Principles for Stakeholder Engagement in Observational Health Research.

This Viewpoint advocates for the inclusion of patients and other stakeholders in interpreting data for observational research studies.

Hospital Readmissions Among Patients Experiencing Homelessness: An Electronic Health Record Data Study.

BACKGROUND: Population-level tracking of hospital use patterns with integrated care organizations in patients experiencing homelessness has been difficult. A California law implemented in 2019 (Senate Bill 1152) aimed to ensure safety for this population after discharge from the hospital by requiring additional documentation for patients experiencing homelessness, which provides an opportunity to evaluate hospital use by this population. METHODS: In a large integrated health system in California, patients experiencing homelessness were identified through documentation change requirements associated with this law and compared with a matched group from the general population. RESULTS: Patients experiencing homelessness had increased rates of hospital readmission after discharge compared to the general population matched on demographics and medical comorbidity in 2019 and 2020. Any address change in the prior year for patients was associated with increased odds of emergency department readmission. Patients experiencing homelessness, both enrolled in an integrated delivery system and not, were successfully identified as having higher readmission rates compared with their housed counterparts. CONCLUSION: Documentation of housing status following Senate Bill 1152 has enabled improved study of hospital use among those with housing instability. Understanding patterns of hospital use in this vulnerable group will help practitioners identify timely points of intervention for further social and health care support.

Identifying Predictors of Homelessness Among Adults in a Large Integrated Health System in Northern California.

Introduction Homelessness contributes to worsening health and increased health care costs. There is little published research that leverages rich electronic health record (EHR) data to predict future homelessness risk and inform interventions to address it. The authors' objective was to develop a model for predicting future homelessness using individual EHR and geographic data covariates. Methods This retrospective cohort study included 2,543,504 adult members (≥ 18 years old) from Kaiser Permanente Northern California and evaluated which covariates predicted a composite outcome of homelessness status (hospital discharge documentation of a homeless patient, medical diagnosis of homelessness, approved medical financial assistance application for homelessness, and/or "homeless/shelter" in address name). The predictors were measured in 2018-2019 and included prior diagnoses and demographic and geographic data. The outcome was measured in 2020. The cohort was split (70:30) into a derivation and validation set, and logistic regression was used to model the outcome. Results Homelessness prevalence was 0.35% in the overall sample. The final logistic regression model included 26 prior diagnoses, demographic, and geographic-level predictors. The regression model using the validation set had moderate sensitivity (80.4%) and specificity (83.2%) for predicting future cases of homelessness and achieved excellent classification properties (area under the curve of 0.891 [95% confidence interval = 0.884-0.897]). Discussion This prediction model can be used as an initial triage step to enhance screening and referral tools for identifying and addressing homelessness, which can improve health and reduce health care costs. Conclusions EHR data can be used to predict chance of homelessness at a population health level.

Psychosocial and diabetes risk factors among racially/ethnically diverse adults with prediabetes.

Psychosocial factors such as self-efficacy may be important in helping high-risk adults prevent diabetes. We aimed to describe psychosocial and diabetes risk factors in adults with prediabetes and evaluate if these varied by demographic characteristics. Cross-sectional data came from baseline surveys and electronic health records (2018-2021) of adults with prediabetes enrolled in a randomized study of peer support for diabetes prevention at Kaiser Permanente Northern California and Michigan Medicine. Linear regression was used to compare differences between racial/ethnic groups, adjusting for age, sex, and clinic. Of 336 participants in the study, 62% were female; median age was 57; 41% were White, 35% African American, 9% Hispanic. Mean autonomous motivation was 6.6 and self-efficacy to prevent diabetes was 6.0 (1-7 scale); mean perceived social support was 47 (12-72 scale). Hispanic adults reported higher autonomous motivation and African American adults reported higher self-efficacy compared to White adults. Hispanic and African American adults had more diabetes risk factors than White adults, including greater family history of diabetes, hypertension, sugar-sweetened beverage consumption, physical inactivity and food insecurity. In conclusion, participants reported high levels of autonomous motivation and self-efficacy at baseline, with Hispanic and African American adults reporting higher levels of some psychosocial factors related to behavior change, suggesting a significant opportunity to engage a diverse population of adults with prediabetes in diabetes prevention strategies. However, Hispanic and African American participants showed greater diabetes risk factors levels. Diabetes prevention efforts should address both to reduce diabetes incidence.

Did the 2015 USPSTF Abnormal Blood Glucose Recommendations Change Clinician Attitudes or Behaviors? A Mixed-Method Assessment.

BACKGROUND: In 2015, the US Preventive Services Task Force (USPSTF) revised clinical recommendations to more broadly recommend abnormal blood glucose screening and more clearly recommend referral to behavioral interventions for adults with prediabetes. OBJECTIVE: To assess the effects of the 2015 USPSTF recommendation changes on abnormal blood glucose screening and referral to behavioral interventions, and to examine physicians' perceptions of the revised recommendation. DESIGN: We utilized a sequential, dependent mixed-methods triangulation design. PARTICIPANTS: A total of 33,444 patients meeting USPSTF abnormal blood glucose screening criteria within 15 health system-affiliated primary care practices and 20 primary care physicians in North Carolina. MAIN MEASURES: We assessed monthly abnormal blood glucose screening rate and monthly referral rate to behavioral interventions. To estimate trend changes in outcomes, we used segmented linear regression analysis of interrupted time-series data. We gathered physicians' perspectives on the 2015 USPSTF abnormal blood glucose recommendation including awareness of, agreement with, adoption of, and adherence to the recommendation. To analyze qualitative data, we used directed content analysis. KEY RESULTS: There was a slight significant change in trend in abnormal blood glucose screening rates post-recommendation. There was a slight, statistically significant decrease in referral rates to behavioral interventions post-recommendation. Physicians were generally unaware of the revisions to the 2015 USPSTF abnormal blood glucose recommendation; however, once the recommendations were described, physicians agreed with the screening recommendation but felt that the behavioral intervention referral recommendation was hard to implement. CONCLUSION: The 2015 USPSTF abnormal blood glucose guideline had little to no effect on abnormal blood glucose screening or referral to behavioral interventions in North Carolina practices. Potential interventions to improve these rates could include clinical decision tools embedded in the electronic health record and better referral systems for community-based diabetes prevention programs.

Race and gender differences in abnormal blood glucose screening and clinician response to prediabetes: A mixed-methods assessment.

The projected three-fold increase in diabetes burden by 2060 in the United States will affect certain race and gender groups disproportionately. The objective of this mixed-methods study was to assess differences in prediabetes screening and clinician response to prediabetes by patient race and gender. We utilized data from 18,742 patients seen between 11/1/15 and 4/30/17 who met criteria for blood glucose screening by the 2015 US Preventive Service Task Force recommendation and had at least one visit to a primary care practice within a large, academic health system located in North Carolina. We utilized generalized estimating equations with logistic regression to assess race and gender differences in two outcomes: prediabetes screening and clinician response to prediabetes. We conducted twenty in-depth interviews (October 2018-May 2019) with physicians to assess their approach to screening for and treating prediabetes. Black patients had 11% higher odds (95% CI:1.02-1.20) of being screened for prediabetes than White patients. Men had 19% higher odds (95% CI:1.09-1.30) of being screened for prediabetes than women. There were no significant differences in clinician response to prediabetes by patient race or gender. Qualitatively, physicians reported a non-systematic approach to prediabetes screening and follow-up care related to: 1) System-level barriers to screening and treatment; 2) Implicit bias; 3) Patient factors; and 4) Physician preferences for prediabetes treatment. Targeted risk-based screening for prediabetes along with increased treatment for prediabetes are critical for preventing diabetes and reducing diabetes-related disparities.

Implicit Racial/Ethnic Bias Among Health Care Professionals and Its Influence on Health Care Outcomes: A Systematic Review.

BACKGROUND: In the United States, people of color face disparities in access to health care, the quality of care received, and health outcomes. The attitudes and behaviors of health care providers have been identified as one of many factors that contribute to health disparities. Implicit attitudes are thoughts and feelings that often exist outside of conscious awareness, and thus are difficult to consciously acknowledge and control. These attitudes are often automatically activated and can influence human behavior without conscious volition. OBJECTIVES: We investigated the extent to which implicit racial/ethnic bias exists among health care professionals and examined the relationships between health care professionals' implicit attitudes about racial/ethnic groups and health care outcomes. SEARCH METHODS: To identify relevant studies, we searched 10 computerized bibliographic databases and used a reference harvesting technique. SELECTION CRITERIA: We assessed eligibility using double independent screening based on a priori inclusion criteria. We included studies if they sampled existing health care providers or those in training to become health care providers, measured and reported results on implicit racial/ethnic bias, and were written in English. DATA COLLECTION AND ANALYSIS: We included a total of 15 studies for review and then subjected them to double independent data extraction. Information extracted included the citation, purpose of the study, use of theory, study design, study site and location, sampling strategy, response rate, sample size and characteristics, measurement of relevant variables, analyses performed, and results and findings. We summarized study design characteristics, and categorized and then synthesized substantive findings. MAIN RESULTS: Almost all studies used cross-sectional designs, convenience sampling, US participants, and the Implicit Association Test to assess implicit bias. Low to moderate levels of implicit racial/ethnic bias were found among health care professionals in all but 1 study. These implicit bias scores are similar to those in the general population. Levels of implicit bias against Black, Hispanic/Latino/Latina, and dark-skinned people were relatively similar across these groups. Although some associations between implicit bias and health care outcomes were nonsignificant, results also showed that implicit bias was significantly related to patient-provider interactions, treatment decisions, treatment adherence, and patient health outcomes. Implicit attitudes were more often significantly related to patient-provider interactions and health outcomes than treatment processes. CONCLUSIONS: Most health care providers appear to have implicit bias in terms of positive attitudes toward Whites and negative attitudes toward people of color. Future studies need to employ more rigorous methods to examine the relationships between implicit bias and health care outcomes. Interventions targeting implicit attitudes among health care professionals are needed because implicit bias may contribute to health disparities for people of color.