Denny Fe G Agana1,2,3,4, Catherine W Striley5, Robert L Cook5,6, Yenisel Cruz-Almeida7,8, Peter J Carek9, Jason L Salemi10. 1. H. James Free MD, Center for Primary Care Education & Innovation, Gainesville, FL, 32610, USA. DennyFe.Agana@bcm.edu. 2. Department of Community Health and Family Medicine, University of Florida, Gainesville, FL, 32603, USA. DennyFe.Agana@bcm.edu. 3. Department of Epidemiology, University of Florida, Gainesville, FL, 32603, USA. DennyFe.Agana@bcm.edu. 4. Department of Family and Community Medicine, Baylor College of Medicine, 3701 Kirby Drive, Suite 600, Houston, TX, 77098, USA. DennyFe.Agana@bcm.edu. 5. Department of Epidemiology, University of Florida, Gainesville, FL, 32603, USA. 6. Department of Internal Medicine, University of Florida, Gainesville, FL, USA. 7. Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, USA. 8. Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, USA. 9. Department of Community Health and Family Medicine, University of Florida, Gainesville, FL, 32603, USA. 10. Department of Family and Community Medicine, Baylor College of Medicine, 3701 Kirby Drive, Suite 600, Houston, TX, 77098, USA.
Abstract
BACKGROUND: Little is known about the frequency, patterns, and determinants of readmissions among patients initially hospitalized for an ambulatory care-sensitive condition (ACSC). The degree to which hospitalizations in close temporal proximity cluster has also not been studied. Readmission patterns involving clustering likely reflect different underlying determinants than the same number of readmissions more evenly spaced. OBJECTIVE: To characterize readmission rates, patterns, and predictors among patients initially hospitalized with an ACSC. DESIGN: Retrospective analysis of the 2010-2014 Nationwide Readmissions Database. PARTICIPANTS: Non-pregnant patients aged 18-64 years old during initial ACSC hospitalization and who were discharged alive (N = 5,007,820). MAIN MEASURES: Frequency and pattern of 30-day all-cause readmissions, grouped as 0, 1, 2+ non-clustered, and 2+ clustered readmissions. KEY RESULTS: Approximately 14% of patients had 1 readmission, 2.4% had 2+ non-clustered readmissions, and 3.3% patients had 2+ clustered readmissions during the 270-day follow-up. A higher Elixhauser Comorbidity Index was associated with increased risk for all readmission groups, namely with adjusted odds ratios (AORs) ranging from 1.12 to 3.34. Compared to patients aged 80 years and older, those in younger age groups had increased risk of 2+ non-clustered and 2+ clustered readmissions (AOR range 1.27-2.49). Patients with chronic versus acute ACSCs had an increased odds ratio of all readmission groups compared to those with 0 readmissions (AOR range 1.37-2.69). CONCLUSIONS: Among patients with 2+ 30-day readmissions, factors were differentially distributed between clustered and non-clustered readmissions. Identifying factors that could predict future readmission patterns can inform primary care in the prevention of readmissions following ACSC-related hospitalizations.
BACKGROUND: Little is known about the frequency, patterns, and determinants of readmissions among patients initially hospitalized for an ambulatory care-sensitive condition (ACSC). The degree to which hospitalizations in close temporal proximity cluster has also not been studied. Readmission patterns involving clustering likely reflect different underlying determinants than the same number of readmissions more evenly spaced. OBJECTIVE: To characterize readmission rates, patterns, and predictors among patients initially hospitalized with an ACSC. DESIGN: Retrospective analysis of the 2010-2014 Nationwide Readmissions Database. PARTICIPANTS: Non-pregnant patients aged 18-64 years old during initial ACSC hospitalization and who were discharged alive (N = 5,007,820). MAIN MEASURES: Frequency and pattern of 30-day all-cause readmissions, grouped as 0, 1, 2+ non-clustered, and 2+ clustered readmissions. KEY RESULTS: Approximately 14% of patients had 1 readmission, 2.4% had 2+ non-clustered readmissions, and 3.3% patients had 2+ clustered readmissions during the 270-day follow-up. A higher Elixhauser Comorbidity Index was associated with increased risk for all readmission groups, namely with adjusted odds ratios (AORs) ranging from 1.12 to 3.34. Compared to patients aged 80 years and older, those in younger age groups had increased risk of 2+ non-clustered and 2+ clustered readmissions (AOR range 1.27-2.49). Patients with chronic versus acute ACSCs had an increased odds ratio of all readmission groups compared to those with 0 readmissions (AOR range 1.37-2.69). CONCLUSIONS: Among patients with 2+ 30-day readmissions, factors were differentially distributed between clustered and non-clustered readmissions. Identifying factors that could predict future readmission patterns can inform primary care in the prevention of readmissions following ACSC-related hospitalizations.
Authors: A De Giorgi; B Boari; R Tiseo; P J López-Soto; F Signani; M Gallerani; R Manfredini; F Fabbian Journal: Eur Rev Med Pharmacol Sci Date: 2016-11 Impact factor: 3.507
Authors: Tobias Freund; Stephen M Campbell; Stefan Geissler; Cornelia U Kunz; Cornelia Mahler; Frank Peters-Klimm; Joachim Szecsenyi Journal: Ann Fam Med Date: 2013 Jul-Aug Impact factor: 5.166
Authors: Maribeth Porter; David Quillen; Denny Fe Agana; Lisa Chacko; Kimberly Lynch; Lauren Bielick; Xiaoqing Fu; Yang Yang; Peter J Carek Journal: J Am Board Fam Med Date: 2019 Jan-Feb Impact factor: 2.657
Authors: Jo M Longman; Megan E Passey; Dan P Ewald; Elizabeth Rix; Geoffrey G Morgan Journal: BMC Health Serv Res Date: 2015-10-16 Impact factor: 2.655
Authors: Jay G Berry; James C Gay; Karen Joynt Maddox; Eric A Coleman; Emily M Bucholz; Margaret R O'Neill; Kevin Blaine; Matthew Hall Journal: BMJ Date: 2018-02-27
Authors: Marc Kowalkowski; Tara Eaton; Andrew McWilliams; Hazel Tapp; Aleta Rios; Stephanie Murphy; Ryan Burns; Bella Gutnik; Katherine O'Hare; Lewis McCurdy; Michael Dulin; Christopher Blanchette; Shih-Hsiung Chou; Scott Halpern; Derek C Angus; Stephanie P Taylor Journal: BMC Health Serv Res Date: 2021-06-02 Impact factor: 2.655