: Adjusting preventive care service delivery in community health clinics during COVID-19.

Purpose: Qualitatively examine how community health clinics delivered cardiovascular disease (CVD) preventive care during COVID-19 using virtual care and related adaptations.

Methods: Semi-structured interviews were conducted in 2021-2022 with nine clinician leaders from four clinics across the country that share an electronic health record. Interviews covered: (1) the pandemic's impact on preventive care delivery, (2) the adaptation and role of virtual care, and (3) management of CVD risk in this context.

Pandemic-related practice changes and CVD risk management in community clinics.

Objectives: Understanding how the COVID-19 pandemic affected cardiovascular disease (CVD) risk monitoring in primary care may inform new approaches for addressing modifiable CVD risks. This study examined how pandemic-driven changes in primary care delivery affected CVD risk management processes.

Study Design: This retrospective study used electronic health record data from patients at 70 primary care community clinics with scheduled appointments from September 1, 2018, to September 30, 2021.

Cardiovascular disease risk management during COVID-19: in-person vs virtual visits.

Objectives: Limited research has assessed how virtual care (VC) affects cardiovascular disease (CVD) risk management, especially in community clinic settings. This study assessed change in community clinic patients' CVD risk management during the COVID-19 pandemic and CVD risk factor control among patients who had primarily in-person or primarily VC visits.

Study Design: Retrospective interrupted time-series analysis.

Does Clinical Decision Support Increase Appropriate Medication Prescribing for Cardiovascular Risk Reduction?

Purpose: To assess the impact of a clinical decision support (CDS) system's recommendations on prescribing patterns targeting cardiovascular disease (CVD) when the recommendations are prioritized in order from greatest to least benefit toward overall CVD risk reduction.

Methods: Secondary analysis of trial data from September 20, 2018, to March 15, 2020, where 70 community health center clinics were cluster-randomized to the CDS intervention (42 clinics; 8 organizations) or control group (28 clinics; 7 organizations). Included patients were medication-naïve and aged 40 to 75 years with ≥1 uncontrolled cardiovascular disease risk factor, with known diabetes or cardiovascular disease, or ≥10% 10-year reversible CVD risk.

Implementation Support for a Social Risk Screening and Referral Process in Community Health Centers.

Evidence is needed about how to effectively support health care providers in implementing screening for social risks (adverse social determinants of health) and providing related referrals meant to address identified social risks. This need is greatest in underresourced care settings. The authors tested whether an implementation support intervention (6 months of technical assistance and coaching study clinics through a five-step implementation process) improved adoption of social risk activities in community health centers (CHCs).

Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers.

Objective: Electronic health record (EHR)-based shared decision-making (SDM) and clinical decision support (CDS) systems can improve cardiovascular disease (CVD) care quality and risk factor management. Use of the CV Wizard system showed a beneficial effect on high-risk community health center (CHC) patients' CVD risk within an effectiveness trial, but system adoption was low overall. We assessed which multi-level characteristics were associated with system use.

Cross-Sectional Associations: Social Risks and Diabetes Care Quality, Outcomes.

Introduction: Social risks (e.g., food/transportation insecurity) can hamper type 2 diabetes mellitus (T2DM) self-management, leading to poor outcomes.

Effect of Clinical Decision Support at Community Health Centers on the Risk of Cardiovascular Disease: A Cluster Randomized Clinical Trial.

Importance: Management of cardiovascular disease (CVD) risk in socioeconomically vulnerable patients is suboptimal; better risk factor control could improve CVD outcomes.

Objective: To evaluate the impact of a clinical decision support system (CDSS) targeting CVD risk in community health centers (CHCs).

Design, Setting, And Participants: This cluster randomized clinical trial included 70 CHC clinics randomized to an intervention group (42 clinics; 8 organizations) or a control group that received no intervention (28 clinics; 7 organizations) from September 20, 2018, to March 15, 2020.

Augmenting usual care SSRIs with cognitive behavioral therapy for insomnia to improve depression outcomes in youth: Design of a randomized controlled efficacy-effectiveness trial.

Importance: Extant treatments for youth depression are only modestly effective. Alternative approaches are needed to improve health outcomes. A novel approach to improve depression outcomes is suggested by epidemiological studies finding that insomnia often predates and may contribute to depression risk.

Predicting adherence to diabetic eye examinations: development of the compliance withAnnual Diabetic Eye Exams Survey.

Objective: To identify variables that predict adherence with annual eye examinations using the Compliance with Annual Diabetic Eye Exams Survey (CADEES), a new questionnaire designed to measure health beliefs related to diabetic retinopathy and annual eye examinations.

Design: Questionnaire development.

Participants: Three hundred sixteen adults with diabetes.

Psychometrics of a new questionnaire to assess glaucoma adherence: the Glaucoma Treatment Compliance Assessment Tool (an American Ophthalmological Society thesis).

Purpose: To report the psychometrics of the Glaucoma Treatment Compliance Assessment Tool (GTCAT), a new questionnaire designed to assess adherence with glaucoma therapy.

Methods: We developed the questionnaire according to the constructs of the Health Belief Model. We evaluated the questionnaire using data from a cross-sectional study with focus groups (n = 20) and a prospective observational case series (n=58).