Response Category Functioning on the Health Care Engagement Measure Using the Nominal Response Model

As part of a scale development project, we fit a nominal response item response theory model to responses to the Health Care Engagement Measure (HEM). When using the original 5-point response format, categories were not ordered as intended for six of the 23 items. For the remaining, the category boundary discrimination between Categories 0 ( not at all true) and 1 ( a little bit true) was only weakly discriminating, suggesting uninformative categories. When the lowest two categories were collapsed, psychometric properties improved greatly. Category boundary discriminations within items, however, varied significantly. Specifically, higher response category distinctions, such as responding 3 ( very true) versus 2 ( mostly true) were considerably more discriminating than lower response category distinctions. Implications for HEM scoring and for improving measurement precision at lower levels of the construct are presented as is the unique role of the nominal response model in category analysis.

Limited English Proficiency and Disparities in Health Care Engagement Among Patients With Breast Cancer

PURPOSE: Race and ethnicity have been shown to affect quality of cancer care, and patients with low English proficiency (LEP) have increased risk for serious adverse events. We sought to assess the impact of primary language on health care engagement as indicated by clinical trial screening and engagement, use of genetic counseling, and communication via an electronic patient portal. METHODS: Clinical and demographic data on patients with breast cancer diagnosed and treated from 2013 to 2018 within the Stanford University Health Care system were compiled via linkage of electronic health records, an internal clinical trial database, and the California Cancer Registry. Logistic and linear regression models were used to evaluate for association of clinical trial engagement and patient portal message rates with primary language group. RESULTS: Patients with LEP had significantly lower rates of clinical trial engagement compared with their English-speaking counterparts (adjusted odds ratio [OR], 0.29; 95% CI, 0.16 to 0.51). Use of genetic counseling was similar between language groups. Rates of patient portal messaging did not differ between English-speaking and LEP groups on multivariable analysis; however, patients with LEP were less likely to have a portal account (adjusted OR, 0.89; 95% CI, 0.83 to 0.96). Among LEP subgroups, Spanish speakers were significantly less likely to engage with the patient portal compared with English speakers (estimated difference in monthly rate: OR, 0.43; 95% CI, 0.24 to 0.77). CONCLUSION: We found that patients with LEP had lower rates of clinical trial engagement and odds of electronic patient portal enrollment. Interventions designed to overcome language and cultural barriers are essential to optimize the experience of patients with LEP.

Physical Activity Support Predicts Safety-Net Patients’ Digital Health-Care Engagement: Implications for Patient Care Delivery

Purpose: Assess relationship among health services received and patients’ digital health-care engagement. Design: Quantitative cross-sectional survey study. Setting: Community health centers in Washington state and DC. Sample: N = 164 adult safety-net patients. Intervention: Not applicable Measures: Outcomes were knowledge and use of health apps. Predictors were health service access (access to specialists and health information); health service delivery (healthy eating and physical activity counsel); health service satisfaction; and perceived service value. Analysis: Descriptive and multivariate regression analyses. Odds ratios (OR) reported for 95% confidence interval (CI). Results: Response rate was 35%. Of all, 71% were knowledgeable of smartphone use for wellness and 48% used health apps. Physical activity (PA) counseling predicted knowledge and health apps use. Respondents receiving PA counseling were 2.61 times more likely to be knowledgeable about using smartphones for health promotion (OR = 2.61; P = .047; 95% CI: 1.01-6.73). Respondents receiving PA counseling were 2.89 times more likely to use health apps (OR = 2.89; P = .022; 95% CI: 1.17-7.17). Health information access predicted health apps use; respondents with easy access to general health information were 0.29 times as likely to use health apps (OR = 0.29; P = .043; 95% CI: 0.09-0.96). Conclusion: Targeted preventive care support encourages digital health-care engagement. mHealth may supplement health-care needs outside clinics.

Primary health care engagement among marginalized people who use drugs in Ottawa, Canada

IntroductionEngagement in primary health care may be lower among marginalized people who use drugs (PWUD) compared to the general population, despite having greater mental and physical healthcare needs as evidenced by higher co-morbidity, and more frequent use of emergency department care. Objectives and ApproachWe investigated which socio-structural factors were related to primary care engagement among PWUD using rich survey data from the Participatory Research in Ottawa: Understanding Drugs cohort study; these data were deterministically linked to several robust provincial-level health administrative databases held at the Institute for Clinical Evaluative Sciences. We defined primary care engagement over the 2 years prior to survey completion (March-December 2013) as: not engaged (<3 outpatient visits to the same family physician) versus engaged in care (3+ outpatient visits to the same family physician). Multi-variable logistic regression was used to identify factors associated with primary health care engagement. ResultsAmong 663 participants, characteristics include: mean age of 41.4 years, 75.6% male, 66.7% in the lowest two income quintiles, and 51.1% with 6+ co-morbidities. 372 (56%) were engaged in primary care, with a mean of 15.97 visits per year (SD=20.18). Engagement was significantly associated with the following factors: receiving drug benefits from either the Ontario Disability Support Program (adjusted odds ratio [AOR] 4.48; 95% confidence interval [95%CI] 2.64 to 7.60) or Ontario Works (AOR 3.41; 95%CI 1.96 to 5.91), having ever taken methadone (AOR 3.05; 95%CI 1.92 to 4.87), mental health co-morbidity (AOR 2.93; 95%CI 1.97 to 4.36), engaging in sex work in the last 12 months (AOR 2.05; 95%CI 1.01 to 4.13), and having stable housing (AOR 1.98; 95%CI 1.30 to 3.01). Conclusion/ImplicationsNearly half of PWUD are not engaged in primary care, representing missed opportunities to improve health. Engagement in primary care may reflect both an increased need for health care, such as mental health disability, and increased access to primary care through other health and social services, such as housing support.

Health Care Utilization Behaviors Predict Disengagement From HIV Care: A Latent Class Analysis

Background The traditional definition of engagement in HIV care in terms of only clinic attendance and viral suppression provides a limited understanding of how persons living with HIV (PLWH) interact with the health care system. Methods We conducted a retrospective analysis of patients with ≥1 HIV clinic visits at the Duke Adult Infectious Diseases Clinic between 2008 and 2013. Health care utilization was characterized by 4 indicators: clinic attendance in each half of the year (yes/no), number of emergency department (ED) visits/year (0, 1, or 2+), inpatient admissions/year (0, 1, 2+), and viral suppression (never, intermittent, always). Health care engagement patterns were modeled using latent class/latent transition analysis. Results. A total of 2288 patients (median age, 46.4 years; 59% black, 71% male) were included in the analysis. Three care engagement classes were derived from the latent class model: “adherent” “nonadherent,” and “sick.” Patients age ≤40 years were more likely to be in the nonadherent class (odds ratio, 2.64; 95% confidence interval, 1.38–5.04) than other cohort members. Whites and males were more likely to transition from nonadherent to adherent the following year. Nonadherent patients were significantly more likely to disengage from care the subsequent year than adherent patients (23.6 vs 0.2%, P &lt; .001). Conclusions A broader definition of health care engagement revealed distinct and dynamic patterns among PLWH that would have been hidden had only previous HIV clinic attendance had been considered. These patterns may be useful for designing engagement-targeted interventions.