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. METHODS: We developed the CADEES based on a review of the literature, the framework of the Health Belief Model, expert opinion, and pilot study data. To examine content validity, we analyzed participant responses to an open-ended question asking for reasons why people do not obtain annual eye examinations. We evaluated construct validity with principal components analysis and examined internal consistency with Cronbach's α. To assess predictive validity, we used multivariate logistic regression with self-reported adherence as the dependent variable. MAIN OUTCOME MEASURES: Associations with self-reported adherence (defined as having a dilated eye examination in the past year). RESULTS: The content analysis showed that CADEES items covered 89% of the reasons given by participants for not obtaining an annual eye examination. The principal components analysis identified 3 informative components that made up 32% of the variance. Multivariate logistic regression modeling revealed several significant predictors of adherence, including beliefs concerning whether insurance covered most of the eye examination cost (P < 0.01), whether there were general barriers that make it difficult to obtain an eye examination (P < 0.01), whether obtaining an eye examination was a top priority (P = 0.02), and whether diabetic eye disease can be seen with an examination (P = 0.05). Lower hemoglobin A1c levels (P < 0.01), having insurance (P = 0.01), and a longer duration of diabetes (P = 0.02) also were associated with adherence. A multivariate model containing CADEES items and demographic variables classified cases with 72% accuracy and explained approximately 24% of the variance in adherence. CONCLUSIONS: The CADEES showed good content and predictive validity. Although additional research is needed before finalizing a shorter version of the survey, our findings suggest that researchers and clinicians may be able to improve adherence by (1) counseling newly diagnosed patients, as well as those with uncontrolled blood glucose, on the importance of annual eye examinations and (2) discussing perceived barriers and misconceptions.

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. METHODS: Data came from an electronic health record shared by 52 community clinics for index (March 1, 2019, to February 29, 2020) and follow-up (July 1, 2020, to February 28, 2022) periods. Analyses compared follow-up period changes in slope and level of population monthly means of 10-year reversible CVD risk score, blood pressure (BP), and hemoglobin A1c (HbA1c) among patients whose completed follow-up period visits were primarily in person vs primarily VC. Propensity score weighting minimized confounding. RESULTS: There were 10,028 in-person and 6593 VC patients in CVD risk analyses, 9874 in-person and 5390 VC patients in BP analyses, and 8221 in-person and 4937 VC patients in HbA1c analyses. The VC group was more commonly younger, female, White, and urban. Mean reversible CVD risk, mean systolic BP, and percentage of BP measurements that were 140/90 mm Hg or higher increased significantly from index to follow-up periods in both groups. Rate of change between these periods was the same for all outcomes in both groups, regardless of care modality. CONCLUSIONS: Among community clinic patients with CVD risk, receiving a majority of care in person vs a majority of care via VC was not significantly associated with longitudinal trends in reversible CVD risk score or key CVD risk factors.

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. METHODS: Analyses examined associations between appointment type and select care process measures: appointment completion rates, time to appointment, and up-to-date documentation for blood pressure (BP) and hemoglobin A1c (HbA1c). RESULTS: Of 1,179,542 eligible scheduled primary care appointments, completion rates were higher for virtual care (VC) vs in-person appointments (10.7 percentage points [PP]; 95% CI, 10.5-11.0; P < .001). Time to appointment was shorter for VC vs in-person appointments (-3.9 days; 95% CI, -4.1 to -3.7; P < .001). BP documentation was higher for appointments completed pre- vs post pandemic onset (16.2 PP; 95% CI, 16.0-16.5; P < .001) and for appointments completed in person vs VC (54.9 PP; 95% CI, 54.6-55.2; P < .001). HbA1c documentation was higher for completed appointments after pandemic onset vs before (5.9 PP; 95% CI, 5.1-6.7; P < .001) and for completed VC appointments vs in-person appointments (3.9 PP; 95% CI, 3.0-4.7; P < .001). CONCLUSIONS: After pandemic onset, appointment completion rates were higher, time to appointment was shorter, HbA1c documentation increased, and BP documentation decreased. Future research should explore the advantages of using VC for CVD risk management while continuing to monitor for unintended consequences.

Zero Suicide Quality Improvement: Developmental and Pandemic-Related Patterns in Youth at Risk for Suicide Attempts.

The Zero Suicide (ZS) approach to health system quality improvement (QI) aspires to reduce/eliminate suicides through enhancing risk detection and suicide-prevention services. This first report from our randomized trial evaluating a stepped care for suicide prevention intervention within a health system conducting ZS-QI describes 1) our screening and case identification process, 2) variation among adolescents versus young adults; and 3) pandemic-related patterns during the first COVID-19 pandemic year. Between April 2017 and January 2021, youths aged 12-24 with elevated suicide risk were identified through an electronic health record (EHR) case-finding algorithm followed by direct assessment screening to confirm risk. Eligible/enrolled youth were evaluated for suicidality, self-harm, and risk/protective factors. Case finding, screening, and enrollment yielded 301 participants showing suicide risk-indicators: 97% past-year suicidal ideation, 83% past suicidal behavior; 90% past non-suicidal self-injury (NSSI). Compared to young adults, adolescents reported: more past-year suicide attempts (47% vs 21%, p<.001) and NSSI (past 6-months, 64% vs 39%, p<.001); less depression, anxiety, posttraumatic stress, and substance use; and greater social connectedness. Pandemic-onset was associated with lower participation of racial-ethnic minority youths (18% vs 33%, p<.015) and lower past-month suicidal ideation and behavior. Results support the value of EHR case-finding algorithms for identifying youths with potentially elevated risk who could benefit from suicide-prevention services, which merit adaptation for adolescents versus young adults. Lower racial-ethnic minority participation after the COVID-19 pandemic-onset underscores challenges for services to enhance health equity during a period with restricted in-person health care, social distancing, school closures, and diverse stresses.

Comparing the effectiveness of telemedicine and traditional surveillance in providing diabetic retinopathy screening examinations: a randomized controlled trial.

OBJECTIVE: To determine the effectiveness of telemedicine for providing diabetic retinopathy screening examinations compared with the effectiveness of traditional surveillance in community health clinics with a high proportion of minorities, including American Indian/Alaska Natives. SUBJECTS AND METHODS: We conducted a multicenter, randomized controlled trial and assigned diabetic participants to one of two groups: (1) telemedicine with a nonmydriatic camera or (2) traditional surveillance with an eye care provider. For those receiving telemedicine, the criteria for requiring follow-up with an eye care provider were (1) moderate nonproliferative diabetic retinopathy or higher, (2) presence of clinically significant macular edema, or (3) "unable to grade" result for diabetic retinopathy or macular edema. RESULTS: The telemedicine group (n=296) was more likely to receive a diabetic retinopathy screening examination within the first year of enrollment compared with the traditional surveillance group (n=271) (94% versus 56%, p<0.001). The overall prevalence of diabetic retinopathy at baseline was 21.4%, and macular edema was present in 1.4% of participants. In the telemedicine group, 20.5% would require further evaluation with an eye care provider, and 86% of these referrals were because of poor-quality digital images. CONCLUSIONS: Telemedicine using nonmydriatic cameras increased the proportion of participants who obtained diabetic retinopathy screening examinations, and most did not require follow-up with an eye care provider. Telemedicine may be a more effective way to screen patients for diabetic retinopathy and to triage further evaluation with an eye care provider. Methods to decrease poor quality imaging would improve the effectiveness of telemedicine for diabetic retinopathy screening examinations.

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. RESULTS: Among eligible encounters with 29,771 patients, the probability of prescribing a medication targeting hypertension was greater at intervention clinic encounters when CDS was used (34.9% [95% CI, 31.5 to 38.3]) versus dismissed (29.6% [95% CI, 26.7 to 32.6]; P < .001), but not when compared with control clinic encounters (34.9% [95% CI, 31.1 to 38.7]; P = .998). Prescribing for dyslipidemia was significantly higher at intervention encounters where the CDS system was used (11.3% [95% CI, 9.3 to 13.3]) compared with dismissed (7.7% [95% CI, 6.1 to 9.3]; P = .003) and to control encounters (8.7% [95% CI, 7.0 to 10.4]; P = .044); smoking cessation medication showed a similar pattern. Except for dyslipidemia, prescribing rates increased according to their prioritization. CONCLUSIONS: Use of this CDS system was associated with significantly higher prescribing targeting most cardiovascular risk factors. These results highlight how displaying prioritized actions to reduce reversible CVD risk could improve risk management. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

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. Materials and Methods: Analyses included 80 195 encounters with 17 931 patients with high CVD risk and/or uncontrolled risk factors at 42 clinics in September 2018-March 2020. Data came from the CV Wizard repository and EHR data, and a survey of 44 clinic providers. Adjusted, mixed-effects multivariate Poisson regression analyses assessed factors associated with system use. We included clinic- and provider-level clustering as random effects to account for nested data. Results: Likelihood of system use was significantly higher in encounters with patients with higher CVD risk and at longer encounters, and lower when providers were >10 minutes behind schedule, among other factors. Survey participants reported generally high satisfaction with the system but were less likely to use it when there were time constraints or when rooming staff did not print the system output for the provider. Discussion: CHC providers prioritize using this system for patients with the greatest CVD risk, when time permits, and when rooming staff make the information readily available. CHCs' financial constraints create substantial challenges to addressing barriers to improved system use, with health equity implications. Conclusion: Research is needed on improving SDM and CDS adoption in CHCs. Trial Registration: ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.

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). Thirty-one CHC clinics were block-randomized to six wedges that occurred sequentially. Over the 45-month study period from March 2018 to December 2021, data were collected for 6 or more months preintervention, the 6-month intervention period, and 6 or more months postintervention. The authors calculated clinic-level monthly rates of social risk screening results that were entered at in-person encounters and rates of social risk-related referrals. Secondary analyses measured impacts on diabetes-related outcomes. Intervention impact was assessed by comparing clinic performance based on whether they had versus had not yet received the intervention in the preintervention period compared with the intervention and postintervention periods. In assessing the results, the authors note that five clinics withdrew from the study for various bandwidth-related reasons. Of the remaining 26, a total of 19 fully or partially completed all 5 implementation steps, and 7 fully or partially completed at least the first 3 steps. Social risk screening was 2.45 times (95% confidence interval [CI], 1.32-4.39) higher during the intervention period compared with the preintervention period; this impact was not sustained postintervention (rate ratio, 2.16; 95% CI, 0.64-7.27). No significant difference was seen in social risk referral rates during the intervention or postintervention periods. The intervention was associated with greater blood pressure control among patients with diabetes and lower rates of diabetes biomarker screening postintervention. All results must be interpreted considering that the Covid-19 pandemic began midway through the trial, which affected care delivery generally and patients at CHCs particularly. Finally, the study results show that adaptive implementation support was effective at temporarily increasing social risk screening. It is possible that the intervention did not adequately address barriers to sustained implementation or that 6 months was not long enough to cement this change. Underresourced clinics may struggle to participate in support activities over longer periods without adequate resources, even if lengthier support is needed. As policies start requiring documentation of social risk activities, safety-net clinics may be unable to meet these requirements without adequate financial and coaching/technical support.

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. To determine the extent to which high-quality care can overcome social risks' health impacts, this study assessed the associations between reported social risks, receipt of guideline-based T2DM care, and T2DM outcomes when care is up to date among community health center patients. METHODS: A cross-sectional study of adults aged ≥18 years (N=73,484) seen at 186 community health centers, with T2DM and ≥1 year of observation between July 2016 and February 2020. Measures of T2DM care included up-to-date HbA1c, microalbuminuria, low-density lipoprotein screening, and foot examination, and active statin prescription when indicated. Measures of T2DM outcomes among patients with up-to-date care included blood pressure, HbA1c, and low-density lipoprotein control on or within 6‒12 months of an index encounter. Analyses were conducted in 2021. RESULTS: Individuals reporting transportation or housing insecurity were less likely to have up-to-date low-density lipoprotein screening; no other associations were seen between social risks and clinical care quality. Among individuals with up-to-date care, food insecurity was associated with lower adjusted rates of controlled HbA1c (79% vs 75%, p<0.001), and transportation insecurity was associated with lower rates of controlled HbA1c (79% vs 74%, p=0.005), blood pressure (74% vs 72%, p=0.025), and low-density lipoprotein (61% vs 57%, p=0.009) than among those with no reported need. CONCLUSIONS: Community health center patients received similar care regardless of the presence of social risks. However, even among those up to date on care, social risks were associated with worse T2DM control. Future research should identify strategies for improving HbA1c control for individuals with social risks. TRIAL REGISTRATION: This study is registered at www. CLINICALTRIALS: gov NCT03607617.

Stepped care for suicide prevention in teens and young adults: Design and methods of a randomized controlled trial.

BACKGROUND: Suicide is the second­leading cause of death among adolescents and young adults in the United States, with rates rising over much of the last decade. The design, testing, and implementation of interventions to prevent suicide in this population is a public health priority. This manuscript outlines the design and methods for a research study that compares two interventions aimed at reducing suicide and suicide attempts in youth. METHODS: We will enroll 300 youth aged 12-24 at high risk for suicide in this randomized controlled parallel group superiority trial. Participants will be randomly assigned to one of two study arms: (1) Zero Suicide Quality Improvement (ZSQI) implemented within the Kaiser Permanente Northwest (KPNW) health system, or (2) ZSQI plus a stepped care intervention for suicide prevention (SC-SP), where the services offered (including care management and dialectical behavior therapy [DBT]) increase based on risk level. Outcomes will be assessed at baseline, as well as 3-, 6-, and 12-months post randomization. The study was conceptualized and designed collaboratively by investigators at UCLA and KPNW. RESULTS: To be reported in future manuscripts. CONCLUSION: The main objective of the study is to determine whether the SC-SP intervention is superior to ZSQI with regard to lowering rates of fatal and nonfatal suicide attempts. Interventions that incorporate the latest research need to be designed and tested under controlled conditions to make progress toward the goal of achieving zero suicide. The results from this trial will directly inform those efforts. CLINICALTRIALS: gov, NCT03092271, https://clinicaltrials.gov/ct2/show/NCT03092271https://clinicaltrials.gov/ct2/show/NCT01379027.

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. Randomization was by CHC organization accounting for organization size. Patients aged 40 to 75 years with (1) diabetes or atherosclerotic CVD and at least 1 uncontrolled major risk factor for CVD or (2) total reversible CVD risk of at least 10% were the population targeted by the CDSS intervention. Interventions: A point-of-care CDSS displaying real-time CVD risk factor control data and personalized, prioritized evidence-based care recommendations. Main Outcomes and Measures: One-year change in total CVD risk and reversible CVD risk (ie, the reduction in 10-year CVD risk that was considered achievable if 6 key risk factors reached evidence-based levels of control). Results: Among the 18 578 eligible patients (9490 [51.1%] women; mean [SD] age, 58.7 [8.8] years), patients seen in control clinics (n = 7419) had higher mean (SD) baseline CVD risk (16.6% [12.8%]) than patients seen in intervention clinics (n = 11 159) (15.6% [12.3%]; P < .001); baseline reversible CVD risk was similarly higher among patients seen in control clinics. The CDSS was used at 19.8% of 91 988 eligible intervention clinic encounters. No population-level reduction in CVD risk was seen in patients in control or intervention clinics; mean reversible risk improved significantly more among patients in control (-0.1% [95% CI, -0.3% to -0.02%]) than intervention clinics (0.4% [95% CI, 0.3% to 0.5%]; P < .001). However, when the CDSS was used, both risk measures decreased more among patients with high baseline risk in intervention than control clinics; notably, mean reversible risk decreased by an absolute 4.4% (95% CI, -5.2% to -3.7%) among patients in intervention clinics compared with 2.7% (95% CI, -3.4% to -1.9%) among patients in control clinics (P = .001). Conclusions and Relevance: The CDSS had low use rates and failed to improve CVD risk in the overall population but appeared to have a benefit on CVD risk when it was consistently used for patients with high baseline risk treated in CHCs. Despite some limitations, these results provide preliminary evidence that this technology has the potential to improve clinical care in socioeconomically vulnerable patients with high CVD risk. Trial Registration: ClinicalTrials.gov Identifier: NCT03001713.

Using Electronic Health Record-Based Clinical Decision Support to Provide Social Risk-Informed Care in Community Health Centers: Protocol for the Design and Assessment of a Clinical Decision Support Tool.

BACKGROUND: Consistent and compelling evidence demonstrates that social and economic adversity has an impact on health outcomes. In response, many health care professional organizations recommend screening patients for experiences of social and economic adversity or social risks-for example, food, housing, and transportation insecurity-in the context of care. Guidance on how health care providers can act on documented social risk data to improve health outcomes is nascent. A strategy recommended by the National Academy of Medicine involves using social risk data to adapt care plans in ways that accommodate patients' social risks. OBJECTIVE: This study's aims are to develop electronic health record (EHR)-based clinical decision support (CDS) tools that suggest social risk-informed care plan adaptations for patients with diabetes or hypertension, assess tool adoption and its impact on selected clinical quality measures in community health centers, and examine perceptions of tool usability and impact on care quality. METHODS: A systematic scoping review and several stakeholder activities will be conducted to inform development of the CDS tools. The tools will be pilot-tested to obtain user input, and their content and form will be revised based on this input. A randomized quasi-experimental design will then be used to assess the impact of the revised tools. Eligible clinics will be randomized to a control group or potential intervention group; clinics will be recruited from the potential intervention group in random order until 6 are enrolled in the study. Intervention clinics will have access to the CDS tools in their EHR, will receive minimal implementation support, and will be followed for 18 months to evaluate tool adoption and the impact of tool use on patient blood pressure and glucose control. RESULTS: This study was funded in January 2020 by the National Institute on Minority Health and Health Disparities of the National Institutes of Health. Formative activities will take place from April 2020 to July 2021, the CDS tools will be developed between May 2021 and November 2022, the pilot study will be conducted from August 2021 to July 2022, and the main trial will occur from December 2022 to May 2024. Study data will be analyzed, and the results will be disseminated in 2024. CONCLUSIONS: Patients' social risk information must be presented to care teams in a way that facilitates social risk-informed care. To our knowledge, this study is the first to develop and test EHR-embedded CDS tools designed to support the provision of social risk-informed care. The study results will add a needed understanding of how to use social risk data to improve health outcomes and reduce disparities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31733.

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. We test whether treating insomnia among youth starting a new course of SSRI antidepressants improves depression outcomes. This paper describes our study design. DESIGN: 2-arm randomized controlled efficacy-effectiveness trial. SETTING: A large non-profit health maintenance organization. PARTICIPANTS: 165 adolescents aged 12-19 with research-confirmed depression and insomnia diagnoses, starting a new episode of selective serotonin reuptake inhibitor (SSRI) antidepressant treatment prescribed by their usual care provider. INTERVENTIONS: Two sleep interventions, each 6-7 sessions, both overlaying "treatment as usual" (TAU) SSRIs: a sleep hygiene (SH) attention control condition, and cognitive-behavioral therapy for insomnia (CBTI). CONCLUSIONS AND RELEVANCE: If CBT-I improved sleep is shown to improve depression-related outcomes, this may provide an additional, easily tolerated intervention for an important public health target. TRIAL REGISTRATION: clinicaltrials.gov, NCT02290496, https://clinicaltrials.gov/ct2/show/NCT02290496.

Challenges involved in establishing a web-based clinical decision support tool in community health centers.

Implementation lessons: Establishing a shared 'hub-and-spoke,' web-based clinical decision support system (CDSS) in an EHR shared by >600 community health centers incurred a myriad of challenges, which are summarized here to guide others seeking to use similar CDSS. Legal and compliance challenges involved ensuring secure data exchanges, determining which entity maintains data records, and deciding which data are sent to the CDSS. Technical challenges involved using lab data from multiple sources and improving the CDSS' cache routine performance in its new setting. Clinical implementation challenges involved identifying optimal strategies for generating data on CDSS use rates, modifying the CDSS functionality for obtaining clinician/staff feedback, and customizing the risk thresholds that trigger the CDSS for the new setting.

Motivational interviewing or reminders for glaucoma medication adherence: Results of a multi-site randomised controlled trial.

OBJECTIVE: Nonadherence reduces glaucoma treatment efficacy. Motivational interviewing (MI) is a well-studied adherence intervention, but has not been tested in glaucoma. Reminder interventions also may improve adherence. DESIGN: 201 patients with glaucoma or ocular hypertension were urn-randomised to receive MI delivered by an ophthalmic technician (OT), usual care or a minimal behavioural intervention (reminder calls). MAIN OUTCOME MEASURES: Outcomes included electronic monitoring with Medication Event Monitoring System (MEMS) bottles, two self-report adherence measures, patient satisfaction and clinical outcomes. Multilevel modelling was used to test differences in MEMS results by group over time; ANCOVA was used to compare groups on other measures. RESULTS: Reminder calls increased adherence compared to usual care based on MEMS, p = .005, and self-report, p = .04. MI had a nonsignificant effect but produced higher satisfaction than reminder calls, p = .007. Treatment fidelity was high on most measures, with observable differences in behaviour between groups. All groups had high baseline adherence that limited opportunities for change. CONCLUSION: Reminder calls, but not MI, led to better adherence than usual care. Although a large literature supports MI, reminder calls might be a cost-effective intervention for patients with high baseline adherence. Replication is needed with less adherent participants.

Long-term Comparative Effectiveness of Telemedicine in Providing Diabetic Retinopathy Screening Examinations: A Randomized Clinical Trial.

IMPORTANCE: Minimal information exists regarding the long-term comparative effectiveness of telemedicine to provide diabetic retinopathy screening examinations. OBJECTIVE: To compare telemedicine to traditional eye examinations in their ability to provide diabetic retinopathy screening examinations. DESIGN, SETTING, AND PARTICIPANTS: From August 1, 2006, through September 31, 2009, 567 participants with diabetes were randomized and followed up to 5 years of follow-up (last date of patient follow-up occurred on August 6, 2012) as part of a multicenter randomized clinical trial with an intent to treat analysis. We assigned participants to telemedicine with a nonmydriatic camera in a primary care medical clinic (n = 296) or traditional surveillance with an eye care professional (n = 271). Two years after enrollment, we offered telemedicine to all participants. MAIN OUTCOMES AND MEASURES: Percentage of participants receiving annual diabetic retinopathy screening examinations, percentage of eyes with worsening diabetic retinopathy during the follow-up period using a validated scale from stage 0 (none) to stage 4 (proliferative diabetic retinopathy), and percentage of telemedicine participants who would require referral to an eye care professional for follow-up care using a cutoff of moderate diabetic retinopathy or worse, the presence of macular edema, or an unable-to-determine result for retinopathy or macular edema. RESULTS: The telemedicine group was more likely to receive a diabetic retinopathy screening examination when compared with the traditional surveillance group during the 6-month or less (94.6% [280/296] vs 43.9% [119/271]; 95% CI, 46.6%-54.8%; P < .001) and greater than 6-month through 18-month (53.0% [157/296] vs 33.2% [90/271]; 95% CI, 16.5%-23.1%; P < .001) time bins. After we offered telemedicine to both groups, we could not identify a difference between the groups in the percentage of diabetic retinopathy screening examinations. Diabetic retinopathy worsened by 2 stages or more in 35 (8.6%) of 409 participants (95% CI, 5.8%-11.2%) and improved by 2 stages or more in 5 (1.2%) of 409 participants (95% CI, 0.1%-2.3%) during the 4-year period. The percent of telemedicine participants requiring referral ranged from 19.2% (52/271) to 27.9% (58/208). CONCLUSIONS AND RELEVANCE: Telemedicine increased the percentage of diabetic retinopathy screening examinations, most participants did not require referral to an eye care professional, and diabetic retinopathy levels were generally stable during the study period. This finding suggests that primary care clinics can use telemedicine to screen for diabetic retinopathy and monitor for disease worsening over a long period. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01364129.

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). Principal components analysis provided assessment of construct validity. We repeated the questionnaire after 3 months for test-retest reliability. We evaluated predictive validity using an electronic dosing monitor as an objective measure of adherence. RESULTS: Focus group participants provided 931 statements related to adherence, of which 88.7% (826/931) could be categorized into the constructs of the Health Belief Model. Perceived barriers accounted for 31% (288/931) of statements, cues-to-action 14% (131/931), susceptibility 12% (116/931), benefits 12% (115/931), severity 10% (91/931), and self-efficacy 9% (85/931). The principal components analysis explained 77% of the variance with five components representing Health Belief Model constructs. Reliability analyses showed acceptable Cronbach's alphas (>.70) for four of the seven components (severity, susceptibility, barriers [eye drop administration], and barriers [discomfort]). Predictive validity was high, with several Health Belief Model questions significantly associated (P <.05) with adherence and a correlation coefficient (R (2)) of .40. Test-retest reliability was 90%. CONCLUSION: The GTCAT shows excellent repeatability, content, construct, and predictive validity for glaucoma adherence. A multisite trial is needed to determine whether the results can be generalized and whether the questionnaire accurately measures the effect of interventions to increase adherence.