Embed Size (px)
Citation preview
Impact of vertical integration on the readmission of individuals with
chronic conditions
Óscar Brito FernandesMaster in Health Management
10th Edition2014-2016
SupervisorsRui Santana, PhDSílvia Lopes, PhD
• 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 MarquesBruno MoitaJoão SarmentoÓscar Brito FernandesRui Santana (Coordinator)Sílvia Lopes
DISCLOSURE
BACKGROUND• Integrated care• Readmissions• Chronic conditions
#1RESEARCH AIMS• Main aim• Specific objectives#2METHODOLOGY#3
RESULTS• Characteristics of the sample• Individuals’ risk factors and
readmission• Impact of vertical integration
#4DISCUSSION• Discussion of results• Study limitations#5FINAL REMARKS#6• Study design
• Data• Variables• Statistical analysis
MULTIMORBIDITY
THE CHANGING GLOBAL CONTEXT
#1 BACKGROUND
AGEING POPULATIONS INNOVATION
RISING COSTS
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
PORTO
VISEUGUARDA
COIMBRA
CASTELO BRANCOLEIRIA
SANTARÉMPORTALEGRE
ÉVORA
VIANA DO CASTELO
BRAGA
VILA REAL
BRAGANÇA
AVEIRO
BEJA
SETÚBAL
LISBOA
FARO
Matosinhos
1999
Alto Minho2008
2008
2009
2007
2008
LitoralAlentejano
2012
NorteAlentejano
BaixoAlentejo
Guarda
CasteloBranco
Nordeste
2011 12% Population PORTUGAL MAINLAND
Local Health Units
15% BudgetNHS 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.
Readmission is a subsequent inpatient admissionto 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
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
• 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
• 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
• 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
6Public hospitals
Treatment
7Local 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.
• 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
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 modelsSAS University Edition
Independent variablesAgePrincipal diagnosisSelected comorbidities
OutcomeIndividual risk of readmission
Dependent variable30-day readmission
#3 METHODOLOGY
METHODOLOGY
Cox regressionIBM SPSS (v.23)
CovariatesGenderAge group# Chronic conditions# Elixhauser comorbidities
OutcomeAssociation between individuals’ risk factors and
time to readmission
Time variableDays until readmission
Status variable1: Readmitted
#3 METHODOLOGY
METHODOLOGYCox 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.
Difference-in-differencesSTATA (v.13)
OutcomeRisk of readmission (odds ratio) for LHU compared
to the control group
Dependent variable30-day readmission
#3 METHODOLOGY
METHODOLOGYDifference-in-differences• Unconditional logit model with
fixed effects using dummy variables;
• Parallel trend assumption tested by a non-linear restriction:
CHARACTERISTICS OF THE SAMPLE0-19 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
#4 RESULTS
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
#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
RISK OF READMISSION: LHU VERSUS CONTROL GROUP
#4 RESULTS
Odds Ratio=1
1.017LHU 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
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
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
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
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.
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
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
Impact of vertical integration on the readmission of individuals with chronic conditions
ØMixed evidence over 30-day readmission of individuals with chronic conditionswithin LHU
Ø It’s a long road to reach integrated care
ØMore research needed to better evaluate, and better serve
Óscar Brito Fernandes
