Cost-effectiveness analysis of direct admission to acute geriatric unit versus admission after an emergency department visit for elderly patients

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Elderly individuals represent an increasing proportion of emergency department (ED) users. In the APHP direct-admission study, direct admission (DA) to an acute geriatric unit (AGU) was associated with a shorter hospital length of stay (LOS), lower post-acute care transfers, and lower risk of an ED return visit in the month following the AGU hospitalization compared with admission after an ED visit. Until now, no economic evaluation of DA has been available. Methods. We aimed to evaluate the cost-effectiveness of DA to an AGU versus admission after an ED visit in elderly patients. This was conducted alongside the APHP direct-admission study which used electronic medical records and administrative claims data from the Greater Paris University Hospitals (APHP) Health Data Warehouse and involved 19 different AGUs. We included all patients ≥ 75 years old who were admitted to an AGU for more than 24 hours between January 1, 2013 and December 31, 2018. The effectiveness criterion was the occurrence of ED return visit in the month following AGU hospitalization. We compared the costs of an AGU stay in the DA versus the ED visit group. The perspective was that of the payer. To characterise and summarize uncertainty, we used a non-parametric bootstrap resampling and constructed cost-effectiveness accessibility curves. Results. At baseline, mean costs per patient were €5113 and €5131 in the DA and ED visit groups, respectively. ED return visit rates were 3.3% (n = 81) in the DA group and 3.9% (n = 160) in the ED group (p = 0.21). After bootstrap, the incremental cost-effectiveness ratio was €-4249 (95%CI= -66001; +45547) per ED return visit averted. Acceptability curves showed that DA could be considered a cost-effective intervention at a threshold of €-2405 per ED return visit avoided. Conclusion. The results of this cost-effectiveness analysis of DA to an AGU versus admission after an ED visit for elderly patients argues in favor of DA, which could help provide support for public decision making. geriatrics hospital costs emergency department Figures Figure 1 Figure 2 Introduction In many industrialised countries, access block as well as emergency department (ED) overcrowding, are well documented ( 1 , 2 ). We know that they are a source of additional morbi-mortality ( 3 , 4 ) and medical errors ( 5 ). Elderly individuals represent an increasing proportion of those requiring admission to the ED ( 6 ). Advanced age brings a higher likelihood of presenting multiple chronic conditions, ( 7 ) and frailty ( 7 , 8 ). These conditions expose individuals to an increased risk of negative health-related outcomes such as disability, hospitalizations, institutionalization, and death ( 7 ). Elderly patients often experience long waiting times in the ED ( 9 , 10 ) and subsequent problems obtaining a hospital bed ( 11 , 12 ). This is particularly true for those living in institutions for whom an ED visit is identified as a possible source of aggravation ( 13 , 14 ). In a report published in 2018 in France, it was established that 45% of hospitalizations of the elderly were preceded by an ED visit ( 14 ). One solution might be to avoid referring elderly patients to the ED and to promote direct admissions (DAs) to an acute geriatric unit (AGU) for those requiring hospitalization. Few studies have compared DAs to an AGU ( 15 – 17 ) with admissions after an ED visit. However, one study showed that admissions after an ED visit were more frequent in elderly patients with a previous history of arrythmia or protein-energy malnutrition, and were associated with a higher likelihood of post-acute care transfer ( 16 ). In another study conducted among people living in nursing homes, admissions after being seen in an ED were more frequent among the most elderly ( 15 ). In the Greater Paris University Hospitals (APHP) direct-admission survey ( 17 ), a multicenter retrospective cohort study using data from the APHP Health Data Warehouse between 2013 and 2018, the aim was to evaluate the benefits on morbidity of DA to an AGU compared with admission after an ED visit, for patients older than 75 years. The study showed that DA was associated with a shorter hospital length of stay (LOS) and that there were no significant associations with the risk of an ED return visit in the month following the AGU hospitalization. However, until now there has been no available economic evaluation of DA. Using data from the APHP direct-admission study, we aimed to evaluate the cost-effectiveness of DA to an AGU versus admission after an ED visit in elderly patients. Materials And Method Study design and setting This economic evaluation was conducted alongside the APHP direct-admission study ( 17 ). Briefly, APHP direct-admission was a retrospective cohort study which used the electronic medical records and administrative claims data from the APHP Health Data Warehouse ( 18 ). It involved sizable data from 19 APHP AGUs covering, for example, demographics, standardised hospitalization reports, coded diagnoses (according to ICD-10), and therapeutic interventions (according to the French Common Classification of Medical Acts [CCAM]). Details regarding the APHP direct-admission study, as well as available data variables, have been described previously ( 17 ). Study Participants ( 17 ) All patients ≥ 75 years old admitted to an AGU for more than 24 hours (inpatient care), between January 1st, 2013 and December 31st, 2018, were included in the APHP direct-admission study. When patients had been admitted several times, we analyzed their latest admission. We excluded all patients who were admitted to the AGU more than 5 days after an ED admission and those who were admitted after hospitalization to an intensive care unit and/or non-geriatric specialty unit. We also excluded all patients presenting at ED with clinical signs of life-threatening conditions (such as mottling, respiratory distress, cyanosis, indrawing, and need for vascular filling) and those with diagnoses that did not adhere to the positivity assumption of propensity score. Intervention The intervention was DA to an AGU (DA group) as opposed to an admission after an ED visit (ED group), which was chosen as the reference strategy. Statistical analysis Using propensity score modeling for DA and inverse-probability treatment weighting (IPTW, see below), patients directly admitted to the AGU were compared with patients admitted to the AGU after an ED visit. Control of confounding We performed multiple imputations in order to handle missing data ( 19 ), and the IPTW approach was used to balance the differences in baseline variables between intervention groups ( 17 , 20 ). Details regarding how multiples imputation, propensity score modeling, and balance diagnostics before and after imputation and inverse probability treatment weighting (IPTW), are available in the original paper of the APHP direct-admission study ( 17 ). Health-economic evaluation Effectiveness criteria. In this cost-effectiveness study, the effectiveness criterion was the occurrence of ED return visit in the month following AGU hospitalization. Cost analysis . The cost analysis was conducted from the payer’s perspective, i.e., the National Health Insurance Fund ( Caisse Nationale d’Assurance Maladie , [CNAM]). The time horizon was the time of the hospitalization. Related costs were direct medical costs charged by the hospital for the hospitalization in acute care (corresponding to AGU hospitalization as well as ED visit). The monetary valuation was made in euros at 2019 rates. For each patient, the duration (in days) of hospitalization was collected and valued. For valuations, data from the Program for the Medicalization of Information Systems (PMSI) was used through diagnosis-related groups ( Groupe Homogène de Malades [GHM]) and their linked tariffs and stay-related groups ( Groupe Homogène de Séjours [GHS]). In France, every type of stay is assigned to a GHM/GHS entity based on the principal diagnosis, procedures performed, LOS, and level of severity (comorbidities and complications). Costs per patient were expressed as median costs (1st and 3rd quartiles) per group. Given the length of follow-up, costs and outcomes were not discounted. Cost-effectiveness analysis. These mean costs were combined with the rate of ED return visit in the month following AGU hospitalization to calculate incremental cost-effectiveness ratios (ICERs). ICERs reflect the additional cost needed to avoid one ED return visit, i.e., the cost per ED return visit averted. Statistical uncertainty surrounding the ICER was expressed with a 95% confidence interval estimated by 5000 non-parametric bootstrap replications. Variability of the ICER was illustrated by plotting a cost-effectiveness plane, where the reference was placed at the origin: the results appear as a scatter of 5000 possible outcomes, with each point representing a bootstrap replication. Results were interpreted with respect to the socially acceptable financial effort, i.e., in the case of our study, the threshold value the National Health Insurance Fund would be willing to pay for an additional unit of effectiveness. To facilitate the decision-making process, we plotted a cost-effectiveness acceptability curve (CEAC): the probability that a treatment is economically acceptable, given a specific cost-effectiveness threshold (i.e., the payer’s willingness to pay), is plotted on the y-axis over a wide range of possible thresholds of costs along the x-axis ( 21 ). R software for Spark (SparkR) was used for analyses. The reporting of this study followed the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines ( 22 ). Results Among the 20,416 patients admitted to an AGU during our study period, 6583 were included in the study: 37.5% (n = 2470) in the DA group and 62.5% (n = 4113) in the ED group. The detailed flowchart with the original results is available in the original paper of the APHP direct-admission study ( 17 ). From all patients, ED return visit rates were 3.3% (n = 81) in the DA group and 3.9% (n = 160) in the ED group. When considering the cost of acute hospital stays in both groups, mean costs per patient were €5131 (Q1: 4296; median: 4942; Q3: 5706) in the ED visit group and €5113 (Q1: 4500; median: 4954; Q3: 5484) in the DA group. The calculated ICER (in the initial sample data set) was €-2788 per ED return visit averted (Table 1 ). After bootstrap resampling, the ICER was €-4249 (95%CI= -66001; +45547) per ED return visit prevented. Table 1 Calculation of the incremental cost-effectiveness ratio (ICER) Rate of ED return visits averted Costs per patient Groups DA ED DA ED Results 0,967329154 0,960797965 5112,864 5131,075 Difference 0,006531189 -18,211 ICER at baseline €-2788 per ED return visit averted DA: Direct admission group; ED: Emergency department group On the cost-effectiveness plane (Fig. 1 ), 59.8% of the 5000 obtained ICERs were situated in the south–east quadrant (DA was dominant, i.e., more effective and less costly than admission after ED visit), 30.7% in the north-east quadrant (DA was more effective and more costly), 6.6% in the south-west quadrant (DA was less effective and less costly) and 2.9% in the north-west quadrant (DA was less effective and more costly). The CEACs (Fig. 2 ) show that DA and admission after an ED visit have equal probabilities of being cost-effective at a threshold of €-2405 per ED return visit avoided. Beyond this threshold, DA has a higher probability of being cost-effective. For example, at a threshold of €0 per ED return visit averted, the probability of being cost-effective is 63% and at a threshold of €1000 per ED return visit prevented, the probability of being cost-effective is 68%. Discussion In our previous APHP direct-admission study, DA to an AGU was associated with greater effectiveness (lower hospital LOS, as well as lower likelihood of post-acute care transfer, including follow-up and rehabilitation care) than admission to an AGU after an ED visit ( 17 ). No significant association was found with the risk of ED return visit ( 17 ). In this economic evaluation, we aimed to assess the cost-effectiveness of DA to an AGU versus admission after an ED visit for the elderly to avoid a return ED admission. At baseline, we found a negative ICER (€-2788 per ED return visit averted), which means that DA was more effective in avoiding an ED return visit and less costly than admission after an ED visit. An acceptability curve showed that DA can be considered a cost-effective intervention at a threshold of €-2409 per ED return visit averted. It also demonstrated that if the payer is not willing to pay additional euros per ED return visit avoided, DA is cost-effective in 63% of cases, i.e., 63% of the 5000 ICERs are situated in the south-east quadrant. Thus, our results are strongly in favor of DA implementation. To our knowledge, this study is the first cost-effectiveness analysis of DA to an AGU for elderly patients, compared with admission after an ED visit. Some observational studies have already shown that admissions to AGUs (compared with non-geriatric units) are associated with better outcomes and lower costs ( 23 , 24 ). Another study, conducted on nearly 1 million ED visits resulting in over 187 acute care hospitalizations in California, found that periods of ED overcrowding were associated with 1% increased costs per admission ( 25 ). However, none of these studies reported ICERs, which are nonetheless essential to inform stakeholders' decision-making. In a context of limited resources, decision makers must consider the allocation of resources. If €100 is allocated to a new health program, for example to gain an additional unit of effectiveness due to the implementation of such a program (here, an ED return visit averted thanks to the implementation of DA to an AGU), it implies that the same €100 cannot be allocated to a competing health program (in the same or alternative field of health) ( 26 ). This is considered to be the opportunity cost ( 21 ). Because of our analysis of the uncertainty surrounding the cost-effectiveness ratios, it should be borne in mind that in 37% of cases, the payer will have to be willing to pay additional euros if they choose to favor DA to an AGU over admission after an ED visit. It is difficult to define what is an acceptable incremental cost-effectiveness ratio. The threshold for willingness to pay may vary depending on the context in which decisions are made, and this may be different between countries due to different health policies, organization, and financing of health care. We therefore used analytical tools such as acceptability curves, a guarantee that cost-effectiveness studies were of good quality, which can inform decision makers about the likelihood that a new health program may be cost-effective, based on a variety of the Willingness to Pay schedule. If we extend the reasoning, as Bourel et al. did in a cost-effectiveness analysis in a completely different field of care, should the payer decide to invest €100,000 in the DA of elderly patients to the AGU rather than continuing to hospitalize this cohort via the emergency room, there is a 68% chance of averting 100,000/1000 = 100 ED return visits ( 26 ). The results of such economic calculations favorable to the implementation of DA of elderly people to the AGU are reinforced by the fact that this group of patients is less likely to be discharged in follow-up and rehabilitation care than those admitted after an ED ( 17 ). Indeed, the daily hospitalization cost in follow-up and rehabilitation care is high, and the LOS is often long, on average 35 days in 2019 ( 27 ), before the patient returns to the institution or home. While the results of the economic analysis are important to consider when choosing one intervention over another, there are other important considerations, such as the feasibility of DA intervention, especially in hospitals with problems related to access block and ED overcrowding ( 17 ). Increasing the total number of AGU beds, as well as follow-up and rehabilitation care beds, might be important levers ( 28 – 32 ). In a large study involving 17,111 patients experiencing acute hospital discharge delays in Canada ( 30 ), patients waiting for nursing home admission accounted for 41.5% of such bed days while only accounting for 8.8% of acute hospital discharge delay patients. This means that a small number of patients with non-medical days waiting for nursing home admission contribute to a substantial proportion of total non-medical days in acute hospitals. Some authors described the end of acute hospitalization as “push” rather than “pull” systems, patients being pushed to the next stage by pressure of patients behind them rather than pulled to the next stage ( 32 ). Higher availability of follow-up and rehabilitation care beds might help the transition to a “pull” system. Increasing the number of both AGU and follow-up and rehabilitation care beds would lead to an obvious increase in a hospital’s functioning costs. However, according to the results of our study, these investments could be offset by the costs of ED return visits averted and related re-hospitalizations. Feasibility of DA is also related to better management of patient flow over the entire geriatric pathway. General practitioners should play an important gatekeeping role for DA, but this is conditional on their availability. In Norway, which has a gatekeeper-based healthcare system, Blinkenberg et al. found that only 65% of the emergency-admitted patients came through the primary healthcare gatekeeping system (general practitioners and out-of-hours doctors) ( 33 ). DAs were more common in central areas (45%), where only 18% of referrals were from a GP. Among hospital inpatients admitted for unscheduled care in the UK, patients able to get a general practice appointment on their last attempt were more likely to have been admitted via a GP than after an ED visit ( 34 ). Better coordination between outpatient and inpatient care results in a reduction in avoidable costs ( 35 ). This study has some limitations. The first, already mentioned in the APHP direct-admission study ( 17 ), relates to the comparison of effectiveness between the two intervention groups: the choice of DA vs. ED was not randomly assigned, and potential confounding by indication could bias our analyses. IPW weighting based on a propensity score was used to balance baseline characteristics between groups, although unmeasured confounding can never be ruled out in observational studies. Second, we were unable to value hospitalizations in follow-up care and rehabilitation, as we used the APHP Health Data Warehouse, in which patient data were not linked to that regarding follow-up and rehabilitation care in public and private hospitals, most often outside the APHP. The cost implications of this lower hospitalization in the DA group have been discussed above. Finally, whilst we could have considered the societal perspective, the method most often used as it is sufficiently broad to take into account all those affected by the treatments studied, it would have been necessary to estimate travel costs, personal expenses, productivity costs/sick days to qualify for such an analysis ( 36 ). The database we used was not designed for such an analysis and our payer perspective analysis follows Peter J. Neumann’s recommendation, according to which "more attention needs to be paid to the question of what cost data decision makers themselves find most useful” ( 36 ). Conclusion The results of this cost-effectiveness analysis of DA to an AGU versus admission after an ED visit for the elderly argues for directly admitting such patients. Our findings could help support public decision making. List Of Abbreviations AGU acute geriatric unit APHP Greater Paris University Hospitals CCMA Classification of Medical Acts CEAC cost-effectiveness acceptability curve CHEERS Consolidated Health Economic Evaluation Reporting Standards CNAM Caisse Nationale d’Assurance Maladie DA direct admission ED emergency department GHM Groupe Homogène de Malades GHS Groupe Homogène de Séjours ICER incremental cost-effectiveness ratios IPTW inverse-probability treatment weighting LOS length of stay PMSI Program for the Medicalization of Information Systems Declarations Ethics approval and consent to participate : This study was carried out in accordance with relevant guidelines and regulations. The study was approved by the Scientific and Ethical Committee of Assistance Publique – Hopitaux de Paris (AP-HP) clinical data warehouse (IRB00011591). The database was authorized by the National Freedom and Informatics Commission (CNIL Number: 1980120). Assistance Publique – Hopitaux de Paris (AP-HP) clinical data warehouse initiative ensures patients’ information and consent regarding the approved studies through a transparency portal in accordance with European Regulation on data protection and authorization (number 1980120) from the National Freedom and Informatics Commission. The need for informed consent was waived by the Scientific and Ethical Committee of Assistance Publique – Hopitaux de Paris (AP-HP) clinical data warehouse, because of the retrospective nature of the study. Consent for publication: not applicable Data availability : Data supporting this study can be made available on request ( [email protected] ), on condition that the research project is accepted by Scientific and Ethical Committee of Assistance Publique – Hopitaux de Paris (AP-HP) clinical data warehouse. Author contribution: DN, NPF, NL and YY were involved in the study data analysis, interpretation of results and drafting of the manuscript. DN and NL were involved in statistical analysis. All authors critically revised the manuscript. Conflict of interest : All authors declare: no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. Funding : There was no funding for this study. Acknowledgement : We acknowledge the Felicity Kay for the english editing. References Shetty AL, Teh C, Vukasovic M, Joyce S, Vaghasiya MR, Forero R. Impact of emergency department discharge stream short stay unit performance and hospital bed occupancy rates on access and patient flowmeasures: A single site study. Emerg Med Australas EMA. 2017 Aug;29(4):407–14. Luo W, Cao J, Gallagher M, Wiles J. Estimating the intensity of ward admission and its effect on emergency department access block. 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Cite Share Download PDF Status: Published Journal Publication published 10 May, 2023 Read the published version in BMC Geriatrics → Version 1 posted Editorial decision: Major revision 03 Apr, 2023 Reviews received at journal 02 Apr, 2023 Reviews received at journal 11 Feb, 2023 Reviewers agreed at journal 03 Feb, 2023 Reviewers invited by journal 09 Dec, 2022 Editor assigned by journal 09 Dec, 2022 Editor invited by journal 29 Nov, 2022 Submission checks completed at journal 29 Nov, 2022 First submitted to journal 24 Nov, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2308875","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":155909246,"identity":"d16e2851-c489-4848-94d5-7b3e1839d2d3","order_by":0,"name":"Diane Naouri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYNACAwbGNhD9AYjZ2EnRwjgDpIWZSHsYG4AEMw+ISUiLOfvhY9IVBQyyfexnDz62+bVNno+ZgfHDxxzcWix70tIkzxgwGLfx5CUb5/bdNmxjZmCWnLkNjy8O5JhJNhgwJLYx5JhJ5/bcZgRqYWPmxafl/BuoFv435r8te27bE9ZyA2aLRI4ZM8OP24lEaHmWbNlgIGHcJvHGWLK34XZyGzNjM36/nE8+eLPhj43s/P4cww8//ty2nd/efPDDRzxaoEACQkHSADiOiAZ/SFE8CkbBKBgFIwUAADXtSi1iePcnAAAAAElFTkSuQmCC","orcid":"","institution":"Université Paris- Saclay, Université Paris-Sud, UVSQ","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Diane","middleName":"","lastName":"Naouri","suffix":""},{"id":155909247,"identity":"c3575be7-1cd2-42b8-8a13-368d90faf59a","order_by":1,"name":"Youri Yordanov","email":"","orcid":"","institution":"Sorbonne Université, APHP, Hôpital Saint Antoine, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, UMR-S 1136","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Youri","middleName":"","lastName":"Yordanov","suffix":""},{"id":155909248,"identity":"8928b443-6f31-47b6-8d47-527e1bb2097a","order_by":2,"name":"Nathanael Lapidus","email":"","orcid":"","institution":"Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, Saint-Antoine Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nathanael","middleName":"","lastName":"Lapidus","suffix":""},{"id":155909249,"identity":"4b3f8b9f-0c92-4d31-b181-51f4fd99b3be","order_by":3,"name":"Nathalie Pelletier-Fleury","email":"","orcid":"","institution":"Université Paris- Saclay, Université Paris-Sud, UVSQ","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nathalie","middleName":"","lastName":"Pelletier-Fleury","suffix":""}],"badges":[],"createdAt":"2022-11-24 11:29:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2308875/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2308875/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12877-023-03985-0","type":"published","date":"2023-05-10T20:47:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":29787592,"identity":"a989febc-7d8c-4ee1-b404-7cb6695a5368","added_by":"auto","created_at":"2022-12-01 18:36:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":287294,"visible":true,"origin":"","legend":"\u003cp\u003eCost-effectiveness plane (5000 bootstrap replications)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: x-axis= additional emergency department (ED) return visit with direct admission (DA)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ey-axis= additional cost with direct admission (DA)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2308875/v1/701b14295cc4ab794c4d33b3.jpg"},{"id":29787593,"identity":"cb7373da-05a4-43dd-9c51-41c8588c4a86","added_by":"auto","created_at":"2022-12-01 18:36:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":240432,"visible":true,"origin":"","legend":"\u003cp\u003eCost-effectiveness acceptability curves\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2308875/v1/ca915174b7468092e885738a.jpg"},{"id":44729425,"identity":"19bd447e-d952-4edd-87a9-22012cb87a87","added_by":"auto","created_at":"2023-10-16 21:15:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":466737,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2308875/v1/12f3b4c3-2e5f-4cc0-912b-f4e134436da7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost-effectiveness analysis of direct admission to acute geriatric unit versus admission after an emergency department visit for elderly patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn many industrialised countries, access block as well as emergency department (ED) overcrowding, are well documented (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). We know that they are a source of additional morbi-mortality (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and medical errors (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElderly individuals represent an increasing proportion of those requiring admission to the ED (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Advanced age brings a higher likelihood of presenting multiple chronic conditions, (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and frailty (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These conditions expose individuals to an increased risk of negative health-related outcomes such as disability, hospitalizations, institutionalization, and death (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Elderly patients often experience long waiting times in the ED (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and subsequent problems obtaining a hospital bed (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This is particularly true for those living in institutions for whom an ED visit is identified as a possible source of aggravation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In a report published in 2018 in France, it was established that 45% of hospitalizations of the elderly were preceded by an ED visit (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne solution might be to avoid referring elderly patients to the ED and to promote direct admissions (DAs) to an acute geriatric unit (AGU) for those requiring hospitalization. Few studies have compared DAs to an AGU (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) with admissions after an ED visit. However, one study showed that admissions after an ED visit were more frequent in elderly patients with a previous history of arrythmia or protein-energy malnutrition, and were associated with a higher likelihood of post-acute care transfer (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In another study conducted among people living in nursing homes, admissions after being seen in an ED were more frequent among the most elderly (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In the Greater Paris University Hospitals (APHP) direct-admission survey (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), a multicenter retrospective cohort study using data from the APHP Health Data Warehouse between 2013 and 2018, the aim was to evaluate the benefits on morbidity of DA to an AGU compared with admission after an ED visit, for patients older than 75 years. The study showed that DA was associated with a shorter hospital length of stay (LOS) and that there were no significant associations with the risk of an ED return visit in the month following the AGU hospitalization. However, until now there has been no available economic evaluation of DA. Using data from the APHP direct-admission study, we aimed to evaluate the cost-effectiveness of DA to an AGU versus admission after an ED visit in elderly patients.\u003c/p\u003e"},{"header":"Materials And Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis economic evaluation was conducted alongside the APHP direct-admission study (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Briefly, APHP direct-admission was a retrospective cohort study which used the electronic medical records and administrative claims data from the APHP Health Data Warehouse (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). It involved sizable data from 19 APHP AGUs covering, for example, demographics, standardised hospitalization reports, coded diagnoses (according to ICD-10), and therapeutic interventions (according to the French Common Classification of Medical Acts [CCAM]). Details regarding the APHP direct-admission study, as well as available data variables, have been described previously (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eAll patients\u0026thinsp;\u0026ge;\u0026thinsp;75 years old admitted to an AGU for more than 24 hours (inpatient care), between January 1st, 2013 and December 31st, 2018, were included in the APHP direct-admission study. When patients had been admitted several times, we analyzed their latest admission. We excluded all patients who were admitted to the AGU more than 5 days after an ED admission and those who were admitted after hospitalization to an intensive care unit and/or non-geriatric specialty unit. We also excluded all patients presenting at ED with clinical signs of life-threatening conditions (such as mottling, respiratory distress, cyanosis, indrawing, and need for vascular filling) and those with diagnoses that did not adhere to the positivity assumption of propensity score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIntervention\u003c/h2\u003e \u003cp\u003eThe intervention was DA to an AGU (DA group) as opposed to an admission after an ED visit (ED group), which was chosen as the reference strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eUsing propensity score modeling for DA and inverse-probability treatment weighting (IPTW, see below), patients directly admitted to the AGU were compared with patients admitted to the AGU after an ED visit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eControl of confounding\u003c/h2\u003e \u003cp\u003eWe performed multiple imputations in order to handle missing data (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and the IPTW approach was used to balance the differences in baseline variables between intervention groups (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Details regarding how multiples imputation, propensity score modeling, and balance diagnostics before and after imputation and inverse probability treatment weighting (IPTW), are available in the original paper of the APHP direct-admission study (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHealth-economic evaluation\u003c/h2\u003e \u003cp\u003e \u003cem\u003eEffectiveness criteria.\u003c/em\u003e In this cost-effectiveness study, the effectiveness criterion was the occurrence of ED return visit in the month following AGU hospitalization.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCost analysis\u003c/em\u003e. The cost analysis was conducted from the payer\u0026rsquo;s perspective, i.e., the National Health Insurance Fund (\u003cem\u003eCaisse Nationale d\u0026rsquo;Assurance Maladie\u003c/em\u003e, [CNAM]). The time horizon was the time of the hospitalization. Related costs were direct medical costs charged by the hospital for the hospitalization in acute care (corresponding to AGU hospitalization as well as ED visit). The monetary valuation was made in euros at 2019 rates. For each patient, the duration (in days) of hospitalization was collected and valued. For valuations, data from the Program for the Medicalization of Information Systems (PMSI) was used through diagnosis-related groups (\u003cem\u003eGroupe Homog\u0026egrave;ne de Malades\u003c/em\u003e [GHM]) and their linked tariffs and stay-related groups (\u003cem\u003eGroupe Homog\u0026egrave;ne de S\u0026eacute;jours\u003c/em\u003e [GHS]). In France, every type of stay is assigned to a GHM/GHS entity based on the principal diagnosis, procedures performed, LOS, and level of severity (comorbidities and complications). Costs per patient were expressed as median costs (1st and 3rd quartiles) per group. Given the length of follow-up, costs and outcomes were not discounted.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCost-effectiveness analysis.\u003c/em\u003e These mean costs were combined with the rate of ED return visit in the month following AGU hospitalization to calculate incremental cost-effectiveness ratios (ICERs). ICERs reflect the additional cost needed to avoid one ED return visit, i.e., the cost per ED return visit averted. Statistical uncertainty surrounding the ICER was expressed with a 95% confidence interval estimated by 5000 non-parametric bootstrap replications. Variability of the ICER was illustrated by plotting a cost-effectiveness plane, where the reference was placed at the origin: the results appear as a scatter of 5000 possible outcomes, with each point representing a bootstrap replication. Results were interpreted with respect to the socially acceptable financial effort, i.e., in the case of our study, the threshold value the National Health Insurance Fund would be willing to pay for an additional unit of effectiveness. To facilitate the decision-making process, we plotted a cost-effectiveness acceptability curve (CEAC): the probability that a treatment is economically acceptable, given a specific cost-effectiveness threshold (i.e., the payer\u0026rsquo;s willingness to pay), is plotted on the y-axis over a wide range of possible thresholds of costs along the x-axis (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eR software for Spark (SparkR) was used for analyses. The reporting of this study followed the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 20,416 patients admitted to an AGU during our study period, 6583 were included in the study: 37.5% (n\u0026thinsp;=\u0026thinsp;2470) in the DA group and 62.5% (n\u0026thinsp;=\u0026thinsp;4113) in the ED group. The detailed flowchart with the original results is available in the original paper of the APHP direct-admission study (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFrom all patients, ED return visit rates were 3.3% (n\u0026thinsp;=\u0026thinsp;81) in the DA group and 3.9% (n\u0026thinsp;=\u0026thinsp;160) in the ED group. When considering the cost of acute hospital stays in both groups, mean costs per patient were \u0026euro;5131 (Q1: 4296; median: 4942; Q3: 5706) in the ED visit group and \u0026euro;5113 (Q1: 4500; median: 4954; Q3: 5484) in the DA group.\u003c/p\u003e\n\u003cp\u003eThe calculated ICER (in the initial sample data set) was \u0026euro;-2788 per ED return visit averted (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). After bootstrap resampling, the ICER was \u0026euro;-4249 (95%CI= -66001; +45547) per ED return visit prevented.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCalculation of the incremental cost-effectiveness ratio (ICER)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRate of ED return visits averted\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCosts per patient\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGroups\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eED\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0,967329154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0,960797965\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5112,864\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5131,075\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDifference\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0,006531189\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-18,211\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eICER at baseline\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026euro;-2788 per ED return visit averted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eDA: Direct admission group; ED: Emergency department group\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eOn the cost-effectiveness plane (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), 59.8% of the 5000 obtained ICERs were situated in the south\u0026ndash;east quadrant (DA was dominant, i.e., more effective and less costly than admission after ED visit), 30.7% in the north-east quadrant (DA was more effective and more costly), 6.6% in the south-west quadrant (DA was less effective and less costly) and 2.9% in the north-west quadrant (DA was less effective and more costly).\u003c/p\u003e\n\u003cp\u003eThe CEACs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) show that DA and admission after an ED visit have equal probabilities of being cost-effective at a threshold of \u0026euro;-2405 per ED return visit avoided. Beyond this threshold, DA has a higher probability of being cost-effective. For example, at a threshold of \u0026euro;0 per ED return visit averted, the probability of being cost-effective is 63% and at a threshold of \u0026euro;1000 per ED return visit prevented, the probability of being cost-effective is 68%.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our previous APHP direct-admission study, DA to an AGU was associated with greater effectiveness (lower hospital LOS, as well as lower likelihood of post-acute care transfer, including follow-up and rehabilitation care) than admission to an AGU after an ED visit (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). No significant association was found with the risk of ED return visit (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In this economic evaluation, we aimed to assess the cost-effectiveness of DA to an AGU versus admission after an ED visit for the elderly to avoid a return ED admission. At baseline, we found a negative ICER (\u0026euro;-2788 per ED return visit averted), which means that DA was more effective in avoiding an ED return visit and less costly than admission after an ED visit. An acceptability curve showed that DA can be considered a cost-effective intervention at a threshold of \u0026euro;-2409 per ED return visit averted. It also demonstrated that if the payer is not willing to pay additional euros per ED return visit avoided, DA is cost-effective in 63% of cases, i.e., 63% of the 5000 ICERs are situated in the south-east quadrant. Thus, our results are strongly in favor of DA implementation.\u003c/p\u003e \u003cp\u003eTo our knowledge, this study is the first cost-effectiveness analysis of DA to an AGU for elderly patients, compared with admission after an ED visit. Some observational studies have already shown that admissions to AGUs (compared with non-geriatric units) are associated with better outcomes and lower costs (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Another study, conducted on nearly 1\u0026nbsp;million ED visits resulting in over 187 acute care hospitalizations in California, found that periods of ED overcrowding were associated with 1% increased costs per admission (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). However, none of these studies reported ICERs, which are nonetheless essential to inform stakeholders' decision-making. In a context of limited resources, decision makers must consider the allocation of resources. If \u0026euro;100 is allocated to a new health program, for example to gain an additional unit of effectiveness due to the implementation of such a program (here, an ED return visit averted thanks to the implementation of DA to an AGU), it implies that the same \u0026euro;100 cannot be allocated to a competing health program (in the same or alternative field of health) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This is considered to be the opportunity cost (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Because of our analysis of the uncertainty surrounding the cost-effectiveness ratios, it should be borne in mind that in 37% of cases, the payer will have to be willing to pay additional euros if they choose to favor DA to an AGU over admission after an ED visit. It is difficult to define what is an acceptable incremental cost-effectiveness ratio. The threshold for willingness to pay may vary depending on the context in which decisions are made, and this may be different between countries due to different health policies, organization, and financing of health care. We therefore used analytical tools such as acceptability curves, a guarantee that cost-effectiveness studies were of good quality, which can inform decision makers about the likelihood that a new health program may be cost-effective, based on a variety of the Willingness to Pay schedule. If we extend the reasoning, as Bourel \u003cem\u003eet al.\u003c/em\u003e did in a cost-effectiveness analysis in a completely different field of care, should the payer decide to invest \u0026euro;100,000 in the DA of elderly patients to the AGU rather than continuing to hospitalize this cohort via the emergency room, there is a 68% chance of averting 100,000/1000\u0026thinsp;=\u0026thinsp;100 ED return visits (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The results of such economic calculations favorable to the implementation of DA of elderly people to the AGU are reinforced by the fact that this group of patients is less likely to be discharged in follow-up and rehabilitation care than those admitted after an ED (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Indeed, the daily hospitalization cost in follow-up and rehabilitation care is high, and the LOS is often long, on average 35 days in 2019 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), before the patient returns to the institution or home.\u003c/p\u003e \u003cp\u003eWhile the results of the economic analysis are important to consider when choosing one intervention over another, there are other important considerations, such as the feasibility of DA intervention, especially in hospitals with problems related to access block and ED overcrowding (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Increasing the total number of AGU beds, as well as follow-up and rehabilitation care beds, might be important levers (\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In a large study involving 17,111 patients experiencing acute hospital discharge delays in Canada (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), patients waiting for nursing home admission accounted for 41.5% of such bed days while only accounting for 8.8% of acute hospital discharge delay patients. This means that a small number of patients with non-medical days waiting for nursing home admission contribute to a substantial proportion of total non-medical days in acute hospitals. Some authors described the end of acute hospitalization as \u0026ldquo;push\u0026rdquo; rather than \u0026ldquo;pull\u0026rdquo; systems, patients being pushed to the next stage by pressure of patients behind them rather than pulled to the next stage (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Higher availability of follow-up and rehabilitation care beds might help the transition to a \u0026ldquo;pull\u0026rdquo; system. Increasing the number of both AGU and follow-up and rehabilitation care beds would lead to an obvious increase in a hospital\u0026rsquo;s functioning costs. However, according to the results of our study, these investments could be offset by the costs of ED return visits averted and related re-hospitalizations. Feasibility of DA is also related to better management of patient flow over the entire geriatric pathway. General practitioners should play an important gatekeeping role for DA, but this is conditional on their availability. In Norway, which has a gatekeeper-based healthcare system, Blinkenberg \u003cem\u003eet al.\u003c/em\u003e found that only 65% of the emergency-admitted patients came through the primary healthcare gatekeeping system (general practitioners and out-of-hours doctors) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). DAs were more common in central areas (45%), where only 18% of referrals were from a GP. Among hospital inpatients admitted for unscheduled care in the UK, patients able to get a general practice appointment on their last attempt were more likely to have been admitted via a GP than after an ED visit (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Better coordination between outpatient and inpatient care results in a reduction in avoidable costs (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study has some limitations. The first, already mentioned in the APHP direct-admission study (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), relates to the comparison of effectiveness between the two intervention groups: the choice of DA vs. ED was not randomly assigned, and potential confounding by indication could bias our analyses. IPW weighting based on a propensity score was used to balance baseline characteristics between groups, although unmeasured confounding can never be ruled out in observational studies. Second, we were unable to value hospitalizations in follow-up care and rehabilitation, as we used the APHP Health Data Warehouse, in which patient data were not linked to that regarding follow-up and rehabilitation care in public and private hospitals, most often outside the APHP. The cost implications of this lower hospitalization in the DA group have been discussed above. Finally, whilst we could have considered the societal perspective, the method most often used as it is sufficiently broad to take into account all those affected by the treatments studied, it would have been necessary to estimate travel costs, personal expenses, productivity costs/sick days to qualify for such an analysis (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The database we used was not designed for such an analysis and our payer perspective analysis follows Peter J. Neumann\u0026rsquo;s recommendation, according to which \"more attention needs to be paid to the question of what cost data decision makers themselves find most useful\u0026rdquo; (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this cost-effectiveness analysis of DA to an AGU versus admission after an ED visit for the elderly argues for directly admitting such patients. Our findings could help support public decision making.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003eAGU acute geriatric unit\u003c/p\u003e\n\u003cp\u003eAPHP Greater Paris University Hospitals\u003c/p\u003e\n\u003cp\u003eCCMA Classification of Medical Acts\u003c/p\u003e\n\u003cp\u003eCEAC cost-effectiveness acceptability curve\u003c/p\u003e\n\u003cp\u003eCHEERS Consolidated Health Economic Evaluation Reporting Standards\u003c/p\u003e\n\u003cp\u003eCNAM \u003cem\u003eCaisse Nationale d\u0026rsquo;Assurance Maladie\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDA direct admission\u003c/p\u003e\n\u003cp\u003eED emergency department\u003c/p\u003e\n\u003cp\u003eGHM \u003cem\u003eGroupe Homog\u0026egrave;ne de Malades\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGHS \u003cem\u003eGroupe Homog\u0026egrave;ne de S\u0026eacute;jours\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eICER incremental cost-effectiveness ratios\u003c/p\u003e\n\u003cp\u003eIPTW inverse-probability treatment weighting\u003c/p\u003e\n\u003cp\u003eLOS length of stay\u003c/p\u003e\n\u003cp\u003ePMSI Program for the Medicalization of Information Systems\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: This study was carried out in accordance with relevant guidelines and regulations. The study was approved by the Scientific and Ethical Committee of Assistance Publique \u0026ndash; Hopitaux de Paris (AP-HP) clinical data warehouse (IRB00011591). The database was authorized by the National Freedom and Informatics Commission (CNIL Number: 1980120). Assistance Publique \u0026ndash; Hopitaux de Paris (AP-HP) clinical data warehouse initiative ensures patients\u0026rsquo; information and consent regarding the approved studies through a transparency portal in accordance with European Regulation on data protection and authorization (number 1980120) from the National Freedom and Informatics Commission. The need for informed consent was waived by the Scientific and Ethical Committee of Assistance Publique \u0026ndash; Hopitaux de Paris (AP-HP) clinical data warehouse, because of the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: Data supporting this study can be made available on request ([email protected]), on condition that the research project is accepted by Scientific and Ethical Committee of Assistance Publique \u0026ndash; Hopitaux de Paris (AP-HP) clinical data warehouse.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e DN, NPF, NL and YY were involved in the study data analysis, interpretation of results and drafting of the manuscript. DN and NL were involved in statistical analysis. All authors critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e: All authors declare: no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: There was no funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e: We acknowledge the Felicity Kay for the english editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShetty AL, Teh C, Vukasovic M, Joyce S, Vaghasiya MR, Forero R. Impact of emergency department discharge stream short stay unit performance and hospital bed occupancy rates on access and patient flowmeasures: A single site study. Emerg Med Australas EMA. 2017 Aug;29(4):407\u0026ndash;14. \u003c/li\u003e\n\u003cli\u003eLuo W, Cao J, Gallagher M, Wiles J. Estimating the intensity of ward admission and its effect on emergency department access block. Stat Med. 2013 Jul 10;32(15):2681\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eJo S, Jin YH, Lee JB, Jeong T, Yoon J, Park B. Emergency department occupancy ratio is associated with increased early mortality. J Emerg Med. 2014 Feb;46(2):241\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eRichardson DB. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med J Aust. 2006 Mar 6;184(5):213\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eKulstad EB, Sikka R, Sweis RT, Kelley KM, Rzechula KH. ED overcrowding is associated with an increased frequency of medication errors. Am J Emerg Med. 2010 Mar;28(3):304\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eSamaras N, Chevalley T, Samaras D, Gold G. Older Patients in the Emergency Department: A Review. Ann Emerg Med. 2010 Sep;56(3):261\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eCesari M, Calvani R, Marzetti E. Frailty in Older Persons. Clin Geriatr Med. 2017;33(3):293\u0026ndash;303. \u003c/li\u003e\n\u003cli\u003eClegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. The Lancet. 2013 Mar 2;381(9868):752\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eHorwitz LI, Bradley EH. Percentage of US emergency department patients seen within the recommended triage time: 1997 to 2006. Arch Intern Med. 2009 Nov 9;169(20):1857\u0026ndash;65. \u003c/li\u003e\n\u003cli\u003eFreund Y, Vincent-Cassy C, Bloom B, Riou B, Ray P, APHP Emergency Database Study Group. Association between age older than 75 years and exceeded target waiting times in the emergency department: a multicenter cross-sectional survey in the Paris metropolitan area, France. Ann Emerg Med. 2013 Nov;62(5):449\u0026ndash;56. \u003c/li\u003e\n\u003cli\u003eCooke MW, Wilson S, Halsall J, Roalfe A. Total time in English accident and emergency departments is related to bed occupancy. Emerg Med J EMJ. 2004 Sep;21(5):575\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eForero R, McCarthy S, Hillman K. Access block and emergency department overcrowding. Crit Care. 2011;15(2):216. \u003c/li\u003e\n\u003cli\u003eCours des comptes. Les urgences hospitali\u0026egrave;res : une fr\u0026eacute;quentation croissante, une articulation avec la m\u0026eacute;decine de ville \u0026agrave; repenser [Internet]. 2014 Sep. Available from: https://www.ccomptes.fr/sites/default/files/EzPublish/rapport_securite_sociale_2014_urgences_hospitalieres.pdf\u003c/li\u003e\n\u003cli\u003eMesnier T. Assurer le premier acc\u0026egrave;s aux soins Organiser les soins non programm\u0026eacute;s dans les territoires [Internet]. 2018 May. Available from: https://solidarites-sante.gouv.fr/IMG/pdf/rapport_snp_vf.pdf\u003c/li\u003e\n\u003cli\u003eAizen E, Swartzman R, Clarfield A. Hospitalization of nursing home residents in an acute-care geriatric department: direct versus emergency room admission. Isr Med Assoc J IMAJ [Internet]. 2001 Oct [cited 2020 Sep 27]; Available from: https://pubmed.ncbi.nlm.nih.gov/11692547/\u003c/li\u003e\n\u003cli\u003eNeouze A, Dechartres A, Legrain S, Raynaud-Simon A, Gaubert-Dahan M, Bonnet-Zamponi D. [Hospitalization of elderly in an acute-care geriatric department]. Geriatr Psychol Neuropsychiatr Vieil [Internet]. 2012 Jun [cited 2020 Sep 27]; Available from: https://pubmed.ncbi.nlm.nih.gov/22713842/\u003c/li\u003e\n\u003cli\u003eNaouri D, Pelletier-Fleury N, Lapidus N, Yordanov Y. The effect of direct admission to acute geriatric units compared to admission after an emergency department visit on length of stay, postacute care transfers and ED return visits. BMC Geriatr. 2022 Jul 4;22(1):555. \u003c/li\u003e\n\u003cli\u003eL\u0026rsquo;Entrep\u0026ocirc;t de Donn\u0026eacute;es de Sant\u0026eacute; [Internet]. Direction de la Recherche Clinique et de l\u0026rsquo;Innovation de l\u0026rsquo;AP-HP. 2016 [cited 2021 Jan 22]. Available from: http://recherche.aphp.fr/eds/\u003c/li\u003e\n\u003cli\u003eSchafer JL, Olsen MK. Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst\u0026rsquo;s Perspective. Multivar Behav Res. 1998 Oct 1;33(4):545\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eRobins JM, Hern\u0026aacute;n MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiol Camb Mass. 2000 Sep;11(5):550\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eDrummond M, Sculpher M, Claxton K, Stoddart G, Torrance G. Methods for the Economic Evaluation of Health Care Programmes. (4th ed.). 2015. (Oxford University Press). \u003c/li\u003e\n\u003cli\u003eHusereau D, Drummond M, Petrou S, Carswell C, Moher D, Greenberg D, et al. Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. Value Health J Int Soc Pharmacoeconomics Outcomes Res. 2013 Apr;16(2):e1-5. \u003c/li\u003e\n\u003cli\u003eFlood KL, MacLennan PA, McGrew D, Green D, Dodd C, Brown CJ. Effects of an Acute Care for Elders Unit on Costs and 30-Day Readmissions. JAMA Intern Med. 2013 Jun 10;173(11):981\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eJayadevappa R, Chhatre S, Weiner M, Raziano DB. Health Resource Utilization and Medical Care Cost of Acute Care Elderly Unit Patients. Value Health. 2006 May 1;9(3):186\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eSun BC, Hsia RY, Weiss RE, Zingmond D, Liang LJ, Han W, et al. Effect of emergency department crowding on outcomes of admitted patients. Ann Emerg Med. 2013 Jun;61(6):605-611.e6. \u003c/li\u003e\n\u003cli\u003eBourel G, Pelletier-Fleury N, Bouyer J, Delbarre A, Fernandez H, Capmas P. Cost-effectiveness analysis of medical management versus conservative surgery for early tubal pregnancy. Hum Reprod Oxf Engl. 2019 Feb 1;34(2):261\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eDREES. Panorama des \u0026eacute;tablissements - Fiche 18 - Les \u0026eacute;tablissements de soins de suite et de r\u0026eacute;adaptation [Internet]. 2021. Available from: https://drees.solidarites-sante.gouv.fr/sites/default/files/2021-07/Fiche%2018%20-%20Les%20%C3%A9tablissements%20de%20soins%20de%20suite%20et%20de%20r%C3%A9adaptation.pdf\u003c/li\u003e\n\u003cli\u003eChamplon S, Cattenoz C, Mordellet B, Roussel-Laudrin S, Jouanny P. D\u0026eacute;terminants de la dur\u0026eacute;e de s\u0026eacute;jour des personnes \u0026acirc;g\u0026eacute;es hospitalis\u0026eacute;es. /data/revues/02488663/002900S1/08003007/ [Internet]. 2008 Jun 4 [cited 2019 Jul 27]; Available from: https://www.em-consulte.com/en/article/167730\u003c/li\u003e\n\u003cli\u003eHolstein J, Saint-Jean O, Verny M, B\u0026eacute;rigaud S, Bouchon JP. Facteurs explicatifs du devenir et de la dur\u0026eacute;e de s\u0026eacute;jour dans une unit\u0026eacute; de court s\u0026eacute;jour g\u0026eacute;riatrique. Sci Soc Sant\u0026eacute;. 1995;13(4):45\u0026ndash;79. \u003c/li\u003e\n\u003cli\u003eCosta AP, Poss JW, Peirce T, Hirdes JP. Acute care inpatients with long-term delayed-discharge: evidence from a Canadian health region. BMC Health Serv Res. 2012 Jun 22;12(1):172. \u003c/li\u003e\n\u003cli\u003eBoaden R, Proudlove N, Wilson M. An exploratory study of bed management. J Manag Med. 1999 Jan 1;13(4):234\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eProudlove NC, Gordon K, Boaden R. Can good bed management solve the overcrowding in accident and emergency departments? Emerg Med J. 2003 Mar 1;20(2):149\u0026ndash;55. \u003c/li\u003e\n\u003cli\u003eBlinkenberg J, Pahlavanyali S, Hetlevik \u0026Oslash;, Sandvik H, Hunskaar S. Correction to: General practitioners\u0026rsquo; and out-of-hours doctors\u0026rsquo; role as gatekeeper in emergency admissions to somatic hospitals in Norway: registry-based observational study. BMC Health Serv Res. 2020 Sep 16;20(1):876. \u003c/li\u003e\n\u003cli\u003eCowling TE, Harris M, Watt H, Soljak M, Richards E, Gunning E, et al. Access to primary care and the route of emergency admission to hospital: retrospective analysis of national hospital administrative data. BMJ Qual Saf. 2016 Jun 1;25(6):432\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eB\u0026eacute;land F, Hollander MJ. Integrated models of care delivery for the frail elderly: international perspectives. Gac Sanit. 2011 Dec 1;25:138\u0026ndash;46. \u003c/li\u003e\n\u003cli\u003eNeumann PJ. Costing and perspective in published cost-effectiveness analysis. Med Care. 2009 Jul;47(7 Suppl 1):S28-32. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"geriatrics, hospital, costs, emergency department","lastPublishedDoi":"10.21203/rs.3.rs-2308875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2308875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eElderly individuals represent an increasing proportion of emergency department (ED) users. In the APHP direct-admission study, direct admission (DA) to an acute geriatric unit (AGU) was associated with a shorter hospital length of stay (LOS), lower post-acute care transfers, and lower risk of an ED return visit in the month following the AGU hospitalization compared with admission after an ED visit. Until now, no economic evaluation of DA has been available.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eWe aimed to evaluate the cost-effectiveness of DA to an AGU versus admission after an ED visit in elderly patients. This was conducted alongside the APHP direct-admission study which used electronic medical records and administrative claims data from the Greater Paris University Hospitals (APHP) Health Data Warehouse and involved 19 different AGUs. We included all patients\u0026thinsp;\u0026ge;\u0026thinsp;75 years old who were admitted to an AGU for more than 24 hours between January 1, 2013 and December 31, 2018. The effectiveness criterion was the occurrence of ED return visit in the month following AGU hospitalization. We compared the costs of an AGU stay in the DA versus the ED visit group. The perspective was that of the payer. To characterise and summarize uncertainty, we used a non-parametric bootstrap resampling and constructed cost-effectiveness accessibility curves.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eAt baseline, mean costs per patient were \u0026euro;5113 and \u0026euro;5131 in the DA and ED visit groups, respectively. ED return visit rates were 3.3% (n\u0026thinsp;=\u0026thinsp;81) in the DA group and 3.9% (n\u0026thinsp;=\u0026thinsp;160) in the ED group (p\u0026thinsp;=\u0026thinsp;0.21). After bootstrap, the incremental cost-effectiveness ratio was \u0026euro;-4249 (95%CI= -66001; +45547) per ED return visit averted. Acceptability curves showed that DA could be considered a cost-effective intervention at a threshold of \u0026euro;-2405 per ED return visit avoided.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003eThe results of this cost-effectiveness analysis of DA to an AGU versus admission after an ED visit for elderly patients argues in favor of DA, which could help provide support for public decision making.\u003c/p\u003e","manuscriptTitle":"Cost-effectiveness analysis of direct admission to acute geriatric unit versus admission after an emergency department visit for elderly patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-01 18:36:14","doi":"10.21203/rs.3.rs-2308875/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-04-03T10:02:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-04-02T10:39:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-02-11T07:58:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37719100-5f06-406f-b59d-7ae8832a0e70","date":"2023-02-03T09:59:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-12-09T13:39:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-12-09T13:28:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-11-29T08:30:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-11-29T08:28:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2022-11-24T11:16:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"014e6af2-6d2e-4388-9a5d-ee1657018c64","owner":[],"postedDate":"December 1st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:03:05+00:00","versionOfRecord":{"articleIdentity":"rs-2308875","link":"https://doi.org/10.1186/s12877-023-03985-0","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2023-05-10 20:47:46","publishedOnDateReadable":"May 10th, 2023"},"versionCreatedAt":"2022-12-01 18:36:14","video":"","vorDoi":"10.1186/s12877-023-03985-0","vorDoiUrl":"https://doi.org/10.1186/s12877-023-03985-0","workflowStages":[]},"version":"v1","identity":"rs-2308875","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2308875","identity":"rs-2308875","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}