Risk factors associated with 30-day hospital readmission of older adults: case-control studyAbstract
Objective To analyze the risk factors associated with hospital readmission of older adults within 30 days.
Method A retrospective, case-control study of 568 patients discharged from hospital inpatient units in southern Brazil, comprising 284 cases (readmitted after discharge) and 284 controls (not readmitted) was conducted. Data were collected by consulting electronic medical records and analyzed using bivariate analysis and multivariate logistic regression.
Results The variables reason for admission, comorbidities, history of hospitalization in previous year, emergency admission, length of stay, and types of discharge instructions, showed significant group differences (p<0.05) on bivariate analysis. On multivariate logistic regression, the protective factors identified were reason for admission due to infectious/parasitic diseases (p=0.007) and comorbidities categorized as “other” (p<0.001). The risk factors identified were reason for admission due to neoplasm (p<0.001), genitourinary comorbidities (p=0.028), history of hospitalization in previous year (p<0.001), emergency admission (p=0.016), length of stay upon admission in days (p<0.001), and discharge guidance for outpatient or surgical procedure (p=0.008).
Conclusion Clinical and organizational risk factors were associated with readmission within 30 days in the older adults and warrant attention when planning care transition actions.
Keywords
Health of the Aged; Patient Readmission; Risk Factors; Patient Discharge
Resumo
Objetivo Analisar os fatores de risco associados à readmissão hospitalar de pessoas idosas em até 30 dias.
Método Trata-se de estudo retrospectivo, do tipo caso-controle. Participaram 568 pacientes que tiveram alta de unidades de internação de hospital no Sul do Brasil, sendo 284 casos (que readmitiram após a alta) e 284 controles (que não readmitiram). Os dados foram coletados por meio de consulta aos prontuários eletrônicos e foram analisados utilizando-se análise bivariada e regressão logística multivariada.
Resultados Variáveis relacionadas a motivo da admissão, comorbidades, histórico de internação no ano anterior, admissão pela emergência, tempo de permanência e tipos de orientações para alta tiveram diferenças significativas (p<0,05) entre os grupos na análise bivariada. Na regressão logística multivariada, identificaram-se como fatores protetores: motivo de admissão por doenças infecciosas/parasitárias (p=0,007) e comorbidades categorizadas como “outros” (p<0,001). Como fatores de risco, identificaram-se: motivo de admissão por neoplasias (p<0,001), comorbidades geniturinárias (p=0,028), histórico de internação no ano anterior (p<0,001), admissão pela emergência (p=0,016), tempo de permanência na admissão em dias (p<0,001), e orientação de alta de retorno para procedimento ambulatorial ou cirúrgico (p=0,008).
Conclusão Evidenciam-se fatores de risco clínicos e organizacionais associados à readmissão em até 30 dias em pessoas idosas, os quais merecem atenção ao se planejar ações de transição do cuidado.
Palavras-Chave:
Saúde do Idoso; Readmissão do Paciente; Fatores de Risco; Alta do Paciente
INTRODUCTION
Hospital readmissions, defined as a second admission to hospital within a given timeframe, although widely debated in studies and interventions, remain prevalent, costly and potentially preventable1,2. Rates of readmission within 30 days are used as a parameter of service quality, with a view to reducing costs and improving care quality, given that patients generally return due to exacerbation of the original admitting condition3,4.
Around 27% of readmissions within 30 days of discharge can be avoided or anticipated by monitoring patients who are at high risk of readmission5. The group of patients aged >60 years is deemed high-risk, with greater likelihood of experiencing hospital readmissions due to being more prone to more complex health needs and social isolation6.
International studies report 30-day readmission rates of 6.4%-18% for patients aged >65 years7-9. Hospital readmissions in this population represent increased morbimortality, loss of functional independence and higher healthcare costs, besides being classified as a primary risk factor for future readmissions and death10.. Thus, readmissions in this population group should be tackled strategically to identify high-risk patients and define measures to prevent this undesirable event.
A number of factors associated with readmission of older adults have been identified by international studies. These factors can be divided into sociodemographic and clinical aspects, such as more advanced age, male gender, ethnicity, living conditions, health status, number of comorbidities and polypharmacy, and into organizational factors, such as the existence of previous unplanned admissions and readmissions, long length of stay, emergency admission, referral method (emergency transfer or from other institutions), discharge destination and clinical stability at discharge9-12. However, the Brazilian literature on risk factors for readmission in this population is scarce, with one study available centered on identifying factors predicting outcome in patients with neurological disorders13.
Identifying the characteristics of patients at risk of hospital readmission can aid recognition of candidates for intervention and help reduce readmission rates in this group11. Some interventions which can be implemented include identification of the risk of readmission, discharge planning, health education during and after hospitalization, medication reconciliation and communication with health services, besides outpatient consultations, telephone contact and post-discharge home visits14. In a North-American study, nurses were protagonists in interventions which significantly reduced 30-day readmissions by acting as patient care transition coordinators15.
Thus, investigation of the aspects which contribute to 30-day readmission of older adults in Brazil is needed to inform the development of care strategies aimed at reducing readmission rates in this population. Therefore, the objective of this study was to analyze the risk factors associated with 30-day hospital readmission of older adults.
METHOD
A retrospective case-control study was conducted in a large general public university hospital foundation located in the southern Brazil. The guidelines of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement were observed16.
Study eligibility criteria were patients aged ≥60 years, admitted to one of the clinical inpatient or surgical units of the hospital, between January and December 2021. Patients whose length of stay was <48 hours or who had been transferred to another hospital at discharge were excluded. Cases encompassed patients with 30-day readmission after hospital discharge, whereas controls included subjects without 30-day readmission. Although cases and controls were at a 1:1 ratio, the groups were not matched.
Sample size calculation was based on a similar study12. Adopting a 5% significance level, 80% power, estimated proportion of readmission of 25% and minimum odds ratio of 1.7 (based on variables education, polypharmacy and length of stay, which increased chances of readmission by over 1.7 times), yielded a minimum of 284 in each group (cases and controls), giving an estimated overall sample size of 568 participants.
A total of 5,122 eligible patients were identified on the report from the institution´s information system. Participants for the control and case groups were selected by random draw in a digital spreadsheet. Overall, 214 cases and 173 controls which failed to meet the inclusion criteria were excluded. Losses were replaced by performing a repeat random draw. The process of selecting participants was deemed complete upon reaching the minimum number for the sample in each group (284).
Data collection was performed between August and October 2022 by directly accessing and consulting electronic patient records held on the information system. For patients that had more than one admission during the year, only the first admission counted for controls whereas, for cases, the first admission followed by a readmission within 30 days was examined.
Data were collected using a form containing the following variables: 1) sociodemographics (age, sex, race/color, education and marital status); 2) clinical (reason for admission and comorbidity); 3) organization (history of admission in previous year, emergency admission, length of stay in days, visit to Intensive Therapy Center (CTI), day of admission discharge, type of admitting unit - clinical or surgical, type of admission under the National Health System (SUS), health insurance or private, and instructions for continuity of care). In the cases group, data were collected on time elapsed between admission discharge and readmission, length of stay on readmission, whether reason for readmission was related to that of first admission, and outcome of readmission (discharge, death or transfer). The variables Reason for admission and Comorbidities were classified according to the chapters of the International Classification of Diseases (ICD-10). For the Comorbidities variable, the diseases that had a frequency of ≤2 in the overall sample of cases and controls were grouped under the “others” category, and not counted in the corresponding ICD-10 chapters. Example diseases classified under this category were Lynch syndrome, solitary kidney, Miller Fisher Syndrome, Fournier´s Gangrene, Sialolithiasis, among others.
For data analysis, the dependent variable was presence of readmission, while the independent variables all of the others. Quantitative variables were expressed as mean and standard deviation or median and interquartile range. Categorical variables were expressed as absolute and relative frequencies. Student´s t-test was used for comparison of means. In the event of asymmetry, the Mann-Whitney U-test was used. For comparing proportions, Pearson´s chi-square test or Fisher´s exact test were used. Multivariate Logistic Regression model was employed to control for confounding factors. The Odds Ratio effect measure was calculated, together with the respective 95% confidence interval. Variables with p<0,20 were entered into the multivariate logistic regression model. When applying the multivariate model, some of the variables inverted their direction from the bivariate analysis, indicating the presence of the multicolinearity effect. Associations between independent variables were tested to ascertain the strength of these associations, and when applicable excluded from the model to prevent this effect. A 5% (p<0.05) level of significance was adopted for all analyses. Missing data were excluded during the analysis process.
This study was approved by the Research Ethics Committee of the Institution under permit no. 5.471.342 and Ethics Appreciation Presentation Certificate (CAAE) 59383522.5.0000.5327. The legal and ethical precepts established in Resolution 466/12 and Resolution 510/2016 were observed.
DATA AVAILABILITY
The complete dataset underpinning the results of the present study are available on the Figshare repository and can be accessed at https://doi.org/10.6084/m9.figshare.26531611.
RESULTS
Participant age ranged from 60 to 98 years. The participants were predominantly male (54.75%), married or in de facto partnership (57.39%), of white ethnicity/color (88.73%), and had incomplete primary education (42.60%).
As shown in Table 1, there were statistically significant group differences in the sociodemographic and clinical characteristics for the variables: Reason for admission due to Certain infectious and parasitic diseases, due to Neoplasms/tumors and due to Diseases of the nervous system; Comorbidities of the class of Diseases of the respiratory system and Diseases of the genitourinary system, and “Others’ class (diseases which had frequency of ≤2 in study), and Number of comorbidities.
Sociodemographic and clinical factors, according to case and control groups (N=568). Porto Alegre city, Rio Grande do Sul state, 2022.
Regarding the organizational factors of admission (Table 2), history of hospital admission the previous year had the strongest association with 30-day readmission, and likewise for emergency service admission. Length of stay also exhibited a positive association with readmission outcome. In addition, lowest prevalence of readmission was found in patients who admitted privately, i.e. as self-paying users. No significant difference was found for the variables Visit to CTI or Day of discharge.
Organizational factors of admission, according to case and control groups (N=568). Porto Alegre city, Rio Grande do Sul state, 2022.
Regarding guidance for care continuity after discharge, advising patients to return to the outpatient service was positively associated with 30-day readmission, and likewise for those advised to return for surgical or outpatient procedure. Instructions to return to the office of the treating physician had the weakest association with the outcome and was the most frequent guidance in the control group.
Variables with p<0.20 on Pearson´s chi-square or Fisher´s exact tests were analyzed in the multivariate logistic regression model. The following variables were excluded due to the multicolinearity effect: Reason for admission due to Diseases of the respiratory system; and Comorbidities classified as Certain infectious and parasitic diseases and Neoplasms/tumors.
The protective factors that reduced the odds of 30-day readmission were: Reason for admission due to Certain infectious and parasitic diseases; and Comorbidities categorized as “others”. Risk factors identified were: Reason for admission due to neoplasms; Comorbidities of the class of diseases of the genitourinary system, history of hospitalization in the year prior to admission, emergency admission, length of stay on admission in days, and discharge guidance to return for outpatient/surgical procedure (Table 3).
Multivariate Logistic Regression Analysis for assessing factors independently associated with 30-day readmission (N=568). Porto Alegre city, Rio Grande do Sul state, 2022.
With regard to readmission of the cases group, median time elapsed between admission and readmission was 10.5 (95%CI: 5-19) days and mean was 12.5 (± 8.9) days. Concerning visits to the emergency service within 30 days of discharge, 81.7% of patient cases made at least one visit to the service where, on many instances, the visit resulted in readmission (80.63%) (p<0.001). In the controls group, however, only 3.5% of subjects were seen in emergency, and did not result in readmission. Length of stay on readmission was a median of 10 (95%CI: 6-16) days and mean of 12.8 (±17.1) days. Of the 243 patients, 85.6% had Reasons for readmission that were related to the first admission. For outcome of readmissions, 242 (85.2%) resulted in discharge, 2 (0.7%) in transfer to another institution, and 40 (14.1%) in death. These results are important in elucidating the characteristics and outcomes of hospital readmissions in older adults.
DISCUSSION
The present study identified a number of aspects associated with greater 30-day readmission of older adults, divided into clinical and organizational factors. Variables exhibiting significant differences between case and control groups were Reason for admission, comorbidities, history of hospitalization in the year prior, emergency admission, length of stay, type of admission and types of guidance for discharge. However, the risk factors for 30-day readmission of older adults on the multivariate analysis were: Reason for admission due to neoplasms, genitourinary comorbidities, history of hospitalization in the year prior, emergency admission, length of stay on admission in days, and discharge guidance to return for outpatient/surgical procedure.
No sociodemographic risk factors, such as age, sex, education or marital status, were identified. Currently, there is no consensus in the literature on these factors for 30-day readmission of older adults. In the United States, more advanced age, male gender and low income represented higher risk for readmission in this population17. However, akin to the present study, other investigations did not find sociodemographic characteristics to be risk factors8-9,12.
Of the different reasons for admission, those for infectious and parasitic diseases constituted a protective factor, i.e. reduced the risk of 30-day readmission, whereas those due to neoplasms represented a risk factor. This finding might be explained by the shift in epidemiological pattern in Brazil, marked by a reduction in morbimortality due to infectious diseases, shifting to a greater prevalence of chronic diseases18. In this context, neoplasms are considered complex health conditions which require long treatment regimens and hospital admissions. A study in Singapore found that 1 in 5 older adults with cancer had a 30-day unplanned readmission19, consistent with the finding of greater risk in this population group.
The current results showed that comorbidities belonging to the group of diseases of the respiratory and genitourinary systems had a stronger association with 30-day readmission in this study population, where genitourinary comorbidities represented a risk factor for the outcome. By contrast, diseases categorized as “others”, having a frequency of <2 in the overall sample, constituted a protective factor. In a Swedish study, a stronger association of the admission outcome with cardiac, respiratory, genitourinary and neoplastic diseases was identified, although these were not classified as risk factors8.
Disparities in diseases and number of comorbidities as risk factors for readmission in older adults were reported in a systematic review. The authors of the study called for caution when interpreting these findings and applying them to clinical practice in settings different to those assessed by the studies reviewed11. The results of present investigation revealed a patient profile, both cases and controls, with complex health problems, requiring greater use of health services and care when implementing actions for preventing readmission.
The history of prior hospital admissions has proven an important factor for risk of readmission12. In the current study, patients with a history of hospitalization in the 12 months leading up to the admission had a 2.65 greater risk of readmission within 30 days of discharge. Hence, health services should remain alert to older individuals with a history of frequent hospitalizations, given that those with a higher mean number of admissions in the year prior, or history of admission in the past 3 months, have a higher likelihood of readmission, and also their risk of readmission rises with each additional hospital stay8,10-11.
In the international literature, older adults whose admission was via the emergency service have greater odds of readmission8,11-12. Similarly, patients in the present study whose first visit occurred at the emergency service readmitted more, showing admission via this route as a risk for readmission. Most patients in the cases group had at least one visit to the emergency service after admission discharge, over 80% of which resulted in readmission. In the control group, only a small proportion of patients were first seen by the emergency service, and did not result in readmission. Entry via the emergency service implies that most of these readmissions were unplanned. It should be noted that the lack of coordinated communication and interprofessional collaboration, as well as structured flows for transition between levels of care, can hamper care continuity within the health care system20. Without this interface, the patient journey is uncertain, potentially leading to more users seeking the emergency service21, particularly for acute or worsening chronic health problems, contributing to hospital readmission.
Length of stay for the admission was also shown to be a risk factor in this study. For each extra day of hospital stay, the risk of 30-day readmission after discharge rose by 4%. Other studies also noted that this variable impacts outcome. A Swedish study found that patients whose length of stay exceeded 5 days had greater chance of readmission8, while a Swiss study identified a 1.4% increase in the chance of readmission for every extra day of hospital stay9. This result explains the impact of hospital admission on older individuals, where stays longer than required can promote a high risk of readmission.
The findings of greater risk associated with history of previous hospitalizations, admission via the emergency service and length of stay, might be related to the level of clinical instability and growing complexity of care needs of older adults. Moreover, these are organizational factors which affect the use of services within the healthcare system and the care journeys of patients11.
Guidance at discharge for patients to return to the treating physician correlated weakly with the readmission outcome, whereas advice for patients to use outpatient serviced and return for surgical/outpatient procedures were more strongly associated, where the latter situation was considered a risk factor for 30-day readmission of older patients. A systematic review showed that discharge planning is able to reduce length of stay and risk of readmission, besides allowing organization of post-discharge services22. Recommendations advising patients to undergo surgical or outpatient procedures after discharge demonstrated a scheduled return, resulting in planned readmission.
In this study, median period elapsed between date of discharge and readmission was 10.5 days, with a mean of 12.5 days, showing a greater number of readmissions during the second week after hospital discharge. Furthermore, 110 (38.7%) patients readmitted 0-7 days after discharge - considered early readmissions, whereas 174 (61.3%) patients readmitted 8-30 days after discharge and were considered late readmissions. These results are similar to the findings of a Brazilian study on readmission of patients discharged from an internal medicine unit23 showing that 28.5% of non-elective readmission were early and 71.5% late. These data are relevant for planning post-discharge follow-up and using timing to prevent readmissions.
In terms of readmission outcome, a mortality rate of 14.1% was established. Readmission represents increased morbimortality in the older population, with losses in functional independence and increase in healthcare costs.
The study has some limitations, such as the non-standardization of discharge summaries, with some incomplete and containing scant information on discharge instructions to patients and lack of intercommunication between information systems, which precludes knowledge on possible readmissions to other institutions. Despite these limitations, the study allowed risk factors for the outcome to be identified.
Although such actions are largely performed outside the hospital setting, the prevention of readmission begins within the hospital itself, by identifying patients at risk of readmission at the time of admission, enabling greater professional focus on this population during their hospital stay11. Hospital services, in a bid to reduce readmission rates, should identify the population with complex health needs and provide the multiprofessional team needed at the time of discharge, such as discharge planning, scheduled follow-up, and transition and continuity of care in primary healthcare services24.
CONCLUSION
Analysis of the results of this study reveal characteristics positively associated with the occurrence of readmission and which represent a risk factor for the outcome. Therefore, intervention actions preventing readmission can be developed for this specific group of users by examining the process of hospital discharge and transition of care in the National healthcare system (SUS), a complex network with multiple points of care.
The following actions are recommended to reduce 30-day readmissions in this population: a highly effective and rapid process, health education, guidance on care continuity, improvements in access to health services and referral to the outpatient service (with consultation within a week of discharge) for post-discharge follow-up, and standardizing of discharge summaries. Future studies should be carried out confirming the effectiveness of these actions for reducing hospital readmission in this specific population.
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Funding: Financial support for this study was received from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Processo nº 433997/2018-4. Auxílio financeiro.
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DATA AVAILABILITY
The complete dataset underpinning the results of the present study are available on the Figshare repository and can be accessed at https://doi.org/10.6084/m9.figshare.26531611.
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» https://doi.org/10.3349/ymj.2021.0838
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Edited by: Camila Alves dos Santos
The complete dataset underpinning the results of the present study are available on the Figshare repository and can be accessed at https://doi.org/10.6084/m9.figshare.26531611.
