System-Level Factors Affecting Long-Term Care Wait Times: A Scoping Review

Abstract

Waitlists for long-term care (LTC) continue to grow, and it is anticipated aging populations will generate additional demand. While literature focuses on individual-level factors, little is known about system-level factors contributing to LTC waitlists. We considered these factors through a scoping review. Inclusion/exclusion included publication year (2000–2022), language, paper focus, and document type. A total of 815 abstracts were identified, only 17 studies were included. Through qualitative content analysis, 10 key factors were identified: (1) waitlist management styles, (2) inconsistent standards of admission, (3) personnel shortage, (4) insufficient community-based care, (5) inequitable distribution of services, (6) lack of system integration, (7) unintended consequences of insurance plans, (8) ranking preferences, (9) the debate of supply and demand, and (10) financial incentives. Targeting interventions to address waitlist management, community-based care capacity, and demographic trends could improve access. More research is needed to address system-level barriers to timely LTC access.

Résumé

Les listes d’attente pour les soins de longue durée continuent de s’allonger, et on s’attend à ce que le vieillissement des populations amplifie la demande. Alors que la littérature se concentre sur les facteurs individuels, on sait peu de choses sur les facteurs systémiques qui contribuent aux listes d’attente pour les soins de longue durée. Nous avons mené une étude de portée pour examiner ces facteurs. Les critères d’inclusion et d’exclusion étaient l’année de publication (2000–2022), la langue, le thème de l’article et le type de document. Au total, 815 résumés ont été recensés, mais seules 17 études ont été retenues. L’analyse qualitative du contenu a permis de répertorier 10 facteurs clés : 1) styles de gestion des listes d’attente, 2) manque d’uniformité des critères d’admission, 3) pénurie de personnel, 4) insuffisance des soins communautaires, 5) répartition inéquitable des services, 6) manque d’intégration des systèmes, 7) conséquences involontaires des régimes d’assurance, 8) critères de priorisation, 9) débat sur l’offre et la demande, et 10) incitatifs financiers. Des interventions ciblées visant la gestion des listes d’attente, les capacités de soins communautaires et les tendances démographiques pourraient améliorer l’accès. D’autres études sont nécessaires pour remédier aux obstacles systémiques à l’accès aux soins de longue durée dans des délais raisonnables.

Introduction

As the population ages, an increasing number of older adults may require support in facility-based long-term care (LTC). LTC is defined as the care and services provided to individuals who are not able to live independently and require on-site nursing care, 24-hour supervision, and/or personal support (Ontario Ministry of Health and Long-Term Care, 2022). Over the past several decades, policy makers have recognized the importance of ensuring access to community-based health and social support for aging populations. However, limited home care budgets, eligibility criteria that target high clinical needs, and long waitlists have limited access for many (Williams et al., 2016). Gaining access to LTC can be difficult and complex, eligibility criteria are variable, and the admissions process is guided by regulatory frameworks and guidelines that are designed to exhaust all community-based care options prior to LTC placement (Canadian Healthcare Association, 2009).

With limited community care budgets and population aging, facility-based LTC has seen an increase in demand with corresponding waitlists of up to 144 days (Financial Accountability Office of Ontario, 2019, 2021). Individuals on LTC waitlists can experience adverse events, including death, and with the demand for LTC within OECD countries rising, it is important to address LTC admission processes to improve access and reduce negative events for those waiting (Tanuseputro et al., 2017).

Literature that focuses on factors that influence waitlists for LTC mostly identifies individual-level factors (e.g., the presence or absence of a caregiver). The risk of admission to LTC is affected by the presence, volume, and mix of unpaid care; the availability of receiving this type of care is decreasing due to shrinking and geographically dispersed caregiving networks (Kuluski et al., 2012; Laporte et al., 2017). A common approach in assessing need/risk when prioritizing access to LTC placement is to aggregate scores on activities of daily living (ADL) and instrumental activities of daily living (IADL) instruments (Kuluski et al., 2012; Laporte et al., 2017). Aside from levels of physical impairments, having formal or unpaid help, living alone or with others, age, cognitive impairments, and gender have often been associated with an increased likelihood of LTC admission (Haken et al., 2002; Kuluski et al., 2012).

As the burden of wait times become evident in health care settings worldwide, studies have also considered the characteristics of specific health care organizations (or ‘organizational-level factors’) and their impact on LTC waitlists. Organizational-level factors can include patient flow, referral processes, acuity thresholds, bed capacity, organizational culture, staffing ratios, staffing model, and organization size (i.e., number of staff, and residents) (Scholl et al., 2018; Valaitis et al., 2018). While individual- and organizational-level factors have been studied, it is pertinent to investigate potential system-level factors that impact LTC waitlists to identify evidence-informed implications of current policy approaches.

System-level factors include the different characteristics of the health care system that guide the work of health care organizations and can include strategies, policies, and guidelines (Scholl et al., 2018). This can include health care legislation, incentives within the system (i.e., the role of payment models and accreditation/certification criteria), clinical guidelines, the culture of health care delivery, provider education, and licensing (Scholl et al., 2018). System-level factors can therefore involve such things as home care budgets, eligibility or admission criteria, payment models that might favour different resident mix (i.e., levels of acuity), incentives to support co-ordination across sectors (i.e., between acute and community), and organizational homogeneity/heterogeneity (i.e., the similarity of the organizations and how they are organized/function, the extent to which residents are clinically similar or different, etc.).

Scoping reviews are exploratory and descriptive (Peters et al., 2020, 2021); they can be used to identify and analyse knowledge gaps and explore the extent and nature of a body of literature, and are particularly helpful when literature is ‘complex and heterogeneous’ (Peters et al., 2020, p. 2121). The goal of this scoping review was to explore which system-level factors may contribute to LTC waitlists. To the best of our knowledge, there has not been a comprehensive review of the literature that identifies system-level factors that contribute to LTC waitlists, and thus this study could provide further insight into the source of wait times across modern health care systems.

Methods

This study used a scoping review method to synthesize the existing international literature on system-level factors that affect LTC waitlists. Scoping reviews are appropriate to explore literature, map, and summarize evidence, and inform future research (Tricco et al., 2018). In addition to Arksey and O’Malley’s (2005) framework, the recommendations of conducting scoping reviews provided by Levac et al. (2010) and Peters et al. (2020, 2021) were consulted in conducting this study. Our research steps included (a) identifying the research question, (b) identifying relevant studies, (c) selecting studies, (d) charting the data through data extraction, and (e) collating, summarizing, and reporting the results (Arksey & O’Malley, 2005; Levac et al., 2010). The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses: extension for Scoping Reviews) guides reporting of this review (Peters et al., 2020; Tricco et al., 2018) (see Supplementary Appendix I for the PRISMA-ScR checklist).

Inclusion and exclusion criteria

Scoping reviews can include multiple forms of evidence (Peters et al., 2021); our goal was to explore and identify a comprehensive overview of relevant literature on the factors associated with LTC wait times. As such, the criteria for inclusion and exclusion were based on relevance and not the quality of the studies. We included only articles written in English, as it is the working language used by the authors of this study; non-English literature was excluded due to feasibility and resources. Originally, our intent was to restrict articles written within the last decade; however, we found that this limited the results available for review and expanded our search to articles published since the year 2000. No restrictions were placed on participants or the location of the study. See Table 1 for detailed inclusion and exclusion criteria.

Table 1. Inclusion and Exclusion Criteria

Search strategy

We developed a search strategy with an academic health sciences librarian. Search terms included ‘long-term care’, ‘nursing home’, ‘continuing care’, ‘waitlist’, ‘wait times’, and ‘wait lists’ to capture a wide variety of results. Keywords or MeSH terms varied based on the index terms available within each database. See Supplementary Appendix II for an example search strategy.

We searched four electronic databases: Cochrane Database of Systematic Reviews, Ovid MEDLINE (in-process and other nonindexed citations; 1946 to December 2020), AgeLine (EBSCO), and EconLit (EBSCO). We excluded systematic reviews due to the risk of duplicity of results; however, we searched reference lists for relevant literature. Additionally, the Canadian Institute for Health Information and the National Institute on Ageing (NIA) were hand searched. The search was performed in August 2022.

Evidence screening and selection

The initial search yielded 815 results. After removing duplicates in EndNote, two reviewers (J.F. and H.S.) independently performed a title and abstract screening using our inclusion and exclusion criteria on the remaining 756 articles using Rayyan software. A total of 724 records were excluded, and 32 full-text articles were screened. Non-English articles were excluded during screening. Discrepancies were resolved through consensus meetings with screeners to ensure consistent interpretation of inclusion criteria, and when consensus could not be reached, a third reviewer assessed eligibility. After conducting the full-text screening, 17 articles met our inclusion criteria (Figure 1).

Figure 1. Article screening and selection.

Data extraction

Three team members (J.F., H.S., and E.S.) reviewed the full text of the articles and constructed an extraction table (see Supplementary Appendix III). Data extracted from the articles included (a) authors, (b) title of the article, (c) year of publication, (d) location (country), (e) study purpose, (f) design/methods (e.g., qualitative, quantitative, and mixed method), (g) key findings, and (h) narrative descriptions of system-level factors (i.e., characteristics of the health care system) attributed to LTC wait times. Key findings were extracted from explicit statements from the articles or from summaries of studies made by research team members. As scoping reviews are an iterative process (Munn et al., 2018), the data extraction form was refined and updated throughout the course of the review and in response to the emergent findings and consensus discussions with team members.

Data analysis

Using qualitative content analysis (Levac et al., 2010), findings from included articles were collated, with analysis consisting of the grouping of similar factors into themes relating to system-level factors that affect LTC waitlists. Themes were developed inductively, remaining open to new and unexpected concepts that emerged throughout the analysis process. Content analysis is a research tool utilized to identify common themes and concepts within text (Barnett-Page & Thomas, 2009; Hsieh & Shannon, 2005). Descriptive qualitative analysis through coding is a useful technique in scoping reviews when the ‘purpose is to identify or clarify concepts’ (Peters et al., 2020, p. 2125). System-level factors were extracted from included studies and summarized; similar factors were collated into higher-level factors where appropriate. Analysis meetings among research team members were held to reach consensus.

Results

The final 17 articles included nine quantitative studies, five qualitative studies, and three mixed methods. Among the 17 studies, 7 studies were from Canada, 6 were from the Netherlands, and 1 was from Spain, Japan, and Poland, respectively (see Data Extraction Table in Supplementary Appendix III for more details).

Based on the scoping review, 10 factors were identified in the reviewed literature (see Table 2): (1) waitlist management styles, (2) inconsistent standards of admission, (3) personnel shortage, (4) insufficient community-based care capacity, (5) inequitable geographic distribution of services, (6) lack of system integration, (7) unintended consequences of insurance plans, (8) ranking preferences, (9) the debate of supply and demand, and (10) financial incentives and funding. While being distinct factors on their own, often these were interrelated and/or influenced each other. Such instances are noted below.

Table 2. Factors identified in the literature

1. Waitlist management styles

Eight articles discussed the method of waitlist management as a reason for longer waitlists (Arntzen et al., 2022; Chafe et al., 2010; Haken et al., 2002; Kommer, 2002; Kuluski et al., 2012; Nakanishi et al., 2012; van Bilsen et al., 2006; Zhang et al., 2012). Five articles mentioned the concept of first-come-first-served waitlist management, particularly through the act of precautionary waitlisting as being a driver for longer waiting times (Arntzen et al., 2022; Chafe et al., 2010; Haken et al., 2002; Kuluski et al., 2012; van Bilsen et al., 2006). Additionally, four studies note the quality of waitlist management data as a factor that can result in longer waiting times (Kommer, 2002; Nakanishi et al., 2012; van Bilsen et al., 2006; Zhang et al., 2012).

Precautionary waitlisting occurs when individuals are placed (by themselves or a proxy) onto the LTC waitlist in anticipation of a long wait for LTC admission. Haken et al. (2002) contend that individuals utilize ‘strategic behaviour’ (p. 354) by placing themselves on the waitlist early. The hope is that when their place in the queue comes up, they will require LTC support. Precautionary waitlisting appears to be an emerging trend in the absence of needed lower-level community support such as housekeeping or meal preparation (see Factor 4) (Kuluski et al., 2012). It is an unintended consequence of long wait times and a first-come-first-served waitlist management style that lacks procedures to prevent this action. While some people may be able to predict the timing of their need for LTC well enough in advance, others put themselves on a waitlist to guard against the possibility that they might need LTC in the future. In effect, this practice causes a feedback loop – people precautionary waitlist, which increases wait times, causing further precautionary waitlisting. van Bilsen et al. (2006) found that 35 per cent of older adults on a waitlist did not actually experience an immediate demand for residential care. Instead, respondents registered out of a sense of precaution, a strategic decision dictated by current shortages in care provision and vulnerable health status (van Bilsen et al., 2006). This can cause people to refuse immediate admission to an available LTC facility due to a lack of immediate need (Chafe et al., 2010). In fact, applying for admission (and being on a waitlist) often does not lead to actual admission (Haken et al., 2002). Precautionary waitlisting can also lead to overestimations of unmet needs and masking the level of demand for specific facilities.

The quality of waitlist data can at times be substandard, in the sense that potential residents can be double registered, meaning they are on the waitlist despite having received care or resident information is incomplete (Kommer, 2002). As a result, unreliable information on waitlists leads to longer wait times. Additionally, some older adults recover and no longer perceive the need to move to an LTC home and yet, do not remove their names from the waitlist (van Bilsen et al., 2006). van Bilsen et al. (2006) found that some older adults registered on a waitlist were already admitted into an LTC home or were deceased and yet remained on the list. In summary, the literature suggests that there may be an opportunity to reduce wait times for LTC by paying closer attention to waitlist management and ensuring that redundancies and missing data do not become contributing factors.

2. Inconsistent standards of admission

Two studies noted that inconsistent standards of admission across LTC homes contribute to wait times and variability in admission criteria (Kuluski et al., 2012; Nakanishi et al., 2012). Thus, some LTC homes can select residents based on acuity thresholds or specific diagnoses. In Japan, where the model of LTC home admission is needs-based, discrepancies between the LTC insurance system and the Public Aid for the Aged Act has allowed LTC homes to establish their own guidelines for evaluating the priority of applicants (Nakanishi et al., 2012). As a result, some LTC homes reject residents requiring individual care (e.g., intravenous feeding or tube feeding) or with behavioural and psychological symptoms of dementia (BPSD) (Nakanishi et al., 2012). Given this discretion, individuals who present with challenging behaviours remain on the waitlist and can extend wait times for individuals who require high levels of care. This occurs regardless of the number of available beds, staffing ratios, presence of allied health care workers (i.e., physiotherapy), or presence of outreach night care (Nakanishi et al., 2012). Some LTC facilities will deny residents admission due to limited resources to care for residents with BPSD (Nakanishi et al., 2012). In Ontario, Canada, Kuluski et al. (2012) examined how in areas where community-based care was less accessible (see Factor 4), the needs threshold for admission was lower with waitlists compiled of individuals with relatively low needs. Eligibility thresholds varied between regions, which could be explained by limited home and community care resources and smaller population size (see Factor 5) (Kuluski et al., 2012). Varied eligibility and admission criteria led to inconsistent patterns of LTC placement, and at times resulted in longer waitlists for those with higher care needs, or who rely on government support to afford LTC.

3. Personnel shortages

Two studies noted personnel shortages as a contributing factor in wait times (Hoek et al., 2000; Raciborski & Samolinski, 2015). In the Netherlands, Hoek et al. (2000) identified personnel shortages in LTC and difficulty in recruiting qualified personnel as complicating waitlists. Subsequently, LTC homes are not admitting as many residents. This was attributed to aging populations – facilities cannot compete with the increase of ‘(very) old people’ (Hoek et al., 2000, p. 215) (i.e., aged 80 and older) – the negative image of LTC homes, inadequate salaries compared with other professions, and a low level of interest in the profession. Hoek et al. (2000) note that insufficiencies in staff are particularly problematic in large cities and are felt to a lesser extent in rural areas (Hoek et al., 2000). Raciborski and Samolinski (2015) state that a personnel deficit is one of the key factors influencing the wait times for LTC. In their survey of medical directors in Poland, 40 per cent noted HR deficiency in caregivers in their facilities, and 61 per cent of directors reported that HR deficits indicate insufficient funds for new jobs as the key reason for such a deficit. This factor is related to the way in which LTC homes are financed (see Factor 10) and the unintended consequences of insurance plans (see Factor 7).

4. Insufficient community-based care capacity

Six studies mentioned the availability of community-based care as a factor contributing to LTC wait times (Berger et al., 2020; Chafe et al., 2010; Kuluski et al., 2012; Laporte et al., 2017; NIA, 2019; Zhang et al., 2012). Community-based care includes any non-medical care (e.g., home support, home care, and community-based nursing) provided outside of institutional settings. This has been a recent priority in modern health care systems as governments aim to support aging in place to respect the preferences of older adults and decrease demand for high-cost institutional care. Many individuals waiting for LTC admission can be redirected to home care or housing with care options while maintaining safety and reducing costs (Williams et al., 2009 as cited in Laporte et al., 2017). Older adults, their caregivers, and service providers may lack awareness about care options that are available in the community (Laporte et al., 2017). Moreover, community support services lack consistency in terms of scope and availability with programs within and between provinces in Canada (Kuluski et al., 2012). NIA (2019) notes that over 40,000 Canadians are currently on waitlists for LTC, in part due to a lack of home- and community-based care. When aging-in-place policy priorities correspond with reduced LTC capacity and are not matched by an expansion of home- and community-based care funding and capacity, an increase occurs in LTC wait times and delayed discharge from the hospital (NIA, 2019). Evidence on community-based care suggests that LTC waitlists do not always imply the need for more LTC beds (Kuluski et al., 2012). These findings highlight the impact of community programming on LTC wait times and demonstrate deficits in the provision of appropriate community-based care.

5. Inequitable geographic distribution of services

Inequitable geographic distribution of services via eligibility thresholds is also identified as a factor contributing to LTC wait times (Kuluski et al., 2012; Laporte et al., 2017). This is associated with insufficient community capacity (see Factor 4). Rural and remote populations tend to receive LTC at lower levels of need (Kuluski et al., 2012). It is suggested that the greater supply of LTC beds along with inadequate community services in rural regions contribute to a higher potential for LTC admission (Coward et al., 1994 as cited in Kuluski et al., 2012). Additional findings pertaining to the rural region include inadequate formal services (i.e., lack of paid caregivers) and the decline of unpaid caregiver networks (Kuluski et al., 2012). Variations between geographical regions impact LTC wait times differently. Although rural areas tend to have fewer community care options than urban communities, they also tend to have more LTC beds per capita (Laporte et al., 2017). Home and community care capacity (see Factor 4) may be limited in rural and remote areas because of distance, low population density, and a lack of service infrastructure, resulting in a higher risk of institutionalization (Laporte et al., 2017). As a result, older adults in rural locations have greater odds of being on an LTC waitlist than urban older adults (Laporte et al., 2017). Considerations in improving services and alleviating wait times must therefore include distributing home- and community-based services equitably across geographic regions.

6. Lack of system integration

Lack of co-ordination between sectors in healthcare such as aged care, community care, acute care, and primary care is associated with LTC wait times (Berger et al., 2020; Kuluski et al., 2012; Nakanishi et al., 2012; NIA, 2019; van Bilsen et al., 2006). Delays in LTC placement and extended wait times can be attributed to a lack of co-ordination between general hospitals, health maintenance organizations, and long-term facilities (Berger et al., 2020). NIA (2019) states that ‘lack of integrated systems, poor coordination of admissions to long term care and overly complex rules related to eligibility and choice are resulting in bottlenecks, duplication, longer wait times and negative resident and family experience’ (Long Term Care Innovation Expert Panel, 2012 as cited in NIA, 2019, p. 68). Nakanishi et al. (2012) also note that preparations for the care of older adults have lagged the growth of aging populations (see Factor 9). Wait times for LTC homes increase as other options such as rehabilitation and community care remain inaccessible for some older adults (i.e., financial constraints and capacity) (Nakanishi et al., 2012). Beyond the hospital, the health care system can be fragmented and interventions to mitigate fragmentation have been incremental, resulting in an uneven and often inadequate system, lacking sufficient care opportunities (Kuluski et al., 2012). This is especially true in rural communities (see Factor 5) with long distances between places of care and people who can deliver such care, sparse populations, and little home and community care infrastructure resulting in a reliance on LTC facilities. Fragmented and hard-to-navigate systems can contribute to LTC wait times and can result in precautionary waitlisting (see Factor 1).

7. Unintended consequences of insurance plans

Legislation and regulation of LTC sectors can ensure equitable access to LTC but have led to unintended consequences (Casado et al., 2000; Nakanishi et al., 2012; NIA, 2019; Roblin et al., 2019). In Japan, the introduction of the Long-Term Care Insurance plan made LTC more accessible for individuals regardless of their economic situation; however, this resulted in an increased number of applicants and long waitlists for placement into LTC (Nakanishi et al., 2012). NIA (2019) notes that LTC is considered an extended health care service (within Canada) and therefore not insured under the Canada Health Act (Canada Health Act, 1985). Each province and territory has developed its own legislation and accompanying policies and regulations to govern the provision and financing of LTC. This has led to varying styles of waitlist management systems (see Factor 1). In Ontario, Canada, there are two forms of residential care for older adults: LTC homes that receive some form of public funds, and retirement homes that are privately funded and are regulated through separate legislation (Roblin et al., 2019). LTC homes provide legally required services, whereas retirement homes differ by owner and by home in the extent of services offered (Roblin et al., 2019). Within a publicly funded health care system such as Canada’s, this has resulted in a two-tiered system with LTC homes having a public responsibility to provide care and retirement homes that do not share the same level of responsibility. Occupancy rates tend to be significantly higher in LTC homes than in retirement homes in Ontario with correspondingly longer waitlists for LTC homes (Roblin et al., 2019). This two-tiered system results in those who are more affluent living in retirement homes, and those who are not waiting for LTC. Funding for LTC within Ontario is based on occupancy targets. According to Roblin et al. (2019), ‘the Ministry [of Health] requires all homes to maintain the occupancy rate of their long-stay beds at a minimum of 97 percent in order to receive 100 percent of the per-person-per-diem-funding…’ (p. 160). Since the demand for LTC beds exceeds the supply of beds available, there are high occupancy rates in LTC homes. In the LTC home sector, demand for long-stay beds continues to grow, with consequent stress on the existing supply (Roblin et al., 2019). In Spain, it was reported that two-thirds of LTC beds are in private institutions (many of which are owned by religious organizations or other non-profit institutions) and only one-third are covered by the social protection system (Casado et al., 2000). Prices of private institutions are not affordable to most older people, meaning there are long waitlists for admission to public LTC facilities (Casado et al., 2000). For those who can afford more privatized care, retirement homes may become a viable option, but for those dependent on publicly subsidized housing, the waitlist is anticipated to grow. The legislation around LTC that has allowed for the emergence of two-tiered systems could increase waits for those reliant on the public system.

8. Ranking preferences

The ability to rank order preferred choice of LTC homes is a result of the waitlist management style (see Factor 1) and can have the unintended consequence of lengthening wait times for admission. Seven studies discussed the ability of individuals to rank order the LTC homes of their choice on their application (Arntzen et al., 2022; Chafe et al., 2010; Haken et al., 2002; Kuluski et al., 2012; Laporte et al., 2017; Qureshi et al., 2021; Roblin et al., 2019). Ranking occurs when admission to LTC is managed by a single-entry point. A study conducted in Ontario, Canada found that the chance of receiving a bed in an LTC home increased with the number of LTC waitlists a person was on (Qureshi et al., 2021). Notably, however, rural and remote areas have been described as relatively over-bedded on a population basis (Kuluski et al., 2012). The need to place oneself on multiple waitlists within rural and remote areas would therefore be less than in densely populated areas. Further, other determinants, aside from physical impairment and age, influence admission to LTC (Haken et al., 2002). Individuals accepting admission to LTC facilities often consider remaining close to home, being in the same facility as their spouse, or being in a facility that is associated with their religion (Chafe et al., 2010). People can be selective about the facilities in which wish to live (Chafe et al., 2010). When older adults have placed their name on a waitlist in anticipated need (see Factor 1) and are offered immediate admission, they may decline the bed offer, resulting in an overestimation of demand (Chafe et al., 2010). Qureshi et al. (2021) contend that there is an especially large shortage of ethnocultural LTC facilities that support the language and cultural needs of residents, resulting in recent immigrants waiting longer to be placed in an LTC home than long-standing residents, as they often desire to be placed within ethno-specific cultural and religious facilities.

Arntzen et al.’s (2022) simulation study on preference-based allocation models found that their allocation model (where older adults provide their willingness to be placed within specific LTC homes) outperforms commonly used facility-based waitlists (where the facility manages its own waitlist) for LTC, while meeting individual preferences to a larger extent. This, however, requires a centralized office to take on the role of placing older adults into LTC. Suggesting that while preference-based ranking results in shorter wait times than facility-based waiting times, more research is required into waitlist management styles. Ranking approaches combined with first-come-first-served waitlist management styles and limited culturally specific beds can contribute to long LTC wait times.

9. The debate of supply and demand

The debate around supply and demand was one of the factors mentioned most throughout this review (Berger et al., 2020; Hoek et al., 2000; Kuluski et al., 2012; Laporte et al., 2017; Meiland et al., 2001; Qureshi et al., 2021; Raciborski & Samolinski, 2015; Roblin et al., 2019). The need for LTC beds/facilities is particularly pronounced when accompanied by limited or insufficient community-based care capacity (see Factor 4). Meiland et al. (2001) contend that the demand for LTC exceeds the supply, contributing to waitlist times for LTC. Prolonged waitlists reflect a significant disconnect between residents’ needs and the availability of appropriate health care services (Berger et al., 2020), particularly in the availability of ethno-specific cultural LTC facilities (Qureshi et al., 2021). Hoek et al. (2000) in their profile of the Netherlands health care system for older adults argue that the increase in the number of facilities cannot compete with the increase in the number of the oldest old, resulting in waitlists for admission to LTC and congestion in the hospital system. Additionally, Raciborski and Samolinski (2015) interviewed directors of LTC in Poland and found that the current supply of LTC homes is insufficient as compared to the demand. They attribute this to population aging and the scale and type of care that this population will require (Raciborski & Samolinski, 2015). While waitlists have been seen as evidence of too few facility-based LTC beds to meet the needs of the aging population, waitlists may also show that there are insufficient community-based care options to support older adults to stay at home (see Factor 4) (Kuluski et al., 2012). An example of this is in rural communities, where there are fewer community care options (see Factor 5), increased beds per capita, and persistent waitlists. The solution is not increased bed numbers; rather, as Berger et al. (2020) state, the solution may be appropriate health and social care services (e.g., home- and community-based supports). More LTC beds do not necessarily mean improved access and less waiting, nor is it the most efficient solution (Kuluski et al., 2012). All care options for older adults are expected to face severe capacity challenges in the coming years (Roblin et al., 2019). This demand for care has been primarily determined by pending demographic changes, including population aging (Roblin et al., 2019). However, it is possible to care for the older population without increasing the number of LTC home beds; Laporte et al. (2017) note that Denmark, with a comprehensive community-based care sector, has built no LTC beds since the late 1980s. However, there are no reliable statistics on wait times in Denmark due to varying wait times between private and public care homes.

10. Financial incentives and funding

Five studies note the funding structure of LTC homes as a system-level factor that contributes to waiting times (Berger et al., 2020; Casado et al., 2000; Qureshi et al., 2021; Raciborski & Samolinski, 2015; Roblin et al., 2019). Berger et al. (2020) explain that in Israel there is a financial conflict between the LTC facilities and the ‘accounting rules between the HMO [health maintenance organization] and the general hospitals’ (p. 4), between the ‘cost of a hospitalization day and the actual expenditure on the service for the detained residents’ (p. 4). This results in little initiative to establish new LTC beds and does not motivate the quick transfer of residents who are waiting in the hospital to an LTC facility (Berger et al., 2020). Raciborski and Samolinski (2015) state that one of the key factors influencing the number of available places and waiting times is insufficient funding. They contend that the financial aspect remains the key barrier to increasing the supply of care institutions and limited resources prevent an increase in the number of available places and employment of additional staff (Raciborski & Samolinski, 2015). Funding in Ontario, Canada for LTC is dependent on bed occupancy levels; as such, there is a financial incentive for LTC homes to be always at or near capacity to receive the maximum per-person-per-diem funding, resulting in longer waitlists (Qureshi et al., 2021; Roblin et al., 2019).

Discussion

Our findings join a growing body of research that suggests that the need for residential LTC beds is in large part determined by access to appropriate, cost-effective community-based care. To the best of our knowledge, this work represents the first attempt to provide a comprehensive exploration of the system-level factors that have been explored to date in the literature. The primary findings of this review are that while the need for more LTC beds and facilities was noted as a common factor, this was primarily due to a system lag in meeting demographic needs, primarily through community-based home care support.

Research has shown that there is insufficient community-based care capacity, and as a result, people are being placed on LTC waitlists (Berger et al., 2020; Chafe et al., 2010; Kuluski et al., 2012; Laporte et al., 2017; NIA, 2019; Zhang et al., 2012). NIA (2019) notes that ‘increases in waitlists often reflect changes to other parts of the system that are not sufficiently alleviating the demand for nursing home care’ (p. 68). Kuluski et al. (2012) contend that LTC waitlists should not be taken as an indication of a need for more institutional LTC beds but might reflect constraints in other sectors of the care continuum. Timely access to the right type of care is important for the health and well-being of older adults and their unpaid caregivers.

One suggestion to reduce LTC wait times has been to introduce a single-entry system to all continuing care services to effectively match service with need (Arntzen et al., 2022; Thompson, 1997). Single-entry systems should, in essence, reduce redundancies in waitlist procedures; however, a lack of system integration (Kuluski et al., 2012; Nakanishi et al., 2012; NIA, 2019; van Bilsen et al., 2006) and problems with waitlist management (Arntzen et al., 2022; Chafe et al., 2010; Kommer, 2002; Nakanishi et al., 2012; van Bilsen et al., 2006; Zhang et al., 2012) procedures can still persist, contributing to wait times. Kommer (2002), for example, notes that individuals can be on a waitlist despite having already been placed. Our findings from this scoping review support the notion that long wait times or ambiguous or unclear admission guidelines when care requirements are high may prompt both families and gatekeepers to the system (i.e., assessors of required need) to put individuals on a waitlist earlier than necessary in order to ensure a spot when it is needed (Thompson, 1997). This precautionary waitlisting is made possible by first-come-first-served waitlist management and is often done in anticipation dictated by perceived shortages in LTC beds and the anticipation of declining health status (Chafe et al., 2010; Haken et al., 2002; Kuluski et al., 2012; van Bilsen et al., 2006).

First-available-bed or first-available-appropriate-living-option policies require organization through a single-entry system and have been introduced in an attempt to facilitate timelier placement into LTC (Health Quality Council of Alberta [HQCA], 2014). However, these types of waitlist management policies are accompanied by ethical issues such as justice or fairness, autonomy or choice, and equitable resource allocation (HQCA, 2014). Shapiro et al. (1992) contend that policy dictates residents have the right to be placed into an LTC facility of their choice and that LTC facilities have the right to refuse admission. Some LTC homes select residents based on acuity thresholds or specific diagnoses when they set their own eligibility/admission criteria (Kuluski et al., 2012; Nakanishi et al., 2012). Our findings from this scoping review suggest that inconsistent patterns of LTC placement have resulted from a variety of eligibility and admission requirements, which has resulted in longer waitlists for those with high degrees of individual care needs or who rely on government assistance to fund LTC.

Another suggestion to reduce LTC wait times has been to introduce housing with support services (i.e., retirement homes, assisted living, or supportive housing facilities) for lower-care residents who do not yet require institutional LTC but cannot remain safely in their own homes. These types of facilities are an alternative older adult congregate housing model. They are a middle ground between community-based home care and residential LTC (Sivananthan et al., 2015). Previous research has shown that approximately 12 per cent of LTC residents in Manitoba, Canada have clinical needs (e.g., ADL, cognitive, behavioural, and continence) similar to individuals who reside in the community (Doupe et al., 2016). Kuluski et al. (2012) contend that in Ontario, Canada, similar studies have demonstrated that between 14 per cent and 49 per cent of individuals wait-listed for LTC could potentially age at home, safely with sufficient community-based care (including IADL support). Campbell-Enns et al. (2020) found that reasons for early admissions are linked to breakdowns in community and health care systems. In essence, older adults were placed onto waitlists for LTC (instead of into assisted living facilities) due to caregivers’ inability to maintain the care levels required to keep someone safe in the community when faced with insufficient community-based care capacity (Campbell-Enns et al., 2020). In the absence of needed community support, placement on an LTC waitlist may become the default option (Sivananthan et al., 2015). Doupe et al. (2016) state that cost disincentives may limit some individuals’ ability to reside within assisted living and as a result be placed into LTC.

Building more LTC bed capacity has been another argument to reduce LTC wait times; however, this comes at a large cost burden to the government (Sawamura et al., 2015). This solution could remain particularly problematic when accompanied by personnel shortages (Nakanishi et al., 2012; Raciborski & Samolinski, 2015). Recruiting and retaining staff for LTC is especially difficult considering the negative image of LTC homes, inadequate salaries compared with other professions, and a low level of interest in the profession (Hoek et al., 2000). While waitlists have been seen as a result of insufficient LTC bed capacity to meet the needs of the aging population, they may in fact indicate that there are insufficient community-based care options available (Kuluski et al., 2012). Additionally, the literature shows that higher volumes of LTC beds do not automatically result in shorter wait times. Sivananthan et al. (2015) argue that some evidence suggests the opposite is true; for example, Manitoba, Canada has a high supply of LTC beds, but also a high volume of individuals in hospitals awaiting LTC placement (Sivananthan et al., 2015). Both the existence and volume of waitlists may inflate the demand for LTC (Kuluski et al., 2012). Sivananthan et al. (2015) argue that there is no ‘right’ or magic number of LTC beds; rather, the number of beds should be related to many factors including perceived need, and the presence or absence of other formal and informal options for supporting older adults to remain in the community.

In the LTC admission process, some groups of people experience more barriers than others in receiving a placement in a timely manner (Qureshi et al., 2021). Our results suggest that system improvement should focus on health equity as a priority, to ensure that all older adults have timely access to care. Ongoing waitlist management issues indicate that a needs-based model may be a more appropriate approach for prioritizing residents; this would require regulation to ensure it is both current and accurate. There is a need to maintain regular contact with those on the waitlist to verify their current level of need, and whether facility preferences have changed to gain a more accurate picture of LTC need (Chafe et al., 2010). Notably, what was missing from the studies reviewed was system-level factors that affect individuals who are already residing within LTC and wanting to transfer facilities. Future research should understand factors that impact a resident’s ability to transfer LTC homes once already in one.

The gap in waiting periods for LTC placement between public and private facilities undermines the right to health care and brings forward the debate of justice and the right to health care (Cioffi, 2021). LTC is intricately linked with the other health care services that exist along the care continuum, which can reduce the need for LTC (Lezovic & Abraham, 2009). There is no single solution to the integration of health and social care, particularly when it comes to the ethical/justice issues of access (Cioffi, 2021; Karlberg & Brinkmo, 2009). It is known that COVID-19 affected access to LTC for many, with delayed placement for facilities with outbreaks, or rushed placements for alternate levels of care patients to free up hospital beds (Cioffi, 2021). It is not yet clear when health care will ‘return to normal’ nor if it should. It is essential to protect the right of all citizens to health care, especially those at the most risk (Cioffi, 2021). To do this, public system funding, as well as proper legislation and regulation, is needed.

Strengths and limitations

This scoping review has some strengths and limitations. This scoping review provides a comprehensive review of relevant studies, allowing for the incorporation of a wide range of study designs to examine the complex concept of system-level factors. The systematic process (replicable, transparent, and rigorous) we undertook allows for the ability to explore and synthesize evidence of system-level factors, a concept with little dedicated interest. Despite the comprehensive search and reflective process of selection and analyses, there are some limitations that may have influenced the review findings. Although we identified many relevant keywords to guide our search, we may have missed articles that used different keywords. This study is limited by including only articles published in English; some relevant articles may have been missed. The age of some included studies may influence the relevancy of their findings; however, challenges faced by the LTC sector have not changed much during the review period, and factors identified in older studies could still be relevant. The lack of consistency in terms used for LTC, as well as a lack of formalized definition of system-level factors, might mean we did not capture all articles. Although focusing our review on system-level factors may have limited the breadth of content areas that we explored, given the abundance of literature on individual- and organizational-level factors, we felt it was prudent to search topics that were relatively unexplored previously.

Conclusion

Overall, this review has helped consolidate system-level factors contributing to LTC wait times. It identifies and creates a map of which factors have been explored in the literature as potentially contributing to waitlists. Predominantly, literature on factors for waitlists includes personal factors; however, this review indicates many system-level factors can impact wait times, regardless of personal factors or unpaid caregiver support. These system-level factors need to be considered when determining LTC system reform strategies. Future research should investigate the issue of waitlist management styles and the ability for precautionary waitlisting to take place. One factor had only been explored qualitatively (personnel shortage), and two factors (inconsistent standards of admission and inequitable geographic distribution of services) have only been explored quantitatively. Both could benefit from further exploratory efforts. Primary studies could focus on further understanding the role that additional community-based supports have on LTC waitlists. A future systematic review, for instance, could evaluate study quality and associations between each of these factors and the wait times. While the need for more LTC beds and facilities was emphasized, this was often due to a system lag in meeting demographic needs through community-based home care support. This scoping review highlights the need to expand the scope of research to understand key factors that contribute to LTC waitlists. While increasing the number of LTC spaces is necessary, it is not sufficient to mitigate the waitlist for the anticipated number of older adults requiring facility-based LTC care.