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Impact of vertical integration on the readmission of individuals with chronic conditions

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INTRODUCTION: Ageing populations and the increasing prevalence of multimorbidity are a challenge for healthcare delivery and health system design. Integrated care has been discussed as a solution to address these challenges. In Portugal, Local Health Units (LHU) promote vertical integration of healthcare, with one of the expected effects being a decrease of readmission rates in individuals with chronic conditions. Readmissions are frequently studied for its negative impacts on individuals, carers, and providers, with excessive unplanned readmission rates among hospitals being a sign of frail integrated care. Thus, we assume as the main aim of this study to assess the impact of vertical integration on the readmission of individuals with chronic conditions.

METHODS: A database including administrative data from 1 679 634 inpatient episodes from years 2002-14 was considered. We identified readmissions with the hospital-wide all-cause unplanned readmission measure methodology of Centers for Medicare and Medicaid Services. The considered outcome was 30-day hospital-wide all-cause unplanned readmissions (1: readmitted), and risk-standardized readmission ratio. Chronic conditions were identified from all diagnoses coded with International Classification of Diseases – 9th version – Clinical Modification codes (1: chronic). In order to assess the impact of LHU on the readmission of individuals with chronic conditions, we compared 30-day readmissions before and after the creation of each LHU. We used difference-in- differences technique to address our main aim. In addition, to understand the associations between individuals’ risk factors and time to readmission, we developed a Cox regression model for LHU and control group.

RESULTS: Difference-in-differences results suggest that vertical integration promoted a decrease on risk-standardized readmission ratio in four LHU, but significant only in LHU 1. In addition, when analysed the individual risk of readmission we observed that it was reduced for four LHU, but only significantly for LHU 3 and LHU 5. A sensitivity analysis was performed for annual evolution of odds ratio of risk of readmission, and initial results were considered stable for most years. Cox regression results suggest that for LHU and control hospitals, female individuals were less at risk of readmission than men, the risk increased with increasing age and number of comorbidities. At LHU, we observed a decreased risk of readmission with increasing number of chronic conditions.

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Impact of vertical integration on the readmission of individuals with chronic conditions

  1. 1. Impact of vertical integration on the readmission of individuals with chronic conditions Óscar Brito Fernandes Master in Health Management 10th Edition 2014-2016 Supervisors Rui Santana, PhD Sílvia Lopes, PhD
  2. 2. • Avaliação do impacto da criação das Unidades Locais de Saúde em Portugal, study carried out by Escola Nacional de Saúde Pública, Universidade NOVA de Lisboa, and funded by Fundação Calouste Gulbenkian (2014-2016). • Research team: Ana Patrícia Marques Bruno Moita João Sarmento Óscar Brito Fernandes Rui Santana (Coordinator) Sílvia Lopes DISCLOSURE
  3. 3. BACKGROUND • Integrated care • Readmissions • Chronic conditions #1 RESEARCH AIMS • Main aim • Specific objectives#2 METHODOLOGY #3 RESULTS • Characteristics of the sample • Individuals’ risk factors and readmission • Impact of vertical integration #4 DISCUSSION • Discussion of results • Study limitations#5 FINAL REMARKS #6• Study design • Data • Variables • Statistical analysis
  4. 4. MULTIMORBIDITY THE CHANGING GLOBAL CONTEXT #1 BACKGROUND AGEING POPULATIONS INNOVATION RISING COSTS
  5. 5. Integrated care is an organizational principle for care delivery[1] as a managerial response to differentiation and fragmentation[2]. INTEGRATED CARE Many integrated care approaches aim to provide a more independent life to individuals with chronic conditions[3-4], highlighting improvements to the patients’ care experience and health outcomes. #1 BACKGROUND
  6. 6. PORTO VISEU GUARDA COIMBRA CASTELO BRANCO LEIRIA SANTARÉM PORTALEGRE ÉVORA VIANA DO CASTELO BRAGA VILA REAL BRAGANÇA AVEIRO BEJA SETÚBAL LISBOA FARO Matosinhos 1999 Alto Minho 2008 2008 2009 2007 2008 Litoral Alentejano 2012 Norte Alentejano Baixo Alentejo Guarda Castelo Branco Nordeste 2011 12% Population PORTUGAL MAINLAND Local Health Units 15% Budget NHS HOSPITALS[5] #1 BACKGROUND Resident population by county in LHU’s catchment area was retrieved from National Statistics Institute on May 2016. Last data update by June 16, 2015.
  7. 7. Readmission is a subsequent inpatient admission to any acute care facility which occurs within 30 days of the discharge date of an eligible index admission[6]. READMISSIONS Excessive unplanned readmission rates among hospitals could be a sign of frail integrated care[7]. #1 BACKGROUND
  8. 8. Chronic conditions[8] include health conditions that persist across time and require healthcare, including non-communicable diseases, mental disorders, some communicable conditions and on- going physical impairments. CHRONIC CONDITIONS Individuals with chronic conditions are more likely to experience hospital readmission since they are more vulnerable to non-effective home transitions after hospital discharge[9]. #1 BACKGROUND
  9. 9. • Describe 30-day readmission frequency in individuals with chronic conditions, from 2002 to 2014. • Analyze the association between individuals’ risk factors and readmission. • Analyze the impact of vertical integration on the readmission rates and risk of readmission of individuals with chronic conditions. Assess the impact of vertical integration on the readmission of individuals with chronic conditions #2 RESEARCH AIMS
  10. 10. • Datasets provided by ACSS, Portuguese Central Administration for Healthcare system; • Data refers to Portugal mainland hospital morbidity from 2002 to 2014. 0201 • Outcome research; • Observational, analytical, longitudinal, and retrospective cohort study. Study Design Data Sources #3 METHODOLOGY METHODOLOGY
  11. 11. • Selected 9 523 432 index admissions; • Treatment and Control group accounted for 1 679 634 index admissions; • Time frame: 8 years, 5 years pre-integration, 3 post-integration. 03 Data Analyzed Variables Statistical Analysis #3 METHODOLOGY METHODOLOGY Control group 6 Public hospitals Treatment 7 Local Health Units Selection criteria • Be part of the same ACSS hospital benchmark group as LHU; • Excluded hospitals with different contexts • Data available from pre- and post-integration periods for each LHU.
  12. 12. • Selected 9 523 432 index admissions; • Treatment and Control group accounted for 1 679 634 index admissions; • Time frame: 8 years, 5 years pre-integration, 3 post-integration. 03 Data Analyzed Variables Statistical Analysis #3 METHODOLOGY METHODOLOGY 18% Treatment group 845 275 Control group 834 359 Analysed sample
  13. 13. Generalized linear mixed model at the specialty cohort (AHRQ) • Readmissions identified using CMS hospital-wide all-cause unplanned readmission measure; • AHRQ Condition Classification System for principal diagnosis; • CMS Condition Category groups for comorbid diseases; • Hierarchical logistic regression models at the specialty cohort. Generalized linear mixed models SAS University Edition Independent variables Age Principal diagnosis Selected comorbidities Outcome Individual risk of readmission Dependent variable 30-day readmission #3 METHODOLOGY METHODOLOGY
  14. 14. Cox regression IBM SPSS (v.23) Covariates Gender Age group # Chronic conditions # Elixhauser comorbidities Outcome Association between individuals’ risk factors and time to readmission Time variable Days until readmission Status variable 1: Readmitted #3 METHODOLOGY METHODOLOGY Cox regression • Elixhauser comorbidity index; • Chronic condition indicator by AHRQ; • Initial assessment of covariates by univariate Cox regression; • Kaplan-Meier plots visual inspection; • Analyses conducted separately for LHU and control group.
  15. 15. Difference-in-differences STATA (v.13) Outcome Risk of readmission (odds ratio) for LHU compared to the control group Dependent variable 30-day readmission #3 METHODOLOGY METHODOLOGY Difference-in-differences • Unconditional logit model with fixed effects using dummy variables; • Parallel trend assumption tested by a non-linear restriction:
  16. 16. CHARACTERISTICS OF THE SAMPLE 0-19 22% 18% 19% 33% 8% 20-44 45-64 65-84 85+ AGE 44% 56% GENDER CHRONIC CONDITIONS 1 2 3 4 5+ 17% 12% 7% 3% 2% ELIXHAUSER COMORBIDITY INDEX 1 2 3 4 5+ 17% 11% 5% 2% 1% #4 RESULTS N=1 679 634
  17. 17. #4 RESULTS
  18. 18. INDIVIDUALS’ RISK FACTORS AND TIME TO READMISSION #4 RESULTS LOCAL HEALTH UNITS CONTROL GROUP Odds Ratio=1 Odds Ratio=1 0.906 0.928 0.839 GENDER (male) FEMALE AGE (0-19) 20-44 45-64 65-84 85+ 1.716 1.281 0.861 0.683 0.713 1.197 1.755
  19. 19. #4 RESULTS LOCAL HEALTH UNITS CONTROL GROUP Odds Ratio=1 Odds Ratio=1 1.298 1.280 1.398 CHRONIC CONDITIONS (0) 1 2 1.287 3 4 5+ 1.266 1.233 1.201 ELIXHAUSER COMORBIDITY INDEX (0) 1 2 3 4 5+ 1.604 1.896 2.296 2.509 1.456 1.472 1.396 1.362 1.285 1.583 1.935 2.192 2.403 INDIVIDUALS’ RISK FACTORS AND TIME TO READMISSION
  20. 20. RISK OF READMISSION: LHU VERSUS CONTROL GROUP #4 RESULTS Odds Ratio=1 1.017 LHU 1 LHU 2 LHU 3 LHU 4 LHU 5 LHU 6 LHU 7 0.991 0.911 1.240 0.860 1.076 0.937 Parallel trend assumption not verified
  21. 21. Vertical integration faces different barriers within each organization. Different interventions addressed to reduce hospital readmissions have different potential of effectiveness.[10-11] The risk of readmission does not follow a clear pattern among LHU. #5 DISCUSSION
  22. 22. In LHU, the risk of readmission decreases with increasing # chronic conditions, after adjusting for gender, age group and comorbidities. Possible evidence of better coordinated care for these patients? Groups with higher #chronic conditions presented decreased risk of readmission. #5 DISCUSSION
  23. 23. Readmission rates reflect not solely the quality of hospital care[12-14] , but also factors in one’s home and communities[15-17] . Lack of national studies to compare results, specifically regarding readmissions and chronic conditions. One cannot measure vertical integration impact solely considering readmission indicator. #5 DISCUSSION
  24. 24. Track the hospitals’ organizational evolution Analytical and selection biasReliability on administrative data LIMITATIONS OF THE STUDY #5 DISCUSSION Limitation due to the model selected to identify readmissions, chronic conditions: Also, the criteria to compose the control group might have incurred in selection bias. Study limited in its ability to prove causation. Difficult to account for the area of residence of individuals treated at LHU, as well as the intense hospital horizontal integration phenomena.
  25. 25. FINAL REMARKS Mixed evidence over 30-day readmission of individuals with chronic conditions More research needed to better evaluate It’s a long road to reach integrated care #6 FINAL REMARKS
  26. 26. REFERENCES #7 REFERENCES [1] Shaw S, Rosen R, Rumbold B. What is integrated care? [Internet]. 2011. Available from: http://www.nuffieldtrust.org.uk/sites/files/nuffield/publication/what_is_integrated_care_research_report_june11.pdf [2] Lillrank P. Integration and coordination in healthcare: an operations management view. J Integr Care [Internet]. Emerald Group Publishing Limited; 2012 Feb 10 [cited 2016 Apr 18];20(1):6– 12. Available from: http://www.emeraldinsight.com/doi/abs/10.1108/14769011211202247 [3] Dorling G, Fountaine T, McKenna S, Suresh B. The Evidence for Integrated Care [Internet]. 2015. Available from: http://www.mckinsey.com/~/media/McKinsey/dotcom/client_service/Healthcare Systems and Services/PDFs/The evidence for integrated care.ashx [4] OECD. Health Reform: Meeting the Challenge of Ageing and Multiple Morbidities [Internet]. Meeting the Challenge of Ageing and Multiple Morbidities. 2011. Available from: http://www.oecd-ilibrary.org/social-issues-migration-health/health-reform_9789264122314-en [5] Portugal. Ministério da Saúde. Administração Central do Sistema de Saúde. Termos de referecia para contratualização hospitalar no SNS: Contrato-Programa 2016 [Terms of reference for hospital contractualization in the NHS. Contract-program 2016] [Internet]. Lisboa; 2016. Available from: http://tinyurl.com/hfumhjr [6] Horwitz L, Grady J, Zhang W, DeBuhr J, Deacon S, Krumholz H, et al. 2015 Measure Updates and Specifications Report: Hospital-Wide All-Cause Unplanned Readmission Measure - Version 4.0. 2015. [7] Bisognano, M, Boutwell A. Improving transitions to reduce readmissions. Front Health Serv Manage. 2009;25(3):3–10. [8] WHO. Innovative care for chronic conditions: building blocks for action: global report. Noncommunicable Diseases and Mental Health. 2002. p. 1–99. [9] Jackson CT, Trygstad TK, DeWalt DA, DuBard CA. Transitional care cut hospital readmissions for North Carolina medicaid patients with complex chronic conditions. Health Aff. 2013;32(8):1407–15. [10] Kansagara D, Chiovaro JC, Kagen D, Jencks S, Rhyne K, O’Neil M, et al. Transitions of care from hospital to home: an overview of systematic reviews and recommendations for improving transitional care in the Veterans Health Administration [Internet]. 2015. Available from: http://tinyurl.com/h52xjlj [11] Hansen LO, Young RS, Hinami K, Leung A, Williams M V. Interventions to reduce 30-day rehospitalization: A systematic review. Ann Intern Med [Internet]. 2011;155(8):520–8. Available from: http://tinyurl.com/h4eh3n5 [12] Bianco A, Molè A, Nobile CGA, Di Giuseppe G, Pileggi C, Angelillo IF. Hospital Readmission Prevalence and Analysis of Those Potentially Avoidable in Southern Italy. PLoS One. 2012;7(11). [13] Fischer C, Lingsma HF, Marang-van De Mheen PJ, Kringos DS, Klazinga NS, Steyerberg EW. Is the readmission rate a valid quality indicator? A review of the evidence. PLoS One. 2014;9(11):1–10. [14] Horwitz LI, Partovian C, Lin Z, Grady JN, Herrin J, Conover M, et al. Development and use of an administrative claims measure for profiling hospital-wide performance on 30-day unplanned readmission. Ann Intern Med. 2014;161:S66–75. [15] Kangovi S, Grande D, Meehan P, Mitra N, Shannon R, Long JA. Perceptions of readmitted patients on the transition from hospital to home. J Hosp Med. 2012;7(9):709–12. [16] Hu J, Gonsahn MD, Nerenz DR. Socioeconomic status and readmissions: evidence from an urban teaching hospital. Health Aff (Millwood) [Internet]. 2014 May;33(5):778–85. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24799574 [17] Joynt KE, Jha AK. A path forward on Medicare readmissions. N Engl J Med [Internet]. 2013 Mar 28;368(13):1175–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23465069
  27. 27. Impact of vertical integration on the readmission of individuals with chronic conditions ØMixed evidence over 30-day readmission of individuals with chronic conditions within LHU ØIt’s a long road to reach integrated care ØMore research needed to better evaluate, and better serve Óscar Brito Fernandes oscar.fernandes@chlc.min-saude.pt

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