Examining Characteristics of Local Public Health Systems With Exceptional Tribal Organization Participation.

OBJECTIVE: To learn feasible ways to increase multisector community partnership with tribal organizations, meaning tribal health authorities or American Indian and Alaska Native (AI/AN)-serving organizations, by examining characteristics of local public health systems with exceptional tribal organization participation. DESIGN, SETTING, AND PARTICIPANTS: In total, 728 local public health departments were surveyed in 2018 to generate a nationally representative sample of local public health systems in the United States. A positive deviance approach using logistic regression helped identify local public health systems that had tribal organization participation despite characteristics that make such participation statistically unlikely. Local public health systems with exceptional tribal organization participation were compared with systems with conventional participation, examining measures known to impact the formation of public health partnerships. MAIN OUTCOME MEASURE: This study used an exploratory logistic regression approach to identify unique characteristics of local public health systems with exceptional tribal organization participation. RESULTS: Of 728 health systems surveyed, 21 were identified as having exceptional tribal organization participation. Across varying thresholds to identify exceptional participation, having a higher network density and prioritizing equity in public health activities were found to consistently distinguish exceptional tribal organization participation in both nonrural and rural areas. CONCLUSIONS: Public health partnerships with tribal organizations are possible even in circumstances that make them unlikely. Efforts to build denser networks of collaborating organizations and prioritize equity may help public health systems achieve success with tribal organization partnerships.

Increasing use of systems science in cardiovascular disease prevention to understand how to address geographic health disparities in communities with a disproportionate burden of risk.

Objective: Marginalized communities shoulder a disproportionate burden of cardiovascular disease (CVD) driven by concentrated neighborhood social risk factors. We provide a case study of systems science application to address geographic CVD health disparities at the community level - informing the science of CVD health disparities research. Methods: We conducted a two-phased, multi-methods needs assessment in the Denver, Colorado area. Phase I consisted of a social network analysis to map a two-mode network of existing CVD prevention interventions and their implementing organizations. In Phase II, group model building (GMB) sessions with key community, public health, and healthcare provider stakeholders, were utilized to identify and visualize community factors contributing to disparities in CVD risk, producing a consensus-based causal loop diagram. Results: Between May 2021 and June 2022, we conducted 24 virtual, semi-structured interviews in Phase I to describe CVD prevention interventions, and 7 virtual GMB sessions in Phase II to describe experiences of disparities in CVD risk. For the purposes of this paper, we focus on a subset of results for both phases. In Phase I we identified 89 active CVD prevention interventions, 29 of which addressed tobacco use. In Phase II, causal loop diagrams revealed root causes of disparities in CVD risk. We provide an example of a causal loop diagram that focuses on the community prevalence of tobacco use, identifying stress as a key underlying factor driving disparities. The integration of findings from both phases highlighted the alignment and misalignment between quit tobacco intervention goals and how they are being experienced in marginalized communities. Conclusion: Systems science methods were useful to organize a large number of CVD prevention efforts, and evaluate the root causes of CVD health disparities in a high risk community. By integrating these two aspects, interventions may be reoriented to more effectively address the root causes of CVD health disparities.

Best of ARM: Evaluating engagement in multisector community health networks: The case of tribal organizations.

OBJECTIVE: To quantify variation in public health system engagement with tribal organizations across a national sample of communities and to identify predictors of engagement. DATA SOURCES: We used 2018 National Longitudinal Survey of Public Health Systems data, a nationally representative cohort of the US public health systems. STUDY DESIGN: Social network analysis measures were computed to indicate the extent of tribal organization participation in public health networks and to understand the sectors and social services that engage with tribal organizations in public health activities. Two-part regression models estimated predictors of tribal engagement. DATA COLLECTION: A stratified random sample of local public health agencies was surveyed, yielding 574 respondents. An additional cohort of oversampled respondents was also surveyed to include jurisdictions from the entire state upon the request of their respective state health departments (n = 154). Analyses were restricted to jurisdictions with a nearby American Indian and Alaska Native (AI/AN) serving health facility, yielding a final sample size of 258 local public health systems. PRINCIPAL FINDINGS: When an AI/AN serving health facility was present in the region, tribal organizations participated in 28% of public health networks and 9% of implemented public health activities. Networks with tribal engagement were more comprehensive in terms of the breadth of sectors and social services participating in the network and the scope of public health activities implemented relative to networks without tribal engagement. The likelihood of tribal engagement increased significantly with the size of the AI/AN population, the presence of a tribal facility with Indian Health Service funding in the region, and geographic proximity to reservation land (p < 0.10). CONCLUSIONS: The vast majority of public health networks do not report engagement with tribal organizations. Even when AI/AN serving health facilities are present, reported engagement of tribal organizations remains low.

Improving Follow-up Attendance for Discharged Emergency Care Patients Using Automated Phone System to Self-schedule: A Randomized Controlled Trial.

BACKGROUND: Automated phone appointment reminders have improved adherence with follow-up appointments in a variety of hospital settings, but have mixed results in patients discharged from the emergency department (ED). Increasing adherence to follow-up care has been a priority in the ED to improve patient outcomes and reduce unnecessary future visits. METHODS: We conducted a prospective randomized open, blinded end-point (PROBE) trial of 278 adult patients discharged from the ED and referred to a provider for follow-up care. Participants in the intervention arm received a self-scheduling text or phone message that automatically connected them to their referral provider to schedule a follow-up appointment and sent them appointment reminders. Those in the control arm received standard-of-care written instructions to contact listed referral providers. The primary outcome was time to appointment. The secondary outcome was time to return visit to the ED. RESULTS: The automated reminders increased the cumulative incidence of keeping the referral appointment after ED discharge (p < 0.001, Gray's test). Of participants randomized to the automated phone intervention, 49.3% (n = 74) kept their follow-up appointment versus 23.4% (n = 30) in the control arm, with a hazard ratio (HR) and 95% confidence interval (CI) over the duration of the study period of 2.4 (1.6 to 3.7; p < 0.001). In a sensitivity analysis using 30 days of follow-up data, 42.0% (n = 63) of participants randomized to the phone intervention kept their follow-up versus 21.1% (n = 27) in the control arm, with a HR (95% CI) of 2.2 (1.4 to 3.5; p < 0.001). There was no difference in ED revisits between the intervention and control group within 120 days postdischarge. CONCLUSIONS: An automated self-scheduling phone system significantly improved follow-up adherence after ED discharge, but did not decrease ED revisits.

Strategies of community engagement in research: definitions and classifications.

Engagement activities are defined along a continuum that analyzes and represents nonacademic stakeholder activities and interactions with academic researchers. Proposed continua begin with none to limited stakeholder inclusion and input into research and continue with descriptions of increasing presence, input, and participation in decision-making. Despite some agreement in the literature, development of consistent terminology and definitions has been recommended to promote the common understanding of strategies in engaged research. This paper sought to develop and understand classifications and definitions of community-engaged research that can serve as the foundation of a measure of engaged research that permits comparisons among engagement strategies and the outcomes that they produce in health- and healthcare-related research studies. Data on academic and stakeholder perceptions and understandings of classifications and definitions were obtained using Delphi process (N = 19) via online and face-to-face survey and cognitive response interviews (N = 16). Participants suggested the need for more nuanced understanding of engagement along portions of the continuum, with active involvement and decision-making as engagement progressed. Cognitive interview responses suggested that outreach and education is a more advanced level of engagement than previously discussed in the literature and viewed consultation negatively because it required work without guaranteeing community benefit. It is possible to define a continuum of patient- and community-engaged research that is understood and accepted by both academic researchers and community members. However, future research should revisit the understanding and depiction of the strategies that are to be used in measure development.