Next Article in Journal
Child Maltreatment Reporting Practices by a Person Most Knowledgeable for Children and Youth: A Rapid Scoping Review
Next Article in Special Issue
The Impact of Social Capital on Multidimensional Poverty of Rural Households in China
Previous Article in Journal
Impact of COVID-19-Related Social Isolation on Behavioral Outcomes in Young Adults Residing in Northern Italy
Previous Article in Special Issue
Wellness Impacts of Social Capital Built in Online Peer Support Forums
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

Risk Factors Associated with Preventable Hospitalisation among Rural Community-Dwelling Patients: A Systematic Review

1
School of Pharmacy and Pharmacology, College of Health and Medicine, University of Tasmania, Hobart, TAS 7000, Australia
2
Huon Valley Health Centre, Huonville, TAS 7109, Australia
3
School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS 7000, Australia
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(24), 16487; https://doi.org/10.3390/ijerph192416487
Submission received: 10 October 2022 / Revised: 2 December 2022 / Accepted: 3 December 2022 / Published: 8 December 2022
(This article belongs to the Special Issue Social Capital and Rural Health)

Abstract

:
Potentially preventable hospitalisations (PPHs) are common and increase the burden on already stretched healthcare services. Increasingly, psychosocial factors have been recognised as contributing to PPHs and these may be mitigated through greater attention to social capital. This systematic review investigates the factors associated with PPHs within rural populations. The review was designed, conducted, and reported according to PRISMA guidelines and registered with Prospero (ID: CRD42020152194). Four databases were systematically searched, and all potentially relevant papers were screened at the title/abstract level, followed by full-text review by at least two reviewers. Papers published between 2000–2022 were included. Quality assessment was conducted using Newcastle–Ottawa Scale and CASP Qualitative checklist. Of the thirteen papers included, eight were quantitative/descriptive and five were qualitative studies. All were from either Australia or the USA. Access to primary healthcare was frequently identified as a determinant of PPH. Socioeconomic, psychosocial, and geographical factors were commonly identified in the qualitative studies. This systematic review highlights the inherent attributes of rural populations that predispose them to PPHs. Equal importance should be given to supply/system factors that restrict access and patient-level factors that influence the ability and capacity of rural communities to receive appropriate primary healthcare.

1. Introduction

Potentially preventable hospitalisations (PPHs) occur when a medical condition that could have been avoided or managed with timely and adequate healthcare in the community results in a hospitalisation [1,2]. Other terms used interchangeably include potentially avoidable hospitalisation (PAH) [3], a hospitalisation due to an ambulatory care-sensitive condition (ACSC) [4], or an ambulatory care-sensitive hospitalisation (ACSH) [5]. Worldwide, these have a negative impact on health systems [6,7,8].
During 2020–2021, just over 672,000 public hospital admissions in Australia were classified as avoidable, equating to 5.7% of all admissions [9]. Admissions for potentially preventable reasons increased by 3.4% between 2012–2013 and 2017–2018 [10]; however, during the COVID-19 pandemic PPH rates decreased due to fewer vaccine-preventable hospital admissions [9]. Diabetes complications are the most frequent reason for PPHs in Australia [9]. Health systems in many western countries, including the USA and Australia, view preventing unnecessary hospital use as integral to maintaining an equitable, efficient, and sustainable health system [8,11]. In Australia, the National Healthcare Agreement uses PPHs as a performance indicator of primary and community health services to ensure the sustainability of the health system [12].
In Australia, 28% of the population live outside of major cities. Incrementally greater distances between these populations and services are reflected in the terms “rural, regional and remote” [13]. Rural populations often experience a unique combination of reduced access to healthcare services and socioeconomic, geographical, or systemic predispositions to avoidable hospitalisation. Access itself can be modulated by patient and population characteristics [14]. Internationally, rural populations are known to experience higher rates of PPHs and many potential targets for intervention have been suggested [15,16,17,18]. Rates of PPHs in Australia are lowest in major cities (22 per 1000) while regional and remote populations have higher rates (29 and 42 per 1000, respectively) [9,13]. The prevalence of chronic diseases and smoking, and health literacy challenges are known to be higher in rural populations [13]. Health system inequalities and the social, economic, and geographical characteristics of rural communities increase their burden of PPHs [19,20,21,22]. In contrast to urban contexts, however, there has been little synthesis of the determinants of PPHs within rural settings, which was the aim of this review.

2. Method

The research question was “what factors are associated with PPHs among rural community-dwelling patients?”. A protocol for the systematic review was developed according to the PRISMA-P guidelines [23] and registered with PROSPERO (ID CRD42020152194) [24].
Prior to commencement, the Cochrane Library [25] and PROSPERO [24] registry were searched for reviews underway or completed on the topic, and none were located. Four databases (MEDLINE, Web of Science, EMBASE and CINAHL) were searched according to the systematic search strategy described in Table A1 in Appendix A. Results from each database search were saved using EndNote X8 software [26] before being exported to Covidence [27] for duplicate removal and screening. A standardised data collection form was used to extract and collate information, including location of the study, definition of PPH used, range of independent variables assessed, and approach to statistical analysis.
The review team consisted of three researchers (AR, GP, and RN). Two reviewers independently screened the studies and excluded those that did not meet the inclusion criteria, firstly based on title and abstract, followed by a full-text reading. Studies were included when two reviewers agreed that the study met the inclusion criteria (Table 1). The third reviewer resolved conflicts where agreement could not be reached. Reasons for a paper being excluded at the full-text level were recorded.
Retained papers were independently assessed for quality by two research team members (AR and one other). The Newcastle–Ottawa Scale (NOS) for non-randomised studies [28] was used for quantitative studies, while qualitative studies were assessed with the CASP Qualitative Checklist [29,30]. A third reviewer (GP or RN) acted as adjudicator if agreement could not be reached by discussion. Because of the heterogenous nature of studies included, a narrative synthesis was used in the analysis [31].

3. Results

The initial search yielded 321 studies. After removal of duplicates (n = 166) and papers with titles or abstracts meeting exclusion criteria (n = 116), there were 39 papers remaining for full-text review. Exclusion of this number of studies was expected based on pilot searches and use of the very broad search terms. A further 26 papers were excluded for not meeting inclusion criteria, leaving 13 papers for review and analysis (Figure 1). Eight papers were quantitative/descriptive studies [32,33,34,35,36,37,38,39] and five were qualitative studies [40,41,42,43,44]. Only one study reported no statistically significant association between the variables studied (physician supply) and rural PPH risk [35].

3.1. Location

Descriptive studies were from either Australia (n = 3) or the USA (n = 5). The American studies were multi-state [35] or nation-wide [33,38], confined to one state (Nebraska) [39], or focused only on Indigenous Americans in rural California [34]. The Australian studies were set in Victoria [32], New South Wales [37], or Tasmania [36]. One study was excluded at the full-text review stage because, despite being conducted in a predominantly sparsely populated region of Spain, full-text review revealed that major cities were included in the analysis [45]. All the qualitative studies were set in Australia.
The population for study was variably described; this included being conducted in administrative areas serviced by “rural Indian Health Services” [34] or rural “health professional shortage areas” in Nebraska [39]. Other studies described a specific rural region of Tasmania [36,40,41], Victoria [32], or New South Wales [37,42,43,44] as the setting. The remainder used large, usually national, datasets from which rural patient data could be extracted.
All the qualitative studies in this review were from two research groups, each with their own geographical location of interest, namely the north coast of New South Wales [42,43,44] or Southern Tasmania [40,41].

3.2. Population

Children, incomplete data, or populations without access to hospitals were some of the exclusion criteria used in the studies (Table 1). An average age of patients in studies or presenting to hospital was not always reported; however, the proportions of people in age groups were often used to describe participants. The reported proportion of people aged >65 years ranged from 20.1% [34] to 49.5% [32]. Other studies reported an average age of participants between 63 years [36] and 75 years [38]. Females representativeness was between 48.6% [36] and 87.0% [42] in qualitative studies, whereas quantitative studies had a more equal distribution of sexes. The study by Korenbrot et al. was designed specifically to determine if there was a disparity in PPH rates between American Indians and Alaska Natives living in rural California [34].

3.3. Definitions and Data Sources

A variety of outcome definitions were used. Korenbrot et al. (2003) [34] used panel consensus to define the “avoidable” nature of admissions, while others used standard definitions of PPH [33,36,37,38].
All but one descriptive study [36] used minimum datasets linked with usage records, such as Medicare claims data [33,38] or national health databases [32,34,35,39]. Slimings et al. (2021) [37] used the Social Health Atlases of Australia in their ecological study, while Ridge et al. [36] linked hospital usage data with a general practice database. All qualitative papers used purposive sampling to recruit participants living in or servicing rural populations.
Characteristics defining rurality were taken from definitions issued by Australian Government organisations [32,36,37,40,41,43,44,46] or similar American bodies [33,35,39,47]. Korenbrot et al. included patients who were treated at rural Indian Health Services [34].

3.4. Analysis Methods

Logistic regression was used in several quantitative studies [32,36,39] to model PPH risk. Other similar statistical methods used included risk ratios [34], multivariate ordinary least squares regression [35], weighted negative binomial regression [33], and generalised linear modelling [37,38]. All the qualitative studies were interpreted using thematic analysis [40,41,42,43,44].

3.5. Predictors of PPH

Access
Restricted access to services was identified as a risk factor for PPH in most studies [32,33,35,38,42]. This included primary healthcare (PHC) physician supply or availability [32,33,36,39,40,42], the presence/absence of a dedicated PHC service in a rural area [38,39], or difficulty in navigating and optimising use of the healthcare system [42]. No further explanation of what was meant by “access to services” was reported by any study. Johnston et al. found that access to specialist care for rural patients, and not PHC physicians, was a driver of PPH [33], while only Laditka et al. found “no evidence that physician supply was associated with ACSH” in rural counties throughout America [35] (p.1161).
Factors related to location included remoteness [32,37,41] and transport [42]. Clinical associations with PPH included greater complexity of care needs [36,42] and overall comorbidity burden [36].
Psychosocial issues
Psychosocial issues linked to PPH included socioeconomic disadvantage or poverty [32,37,42], education or current occupation [32], isolation or living alone [36,41,43], health behaviours, beliefs or attitudes [36,41,42,44], and health literacy [36,41]. Racial or ethnic factors were confirmed or identified as a risk by Korenbrot et al. [34] and Slimings et al. [37]. Table 2 contains a summary of all data extracted from the 13 retained papers.

3.6. Quality Assessment

The quantitative papers scored highly for patient selection and outcome assessment criteria due to their use of large datasets; both patients with and without PPHs were taken from very representative datasets, and the predominantly retrospective approaches used ensured most outcomes were captured. Some papers scored less well for comparability criteria as only a limited number of potentially confounding factors were adequately controlled for in the statistical analyses; however, the findings of these studies are unlikely to be changed by this (Table 3).
The qualitative papers were assessed as generally having good validity, although the researchers’ role in collection and interpretation of data was often inadequately explained [41,43,44]. Several papers used pre-existing data from larger projects. The appropriateness of this data for new analyses and ethical considerations of re-using the data were not explicitly stated; however, the influence on the studies’ findings is probably insignificant [43,44] (Table 4).

4. Discussion

This systematic review brings together both descriptive and qualitative research, and thereby extends the understanding of risks associated with PPH. Specifically it sought to answer “what factors are associated with PPHs among rural community-dwelling patients?”.
Risks identified by this review are likely to be applicable to rural settings irrespective of nationality [48,49]. The effects of proximity to services, accessing the available healthcare workforce, and burden of socioeconomic disadvantage on PPH appear to be relevant to all rural settings. The disproportionate effect of lifestyle, behavioural, and environmental factors experienced by rural populations is a barrier to reducing health outcome disparities [50].
Poor access to PHC is an established risk factor contributing to PPH [51,52,53]. While a comparison of “access” in different healthcare systems is not always straightforward, it is evident from the current review that limited access is a common factor which predisposes rural populations to PPH. All but two papers suggested access to PHC services reduced PPH rates; Laditka et al. found no association between supply of PHC physician and PPH [35], while Johnston et al. showed that limited access to specialist medical, but not PHC, services was associated with PPH risk [33]. These findings are a reminder that, firstly, supply of PHC services is not always the most important determinant of PHC outcomes and, secondly, that the diversity of services available in rural areas should be governed by local community needs.
Penchansky et al. (1981) [54] previously described five dimensions (availability, accessibility, affordability, accommodation, and acceptability) of access which are evident in the 13 studies included in this review. The alignment of patient needs with the services offered by the healthcare system determines the ease, or difficulty, with which patients can receive healthcare. Notably, limited availability of an appropriate volume and range of services in rural communities was frequently identified as a risk for PPH [32,33,35,38,39,40,41,42,44]. Health workforce maldistribution is a known cause of lower numbers of health professionals serving rural areas [20]. Shortages in rural areas of general, and specialist, services limit patient access to PHC [33,55]. Collegial, social, and economic barriers to increasing the number of general practitioners (GPs) in rural Australia [56] contribute to GP visits per capita being half that of urban areas, with excessive wait times to see a GP in many rural settings [57]. Similar barriers to using primary healthcare have been observed in the USA [58]. Strategies beyond increasing the healthcare workforce and financial incentivisation of rural practice need to be considered to improve the volume and range of healthcare services for communities living in rural areas [57].
Accessibility (viz. geographical and transport barriers) and affordability of services were also identified in this review to be contributing to PPH amongst rural-dwelling patients. This finding could perhaps be a consequence of their easily quantifiable nature at an individual or population level. Providing and prioritising health services close to areas of high demand is an approach to resource allocation previously identified as crucial to reducing PPH [20].
Health system accommodation (the suitability of services to patients’ preferences) and acceptability in rural communities was demonstrated subjectively by patients’ preference to self-refer to hospitals for treatment and general satisfaction with local healthcare providers [40,41,44]. Providing different models of care, such as parallel provision of low-acuity and specialist services, could better accommodate patient preferences [59]. Involving under-utilised healthcare professionals, such as pharmacists and paramedics, has previously been suggested as a low-cost improvement to improving access [57].
An expanded understanding of access was offered by Levesque et al. (2013) [14], who identified five abilities that impact access. Initially, the ability to perceive, seek, reach, afford, and engage with healthcare services appear to mirror the domains identified by Penchansky et al. [54]. However, the qualitative papers reviewed here highlight how psychosocial factors influence “ability” and therefore access. Specific factors included health literacy [40,41], social determinants [40,41,42,43,46], and patient enablement [40,42]. Interestingly, all three factors are interconnected and exert their influence on each other and upon a person’s overall health across their entire life-course [60,61,62]. The social capital mechanisms [63] that improve or limit an individual’s access to healthcare were not explored in the studies included in this review, but provide an important focus for future research.
Disaggregating access into more nuanced components helps to shift the focus away from “supply and demand” thinking [14]. Understanding the non-clinical factors that influence a patient’s ability to interact with the health system could help to identify novel areas for interventions. Health literacy is lower in rural areas [21] and is known to be associated with poorer health outcomes [64,65], including a higher risk of hospitalisation [66,67]. A health-literate population would find it easier to access, understand and use healthcare information and services. It is important to understand that the health literacy strengths and challenges in rural communities are context-specific and may be different for each community. In order for healthcare services to be health literacy responsive [68] they must consider health literacy at both the community and individual level.
Related to health literacy, patient enablement is the “extent to which people understand their health conditions and have the confidence, skills, knowledge and ability to manage their health and wellbeing” [69]. Improving patient enablement ensures people can actively manage their own health, remain well, and avoid hospitalisations [70,71]. A passive approach to patient participation in healthcare was demonstrated in two of the qualitative studies included in this review [40,41], which is the direct opposite of patient enablement.
Influenced by a person’s social capital, the loneliness and social isolation they experience further limits an individual’s ability to harness support in times of need or to prevent ill health [72]. Otherwise referred to as distributed health literacy, a limited sphere of contact reduces the health literacy resources that can be used by patients, who themselves have low health literacy [73]. Compounding this further, isolation and loneliness can independently adversely affect their health [74,75]. Restrictions imposed during the COVID-19 pandemic highlighted the impact that isolation has on health outcomes [76], and provoked thought as to how isolated people can be reached and supported (e.g., telehealth) [76,77,78,79,80]. Reducing loneliness and social isolation may form part of a strategy to improve health literacy for individuals, communities, and organisations. In turn, this may reduce PPHs in rural communities. (Figure 2).
An emerging approach to addressing social factors, social prescribing, was mentioned in one paper [40]. This approach to holistic patient care provides a link between PHC and sources of support within the community [81]. The benefits of social prescribing are thought to be particularly important for rural populations [82]. This approach to reducing isolation and loneliness, while fostering enablement and building social capital [83], has increasing support at the policy level in Australia and overseas [84,85,86,87,88,89,90].

Limitations

Only studies from Australia and the USA met the inclusion criteria and were included in the final analysis. The initial search results were reviewed and found to be accurate. Studies from several other countries were excluded for valid reasons (for example, Ingold et al. (2000) [91], Burgdorf et al. (2014) [92], Cloutier-Fisher et al. (2006) [93], O’Cathain et al. (2014) [94], Lynch et al. (2018) [95]). Further, the four qualitative papers included here were from two groups of researchers (both based in Australia). This may point to a need for greater emphasis on PPH research in rural settings, that is designed to capture stakeholder perceptions. These stakeholder insights are critical for informing policy and system improvements that meet local needs.
There is often a trade-off between producing statistically robust evidence and contextualisation of results; this was observed in the papers included in this review. Using large, highly linked data sets may identify prominent risks for PPH, but the applicability to local regions then needs careful consideration. Small quantitative studies, as always, are prone to possible biases and can be less applicable to other larger settings. For example, Ridge et al. (2021) [36] provides a useful snapshot of PPH patterns and risks in rural Tasmania. However, applying these findings to other rural areas in Australia or overseas ignores the specific geographic, demographic, socioeconomic, and healthcare system influences that are an integral part of each rural community.

5. Conclusions

This review is the first to highlight the importance of non-clinical determinants in contributing to PPH in rural communities and reinforces why elements within the access framework reported by Levesque et al. (2013) [14] should be considered. While poor “access” is a driver of PPH, considering factors beyond the “supply” of health services in rural areas is increasingly important. Patient-level attributes of health literacy, social isolation, and loneliness are important determinants of health. Rather than revisiting means of increasing healthcare provider numbers in rural areas, building the capacity of individuals, communities, and organisations to optimise their existing healthcare system is worthy of consideration. Employing a social capital approach to preventing PPHs may well be the answer.

Author Contributions

All three authors contributed to the study conception, development, and manuscript preparation. Database search planning and execution: A.R. (with G.M.P. and R.N.); Removal of duplicates: A.R. (and G.M.P. or R.N.); Title/abstract screening: A.R. (and G.M.P. or R.N.); Full-text review: A.R. (and G.M.P. or R.N.); Data extraction: A.R. (and G.M.P. or R.N.); Quality assessment/risk of bias assessment: A.R. (and G.M.P. or R.N.); Original Draft Preparation: A.R.; Review and Editing: A.R., G.M.P. and R.N.; Supervision: G.M.P. and R.N. All authors have read and agreed to the published version of the manuscript.

Funding

The article processing charge was funded by the University of Tasmania’s Pharmacy Appeal Fund.

Institutional Review Board Statement

The study was approved by Prospero (ID CRD42020152194).

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Database-specific search strategies.
Table A1. Database-specific search strategies.
Research Question“What Factors Are Associated with Preventable Hospitalisation in Rural Areas”
ConceptsFactorsPreventable HospitalisationRural
SynonymsPredictor
Risk
Factor
Cause
Determinant
Influence
Preventable
Avoidable
Unnecessary
Hospitalisation
Admission
Rural
Non-urban
EMBASE
Embase < 1974 to 2022 May 31>
1
(predict* or risk* or factor* or caus* or determin* or influenc*).ab,kf,ti.15023263
2
risk factor/ 1212808
3
predictor variable/ 29645
4
1 or 2 or 3 15231026
5
(rural* or “non-urban” or “non urban”).ab,kf,ti. 193644
6
rural health/or rural population/or rural area/or rural hospital/or rural health care/ 124869
7
5 or 6 220201
8
((prevent* or avoid*) adj2 (hospitali* or admission or admit*)).ab,ti. 5973
9
4 and 7 and 8 83
MEDLINE
Ovid MEDLINE(R) ALL <1946 to May 31, 2022>
1
(predict* or risk* or factor* or caus* or determin* or influenc*).ab,kf,ti. 11822934
2
Risk Factors/ 924309
3
1 or 2 11988606
4
(rural* or “non-urban” or “non urban”).ab,kf,ti. 164085
5
Rural Health/or Hospitals, Rural/or Rural Population/or Rural Health Services/ 103381
6
4 or 5 190189
7
((prevent* or avoid*) adj2 (hospitali* or admission or admit*)).ab,ti. 3647
8
3 and 6 and 7 68
CINHAL
S8
S3 AND S6 AND S7 (59)
S7
TI ((prevent* OR avoid*) N2 (hospitali* OR admission OR admit*)) OR AB ((prevent* OR avoid*) N2 (hospitali* OR admission OR admit*)) OR MH ((prevent* OR avoid*) N2 (hospitali* OR admission OR admit*)) (3,273)
S6
S4 OR S5 (81,259)
S5
(MH “Hospitals, Rural”) OR (MH “Rural Population”) OR (MH “Rural Health Services”) OR (MH “Rural Areas”) OR (MH “Rural Health”) (51,135)
S4
TI (rural* OR “non-urban” OR “non urban”) OR AB (rural* OR “non-urban” OR “non urban”) OR MH (rural* OR “non-urban” OR “non urban”) (80,334)
S3
S1 OR S2 (2,367,920)
S2
(MH “Risk Factors”) (197,634)View DetailsEdit
S1
TI (predict* OR risk* OR factor* OR caus* OR determin* OR influenc*) OR AB (predict* OR risk* OR factor* OR caus* OR determin* OR influenc*) OR MH (predict* OR risk* OR factor* OR caus* OR determin* OR influenc*) (2,367,920)
Web of Science
4
#3 AND #2 AND #1 111
3
((TI = ((prevent* or avoid*) near/2 (hospitali* or admission or admit*))) OR AB = ((prevent* or avoid*) near/2 (hospitali* or admission or admit*))) OR TS = ((prevent* or avoid*) near/2 (hospitali* or admission or admit*)) 5,726
2
((TI = (rural* or “non-urban” or “non urban”)) OR AB = (rural* or “non-urban” or “non urban”)) OR TS = (rural* or “non-urban” or “non urban”) 302,206
1
((TI = (predict* or risk* or factor* or caus* or determin* or influenc*)) OR AB = (predict* or risk* or factor* or caus* or determin* or influenc*)) OR TS = (predict* or risk* or factor* or caus* or determin* or influenc*) 20,632,004

References

  1. Australian Institute of Health and Welfare (AIHW). Australia’s Health 2018-In Brief; Australian Institute of Health and Welfare: Canberra, Australia, 2018. [Google Scholar]
  2. Billings, J.; Zeitel, L.; Lukomnik, J.; Carey, T.S.; Blank, A.E.; Newman, L. Impact of socioeconomic status on hospital use in New York City. Health Aff. 1993, 12, 162–173. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Baker, J.; White, N.; Mengersen, K.; Rolfe, M.; Morgan, G.G. Joint modelling of potentially avoidable hospitalisation for five diseases accounting for spatiotemporal effects: A case study in New South Wales, Australia. PLoS ONE 2017, 12, e0183653. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Agency for Healthcare Research and Quality. Potentially Avoidable Hospitalizations. Available online: https://www.ahrq.gov/research/findings/nhqrdr/chartbooks/carecoordination/measure3.html (accessed on 19 September 2022).
  5. Lugo-Palacios, D.G.; Cairns, J. Using ambulatory care sensitive hospitalisations to analyse the effectiveness of primary care services in Mexico. Soc. Sci. Med. 2015, 144, 59–68. [Google Scholar] [CrossRef] [PubMed]
  6. Falster, M.; Jorm, L. A Guide to the Potentially Preventable Hospitalisations Indicator in Australia; Centre for Big Data Research in Health, University of New South Wales in Consultation with Australian Commission on Safety and Quality in Health Care and Australian Institute of Health and Welfare: Sydney, Australia, 2017. [Google Scholar]
  7. Solberg, L.I.; Ohnsorg, K.A.; Parker, E.D.; Ferguson, R.; Magnan, S.; Whitebird, R.R.; Neely, C.; Brandenfels, E.; Williams, M.D.; Dreskin, M.; et al. Potentially Preventable Hospital and Emergency Department Events: Lessons from a Large Innovation Project. Perm. J. 2018, 22, 17–102. [Google Scholar] [CrossRef] [Green Version]
  8. Australian Institute of Health and Welfare (AIHW). Admitted Patient Care 2017–2018: Australian Hospital Statistics; AIHW: Canberra, Australia, 2019. [Google Scholar]
  9. Australian Institute of Health and Welfare. Admitted Patient Safety and Quality. Available online: https://www.aihw.gov.au/reports-data/myhospitals/intersection/quality/apc (accessed on 12 September 2022).
  10. Australian Institute of Health and Welfare. Atlas 2017-1. Chronic Disease and Infection: Potentially Preventable Hospitalisations. Available online: https://www.safetyandquality.gov.au/our-work/healthcare-variation/atlas-2017/atlas-2017-1-chronic-disease-and-infection-potentially-preventable-hospitalisations (accessed on 12 September 2022).
  11. Sheringham, J.; Asaria, M.; Barratt, H.; Raine, R.; Cookson, R. Are some areas more equal than others? Socioeconomic inequality in potentially avoidable emergency hospital admissions within English local authority areas. J. Health Serv. Res. Policy 2017, 22, 83–90. [Google Scholar] [CrossRef] [Green Version]
  12. Australian Institute of Health and Welfare (AIHW). National Healthcare Agreement: PI 18–Selected Potentially Preventable Hospitalisations. 2019. Available online: https://meteor.aihw.gov.au/content/index.phtml/itemId/698904 (accessed on 28 July 2022).
  13. Australian Institute of Health and Welfare. Rural & Remote Health. Available online: https://www.aihw.gov.au/reports/rural-remote-australians/rural-remote-health (accessed on 23 November 2021).
  14. Levesque, J.-F.; Harris, M.F.; Russell, G. Patient-centred access to health care: Conceptualising access at the interface of health systems and populations. Int. J. Equity Health 2013, 12, 18. [Google Scholar] [CrossRef] [Green Version]
  15. Greenwood-Ericksen, M.B.; Macy, M.L.; Ham, J.; Nypaver, M.M.; Zochowski, M.; Kocher, K.E. Are Rural and Urban Emergency Departments Equally Prepared to Reduce Avoidable Hospitalizations? West. J. Emerg. Med. 2019, 20, 477–484. [Google Scholar] [CrossRef] [Green Version]
  16. Johnston, K.J.; Wen, H.; Kotwal, A.; Joynt Maddox, K.E. Comparing Preventable Acute Care Use of Rural Versus Urban Americans: An Observational Study of National Rates During 2008–2017. J. Gen. Intern. Med. 2021, 36, 3728–3736. [Google Scholar] [CrossRef]
  17. Rust, G.; Baltrus, P.; Ye, J.L.; Daniels, E.; Quarshie, A.; Boumbulian, P.; Strothers, H. Presence of a Community Health Center and Uninsured Emergency Department Visit Rates in Rural Counties. J. Rural. Health 2009, 25, 8–16. [Google Scholar] [CrossRef] [Green Version]
  18. Vest, J.R.; Gamm, L.D.; Oxford, B.A.; Gonzalez, M.I.; Slawson, K.M. Determinants of preventable readmissions in the United States: A systematic review. Implement. Sci. 2010, 5, 88. [Google Scholar] [CrossRef]
  19. Bourke, L.; Humphreys, J.S.; Wakerman, J.; Taylor, J. Understanding drivers of rural and remote health outcomes: A conceptual framework in action. Aust. J. Rural. Health 2012, 20, 318–323. [Google Scholar] [CrossRef] [PubMed]
  20. Thomas, S.L.; Wakerman, J.; Humphreys, J.S. Ensuring equity of access to primary health care in rural and remote Australia-what core services should be locally available? Int. J. Equity Health 2015, 14, 111. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Aljassim, N.; Ostini, R. Health literacy in rural and urban populations: A systematic review. Patient Educ. Couns. 2020, 103, 2142–2154. [Google Scholar] [CrossRef]
  22. Department of Health and Ageing. Report on the Audit of Health Workforce in Rural and Regional Australia; Commonwealth of Australia Canberra: Canberra, Australia, 2008. [Google Scholar]
  23. Moher, D.; Shamseer, L.; Clarke, M.; Ghersi, D.; Liberati, A.; Petticrew, M.; Shekelle, P.; Stewart, L.A.; Group, P.-P. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 2015, 4, 1. [Google Scholar] [CrossRef] [Green Version]
  24. Centre for Reviews and Dissemination. PROSPERO: International Prospective Register of Systematic Reviews. Available online: https://www.crd.york.ac.uk/PROSPERO/ (accessed on 12 September 2022).
  25. Cochrane Library. Available online: https://www.cochranelibrary.com/cdsr/reviews (accessed on 12 September 2022).
  26. Clarivate Analytics. EndNote Version 20; Clarivate Analytics: San Francisco, CA, USA, 2018. [Google Scholar]
  27. Covidence Systematic Review Software. Available online: www.covidence.org (accessed on 12 September 2022).
  28. Wells, G.A.; Shea, B.; O’Connell, D.; Peterson, J.; Welch, V.; Losos, M.; Tugwell, P. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. Available online: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (accessed on 12 September 2022).
  29. Critical Appraisal Skills Programme. CASP Qualitative Checklist. Available online: https://casp-uk.b-cdn.net/wp-content/uploads/2018/03/CASP-Qualitative-Checklist-2018_fillable_form.pdf (accessed on 26 July 2022).
  30. Stang, A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur. J. Epidemiol. 2010, 25, 603–605. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Snilstveit, B.; Oliver, S.; Vojtkova, M. Narrative approaches to systematic review and synthesis of evidence for international development policy and practice. J. Dev. Eff. 2012, 4, 409–429. [Google Scholar] [CrossRef]
  32. Ansari, Z.; Barbetti, T.; Carson, N.J.; Auckland, M.J.; Cicuttini, F.M. The Victorian ambulatory care sensitive conditions study: Rural and urban perspectives. Soz. Und Prav. 2003, 48, 33–43. [Google Scholar] [CrossRef]
  33. Johnston, K.J.; Hefei, W.; Joynt Maddox, K.E. Lack Of Access To Specialists Associated With Mortality And Preventable Hospitalizations Of Rural Medicare Beneficiaries. Health Aff. 2019, 38, 1993–2002. [Google Scholar] [CrossRef]
  34. Korenbrot, C.C.; Ehlers, S.; Crouch, J.A. Disparities in hospitalizations of rural American Indians. Med. Care 2003, 41, 626–636. [Google Scholar] [CrossRef]
  35. Laditka, J.N.; Laditka, S.B.; Probst, J.C. More may be better: Evidence of a negative relationship between physician supply and hospitalization for ambulatory care sensitive conditions. Health Serv. Res. 2005, 40, 1148–1166. [Google Scholar] [CrossRef]
  36. Ridge, A.; Peterson, G.; Kitsos, A.; Seidel, B.; Anderson, V.; Nash, R. Potentially Preventable Hospitalisations in rural Community-Dwelling Patients. Int. Med. J. 2021, in press. [Google Scholar] [CrossRef] [PubMed]
  37. Slimings, C.; Moore, M. Geographic variation in health system performance in rural areas of New South Wales, Australia. Aust. J. Rural. Health 2021, 29, 41–51. [Google Scholar] [CrossRef] [PubMed]
  38. Wright, B.; Akiyama, J.; Potter, A.J.; Sabik, L.M.; Stehlin, G.G.; Trivedi, A.N.; Wolinsky, F.D. Health center use and hospital-based care among individuals dually enrolled in Medicare and Medicaid, 2012–2018. Health Serv. Res. 2022, 57, 1045–1057. [Google Scholar] [CrossRef] [PubMed]
  39. Zhang, W.Q.; Mueller, K.J.; Chen, L.W.; Conway, K. The role of rural health clinics in hospitalization due to ambulatory care sensitive conditions: A study in Nebraska. J. Rural. Health 2006, 22, 220–223. [Google Scholar] [CrossRef]
  40. Ridge, A.; Peterson, G.M.; Seidel, B.M.; Anderson, V.; Nash, R. Rural Patients’ Perceptions of Their Potentially Preventable Hospitalisation: A Qualitative Study. J. Patient Exp. 2022, 9. [Google Scholar] [CrossRef]
  41. Ridge, A.; Peterson, G.M.; Seidel, B.M.; Anderson, V.; Nash, R. Healthcare Providers’ Perceptions of Potentially Preventable Rural Hospitalisations: A Qualitative Study. Int. J. Environ. Res. Public Health 2021, 18, 12767. [Google Scholar] [CrossRef]
  42. Longman, J.M.; Singer, J.B.; Gao, Y.; Barclay, L.M.; Passey, M.E.; Pirotta, J.P.; Heathcote, K.E.; Ewald, D.P.; Saberi, V.; Corben, P.; et al. Community based service providers’ perspectives on frequent and/or avoidable admission of older people with chronic disease in rural NSW: A qualitative study. BMC Health Serv. Res. 2011, 11, 265. [Google Scholar] [CrossRef] [Green Version]
  43. Longman, J.; Passey, M.; Singer, J.; Morgan, G. The role of social isolation in frequent and/or avoidable hospitalisation: Rural community-based service providers’ perspectives. Aust. Health Rev. 2013, 37, 223–231. [Google Scholar] [CrossRef] [Green Version]
  44. Longman, J.; Johnston, J.; Ewald, D.; Gilliland, A.; Burke, M.; Mutonga, T.; Passey, M. What could prevent chronic condition admissions assessed as preventable in rural and metropolitan contexts? An analysis of clinicians’ perspectives from the DaPPHne study. PLoS ONE 2021, 16, e0244313. [Google Scholar] [CrossRef]
  45. Borda-Olivas, A.; Fernandez-Navarro, P.; Otero-Garcia, L.; Sanz-Barbero, B. Rurality and avoidable hospitalization in a Spanish region with high population dispersion. Eur. J. Public Health 2013, 23, 946–951. [Google Scholar] [CrossRef]
  46. Longman, J.M.; Rolfe, M.I.; Passey, M.D.; Heathcote, K.E.; Ewald, D.P.; Dunn, T.; Barclay, L.M.; Morgan, G.G. Frequent hospital admission of older people with chronic disease: A cross-sectional survey with telephone follow-up and data linkage. BMC Health Serv. Res. 2012, 12, 373. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Wright, B.; Potter, A.J.; Trivedi, A.N.; Mueller, K.J. The Relationship Between Rural Health Clinic Use and Potentially Preventable Hospitalizations and Emergency Department Visits Among Medicare Beneficiaries. J. Rural. Health 2018, 34, 423–430. [Google Scholar] [CrossRef] [PubMed]
  48. van der Pol, M.; Olajide, D.; Dusheiko, M.; Elliott, R.; Guthrie, B.; Jorm, L.; Leyland, A.H. The impact of quality and accessibility of primary care on emergency admissions for a range of chronic ambulatory care sensitive conditions (ACSCs) in Scotland: Longitudinal analysis. BMC Fam. Pract. 2019, 20, 32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Elek, P.; Molnar, T.; Varadi, B. The closer the better: Does better access to outpatient care prevent hospitalization? Eur. J. Health Econ. 2019, 20, 801–817. [Google Scholar] [CrossRef] [Green Version]
  50. World Health Organisation. Human Rights and Health. Available online: https://www.who.int/news-room/fact-sheets/detail/human-rights-and-health (accessed on 12 September 2022).
  51. Agency for Healthcare Research and Quality. Access and Disparities in Access to Health Care; Agency for Healthcare Research and Quality: Rockville, MD, USA, 2018. [Google Scholar]
  52. Mazumdar, S.; Chong, S.; Arnold, L.; Jalaludin, B. Spatial clusters of chronic preventable hospitalizations (ambulatory care sensitive conditions) and access to primary care. J. Public Health 2019, 42, e134–e141. [Google Scholar] [CrossRef]
  53. Carmeiro, C.S. Hospitalisation of ambulatory care sensitive conditions and access to primary care in Portugal. Public Health 2018, 165, 117–124. [Google Scholar] [CrossRef]
  54. Penchansky, R.; Thomas, J.W. The Concept of Access: Definition and Relationship to Consumer Satisfaction. Med. Care 1981, 19, 127–140. [Google Scholar] [CrossRef]
  55. Swerissen, H.; Duckett, S.; Moran, G. Mapping Primary Care in Australia (Grattan Institute Report No. 2018–09); Grattan Institute Melbourne, Australia: Carlton, Australia, 2018. [Google Scholar]
  56. The Royal Australian College of General Practitioners. General Practice: Health of the Nation 2020; RACGP: East Melbourne, VIC, Australia, 2020. [Google Scholar]
  57. Duckett, S.; Breadon, P. Access All Areas: New Solutions for GP Shortages in Rural AUSTRALIA; Grattan Institute Melbourne, Australia: Carlton, Australia, 2013. [Google Scholar]
  58. Cyr, M.E.; Etchin, A.G.; Guthrie, B.J.; Benneyan, J.C. Access to specialty healthcare in urban versus rural US populations: A systematic literature review. BMC Health Serv. Res. 2019, 19, 974. [Google Scholar] [CrossRef] [Green Version]
  59. Cheek, C.; Allen, P.; Shires, L.; Parry, D.; Ruigrok, M. Low-acuity presentations to regional emergency departments: What is the issue? Emerg. Med. Australas. 2016, 28, 145–152. [Google Scholar] [CrossRef]
  60. Rowlands, G.; Shaw, A.; Jaswal, S.; Smith, S.; Harpham, T. Health literacy and the social determinants of health: A qualitative model from adult learners. Health Promot. Int. 2017, 32, 130–138. [Google Scholar] [CrossRef]
  61. Pelikan, J.M.; Ganahl, K.; Roethlin, F. Health literacy as a determinant, mediator and/or moderator of health: Empirical models using the European Health Literacy Survey dataset. Glob. Health Promot. 2018, 25, 57–66. [Google Scholar] [CrossRef] [PubMed]
  62. Stormacq, C.; Van Den Broucke, S.; Wosinski, J. Does health literacy mediate the relationship between socioeconomic status and health disparities? Integrative review. Health Promot. Int. 2019, 34, e1–e17. [Google Scholar] [CrossRef] [PubMed]
  63. Villalonga-Olives, E.; Kawachi, I. The dark side of social capital: A systematic review of the negative health effects of social capital. Soc. Sci. Med. 2017, 194, 105–127. [Google Scholar] [CrossRef] [PubMed]
  64. Ehmann, A.T.; Groene, O.; Rieger, M.A.; Siegel, A. The Relationship between Health Literacy, Quality of Life, and Subjective Health: Results of a Cross-Sectional Study in a Rural Region in Germany. Int. J. Environ. Res. Public Health 2020, 17, 1683. [Google Scholar] [CrossRef] [Green Version]
  65. Berkman, N.D.; Sheridan, S.L.; Donahue, K.E.; Halpern, D.J.; Crotty, K. Low health literacy and health outcomes: An updated systematic review. Ann. Intern. Med. 2011, 155, 97–107. [Google Scholar] [CrossRef]
  66. Balakrishnan, M.P.; Herndon, J.B.; Zhang, J.N.; Payton, T.; Shuster, J.; Carden, D.L. The Association of Health Literacy With Preventable Emergency Department Visits: A Cross-sectional Study. Acad. Emerg. Med. 2017, 24, 1042–1050. [Google Scholar] [CrossRef] [Green Version]
  67. Schumacher, J.R.; Hall, A.G.; Davis, T.C.; Arnold, C.L.; Bennett, R.D.; Wolf, M.S.; Carden, D.L. Potentially preventable use of emergency services: The role of low health literacy. Med. Care 2013, 51, 654. [Google Scholar] [CrossRef] [Green Version]
  68. Trezona, A.; Dodson, S.; Osborne, R.H. Development of the organisational health literacy responsiveness (Org-HLR) framework in collaboration with health and social services professionals. BMC Health Serv. Res. 2017, 17, 513. [Google Scholar] [CrossRef] [Green Version]
  69. Agency for Clinical Innovation. Consumer Enablement: A Clinician’s Guide. Available online: https://aci.health.nsw.gov.au/resources/primary-health/consumer-enablement/guide (accessed on 19 September 2022).
  70. Batterham, R.; Osborne, R.; Mcphee, C.; Townsend, B. Consumer Enablement: An Evidence Check Rapid Review Brokered by the Sax Institute for the Agency for Clinical Innovation; Sax Institute: Ultimo, NSW, Australia, 2017. [Google Scholar]
  71. Kurosawa, S.; Matsushima, M.; Fujinuma, Y.; Hayashi, D.; Noro, I.; Kanaya, T.; Watanabe, T.; Tominaga, T.; Nagata, T.; Kawasaki, A.; et al. Two Principal Components, Coping and Independence, Comprise Patient Enablement in Japan: Cross Sectional Study in Tohoku Area. Tohoku J. Exp. Med. 2012, 227, 97–104. [Google Scholar] [CrossRef] [Green Version]
  72. Veazie, S.; Gilbert, J.; Winchell, K.; Paynter, R.; Guise, J.-M. Addressing Social Isolation to Improve the Health of Older Adults: A Rapid Review. Available online: https://effectivehealthcare.ahrq.gov/products/social-isolation/rapid-product (accessed on 3 December 2021).
  73. Edwards, M.; Wood, F.; Davies, M.; Edwards, A. ‘Distributed health literacy’: Longitudinal qualitative analysis of the roles of health literacy mediators and social networks of people living with a long-term health condition. Health Expect. 2015, 18, 1180–1193. [Google Scholar] [CrossRef]
  74. Valtorta, N.K.; Kanaan, M.; Gilbody, S.; Ronzi, S.; Hanratty, B. Loneliness and social isolation as risk factors for coronary heart disease and stroke: Systematic review and meta-analysis of longitudinal observational studies. Heart 2016, 102, 1009–1016. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Cene, C.W.; Beckie, T.M.; Sims, M.; Suglia, S.F.; Aggarwal, B.; Moise, N.; Jimenez, M.C.; Gaye, B.; McCullough, L.D.; American Heart Association Social Determinants of Health Committee of the Council on Epidemiology and Prevention and Council on Quality of Care and Outcomes Research; et al. Effects of Objective and Perceived Social Isolation on Cardiovascular and Brain Health: A Scientific Statement From the American Heart Association. J. Am. Heart Assoc. 2022, 11, e026493. [Google Scholar] [CrossRef] [PubMed]
  76. Smith, B.; Lim, M. How the COVID-19 pandemic is focusing attention on loneliness and social isolation. Public Health Res. Pract. 2020, 30, 3022008. [Google Scholar] [CrossRef]
  77. Duckett, S. What should primary care look like after the COVID-19 pandemic? Aust. J. Prim. Health 2020, 26, 207–211. [Google Scholar] [CrossRef] [PubMed]
  78. Greenhalgh, T.; Koh, G.C.H.; Car, J. COVID-19: A remote assessment in primary care. BMJ 2020, 368, m1182. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Mental Health Council of Tasmania. COVID-19: A Mental Health Response for Older Tasmanians; Mental Health Council of Tasmania: Hobart, Tasmania, 2021. [Google Scholar]
  80. Rumas, R.; Shamblaw, A.L.; Jagtap, S.; Best, M.W. Predictors and consequences of loneliness during the COVID-19 Pandemic. Psychiatry Res. 2021, 300, 113934. [Google Scholar] [CrossRef] [PubMed]
  81. Chatterjee, H.; Camic, P.; Lockyer, B.; Thomson, L. Non-clinical community interventions: A systematised review of social prescribing schemes. Arts Health 2018, 10, 97–123. [Google Scholar] [CrossRef] [Green Version]
  82. Fitzmaurice, C. Social prescribing: A new paradigm with additional benefits in rural Australia. Aust. J. Rural. Health 2022, 30, 298–299. [Google Scholar] [CrossRef]
  83. Tierney, S.; Wong, G.; Roberts, N.; Boylan, A.-M.; Park, S.; Abrams, R.; Reeve, J.; Williams, V.; Mahtani, K.R. Supporting social prescribing in primary care by linking people to local assets: A realist review. BMC Med. 2020, 18, 49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. TASCOSS. Legislative Council Government Administration Committee A: Inquiry into Rural Health Services; Tasmanian Council of Social Service: Hobart, Tasmania, 2021. [Google Scholar]
  85. Department of Health. National Preventive Health Strategy 2021–2030; Commonwealth of Australia: Canberra, Australia, 2021. [Google Scholar]
  86. Royal Australian College of General Practitioners and Consumers Health Forum of Australia. Social Prescribing Roundtable November 2019-Report; Royal Australian College of General Practitioners: Melbourne, Australia, 2020. [Google Scholar]
  87. NHS England and NHS Improvement. Social Prescribing and Community-Based Support Summary Guide; NHS: London, UK, 2020. [Google Scholar]
  88. Marmot, M. Health equity in England: The Marmot review 10 years on. BMJ 2020, 368, m693. [Google Scholar] [CrossRef] [PubMed]
  89. Health Service Executive. Building the Capacity for the Evaluation of Social Prescribing: An Evaluability Assessment; Department of Health: Dublin, Ireland, 2020. [Google Scholar]
  90. Frankston Mornington Peninsula Primary Care Partnership. Frankston Mornington Peninsula Social Prescribing Program. Available online: https://fmppcp.org.au/fmpsocialprescribingprogram/# (accessed on 22 March 2022).
  91. Ingold, B.B.; Yersin, B.; Wietlisbach, V.; Burckhardt, P.; Burnand, B.; Büla, C.J. Characteristics associated with inappropriate hospital use in elderly patients admitted to a general internal medicine service. Aging Clin. Exp. Res. 2000, 12, 430–438. [Google Scholar] [CrossRef]
  92. Burgdorf, F.; Sundmacher, L. Potentially Avoidable Hospital Admissions in Germany. Dtsch. Arztebl. Int. 2014, 111, 215–223. [Google Scholar] [CrossRef] [Green Version]
  93. Cloutier-Fisher, D.; Penning, M.J.; Zheng, C.; Druyts, E.-B.F. The devil is in the details: Trends in avoidable hospitalization rates by geography in British Columbia, 1990–2000. BMC Health Serv. Res. 2006, 6, 104. [Google Scholar] [CrossRef] [Green Version]
  94. O’Cathain, A.; Knowles, E.; Maheswaran, R.; Pearson, T.; Turner, J.; Hirst, E.; Goodacre, S.; Nicholl, J. A system-wide approach to explaining variation in potentially avoidable emergency admissions: National ecological study. BMJ Qual. Saf. 2014, 23, 47–55. [Google Scholar] [CrossRef]
  95. Lynch, B.M.; Fitzgerald, T.; Corcoran, P.; Buckley, C.; Healy, O.; Browne, J. Avoidable emergency admissions for ambulatory care sensitive conditions in the Republic of Ireland: Analysis of regional determinants. Int. J. Integr. Care (IJIC) 2018, 18, 352. [Google Scholar] [CrossRef]
Figure 1. PRISMA flowchart for the review.
Figure 1. PRISMA flowchart for the review.
Ijerph 19 16487 g001
Figure 2. Key psychosocial factors that contribute to PPH for rural-dwelling patients.
Figure 2. Key psychosocial factors that contribute to PPH for rural-dwelling patients.
Ijerph 19 16487 g002
Table 1. Inclusion and exclusion criteria.
Table 1. Inclusion and exclusion criteria.
Inclusion CriteriaExclusion Criteria
Participants/Population
  • population representative of rural, community-dwelling patients, or providers of healthcare services in rural areas in developed economies at risk of a PPH.
  • participants aged 18 years or over, or aged-based sub-groups of the population who are 18 years or over.
Types of study to be included
  • any original, full-text study where a factor, or factors, associated with potentially avoidable hospitalisation are identified and used to describe the risk of PPH in a rural population. Factors influencing PPH occurrence may be patient-level clinical, demographic, or social factors, or may reflect access to, or functioning of, the health system.
  • set in rural areas or providing a comparison between rural and urban areas.
  • published in the English language, and
  • published after 1/1/2000
Types of studies to be excluded
  • studies that identify factors associated with a repeat admission to hospital (i.e., a readmission within a specific timeframe).
  • systematic reviews, meta-analyses, feasibility or pilot studies, case studies, correspondence, or conference/symposia abstracts.
  • studies that include rurality as an independent variable in modelling (i.e., rurality was an investigated risk factor and not the outcome focus).
  • studies based in residents of aged care facilities.
  • studies investigating the impact of a single medication (or medication class) on PPH.
  • studies in the context of one specific medical condition.
Table 2. Description of papers included in the review.
Table 2. Description of papers included in the review.
Study (Year) CountryStudy TypeNumber of Participants–Data SourceAgeSex
(% Female)
Analysis MethodKey Findings
Korenbrot et al. (2003) [34]
USA
Cross sectional3920
California Department of Health Services
20.1% > 65 years (AI/AN group)60.1risk ratio with stratification by age, sexAI/AN status increased PPH risk ratio for men (RR 2.26; 95%CI 1.39–2.98) & women (RR 1.87; 95%CI 1.46–3.17)
Rates of PPH were only significantly higher for adult males aged > 45 years & women aged < 75 years after adjusting for age-group
Ansari et al. (2003) [32]
Australia
Cross sectional4,403,637
Victorian Admitted Episodes Dataset
42.0% aged > 65 years (ACSC group)49.5random effects multilevel regressionStrong associations with ACSH were observed for the following factors:
  • insurance status reduces risk by 24% (ORadj = 0.76; 95%CI 0.75–0.77))
  • greatest remoteness increases risk by 17% (ORadj = 1.17; 95%CI 1.14–1.21)
  • high population density reduces risk by 24% (ORadj = 0.76; 95%CI 0.74–0.78)
  • greater GP presence reduces risk by 21% (ORadj = 0.79; 95%CI 0.78–0.81)
  • more frequent GP visits reduces risk by 35% (ORadj = 0.65; 95%CI 0.64–0.67)
  • the most disadvantaged socioeconomic areas had 40% higher risk (ORadj = 1.40; 95%CI 1.35–1.45)
  • greater educational and occupational disadvantage increases risk by 48% (ORadj = 1.48; 95%CI 1.32–1.65)
  • greater economic disadvantage increases risk by 56% (ORadj = 1.56; 95%CI 1.49–1.64)
Laditka et al. (2005) [35]
USA
Cross sectional948 counties
Agency for Healthcare Research and Quality
n.s.n.s.multivariate ordinary least squares regressionPhysician supply was not associated with ACSH in rural areas
Zhang et al. (2006) [39]
USA
Cross sectional538,580
Nebraska hospital discharge data
(1999–2001)
n.s.n.s.multilevel logistic regressionPresence of ≥1 rural health clinic in rural areas was associated with a 5.5% reduction in risk of a ACSH due to a chronic disease among elderly patients (ORadj = 0.945; 95%CI 0.893–0.997)
Longman et al. (2011) [42]
Australia
Qualitative15 semi-structured interviews with healthcare providersn.s.86.7thematic analysisExternal barriers influencing PPH risk: complexity of services; availability, awareness, and ability to access services; greater care needs; patient poverty; rurality; transport
Internal barriers influencing PPH risk: fear of change; “stoic” attitudes; difficulty accepting change in health status
Longman et al. (2013) [43]
Australia
Qualitative15 semi-structured interviews with healthcare providersn.s.n.s.thematic analysisSocial isolation consistently identified as a risk factor for PPH among older patients with chronic diseases. Dimensions of social isolation included living alone, not socialising and being isolated from family
Johnston et al. (2019) [33]
USA
Cross sectional11,581
Centres
for Medicare and Medicaid Services
n.s.n.s.weighted negative
binomial regression
One or more specialist visits during the previous year was associated with a 15.9% lower preventable hospitalisation rate (explained 55% of the difference in preventable hospitalisation rates between rural and urban groups)
Overall morbidity, heart failure (independently), lower income and being unmarried were all associated with a higher preventable hospitalisation risk.
Ridge et al. (2021) [41]
Australia
Qualitative14 semi-structured interviews with healthcare providers n.s.n.s.thematic analysisHealth literacy challenges; access to PHC; perceived convenience of hospital treatment
Longman et al. (2021) [44]
Australia
Qualitative148 preventable admissions reviewed by expert paneln.s.n.s.thematic analysisSystem issues: community-based services inadequate or not referred to; poor connections between services; problems with specialist services
Clinician issues: GP care inadequate
Patient issues: adherence/self-management; patient’s engagement with existing services
Slimings et al. (2021) [37]
Australia
Ecological89 LGAs
Social Health Atlases of Australia
20.8% aged > 65 years49.80multivariable analysis using generalised linear modelRemoteness, Indigenous percentage, and socioeconomic disadvantage were independently associated with preventable hospitalisation in rural NSW. Socioeconomic factors (measured by internet access) and Indigenous percentage remained significant in the adjusted model with 416.5 fewer (95%CI−597.6-−235.5; p <0.001) and 367.0 (95%CI 68.8–665.2; p = 0.041) more preventable hospitalisations per 100,000 population, respectively, between 2013–2017.
Ridge et al. (2021) [36]
Australia
Cross sectional436
Admitted Patient Data Collection dataset
62.5 years48.6multivariate logistic
regression
Being single/unmarried (OR 2.43; 95%CI 1.38–4.28), greater comorbidity burden (as measured by higher Charlson Comorbidity Index scores) (OR 1.40; 95%CI 1.13–1.74) and number of general practice visits in the preceding 12 months (OR 1.09, 95%CI 1.05–1.14) were all associated with a higher risk of PPH
Ridge et al. (2022) [40]
Australia
Qualitative10 semi-structured patient interviews 68 years (range 47–91)40.0thematic analysisPatient self-efficacy and health literacy; community support networks; access to PHC services
Wright et al. (2022) [38]
USA
Cross sectional8,483,758 person-year observations
Medicare claims & Master Beneficiary Summary File
(2012–2018)
75.1 years (IQR 69–80)68.3negative binomial
and linear probability models
Dual-registered persons in rural areas receiving 100% of their PHC at a FQHC demonstrated a lower propensity for ACSH (marginal effect 0.3%; 95%CI 0.1–0.4)
ACSC = ambulatory care sensitive condition; ACSH = ambulatory care sensitive hospitalisation; AHPF = Australian Health Performance Framework; AHRQ = Agency for Healthcare Research and Quality; AI/AN = American Indians/Alaska Natives; FQHC = Federally Qualified Health Centre; GP = general practitioner; IQR = interquartile range; LGA = Local Government Area; n.s. = not stated; NSW = New South Wales; ORadj = adjusted odds ratio; PHC = primary healthcare; PPH = Potentially preventable hospitalisation; RR = risk ratio.
Table 3. Newcastle–Ottawa Scores for quantitative papers.
Table 3. Newcastle–Ottawa Scores for quantitative papers.
Korenbrot et al. (2003) [34]Ansari et al. (2003) [32]Laditka et al. (2005) [35]Zhang et al. (2006) [39]Johnston et al. (2019) [33]Slimings et al. (2021) [37]Ridge et al. (2021) [36]Wright et al. (2022) [38]
Selection44444444
Comparability12222121
Outcome33333333
nb: score based on the number of Newcastle-Ottawa Score domains that are met, with a maximum score of 9 possible across the three items (‘selection’, ‘comparability’ and ‘outcome’) [27].
Table 4. CASP rating for qualitative papers.
Table 4. CASP rating for qualitative papers.
Longman et al. (2011) [42]Longman et al. (2013) [43]Longman et al. (2021) [44]Ridge et al. (2021) [41]Ridge et al. (2022) [40]
Section A: Are the results valid?
1.
Was there a clear statement of the aims of the research?
2.
Is a qualitative methodology appropriate?
3.
Was the research design appropriate to address the aims of the research?
?
4.
Was the recruitment strategy appropriate to the aims of the research?
?
5.
Was the data collected in a way that addressed the research issue?
?
6.
Has the relationship between researcher and participants been adequately considered?
xx??
Section B: What are the results?
7.
Have ethical issues been taken into consideration?
??
8.
Was the data analysis sufficiently rigorous?
x?
9.
Is there a clear statement of findings?
Section C: Will the results help locally?
10.
How valuable is the research?
7 ✓
2 x
3 ✓
5 ?
1 x
7 ✓
2 ?
8 ✓
1 ?
9 ✓
CASP scoring as per Critical Appraisal Skills Programme (2018) b [29]: ✓-yes, ?–cannot tell, x-no.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ridge, A.; Peterson, G.M.; Nash, R. Risk Factors Associated with Preventable Hospitalisation among Rural Community-Dwelling Patients: A Systematic Review. Int. J. Environ. Res. Public Health 2022, 19, 16487. https://doi.org/10.3390/ijerph192416487

AMA Style

Ridge A, Peterson GM, Nash R. Risk Factors Associated with Preventable Hospitalisation among Rural Community-Dwelling Patients: A Systematic Review. International Journal of Environmental Research and Public Health. 2022; 19(24):16487. https://doi.org/10.3390/ijerph192416487

Chicago/Turabian Style

Ridge, Andrew, Gregory M. Peterson, and Rosie Nash. 2022. "Risk Factors Associated with Preventable Hospitalisation among Rural Community-Dwelling Patients: A Systematic Review" International Journal of Environmental Research and Public Health 19, no. 24: 16487. https://doi.org/10.3390/ijerph192416487

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop