Comparative Use of Technologies for Coronary Heart Altamiro Costa Pereira, PI, FMUP Disease Sharon-Lise Normand, PI, HMS JosĂŠ Pereira Miguel, PI, FMUL
HMS-PORTUGAL DECEMBER 2013 RETREAT
introduction: rationale and aims of cuteheart project (altamiro costa pereira)
Rationale & Motivation • Evidence-based medicine and health policy are crucial tools to ensure quality, safety and reduced costs in healthcare. • Sources of evidence: – Basic, clinical and health services research (translational research) results.
• Clinical trials: evidence for efficacy and safety of health technologies • Health service research: evidence for effectiveness and efficiency of health technologies.
Rationale & Motivation • Limited number of comparative effectiveness studies • Poor quality of available information • Inadequate integration between hospitals and primary care datasets CUTEheart, a comparative effectiveness research study, addressing current gaps in evidence, particularly with respect to the effectiveness and efficiency of health technologies and healthcare systems.
Main Aims • To compare the use of health technologies between U.S. and Portugal health systems, focusing on hospital management of coronary heart disease (CHD) • To compare the diffusion rates of new technologies (namely medical devices and surgical procedures) for treating CHD, and identify patient and hospital determinants of use in Portugal and U.S. • To assess the impact of health technology diffusion on clinical outcomes in high risk patient cohorts.
Main Aims • To compare clinical effectiveness of the use of procedures to treat CHD as a function of patient, hospital, and health system characteristics. • To compare cost effectiveness of treating CHD in Portugal and USA. • To develop a framework for designing and implementing health services research between two health care systems.
Research Team • Multidisciplinary team of researchers and technicians from Harvard Medical School and the Faculties of Medicine of Universities of Porto and Lisbon. • Disciplines: cardiologists, statisticians, health economists, computer scientists, epidemiologists and health data coding specialists.
HMS-PORTUGAL DECEMBER 2013 RETREAT
Paper 1: Comparison of healthcare systems (Mariana Lobo)
Motivation and Objective Motivation: Different health systems have different abilities to adopt new health technologies. Goal: Identify the main differences and similarities between the health systems of Portugal and the U.S. Focus: Coronary heart disease is highly prevalent and represents a high economic burden in high-income countries. Methods: Comprehensive literature review.
HEALTH SYSTEM DIFFERENCES • 100% public health insurance (PT) vs. 29% (U.S.) – 16% no insurance in U.S. – 10%-20% private additional in PT
• Health care coverage: – Universal access (PT) vs. variable access (U.S.)
• Health expenditure (% GDP): – 10.4 (PT) vs. 17.9 (U.S.)
U.S. vs. Portuguese Health Systems (WHO, 2000)
Demographic and Macroeconomic Context, (2010 or closest year available)
Area Total Population (1 000 000) Population aged > 65 years (%) GDP per capita (US$)
European Union
Portugal
United States
Massachusetts
502.0
10.6
307.9
6.5
17
18
13
14
31 182
22 226
46 588
56 170
Source: US Census bureau, Europa.eu, US Bureau of Economic Analysis
Key Differences Between US and Portugal • Fact: Incidence of Coronary Heart Disease deaths in Portugal is almost half of the U.S./MA • Backup cardiac surgery in elective PCI: • Portugal: optimal but not mandatory • U.S.: State specific, mandatory in Massachusetts • Medical device approval system: • Decentralized approval system (EU/PT) • Centralized approval system (U.S./MA)
Medical Device Approval Times (U.S. vs. EU)
•
The median difference in approval between the U.S. and EU is 35
months.
BCG. Regulation and Access to Innovative Medical Technologies: A comparison of the FDS and EU Approval Process and their impact on Patients and Industry. June •Sources: differences between the two systems affect performance. 2012;The BCG. EU Medical Device Approval Safety Assessment: A comparative analysis of medical devicedo recallsnot 2005-2009. January 2011
Summary • Financing of health vastly different – Public-based vs. Private-based
• % of GDP per capita health spending higher in U.S. • Medical device approval faster in Europe • Overall level of health reported in Portugal higher • How do these factors impact patient care? – Case study in an acute condition [heart attack]
HMS-PORTUGAL DECEMBER 2013 RETREAT
Paper 2: In-HOSPITAL TREATMENT PATTERNS AND OUTCOMES FOR AMI PATIENTS (Vanessa Azzone)
OBJECTIVES Background: Different health systems have different abilities to adopt new medical technologies. Treatment of AMI has changed over the past decade. Objective: Compare hospital care to AMI patients between the U.S. and Portugal during 2000 - 2009. Methods: Repeated cross-sectional observational cohorts using billing data. Revascularization rates, mortality rates, and length of stay. Aggregated diffusion rates of drug eluting stents, off-pump CABG surgery, and brachytherapy determined.
DATA SOURCES • January 1, 2000 – December 31, 2009 • United States – 20% nationwide inpatient stratified random sample (Health Care Utilization Project) – 100% Massachusetts inpatient discharge data (Center for Health Information Analysis)
• Portugal – 100% Portugal inpatient discharge data from public sector (Administração Central do Sistema de Saúde) – 84% of all Portugal inpatient discharge data
INPATIENT BILLING DATA • Similarities
• Differences
• Diagnosis and procedure codes
•No. of codes:
• Demographic information • Primary discharge diagnosis • Main variables have equivalent definitions and categorical values
–15 [U.S.] versus 20 [Portugal]
•No race information in PT •Unique patient ID only MA data •Some coding practice differences: –DNR –Discharge disposition status •Charges/prices for discharges –Charges in U.S. –DRGs in PT
DATA SHARING & ANALYTICAL APPROACHES • Data use policy prohibits sharing of individual-level data – Can share summary information
• Adopted a distributed computing & modeling approach – Harmonize data elements and variable names – Distribute SAS code between teams (coding integrity, logical errors) – Distribute summaries
AMI COHORTS
January 1, 2000 – December 31, 2009 United States All adult (> 17 yrs) discharges (N= 78,822,431)
Massachusetts All adult (>17 yrs) discharges (N = 7,082,762)
Portugal All adult (> 17 yrs) discharges (N = 7,834,879)
Excluded: AMA, Discharged to hospice, Elective admissions
Excluded: AMA, Discharged to hospice, DNR or comfort measures only, Elective admissions
Excluded: AMA, Discharged to hospice, Elective admissions
Number of AMI discharges:
Number of AMI discharges:
Number of AMI discharges:
1,144,926 (1.45%)
160,697 (2.27%)
110,262 (1.41%)
HMS-PORTUGAL DECEMBER 2013 RETREAT
Paper 2 FINDINGS: In-HOSPITAL TREATMENT PATTERNS AND OUTCOMES FOR AMI PATIENTS (sharon-Lise Normand)
Source: Authors’ calculations using HCUP, CHIA, and Administração Central do Sistema de Saúde data files.
Source: Authors’ calculations using HCUP, CHIA, and Administração Central do Sistema de Saúde data files.
Source: Authors’ calculations using HCUP, CHIA, and Administração Central do Sistema de Saúde data files.
• DES approved in 2003, USA • FDA warning issued in 2006 • DES approved in 2002, Portugal
REGISTRY DATA BILLING DATA
BMS & DES Among PCI Following AMI BILLING DATA
DES-36.07 BMS-36.06
HMS-PORTUGAL DECEMBER 2013 RETREAT
CUTEHEART: CONCLUSIONs and next steps (Armando teixeira-Pinto)
CONCLUSIONS • Important differences observed in case study of AMI (procedures, technology use, and outcomes) • Life-saving technologies underused in PT (e.g., CABG surgery) and higher mortality • No information about care outside of the hospitalization (e.g., primary care use)
CONCLUSIONS
Admission-based in-hospital case-fatality after admission for AMI - 2009 (OECD report Health at a Glance: Europe 2012)
NEXT STEPS • Further analyses of current data • Validation of PT hospital billing data – Review of medical records for a sample of patients treated at a few hospitals – More work with National Registry for Cardiology
• Linking data across different sources of information – Longitudinal tracking of patients (readmission rates, safety issues, quality & effectiveness of care) – Electronic medical records (primary care), vital statistics, pharmacy data, socio-demographic characteristics
Many thanks to the FCT and HMSP-