Population health interventions for cardiometabolic diseases in primary care: a scoping review and RE-AIM evaluation of current practices

Introduction Cardiometabolic diseases (CMD) are the leading cause of death in high-income countries and are largely attributable to modifiable risk factors. Population health management (PHM) can effectively identify patient subgroups at high risk of CMD and address missed opportunities for preventive disease management. Guided by the Reach, Efficacy, Adoption, Implementation and Maintenance (RE-AIM) framework, this scoping review of PHM interventions targeting patients in primary care at increased risk of CMD aims to describe the reported aspects for successful implementation. Methods A comprehensive search was conducted across 14 databases to identify papers published between 2000 and 2023, using Arksey and O’Malley’s framework for conducting scoping reviews. The RE-AIM framework was used to assess the implementation, documentation, and the population health impact score of the PHM interventions. Results A total of 26 out of 1,100 studies were included, representing 21 unique PHM interventions. This review found insufficient reporting of most RE-AIM components. The RE-AIM evaluation showed that the included interventions could potentially reach a large audience and achieve their intended goals, but information on adoption and maintenance was often lacking. A population health impact score was calculated for six interventions ranging from 28 to 62%. Discussion This review showed the promise of PHM interventions that could reaching a substantial number of participants and reducing CMD risk factors. However, to better assess the generalizability and scalability of these interventions there is a need for an improved assessment of adoption, implementation processes, and sustainability.


Introduction
Cardiometabolic diseases (CMD), which include cardiovascular disease, diabetes mellitus, and chronic renal failure, are the leading cause of death in high-income countries and are increasing worldwide.If this situation continues unchecked it could potentially compromise the sustainability of healthcare systems (1)(2)(3).Cardiometabolic diseases can be prevented for a large part by addressing modifiable risk factors, such as elevated blood pressure, unhealthy dietary habits, and smoking (4)(5)(6)(7).To accomplish this, the proactive identification of high-risk patients is essential for early detection of these modifiable risk factors (8).
Population health management (PHM) is a strategy that supports proactive care by identifying and addressing missed opportunities in chronic disease management (9).Population Health Management, in a clinical context, is also known as panel management and can be defined as 'the proactive management of a total population at risk for adverse outcomes through various individual, organizational and cultural interventions based on a risk-stratified needs assessment of the population' (9).Primary care occupies a central position in the implementation of PHM thanks to its inherent capacity for care coordination and integration, coupled with access to comprehensively coded routine care data.These unique characteristics of primary care also promote the effective identification of individuals at increased risk of CMD progression and the provision of appropriate care related to identified risk (10, 11).
While there is increasing interest in PHM in relation to CMD, a clear overview detailing how PHM interventions are best implemented in primary care is lacking.Although various implementation theories and frameworks are available, the RE-AIM framework provides a vital tool for evaluating and comprehending the effectiveness and sustainability of PHM interventions in primary care.The RE-AIM framework assesses the impact of population health intervention initiatives using five critical factors: Reach, Efficacy, Adoption, Implementation, and Maintenance (12).Additionally, this framework aids in determining the potential population health impact of these interventions.
This scoping review aimed to identify PHM interventions, which were targeted at patients with a high risk of CMD in the primary care setting.This was accomplished by obtaining information according to the dimensions outlined in the RE-AIM framework and estimating their potential population health impact.In doing so, the study has the objective of contributing to an understanding of the implementation of PHM interventions, their potential population health impact, and to better inform future research efforts.

Search strategy
This scoping review followed the recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) (13).The search was conducted in the electronic databases Pubmed, Embase, Web of Science, COCHRANE Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAi.org, arXiv, Epistemonikos, PsycINFO and Google Scholar.The search was formulated as a combination of terms that included PHM, primary care, and implementation.The terms were identified through searches of the National Library of Medicine MeSH Tree Structures and by the review team expert.The full search strategy can be found in Supplementary File 1.

Eligibility criteria
Peer-reviewed journal papers were included when they met the following eligibility criteria: (i) published between 2000-2023, (ii) written in English, (iii) published as original results, (iv) focused on risk-based identification of patient groups (panels) with a high risk of (progression of) CMD using a primary care data source or within a primary care setting, and (v) focused on original data about implementation as part of (pragmatic) randomized controlled trials, clinical trials, (retrospective) cohort, case-control, implementation studies, cost-effectiveness or (pilot) feasibility studies.Studies that focused on developing theoretical PHM interventions and those in which the strategy was implemented in a setting other than primary care were excluded.

Data extraction
Using the inclusion and exclusion criteria, two reviewers (M.M.R and S.P.C.P) independently screened all articles on title and abstract.Reading the full text, two team members (M.M.R and S.P.C.P) assessed the selected articles for eligibility.Disagreements were discussed by the core team (M.M.R, N.E.v.H, and S.P.C.P) until consensus was reached.Subsequently, the core team members (M.M.R., N.E.v.H, and S.P.C.P) independently completed full data extraction of study characteristics (publication year, purpose, target population, study design and steps within PHM), and the five dimensions of the RE-AIM framework (14).The three assessors addressed their differences until they came to a complete understanding.
For data extraction focused on RE-AIM dimensions, researchers employed a modified extraction technique created specifically designed for systematic reviews using a RE-AIM framework (see Supplementary File 2) (15,16).Each of the five RE-AIM dimensions was broken down into a number of components, and the core team (M.M.R., N.E.v.H, and S.P.C.P) categorized each recorded article in relation to whether they reported on specific components.Components were based on inclusion criteria: process interventions to improve clinical health outcomes of a defined group of individuals through proactive care coordination and patient engagement.The components for Reach were: the description of the target population, method of identifying the population, recruitment strategies, inclusion/exclusion criteria, participation rate, and cost of the recruitment.For Effectiveness, quality of life measures, positive outcomes, unintended or negative consequences, and cost-effectiveness were reported.Adoption was extracted based on the site and staff participation rate, description of the intervention location, method of identifying setting and staff, level of expertise of providers, and inclusion or exclusion criteria for providers.3 Results

Intervention characteristics of the studies reviewed
Of the 1,110 studies initially identified, 78 remained after removing duplicates and screening titles and abstracts.Full-text screening led to the inclusion of 26 studies, representing 21 unique interventions (see Figure 1).Most PHM interventions were published in the last five years (13 of 21).Seven of the 21 included interventions were randomized controlled studies, and eight were prospective cohort studies.The characteristics of the reviewed interventions included in the analysis are summarized in Table 1.

RE-AIM evaluation
A comprehensive overview of RE-AIM dimensions, including individual components, can be found in Table 2 (detailed data extraction is provided in Supplementary File 3).Three of the PHM Preferred reporting items for systematic reviews and meta-analyses flow chart, which included searches of databases.

Reach
Among all the evaluated dimensions in the included interventions, reach was documented most extensively.In total, 15 interventions reported five out of seven reach components (19, 20, 22-25, 27-29, 34, 36-38, 44).All interventions provided information on the target

Implementation
Descriptions of the intervention were provided for all 21 interventions.Only three interventions (14%) explained the theories or principles that guided the creation of the intervention (27,34,36).The frequency, duration, and timing of visits varied across interventions and were sometimes inadequately described.Patient engagement in intervention design was reported in only a single study (25), while five interventions (24%) involved healthcare professionals, experts, and local stakeholders in developing specific intervention components (19,25,26,36,44).

Maintenance
Maintenance was least often reported across all interventions.Five interventions (24%) reported on the continuation of the program after the study period (27,29,30,33,41), with just one providing details (27).Additionally, while three interventions (14%) reported modifications to the original program, these changes were implemented during the study period, not after completion (24, 25, 27).

Potential population health impact
Calculating the potential population health impact was possible for six out of 21 interventions (see Table 3), with scores ranging from 45 to 89%.The RE-AIM mean exhibited clear variation attributable to differences within each dimension, except for the implementation score, which remained consistent across all interventions.

Discussion
A total of 21 PHM interventions for patients at high risk of CMD in a primary care setting were identified.These interventions showed promise in engaging a substantial number of participants and reducing CMD risk factors.However, this study also revealed a widespread deficiency in reporting across most RE-AIM components.While the included interventions exhibited higher reporting accuracy concerning Reach, followed by Adoption and Implementation, the constructs Effectiveness and Maintenance were barely addressed.A similar trend was found regarding the population health impact score, as the score could only be calculated for six interventions.
Compared with previous systematic reviews (15, 49-52), Reach (especially the description and the method of identifying the target population) was well described, with most interventions using algorithms or risk stratification tools in electronic health records to identify potential participants.Population surveys or routine care checks were employed for those who did not use electronic health records.However, it is worth noting that the risk calculation primarily relied on clinical health outcomes and did not incorporate health behaviors or social determinants of health.Given their significance in determining the risk of a particular group (53, 54), integrating health behaviors and social determinants into the risk model may be crucial to ensure that all potentially suitable patients are included.
Secondly, most studies reported positive outcomes while neglecting to address negative consequences of the intervention adequately.Awareness of negative outcomes, such as attrition and adverse outcomes, is essential for developing effective implementation strategies and ensuring the sustainability of interventions (55).Moreover, most interventions lacked follow-up data and information on attrition, which raises concerns about their long-term effectiveness.However, the short observation period of many interventions may be attributed to the research funding structure, often relying on one-off grants with limited duration and insufficient structural support (56).Nonetheless, long-term results on maintenance and sustainability are crucial for reliable cost-effectiveness analysis, which policymakers and healthcare providers weigh when deciding whether or not to scale up and implement health interventions (16,57).
Another issue was the lack of comprehensive information regarding Adoption, a multifaceted process involving two levels: setting and staff.Specifically, the descriptions of staff involvement were inadequate, potentially leading to a lack of clarity regarding the qualifications necessary to properly implement an intervention.Effective staff involvement is of paramount importance.Previous studies have highlighted the significance of implementation strategies such as education, training, and staff participation in decision-making in promoting successful implementation.Additionally, utilizing champions and opinion leaders to facilitate intervention implementation has been recommended in previous research (16,58,59).A lack of reporting on the components of Adoption and Maintenance makes it challenging to determine whether success can be attributed to the intervention itself, the elements of its implementation, or a combination of both.This consequently limits the prospects of disseminating results and thus extends the reach of an intervention (60).
Finally, an assessment of potential population health impact was conducted for six interventions, utilizing the RE-AIM average score as defined by Glasgow et al. (12).It is important to note that this score does not encompass all facets of the RE-AIM dimensions, necessitating caution in its interpretation.Two interventions resulted in the highest potential population impact scores, which may be linked to their higher participation rates in the Reach dimension (19,20).This can in turn, be attributed to contacting eligible patients via email and telephone, as well as maintenance of extensive intervention contacts.These contacts, including navigator support, website and telephone services, were associated with significant reductions in risk factors for CMD.Moreover, these interventions consistently delivered all components as intended in their respective settings (19, 20).

Limitations of this review
Several limitations need to be taken into account when interpreting the results.Firstly, the search strategy was designed to capture information from English language publications only.Consequently, valuable publications utilizing other languages, housed in other databases, or employing alternative applicable MeSH terms may have been overlooked.Two widely used terms, "panel management" and "PHM, " were utilized to describe the proactive management of an entire population at risk of adverse outcomes.These terms are frequently used interchangeably in the literature, but their recent emergence suggests that a broader search might have yielded more publications.Secondly, current study focused exclusively on the RE-AIM framework and did not explore other frameworks such as the widely used Consolidated Framework for Implementation Research.This decision may have limited the exploration of potential barriers and facilitators to successful implementation.Nonetheless, as the objective was to better understand the potential of interventions for broader dissemination and adoption, the RE-AIM framework was intentionally selected because of its specific emphasis on assessing how interventions perform in real-world implementation settings.We acknowledge that the RE-AIM framework is not the only framework.Rather, it was used as an appropriate framework in which to present carefully systematized findings to enable readers to exercise their own discernment.Finally, another noteworthy limitation pertains to the scope of this review, which was centered on primary care.As the organization of primary care can vary considerably across different countries, it is prudent to exercise caution when applying the findings to countries with different healthcare systems.Nonetheless, it is worth emphasizing that the shared goal of providing accessible and appropriate care to all patients remains a common thread across these diverse settings.

Implications for research and practice
In line with the findings in this study, other health interventions tend to underreport aspects covered by RE-AIM dimensions, which may result in a poor understanding of the factors contributing to the success or failure of intervention implementation (15, [49][50][51][52].Providing clear, standardized documentation of the effectiveness of implementation would improve understanding of potential public health impacts and better inform future research efforts (16,61).Moreover, a better understanding would help demonstrate practical impacts and potentially stimulate wider adoption of such interventions.
Decision-makers can use the population health impact score to assess the feasibility of implementing an intervention within their specific setting (55).However, caution is advised when interpreting an average score because it may not encompass all dimensions outlined in the RE-AIM framework.It may be more insightful to compare the scores for each dimension across different PHM interventions (62).This practical method allows for better visual communication with relevant stakeholders (63), providing a comprehensive view of intervention strengths and weaknesses regarding reach, effectiveness, adoption, implementation, and maintenance.
In conclusion, while many interventions did not fully report results across all RE-AIM dimensions, those that reported on Reach, Effectiveness, Adoption, Implementation, and Maintenance showed positive outcomes.Population Health Management interventions demonstrated their potential by reaching a significant number of participants and reducing CMD risk factors.Assessment of the RE-AIM average indicated that achieving the highest potential population health impact required reaching eligible participants through email or telephone, maintaining extensive intervention contacts via navigator support, website and telephone services, and consistently delivering all intended components within a specific setting.However, to further substantiate these results, reporting on adoption, implementation processes and the sustainability of these interventions must improve.

TABLE 2
Number of interventions reporting the RE-AIM dimensions.

TABLE 3
Potential population health impact (RE-AIM average).