, with themes and subthemes identified and finalized by consensus. This qualitative study was conducted from May 4, 2021, through December 7, 2022. Forty-two organization representatives (eg, clinical professional societies, universities, government agencies, payers, and health technology organizations) and individuals responded to the request for information. Main Outcomes and Measures: Identification of algorithms with the potential for race- and ethnicity-based biases and qualitative themes. Results: Forty-two respondents identified 18 algorithms currently in use with the potential for bias, including, for example, the Simple Calculated Osteoporosis Risk Estimation risk prediction tool and the risk calculator for vaginal birth after cesarean section. The 7 qualitative themes, with 31 subthemes, included the following: (1) algorithms are in widespread use and have significant repercussions, (2) bias can result from algorithms whether or not they explicitly include race, (3) clinicians and patients are often unaware of the use of algorithms and potential for bias, (4) race is a social construct used as a proxy for clinical variables, (5) there is a lack of standardization in how race and social determinants of health are collected and defined, (6) bias can be introduced at all stages of algorithm development, and (7) algorithms should be discussed as part of shared decision-making between the patient and clinician. Conclusions and Relevance: This qualitative study found that participants perceived widespread and increasing use of algorithms in health care and lack of oversight, potentially exacerbating racial and ethnic inequities. Increasing awareness for clinicians and patients and standardized, transparent approaches for algorithm development and implementation may be needed to address racial and ethnic biases related to algorithms.

Primary Care's Challenges and Responses in the Face of the COVID-19 Pandemic: Insights From AHRQ's Learning Community.

The COVID-19 pandemic dramatically disrupted health care systems and delivery in the United States. Despite emotional, psychological, logistical, and financial stress, primary care clinicians responded to the challenges that COVID-19 presented and continued to provide essential health services to their communities. As the lead federal agency for primary care research, the Agency for Healthcare Research and Quality (AHRQ) identified a need to engage and support primary care in responding to COVID-19. AHRQ initiated a learning community from December 2020-November 2021 to connect professionals and organizations that support primary care practices and clinicians. The learning community provided a forum for participants to share learning and peer support, better understand the stressors and challenges confronting practices, ascertain needs, and identify promising solutions in response to the pandemic. We identified challenges, responses, and innovations that emerged through learning community engagement, information sharing, and dialog. We categorized these across 5 domains that reflect core areas integral to primary care delivery: patient-centeredness, clinician and practice, systems and infrastructure, and community and public health; health equity was crosscutting across all domains. The engagement of the community to identify real-time response and innovation in the context of a global pandemic has provided valuable insights to inform future research and policy, improve primary care delivery, and ensure that the community is better prepared to respond and contribute to ongoing and future health challenges.

From precision medicine to precision care: Choosing and using precision medicine in the context of multimorbidity.

Rapid advances in precision medicine promise dramatic reductions in morbidity and mortality for a growing array of conditions. To realize the benefits of precision medicine and minimize harm, it is necessary to address real-world challenges encountered in translating this research into practice. Foremost among these is how to choose and use precision medicine modalities in real-world practice by addressing issues related to caring for the sizable proportion of people living with multimorbidity. Precision medicine needs to be delivered in the broader context of precision care to account for factors that influence outcomes for specific therapeutics. Precision care integrates a person-centered approach with precision medicine to inform decision making and care planning by taking multimorbidity, functional status, values, goals, preferences, social and societal context into account. Designing dissemination and implementation of precision medicine around precision care would improve person-centered quality and outcomes of care, target interventions to those most likely to benefit thereby improving access to new therapeutics, minimize the risk of withdrawal from the market from unanticipated harms of therapy, and advance health equity by tailoring interventions and care to meet the needs of diverse individuals and populations. Precision medicine delivered in the context of precision care would foster respectful care aligned with preferences, values, and goals, engendering trust, and providing needed information to make informed decisions. Accelerating adoption requires attention to the full continuum of translational research: developing new approaches, demonstrating their usefulness, disseminating and implementing findings, while engaging patients throughout the process. This encompasses basic science, preclinical and clinical research and implementation into practice, ultimately improving health. This article examines challenges to the adoption of precision medicine in the context of multimorbidity. Although the potential of precision medicine is enormous, proactive efforts are needed to avoid unintended consequences and foster its equitable and effective adoption.

Association of Early Physician Follow-up With Readmission Among Patients Hospitalized for Acute Myocardial Infarction, Congestive Heart Failure, or Chronic Obstructive Pulmonary Disease.

Importance: A better understanding of the association between early physician follow-up after discharge and adverse outcomes among hospitalized patients may inform interventions aimed at reducing readmission for common chronic conditions. Objective: To assess whether hospitalized patients with early physician follow-up after discharge had lower rates of overall and condition-specific readmissions within 30 days and 90 days of discharge. Design, Setting, and Participants: This cohort study was conducted among Ontario, Canada, adults with first admission for acute myocardial infarction (AMI), congestive heart failure (CHF), or chronic obstructive pulmonary disease (COPD) during 2005 to 2019. The exposure was follow-up visit with a primary care physician or relevant specialist within 7 days of discharge. Cox proportional hazards models were used to compare patients with vs without early follow-up, adjusting for sociodemographic factors and comorbidities, weighting by propensity score-based overlap weights. Data were analyzed from January through July 2021. Main Outcomes and Measures: Primary outcomes were 30-day and 90-day readmissions, cardiac readmissions (readmission for AMI, CHF, or angina) for patients with cardiac conditions, and COPD-related readmissions for patients with COPD. Mortality at 30 days and 90 days was a secondary outcome. All percentages reported in Results are unweighted. Results: The study cohort comprised 450 746 patients, including 198 854 patients with AMI, 133 058 patients with CHF, and 118 834 patients with COPD; the median (IQR) age was 66 (56-77) years for AMI, 78 (68-85) years for CHF, and 73 (64-81) years for COPD, and there were 64 339 (32.35%) women, 62 575 (47.03%) women, and 59 179 (49.80%) women, respectively. There were 91 182 patients (45.85%), 56 491 patients (42.46%), and 40 159 patients (33.79%), respectively, who received an early follow-up visit. Overall, patients with early follow-up had higher rates of collaborative care (eg, CHF: 20 931 patients [37.85%] vs 11 101 of 76 567 patients [14.85%]) and visits to a specialist within 30 days (eg, CHF: 25 797 patients [45.67%] vs 20 548 patients [26.84%]). Those with early follow-up had lower 90-day readmission rates among patients with CHF (15 934 patients [28.21%] vs 23 121 patients [30.20%]; adjusted hazard ratio [aHR], 0.98; 95% CI, 0.96-0.99) and among those with COPD (8784 patients [21.87%] vs 18 097 of 78 675 patients [23.00%]; aHR, 0.95; 95% CI, 0.93-0.98). Among patients with COPD, those with early follow-up had lower 90-day COPD-related readmission rates (4015 patients [10.00%] vs 8449 patients [10.74%]; aHR, 0.93; 95% CI, 0.89-0.96), and among patients with CHF, those with early follow-up had lower 90-day mortality rates (4044 patients [7.16%] vs 6281 patients [8.20%]; aHR, 0.93; 95% CI, 0.90-0.97). There were no significant benefits at 30 days or for patients with AMI. Conclusions and Relevance: These findings suggest that early follow-up in conjunction with a comprehensive transitional care strategy for hospitalized patients with medically complex conditions coupled with ongoing effective chronic disease management may be associated with reduced 90-day readmissions.

Assessing Progress Toward the Vision of a Comprehensive, Shared Electronic Care Plan: Scoping Review.

BACKGROUND: Care plans are central to effective care delivery for people with multiple chronic conditions. But existing care plans-which typically are difficult to share across care settings and care team members-poorly serve people with multiple chronic conditions, who often receive care from numerous clinicians in multiple care settings. Comprehensive, shared electronic care (e-care) plans are dynamic electronic tools that facilitate care coordination and address the totality of health and social needs across care contexts. They have emerged as a potential way to improve care for individuals with multiple chronic conditions. OBJECTIVE: To review the landscape of e-care plans and care plan-related initiatives that could allow the creation of a comprehensive, shared e-care plan and inform a joint initiative by the National Institutes of Health and the Agency for Healthcare Research and Quality to develop e-care planning tools for people with multiple chronic conditions. METHODS: We conducted a scoping review, searching literature from 2015 to June 2020 using Scopus, Clinical Key, and PubMed; we also searched the gray literature. To identify initiatives potentially missing from this search, we interviewed expert informants. Relevant data were then identified and extracted in a structured format for data synthesis and analysis using an expanded typology of care plans adapted to our study context. The extracted data included (1) the perspective of the initiatives; (2) their scope, (3) network, and (4) context; (5) their use of open syntax standards; and (6) their use of open semantic standards. RESULTS: We identified 7 projects for e-care plans and 3 projects for health care data standards. Each project provided critical infrastructure that could be leveraged to promote the vision of a comprehensive, shared e-care plan. All the e-care plan projects supported both broad goals and specific behaviors; 1 project supported a network of professionals across clinical, community, and home-based networks; 4 projects included social determinants of health. Most projects specified an open syntax standard, but only 3 specified open semantic standards. CONCLUSIONS: A comprehensive, shared, interoperable e-care plan has the potential to greatly improve the coordination of care for individuals with multiple chronic conditions across multiple care settings. The need for such a plan is heightened in the wake of the ongoing COVID-19 pandemic. While none of the existing care plan projects meet all the criteria for an optimal e-care plan, they all provide critical infrastructure that can be leveraged as we advance toward the vision of a comprehensive, shared e-care plan. However, critical gaps must be addressed in order to achieve this vision.

Realizing the Dream: The Future of Primary Care Research.

Primary care research is central to the successful transformation of care delivery, providing the crucial evidence to overcome the longstanding and widespread threats and challenges to the realization of primary care's full potential. The Agency for Healthcare Research and Quality (AHRQ), as the federal agency specifically charged with conducting and supporting primary care research, plays a pivotal role in supporting the research and generating the evidence needed to advance primary care. Drawing upon decades of AHRQ-supported research studies, extensive stakeholder consultation, and a Primary Care Research Summit held in fall 2020, we discuss the primary care research central to successful primary care transformation and for realizing the vision of a high-performing US health system to effectively serve all Americans and their communities while advancing health equity.Realizing the potential of primary care will require wise investments in primary care research. Newly generated evidence needs to be rapidly incorporated into the design of the delivery system, clinical care, and community interventions. Investments in evidence-informed primary care redesign can catalyze progress to achieving the quintuple aim-improved health outcomes, increased value, better patient and clinician experience, and health equity. Primary care research can provide the evidence to help stem the twin epidemics of clinician burnout and lack of trust in the health system. Actualizing this vision will require a concerted and coordinated effort by policy makers, researchers, clinicians, and community members and a commitment to ensuring people and communities have ready access to primary care.Appeared as Annals "Online First" article.

Health information technology to improve care for people with multiple chronic conditions.

OBJECTIVE: To review evidence regarding the use of Health Information Technology (health IT) interventions aimed at improving care for people living with multiple chronic conditions (PLWMCC) in order to identify critical knowledge gaps. DATA SOURCES: We searched MEDLINE, CINAHL, PsycINFO, EMBASE, Compendex, and IEEE Xplore databases for studies published in English between 2010 and 2020. STUDY DESIGN: We identified studies of health IT interventions for PLWMCC across three domains as follows: self-management support, care coordination, and algorithms to support clinical decision making. DATA COLLECTION/EXTRACTION METHODS: Structured search queries were created and validated. Abstracts were reviewed iteratively to refine inclusion and exclusion criteria. The search was supplemented by manually searching the bibliographic sections of the included studies. The search included a forward citation search of studies nested within a clinical trial to identify the clinical trial protocol and published clinical trial results. Data were extracted independently by two reviewers. PRINCIPAL FINDINGS: The search yielded 1907 articles; 44 were included. Nine randomized controlled trials (RCTs) and 35 other studies including quasi-experimental, usability, feasibility, qualitative studies, or development/validation studies of analytic models were included. Five RCTs had positive results, and the remaining four RCTs showed that the interventions had no effect. The studies address individual patient engagement and assess patient-centered outcomes such as quality of life. Few RCTs assess outcomes such as disability and none assess mortality. CONCLUSIONS: Despite a growing body of literature on health IT interventions or multicomponent interventions including a health IT component for chronic disease management, current evidence for applying health IT solutions to improve care for PLWMCC is limited. The body of literature included in this review provides critical information on the state of the science as well as the many gaps that need to be filled for digital health to fulfill its promise in supporting care delivery that meets the needs of PLWMCC.

Measuring Multimorbidity: Selecting the Right Instrument for the Purpose and the Data Source.

BACKGROUND: Adults have a higher prevalence of multimorbidity-or having multiple chronic health conditions-than having a single condition in isolation. Researchers, health care providers, and health policymakers find it challenging to decide upon the most appropriate assessment tool from the many available multimorbidity measures. OBJECTIVE: The objective of this study was to describe a broad range of instruments and data sources available to assess multimorbidity and offer guidance about selecting appropriate measures. DESIGN: Instruments were reviewed and guidance developed during a special expert workshop sponsored by the National Institutes of Health on September 25-26, 2018. RESULTS: Workshop participants identified 4 common purposes for multimorbidity measurement as well as the advantages and disadvantages of 5 major data sources: medical records/clinical assessments, administrative claims, public health surveys, patient reports, and electronic health records. Participants surveyed 15 instruments and 2 public health data systems and described characteristics of the measures, validity, and other features that inform tool selection. Guidance on instrument selection includes recommendations to match the purpose of multimorbidity measurement to the measurement approach and instrument, review available data sources, and consider contextual and other related constructs to enhance the overall measurement of multimorbidity. CONCLUSIONS: The accuracy of multimorbidity measurement can be enhanced with appropriate measurement selection, combining data sources and special considerations for fully capturing multimorbidity burden in underrepresented racial/ethnic populations, children, individuals with multiple Adverse Childhood Events and older adults experiencing functional limitations, and other geriatric syndromes. The increased availability of comprehensive electronic health record systems offers new opportunities not available through other data sources.

Developing AHRQ's Feasibility Assessment Criteria for Wide-Scale Implementation of Patient-Centered Outcomes Research Findings.

BACKGROUND: The Agency for Healthcare Research and Quality's (AHRQ) Patient-Centered Outcomes Research (PCOR) Dissemination and Implementation (D&I) Initiative identifies and prioritizes PCOR findings that could improve health care if widely implemented. To inform PCOR implementation investments, AHRQ sought to assess feasibility of widely implementing impactful PCOR findings with good strength of evidence in clinical practice. OBJECTIVE: To develop criteria to assess the feasibility of widely implementing nominated PCOR findings. METHODS: We reviewed literature and interviewed thirteen D&I experts to identify factors affecting feasibility of implementing PCOR findings. We grouped similar factors into themes. Fourteen technical expert panel (TEP) members discussed the face-validity and relative merits of the themes and additional factors, applied themes to fictional case studies, and prioritized themes for assessing feasibility. We developed criteria and guiding questions with a 3-point Likert scale. Seven D&I experts pilot-tested the criteria using sample nominations of PCOR findings. Experts represented diverse views of implementation from federal and state government agencies, research institutions, and quality improvement and advocacy organizations. KEY RESULTS: We developed a set of three essential criteria for AHRQ to assess feasibility of widely implementing PCOR findings to be widely implementable: (1) acceptability to the implementers; (2) generalizability, adaptability, and ease of implementing with fidelity; and (3) alignment with external policies and incentives. Two supplemental criteria, (1) the presence of a plan or toolkit supporting implementation, or (2) evidence supporting implementation outside the research setting, can enhance reviewers' confidence in the intervention's feasibility. Each criterion includes "guiding questions" to parse out specific components that could be more readily assessed. CONCLUSIONS: The criteria and guiding questions are a valuable tool for informing AHRQ's investment decisions regarding implementing PCOR findings. Although developed for AHRQ's needs, the criteria may help other funders and health care organizations determine the feasibility of implementing evidence-based practices.

Gender-Based Differences in Outcomes Among Resuscitated Patients With Out-of-Hospital Cardiac Arrest.

BACKGROUND: Studies examining gender-based differences in outcomes of patients experiencing out-of-hospital cardiac arrest have demonstrated that, despite a higher likelihood of return of spontaneous circulation, women do not have higher survival. METHODS: Patients successfully resuscitated from out-of-hospital cardiac arrest enrolled in the CCC trial (Trial of Continuous or Interrupted Chest Compressions during CPR) were included. Hierarchical multivariable logistic regression models were constructed to evaluate the association between gender and survival after adjustment for age, gender, cardiac arrest rhythm, witnessed status, bystander cardiopulmonary resuscitation, episode location, epinephrine dose, emergency medical services response time, and duration of resuscitation. Do not resuscitate (DNR) and withdrawal of life-sustaining therapy (WLST) order status were used to assess whether differences in postresuscitation outcomes were modified by baseline prognosis. The analysis was replicated among ALPS trial (Amiodarone, Lidocaine, or Placebo in Out-of-Hospital Cardiac Arrest) participants. RESULTS: Among 4875 successfully resuscitated patients, 1825 (37.4%) were women and 3050 (62.6%) were men. Women were older (67.5 versus 65.3 years), received less bystander cardiopulmonary resuscitation (49.1% versus 54.9%), and had a lower proportion of cardiac arrests that were witnessed (55.1% versus 64.5%) or had shockable rhythm (24.3% versus 44.6%, P<0.001 for all). A significantly higher proportion of women received DNR orders (35.7% versus 32.1%, P=0.009) and had WLST (32.8% versus 29.8%, P=0.03). Discharge survival was significantly lower in women (22.5% versus 36.3%, P<0.001; adjusted odds ratio, 0.78 [95% CI, 0.66-0.93]; P=0.005). The association between gender and survival to discharge was modified by DNR and WLST order status such that women had significantly reduced survival to discharge among patients who were not designated DNR (31.3% versus 49.9%, P=0.005; adjusted odds ratio, 0.74 [95% CI, 0.60-0.91]) or did not have WLST (32.3% versus 50.7%, P=0.002; adjusted odds ratio, 0.73 [95% CI, 0.60-0.89]). In contrast, no gender difference in survival was noted among patients receiving a DNR order (6.7% versus 7.4%, P=0.90) or had WLST (2.8% versus 2.4%, P=0.93). Consistent patterns of association between gender and postresuscitation outcomes were observed in the secondary cohort. CONCLUSIONS: Among patients resuscitated after experiencing out-of-hospital cardiac arrest, discharge survival was significantly lower in women than in men, especially among patients considered to have a favorable prognosis.

Neighborhood walkability and pre-diabetes incidence in a multiethnic population.

INTRODUCTION: We examined whether adults living in highly walkable areas are less likely to develop pre-diabetes and if so, whether this association is consistent according to immigration status and ethnicity. RESEARCH DESIGN AND METHODS: Population-level health, immigration, and administrative databases were used to identify adults aged 20-64 (n=1 128 181) who had normoglycemia between January 2011 and December 2011 and lived in one of 15 cities in Southern Ontario, Canada. Individuals were assigned to one of ten deciles (D) of neighborhood walkability (from lowest (D1) to highest (D10)) and followed until December 2013 for the development of pre-diabetes. RESULTS: Overall, 220 225 individuals in our sample developed pre-diabetes during a median follow-up of 8.4 years. Pre-diabetes incidence was 20% higher among immigrants living in the least (D1) (adjusted HR 1.20, 95% CI 1.15 to 1.25, p<0.0001) versus most (D10, referent) walkable neighborhoods after accounting for age, sex, and area income. Findings were similar among long-term residents and across sexes. However, susceptibility to walkability varied by ethnicity where D1 versus D10 adjusted HRs ranged from 1.17 (95% CI 1.02 to 1.35, p=0.03) among West Asian and Arab immigrants to 1.32 (95% CI 1.19 to 1.47, p<0.0001) in Southeast Asians. Ethnic variation in pre-diabetes incidence was more marked in low walkability settings. Relative to Western Europeans, the adjusted HR for pre-diabetes incidence was 2.11 (95% CI 1.81 to 2.46, p<0.0001) and 1.50 (95% CI 1.27 to 1.77, p<0.0001) among Sub-Saharan African and the Carribean and Latin American immigrants, respectively, living in the least walkable (D1) neighborhoods, but only 1.24 (95% CI 1.08 to 1.42, p=0.002) and 1.00 (95% CI 0.87 to 1.15, p=0.99) for these same groups living in the most walkable (D10) neighborhoods. CONCLUSIONS: Pre-diabetes incidence was reduced in highly walkable areas for most groups living in Southern Ontario cities. These findings suggest a potential role for walkable urban design in diabetes prevention.

Generalized shared decision making approaches and patient problems. Adapting AHRQ's SHARE Approach for Purposeful SDM.

OBJECTIVE: Generalized shared decision making (SDM) describes the involvement of patients in choosing options. However, there are many situations in which patients and clinicians make decisions together that don't focus on choosing between options, e.g. problem-solving dialysis and insulin use while traveling. Poor uptake associated with clinicians' perception that SDM doesn't apply to clinical situations they face may reflect the lack of adaptation of generalized SDM approaches to patients' problems. The Purposeful SDM schema published in 2019 identifies problems for which different kinds of SDM are appropriate. METHODS: The U.S. Agency for Healthcare Research and Quality developed SHARE as a generalized SDM approach. We sought to adapt SHARE to the different problems that patients face using a matrix to relate SHARE steps and Purposeful SDM modes and describe changes in generalized concepts and practices of SDM across these modes. RESULTS: Many SHARE communicative behaviors applied across modes, although the meaning of SDM terms and practices, e.g. patients involved as problem solvers versus experts, varied substantially. CONCLUSION: Aspects of SHARE require adaptation to different patient problems. PRACTICE IMPLICATIONS: SDM in education, practice, and tools may be supported by adapting generalized SDM approaches to patients' problems.

Universal Drug Coverage and Socioeconomic Disparities in Health Care Costs Among Persons With Diabetes.

OBJECTIVE: To examine whether neighborhood socioeconomic status (SES) is a predictor of non-drug-related health care costs among Canadian adults with diabetes and, if so, whether SES disparities in costs are reduced after age 65 years, when universal drug coverage commences as an insurable benefit. RESEARCH DESIGN AND METHODS: Administrative health databases were used to examine publicly funded health care expenditures among 698,113 younger (20-64 years) and older (≥65 years) adults with diabetes in Ontario from April 2004 to March 2014. Generalized linear models were constructed to examine relative and absolute differences in health care costs (total and non-drug-related costs) across neighborhood SES quintiles, by age, with adjustment for differences in age, sex, diabetes duration, and comorbidity. RESULTS: Unadjusted costs per person-year in the lowest SES quintile (Q1) versus the highest (Q5) were 39% higher among younger adults ($5,954 vs. $4,270 [Canadian dollars]) but only 9% higher among older adults ($10,917 vs. $9,993). Adjusted non-drug costs (primarily for hospitalizations and physician visits) were $1,569 per person-year higher among younger adults in Q1 vs. Q5 (modeled relative cost difference: 35.7% higher) and $139.3 million per year among all individuals in Q1. Scenarios in which these excess costs per person-year were decreased by ≥10% or matched the relative difference among seniors suggested a potential for savings in the range of $26.0-$128.2 million per year among all lower-SES adults under age 65 years (Q1-Q4). CONCLUSIONS: SES is a predictor of diabetes-related health care costs in our setting, more so among adults under age 65 years, a group that lacks universal drug coverage under Ontario's health care system. Non-drug-related health care costs were more than one-third higher in younger, lower-SES adults, translating to >$1 billion more in health care expenditures over 10 years.

Ethnic variation in the conversion of prediabetes to diabetes among immigrant populations relative to Canadian-born residents: a population-based cohort study.

OBJECTIVE: The aim of this study was to compare absolute and relative rates of conversion from prediabetes to diabetes among non-European immigrants to Europeans and Canadian-born residents, overall, and by age and level of glycemia. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort population-based study using administrative health databases from Ontario, Canada, to identify immigrants (n=23 465) and Canadian born (n=1 11 085) aged ≥20 years with prediabetes based on laboratory tests conducted between 2002 and 2011. Individuals were followed until 31 December 2013 for the development of diabetes using a validated algorithm. Immigration data was used to assign ethnicity based on country of origin, mother tongue, and surname. Fine and Gray's survival models were used to compare diabetes incidence across ethnic groups overall and by age and glucose category. RESULTS: Over a median follow-up of 5.2 years, 8186 immigrants and 39 722 Canadian-born residents developed diabetes (7.1 vs 6.1 per 100 person-years, respectively). High-risk immigrant populations such as South Asians (HR: 1.72, 95% CI 1.55 to 1.99) and Southeast Asians (HR: 1.65, 95% CI 1.46 to 1.86) had highest risk of converting to diabetes compared with Western Europeans (referent). Among immigrants aged 20-34 years, the adjusted cumulative incidence ranged from 18.4% among Eastern Europeans to 52.3% among Southeast Asians. Conversion rates increased with age in all groups but were consistently high among South Asians, Southeast Asians and Sub-Saharan African/Caribbeans after the age of 35 years. On average, South Asians converted to diabetes 3.1-4.6 years earlier than Western Europeans and at an equivalent rate of conversion to Western Europeans who had a 0.5 mmol/L higher baseline fasting glucose value. CONCLUSIONS: High-risk ethnic groups converted to diabetes more rapidly, at younger ages, and at lower fasting glucose values than European populations, leading to a shorter window for diabetes prevention.