Challenges exist for the management of diabetes care in First Nations populations. RADAR (Reorganizing the Approach to Diabetes through the Application of Registries) is a culturally appropriate, innovative care model that incorporates a disease registry and electronic health record for local care provision with remote coordination, tailored for First Nations people. This study assessed the effectiveness of RADAR on patient outcomes and diabetes care organization in participating communities in Alberta, Canada. It revealed significant improvements in outcomes after 2 years, with 91% of patients achieving a primary combined end point of a 10% improvement in or persistence at target for A1C, systolic blood pressure, and/or LDL cholesterol. Qualitative assessment showed that diabetes care organization also improved. These multimethod findings support tailored diabetes care practices in First Nations populations.

In Canada, chronic diseases have reached critical imbalances for First Nations (FN) people (1). Diabetes is three to five times more common in FN than in non-FN populations (2,3). Higher rates of diabetes-related complications are observed in FN populations (4,5). The high burden of disease, complications, and comorbidity that FN people with diabetes experience is compounded by the significant and historical social, physical, and economic challenges they face (5,6).

Primary care for people with diabetes is typically delivered using a chronic care model (7), although its implementation within FN communities is challenging. FN communities are often remote, with limited access to comprehensive diabetes care. Current federal and provincial approaches to managing diabetes care among FN people has led to suboptimal and fragmented care for people living both on and off reserves. Indeed, we previously found that such diabetes care was reactive, relying on FN people to navigate a complicated health care system (8).

Within a chronic care model, one framework for organizing and delivering optimal diabetes care is called the 5Rs, which stands for Recognize (performing screening/risk factor assessment), Register (systematically tracking patients), Relay (facilitating information-sharing), Recall (providing timely review and reassessment), and Resource (supporting self-management) (7). Furthermore, FN-specific clinical practice guidelines (CPGs) recommend that diabetes prevention, care, and education be grounded in communities’ social, cultural, and health service contexts (6). The organization of diabetes care (7) and the principles of self-determination and governance of FN communities (9) guided the development of RADAR (Reorganizing the Approach to Diabetes through the Application of Registries) (10), in partnership with FN communities and OKAKI Health Intelligence, Inc., a private, social enterprise in Alberta, Canada, with >12 years working with FN communities.

The RADAR model (10) uses remote care coordinators (CCs), who are registered nurses or dietitians, to support local health care providers (HCPs) (e.g., registered nurses, licensed practical nurses, and dietitians) in FN communities through telehealth to 1) populate an electronic medical record (EMR) diabetes registry called the CARE platform, which was developed specifically for FN communities; 2) coordinate population-level care to identify priorities and gaps in care; and 3) apply current CPGs (11) through regular client review and case conferencing to consider therapeutic changes and coordinate referrals (Figure 1).

RADAR was implemented collaboratively with FN communities from Treaty 6, 7, and 8 territories in Alberta, Canada, where local HCPs found the model appropriate, acceptable, and valuable for both HCPs and patients (12). Here, we describe the effectiveness of RADAR with regard to patient outcomes and the organization of diabetes care in these communities.

Design

We used a modified stepped-wedge design supplemented with a descriptive qualitative assessment to evaluate RADAR’s effectiveness (10). Eligible patients were ≥18 years of age, diagnosed with type 2 diabetes, and actively engaged in and received care from the FN health facility on a reserve and had provided verbal consent for HCPs to manage their diabetes (10). For the qualitative assessment, we purposefully sampled remote care coordinators and local HCPs who were implementing RADAR.

The decision to participate in RADAR was made by FN leaders (e.g., chiefs and council, chief executive officers, and/or community health directors) in each participating community, who reviewed and endorsed the project. OCAP (Ownership, Control, Access, and Possession) principles were followed (13,14). Findings were shared with FN health leadership to incorporate their feedback. RADAR was approved by the University of Alberta’s Health Research Ethics Board (Study ID Pro00048714).

Data Collection and Analysis

The primary outcome was a 10% improvement in A1C, systolic blood pressure (SBP), or LDL cholesterol during the 2-year follow-up relative to baseline control periods, representing a clinically important difference (15). For patients who were already at target at baseline, we considered persistence at target (i.e., maintaining values within 10% of baseline) as achieving the primary outcome (10). Outcomes were measured continuously throughout the follow-up period and at the time of each community transition (i.e., step) as per traditional stepped-wedge designs.

The primary outcome was first analyzed using an intention-to-treat (ITT) framework, which included all patients identified by the communities in their baseline assessments, irrespective of the follow-up care they received. Additionally, we used a per-protocol (PP) framework, removing patients who died, moved, or subsequently refused care at FN community health centers. We fit a generalized linear mixed model with random effect for cluster (i.e., community) and fixed effect for each step (i.e., transitions from baseline to follow-up intervention) (16). We also included calendar year in our models as a fixed effect to control for any temporal confounding effects, although calendar year was not significant in the models (P >0.10). Patients with insufficient primary outcome data were assumed to be nonresponders (i.e., individuals who had failed to achieve the primary end point). In addition, the mean change in A1C, SBP, and LDL was assessed for patients with A1C ≥7.5% (58 mmol/mol), SBP ≥140 mmHg, and LDL cholesterol ≥2.5 mmol/L (i.e., at-risk patients), after accounting for the baseline value, year, and community clustering in alignment with Diabetes Canada CPGs (10,11). Outcome data are not reported by community to maintain confidentiality as per our data-sharing agreements; however, the consistency of effects across the communities was assessed and is expressed as ranges. Quantitative analyses were performed using Stata, v. 14.2, statistical software (StataCorp, College Station, TX).

We used a descriptive qualitative approach (17) to assess the effectiveness of RADAR on the organization of diabetes care within participating communities, which were diverse by treaty, geography, population, and proximity to urban centers. Data sources included individual interviews with remote CCs and local HCPs. Qualitative data were managed using ATLAS.ti, v. 8, software (Scientific Software Development, Berlin, Germany) and analyzed using summative content analysis within the 5Rs framework, including negative case analysis for meaning saturation to understand the diversity of experiences (18,19). We followed the Consolidated Criteria for Reporting Qualitative Research framework (Supplementary Table S1) (20).

Primary Outcomes

The quantitative assessment included seven communities at the end of 2 years. At baseline, 516 patients with type 2 diabetes were registered in RADAR, ranging from 28 to 129 per community. The mean age was 60 years (SD 13.6 years, mean community range [MCR] 55–65 years), 57% were female (MCR 50–66%), the mean A1C was 8.3% (67 mmol/mol) (SD 2.0%, MCR 8.0–8.7%), the mean SBP was 131 mmHg (SD 19.1 mmHg, MCR 122–137 mmHg), and the mean LDL cholesterol was 2.0 mmol/L (36 mg/dL) (SD 0.9, MCR 1.7–2.3 mmol/L [30.6–41.4 mg/dL]) (Table 1).

Overall, after 2 years of RADAR across all communities, we found significant improvements for the primary combined end point and in all three outcomes individually (Figure 2). Achievement of the combined end point was 91% (95% CI 89–94%) in the ITT population (n = 516) and 93% (95% CI 91–95%) in the PP population (n = 419) (intracluster correlation ρ = 0.008). Within the combined primary end point (ITT population), the majority (377 of 465, or 80%) had a 10% improvement in any one parameter (A1C, SBP, or LDL cholesterol) over baseline, while 78% of the remaining patients (40 of 51) were persistently at target (Supplementary Figure S1). When examining the primary combined outcome by community, achievement ranged from 87 to 100% (Supplementary Figure S2).

When examining the individual components of the primary end point, the biggest driver of the improvement was A1C. Two-thirds of patients achieved a 10% reduction in or persistence at target for A1C (ITT 66% [95% CI 55–77%] and PP 65% [95% CI 54–76]). The proportion of patients achieving a 10% reduction (221 of 336, or 64%) was similar to the number of patients with persistence at target (130 of 180, or 71%) in the ITT population. The mean change in A1C during follow-up was −0.93% (95% CI −0.59 to −1.28%, P = 0.001; MCR −1.26 to −0.16%). Among those with an A1C ≥7.5% (58 mmol/mol) at baseline (mean 9.70%, SD 1.66%; n = 303), mean change in A1C during follow-up was −1.62% (95% CI −2.18 to −1.06%, P <0.001; MCR −2.43 to −0.54%).

With regard to the other individual outcomes, SBP improved for 54% (95% CI 39–68%) of the ITT population and 58% (95% CI 46–69%) of the PP population, and LDL cholesterol improved for 40% (95% CI 32–48%) of the ITT population and 42% (95% CI 34–50%) of the PP population. Unlike A1C, achieving the SBP or LDL cholesterol target was driven by achieving a 10% reduction: 134 of 187 (70%) for SBP and 159 of 236 (66%) for LDL cholesterol in ITT populations, whereas a smaller proportion persisted at target for SBP (150 of 329, or 45%) or LDL cholesterol (55 of 280, or 18%). The mean change in SBP during follow-up was −6.03 mmHg (95% CI −2.68 to −9.38 mmHg, P = 0.005; MCR −19.64 to −0.82 mmHg). Among those with an SBP ≥140 mmHg at baseline (mean 156 mmHg, SD 14.9 mmHg; n = 193), mean change in SBP during follow-up was −14.19 mmHg (95% CI −22.55 to −5.82, P = 0.006; MCR −36.17 to −3.45 mmHg). The mean change in LDL cholesterol during follow-up was −0.17 mmol/L (95% CI −0.10 to −0.24 mmol/L, P = 0.001; MCR −0.38 to −0.02 mmol/L). Among those with LDL cholesterol ≥2.5 mmol/L at baseline (mean 3.17 mmol/L, SD 0.57 mmol/L; n = 215), mean change in LDL during follow-up was −0.33 mmol/L (95% CI −0.46 to −0.21 mmol/L, P = 0.01; MCR −0.71 to −0.03 mmol/L).

Organization of Diabetes Care

We conducted 21 semistructured interviews with 11 individual participants: three remote CCs and eight local HCPs (e.g., registered nurses, licensed practical nurses, and dietitians) from May 2015 to February 2019 (Table 2). Overall, we found improvements in the organization of diabetes care by the 5Rs, as presented below, with illustrative quotes including position (i.e., CC or HCP) and anonymous study code number for each participant.

Recognize and Register

Overall, RADAR facilitated the recognition and registration of patients with diabetes. RADAR “identified our [diabetes] population” (HCP 5), including “younger people that we didn’t know had diabetes” (HCP 4), thus capturing “a lot more of the population” (HCP 7). Nevertheless, RADAR was limited in identifying new patients because only physicians can diagnose. “I don’t know if RADAR had any impact on recognizing clients . . . [because] we never can diagnose” (HCP 6). As such, “the registry was only those [patients] already diagnosed with type 2 diabetes” (HCP 3).

Regardless, RADAR resulted in a “client registry that has been created” (HCP 1). The act of centrally registering patients in CARE with CC support confirmed the type of diabetes for some patients because “many patients had been indicated as ‘other’ diabetes because the HCPs weren’t sure [of the diagnosis]” (CC 2). CCs helped identify type of diabetes by “looking on [the provincial EMR] through discharge summaries to find data to support whether [patients had] type 2 diabetes” (CC 2) or asking “the nurse to confirm with [the patient’s] physician” (CC 1). However, centrally registering patients also resulted in “increasing our overall client load” (HCP 1) or feeling overwhelmed by “seeing my whole population and saying, ‘I should be doing all these things for everybody’” (HCP 6) in light of limited resources.

Relay

Overall, RADAR improved HCP access to and sharing of information. Within health centers, local HCPs described efficient and comprehensive access to information through CARE versus relying on patients’ recollections or paper charts. “You don’t have to go into the [paper] chart every time. Even when you’re unfamiliar with a client, all that information is [in CARE] . . . . Because, sometimes, your clients are not great historians” (HCP 7). CCs also enabled access of local HCPs to clinical information contained in other EMRs. “It can take a lot of time to get access to [the provincial EMR], if we are given it at all. With RADAR, [the CCs] populated CARE with medical information that is important to the level of care we can provide” (HCP 1). Moreover, CARE resulted in “improved communication amongst the team” (HCP 7), allowing local HCPs “to communicate everything you’ve done with a client” (HCP 5). This feature was especially important given staffing challenges. “We really have a good flow [of information] regardless of the staff turnover” (CC 1).

Finally, RADAR improved communication with providers outside the health centers. “Prior to RADAR, there was no communication between on- and off-reserve medical services,” but the CCs helped “connect us to the off-reserve services, such as the physicians . . . [and] the communication has become more open” (HCP 1). Furthermore, CARE facilitated communication and coordinated diabetes specialty care such as ophthalmology (HCP 7) and endocrinology (HCP 6) through report and letter templates. “I use the [patient] report summary, and I can easily fax that to the [physician] clinic” (HCP 2). However, improved relay of information was not reported in all communities, indicating that more work is needed.

Recall

RADAR improved timely review and reassessment of patients with diabetes by local HCPs through CARE’s features (e.g., tasks, reminders, and patient summaries) and CC support. “You have very specific tasks that you need to accomplish to improve people’s health, and they’re right in the forefront [in CARE]” (HCP 7). CCs facilitated recall by “looking at the total population, including the ones not accessing care” (CC 1) and identifying for HCPs which patients “should be targeted at this time” (HCP 4). As a result, “we have been able to reach out and connect with a lot more community members who may not have been utilizing our services for diabetes care” (HCP 1). In addition, CCs reviewed “the diabetes guidelines and asked us to follow up on as much as we can with that client” (HCP 6). As a result, processes of care were completed. “Blood pressures were taken; foot exams and lab work were done” (CC 1). Recall support was crucial in the context of busy clinics. “It gets busy, and you get sidetracked, so it helps to have that person saying, ‘This is who you’ve got to follow up with next; this person needs [this]’” (HCP 4).

However, not all HCPs believed CARE improved recall beyond what existed “because we already set up regular reminders, like [for A1Cs], foot exams, eye exams . . . into the [physician] EMR” (HCP 3). Furthermore, in some communities, “we only case-reviewed half the patients” (CC 1), thus limiting recall. Some HCPs were uncomfortable recalling patients, saying, “That’s not our job’” (CC 1). HCPs resisted recalling patients they believed did not want their care. “Some of these clients that [the CCs] asked me to review were ones that don’t access care here regularly. We know their [glucose] is high [and] that they have other things going on in their life, so they don’t come in for appointments . . . . Chasing them down, [is] not the best use of my time” (HCP 3). Another HCP explained, “We tell [the CC] this person is not engaged . . . and [to] just leave them alone” (HCP 6).

Resource

RADAR helped improve local HCPs’ ability to support patients’ diabetes self-management by informing patients of health center services and supports, developing care plans, and increasing local HCPs’ diabetes knowledge. For example, one respondent said, “Based on the registry, we would call clients, introduce ourselves, [and] introduce the program” (HCP 1) or inform patients that “we have a diabetes management support system . . . [and ask] ‘Would you like some help?’” (CC 2). This resulted in patients accessing services. “There’s probably 50% more people coming for their monthly foot care” (HCP 5).

Furthermore, CCs helped local HCPs develop care plans informed by CPGs. “[The CC] reviewed the guidelines with us and helped us incorporate them into our day-to-day care with clients” (HCP 1). This, in part, helped to increase local HCPs’ diabetes knowledge and “build local capacity” (CC 1). For example, one respondent said, “[The CC showed us] the gold standards for diabetes treatment . . . . I am not an expert on diabetes. I have learned so much” (HCP 7). Diabetes education was especially valuable to HCPs who did not have time or resources to stay current in their knowledge. “Even though we’re close to the nearest town and physicians, you’re still isolated from information . . . . There may be new information, and [the CC is] helpful that way, with new stuff or if we have a question” (HCP 4). As a result, local HCPs reported increased confidence in their diabetes knowledge. “[The CC] has made a huge difference in my confidence with diabetes knowledge” (HCP 1). However, not all HCPs believed RADAR improved their diabetes knowledge. “I have a lot of knowledge in diabetes, so it is not anything new” (HCP 2). One respondent noted that the community already had a certified diabetes care and education specialist (HCP 3). Finally, some HCPs requested further education, such as “10-minute education sessions on new [CPGs]” (HCP6), indicating an ongoing need for up-to-date diabetes knowledge.

We found that RADAR improved patient-level outcomes through improved organization of diabetes care in FN communities. In addition, we found significant achievement of our primary outcomes for RADAR participants over 2 years, with the largest effect observed for change in A1C. This finding is similar to an intervention study of rural (non-FN) patients with type 2 diabetes, in which improvements in SBP and/or LDL cholesterol were smaller than improvements in A1C (21). Although glycemic control is essential, further improvements in SBP and/or LDL cholesterol would provide substantial macro- and microvascular benefits (22).

Our results also demonstrated improved diabetes care organization (8), which is foundational to improving patient-level outcomes (7). With regard to the 5Rs, RADAR resulted in central registries, helping communities verify, visualize, and recall their diabetes population. The use of patient registries for diabetes management is associated with better processes and outcomes of care (23,24). Furthermore, FN health directors can use their own registry data as a resource to inform decision-making for local program planning, meet federal reporting requirements, and support funding requests based on the needs of and trends in the local patient population. RADAR allows for a community-led response based in data sovereignty (25) and aligned with the principles of self-governance and self-determination (9).

Second, RADAR facilitated relay of clinical information essential for continuity and coordination of team-based diabetes care (11). This effort included overcoming the restricted access of local HCPs to patients’ provincial EMR data in the current fragmented system, with multiple isolated technologies hindering clinical information-sharing necessary for diabetes care (26,27). Within health centers, centralized compilation of patients’ clinical information and care plans in CARE was crucial in the context of the high staff turnover that FN communities face (27). In addition, team-based care was enhanced through relationship-building among HCPs through CCs’ professional networks, including diabetes specialists. This was especially important for remote communities with irregular access to services necessary for comprehensive diabetes care (11,27).

Finally, RADAR increased local HCP capacity (i.e., knowledge and confidence) to support diabetes self-management (resource) through education (e.g., of current CPGs) and peer support, promoting the long-term sustainability of primary outcomes. This is important because lack of adequate diabetes knowledge among local HCPs is a barrier to diabetes care and is largely affected by time/competing priorities, staff turnover, and changing guidelines (4,28). Furthermore, local HCPs, who are the experts in their communities, can apply RADAR, including enhanced knowledge of diabetes care, with local patients in appropriate ways that recognize contextual and cultural factors (12).

Although RADAR improved diabetes care organization overall, challenges remain. Centrally registering patients does not necessarily recognize new patients through screening, thereby limiting the population health impact. Future RADAR activities may include recommended earlier and more frequent screening of type 2 diabetes for FN people (6). Furthermore, centrally registering patients with recall functionality increased local HCPs’ workload, contributing to feelings of stress. In addition, although RADAR encouraged local HCPs to actively engage or re-engage patients, some HCPs resisted recalling patients, thus limiting the proactive care essential to improving patient outcomes (11). This challenge is not unique to FN communities, as HCPs must balance proactive care with patients’ right to decline care. However, it can become a greater challenge in FN communities, as patients may have competing priorities that take precedence over diabetes self-management (resource), such as housing or food insecurity related to historic and social injustices (5,9). Although local HCPs are experts in their communities, including knowing which patients want to engage or re-engage with diabetes care, some HCPs may believe it is patients’ responsibility to seek care and outside of their role as care providers to facilitate patient engagement proactively. This opinion may be related to local HCPs’ feelings of stress, which is understandable in the context of their limited resources. Nonetheless, this belief may limit community-level comprehensive diabetes care and requires further exploration. Indeed, we previously found that the 5Rs of diabetes care organization are interrelated and influenced by financial and human resources (the sixth R) (8). Regardless, RADAR created the conditions for local HCPs and patients to collaborate in diabetes self-management in this resource-challenged context.

Strengths and Limitations

Our incorporation of quantitative and qualitative components to comprehensively measure effectiveness for both clinical outcomes and diabetes care organization is a strength. However, this study is not without limitations. First, given our follow-up of 2 years, we used intermediate outcomes as opposed to hard clinical end points (e.g., occurrence of heart attack or stroke). Nevertheless, A1C, SBP, and LDL cholesterol are well established diabetes care targets within guidelines (11) and have been shown in many large-scale randomized controlled trials (RCTs) to confer substantial benefits with regard to macro- or microvascular outcomes (11,21). Second, we designed a pragmatic, controlled, community-based intervention aimed at local HCPs as opposed to patients per se. Individual patients or local HCPs within the community could not be randomized to care with and without RADAR because of the threat of “contamination” via exposure to some aspects of the intervention within the same community team or health facility. Moreover, our community partners were not interested in a traditional RCT; a stepped-wedge design was used, as few communities would accept being the control community for an extended duration. Although the participating FN communities were diverse, they may not be representative of all FN communities. Additionally, the participating local HCPs who were interviewed for the qualitative component of the study are a sample of the health care workers involved in the implementation of RADAR. Although almost all FN communities are dealing with similar issues in providing diabetes care, it is possible that some of our findings may not be applicable to all communities, depending on local resources.

RADAR is an effective, tailored approach to support the organization of and capacity building in diabetes care for FN communities. From our findings, it is reasonable to assume that this intervention could be applied to other indigenous populations in jurisdictions around the globe.

Acknowledgments

The authors thank their FN partners for their ongoing and generous support and recognize that this work was implemented in the traditional territories of their Treaty 6, 7, and 8 FN partners. The authors thank OKAKI for their support and participation in the evaluation of RADAR. They also acknowledge the significant contribution of Dr. Sumit (Me2) R. Majumdar, who died in 2018, to the study design.

Funding

This work was funded by the Canadian Institutes of Health Research (MOP #143562), Alberta Innovates Health Solutions, and the Lawson Foundation.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

D.T.E. and S.S. conceived the study, designed its protocols, and received ethics approval and funding. D.T.E. led the quantitative data collection and analysis, with support from J.K.M.-S. L.A.W. led the evaluation design and conducted qualitative data collection and analysis. D.T.E., L.A.W., and A.S. drafted the manuscript. L.C. and J.A.J. actively contributed to the study design. All authors read and approved the final manuscript. D.T.E. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

This article contains supplementary material online at https://doi.org/10.2337/figshare.21872040.

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