Extrapolation and potential impact of IPHS deployment in Europe

Transcription

1 SIMPHS2 Validation Workshop Brussels, 31 Jan SIMPHS2 Validation Workshop Extrapolation and potential impact of IPHS deployment in Europe JRC IPTS IS Unit Ioannis Maghiros, Fabienne Abadie, Maria Lluch, Francisco Lupiañez, Elena Villalba, Bernarda Zamora Disclaimer: "The views expressed in this presentation are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission

2 Overview SIMPHS2 Validation Workshop Brussels, 31 Jan Policy objective Quantifying value of ehealth market in Europe and potential benefits with view to assessing the costseffectiveness of ehealth treatments Outline Methodology Analysis of Prevalence, mortality, and health costs Definition of Indicators linked to objectives Extrapolation and Results

3 Methodology SIMPHS2 Validation Workshop Brussels, 31 Jan Monitoring: assessing expected results Collect, analyze and complete country-level statistics on health status, resources and costs for the three main chronic diseases in the EU27 countries Translate objectives into quantitative indicators Link health status, health resources and costs to objectives and assess the level at which they can be reached through telehealth and telecare based on static ASSUMPTIONS OF IMPACT

4 Methodology SIMPHS2 Validation Workshop Brussels, 31 Jan Limitations: Impact Evaluation and Modelling Causal contribution not estimated (but assumed) Spillover effects such as economies of scale and scope not considered Dynamic feedback effects of the objectives reached to initial health status, health resources and costs not considered

5 Methodology SIMPHS2 Validation Workshop Brussels, 31 Jan Indicators linked to objectives Value of ehealth potential market: as the product of prevalence (number of people suffering a disease) by the assumed levels of ehealth deployment and of monitoring costs of the ITC components Savings in hospitalization: as the product of total hospital bed days used by its costs, and by the assumed parameters about ehealth deployment and about impact on reduced hospitalization Value of reduced mortality: as the product of number of deaths caused by disease by the assumed parameters about ehealth deployment and about impact on reduced mortality Break-even indicator: costs per ehealth monitored day such that the Value of ehealth potential market equals the Savings in hospitalization

6 Health Status: Chronic Diseases SIMPHS2 Validation Workshop Brussels, 31 Jan Are there systematically healthier countries? Life Expectancy Prevalence 3-Chronic Life Expectancy and Morbidity in EU27 IT DE EL BU LV PT SE EE BEAT HU FR FI LT SI ES UK RO CZ NL MT PL CY SK IE GDP per capita - EUR 2010 Life Expectancy PL LT EL BU HU PT RO ES CZ CY IT EE LV SKMTSI UK DE AT FR FINLSE BEIE DK Percentage 65 and older GDP per capita - EUR 2010 Prevalence Source:Authors'elaboration from different databases. 3-Chronic: CHF, COPD and Diabetes. DK Mortality LU LU Percentage 65 and older Mortality 3-Chronic EU27 Life exp.: 79.4 years EU27 GDPpc: 24,442 EU27 %65 and older: EU27 Prevalence 3-chronic: 14.24% EU27 Mortality 3-chronic: 0.125% Pearson/Spearman Rank Corr. Prevalence ~ Life -.08/-.03 expectancy Prevalence ~ Mortality.42*/.55* 3-chronic Prevalence ~GDPpc -.22/-.26* Life expect. ~ GDPpc.65*/.71* Mortality ~ GDPpc -.55*/-.69* Mortality 3-chronic ~ Life expectancy Prevalence ~ % 65 and older Mortality 3-chronic ~ % 65 and older -.64*/-.52*.51*/.49*.13/.23*

7 Health Status: Chronic Diseases SIMPHS2 Validation Workshop Brussels, 31 Jan Prevalence: Data availability and imputations Diabetes: Diabetes Atlas 4 th Edition, 2010, national series (measured over age group) CHF: No international statistics in prevalence of coronary diseases Literature estimates range of 1% to 3% for CHF prevalence Frost&Sullivan 2010 (F&S) collects data on CVD prevalence for 12 of the EU27 countries - Assumption: the variability of CHF prevalence is highly correlated to the percentage of hospital discharges diagnosed as Heart Failure (HF) over total hospital discharges. - Data on hospital discharges in HF available from HMDB WHO/Europe and Eurostat - Average EU27 % hospital discharges HF = 1.96%. - Correlation % hospital discharges HF and CVD prevalence = 33%. COPD: F&S collects prevalence statistics for 18 countries. The data for the remaining countries have been completed first from Health For All Database WHO/Europe (HFA-DB) and by taking the percentage of hospital discharges in COPD for the cases of Cyprus, Estonia and Slovenia. The correlation coefficient between the series taken from F&S and the alternative series reach a 36%.

8 Health Status: Chronic Diseases SIMPHS2 Validation Workshop Brussels, 31 Jan Prevalence: Data availability and imputations Finland Sweden Denmark Portugal Hungary Italy Spain Cyprus Malta Latvia Ireland UK Prevalence of Chronic Diseases Cluster A Cluster B Germany Austria France Netherlands Poland Greece Lithuania Bulgaria Romania Czech Rep. Estonia Belgium Slovenia Luxembourg Slovakia Source:Authors'elaboration from different databases. CHF prevalence is assumed equal to percentage of hospital discharges in Heart Failure. COPD prevalence combines Frost & Sullivan and WHO/Europe statistics. Diabetes prevalence is taken from Diabetes Atlas 4th Edition on national series. Cluster D Cluster C EU27: CHF:1.96% COPD :3.32% Diabetes:8.96% CHF COPD Diabetes Data on prevalence in scientific literature: CHF: 1-3% COPD: 4-10% Diabetes: 8.9%

9 Health Status: Chronic Diseases SIMPHS2 Validation Workshop Brussels, 31 Jan Prevalence: Data availability and imputations Percentage of Hospital Discharges Cluster A Cluster D Sweden Germany Austria Finland France Denmark Netherlands Cluster B Cluster C Rank correlation hospital discharges and prevalence: - 3 chronic: 0.48* - diabetes: 0.22* - COPD: 0.15* Spain Hungary Malta Italy Ireland UK Cyprus Latvia Portugal Source:Authors'elaboration from different databases. Greece Bulgaria Romania Poland Lithuania Belgium Estonia Czech Rep. Luxembourg Slovakia Slovenia EU27: CHF:1.96 COPD:1.40 Diabetes: CHF COPD Diabetes

10 Health Status: Chronic Diseases SIMPHS2 Validation Workshop Brussels, 31 Jan Mortality: Data availability and imputations Annual mortality rates are reported for Diabetes. For CHF and COPD, mortality data correspond to the higher classification of diseases: CVD and All Respiratory Diseases, respectively. Assumption: to obtain mortality caused by CHF and COPD a fraction equal to the part of hospital discharges in CHF over CVD, and to the part of hospital discharges in COPD over all respiratory diseases, is multiplied to the reported mortality caused by CVD and all respiratory diseases, respectively. WHO Department of Measurement and Health Information, reports number of deaths caused by COPD.

11 Health Status: Chronic Diseases SIMPHS2 Validation Workshop Brussels, 31 Jan Mortality: Data availability and imputations Sweden Chronic Diseases Mortality Cluster A Germany (Cluster D EU27 Total deaths caused by: Finland Denmark Hungary Malta Italy Spain Portugal Latvia UK Cyprus Ireland Cluster B Austria France Netherlands Bulgaria Romania Greece Lithuania Poland Estonia Czech Rep. Slovakia Belgium Slovenia Luxembourg Cluster C EU27: CHF:.055% COPD:.014% Diabetes:.055% CHF: 277,161 - COPD: 71,456 - Diabetes: 277,896 Total deaths of 65+ population: 3,884,592 CHF COPD Diabetes Source:Authors'elaboration from different databases. CHF mortality is assumed equal to a fraction of CVD mortality, the fraction being %hospital discharges in HF over CVD COPD mortality is assumed equal to a fraction of respiratory diseases mortality, the fraction being %hospital discharges COPD over respiratory diseases. Diabetes mortality is taken from Diabetes Atlas 4th Edition.

12 Healthcare expenditures SIMPHS2 Validation Workshop Brussels, 31 Jan CHF per in-patient costs are assumed equal to CVD per in-patient costs, with total-inpatient costs taken from the European Cardiovascular Disease Statistics 2008 (British Heart Foundation). COPD per-inpatient costs are assumed equal to average for all diseases. Diabetes in-patient costs are assumed a 35% of the costs reported by the Diabetes Atlas, being 35% the percentage of in-patient expenditures over health expenditures at EU27 level. Number of in-patients and number of bed days data from HMDB WHO/Europe and Eurostat. Series of hospital costs per in-patient Series of costs per hospital bed day

13 Healthcare expenditures SIMPHS2 Validation Workshop Brussels, 31 Jan Assumption: COPD average costs per hospital bed day equal average costs per hospital bed day for all diseases, estimated at 649 Figure: COPD, the in-patient costs are estimated at 5,400 per in-patient ( 602 per hospital bed day) for all EU27 countries. Mapel D.W. (2005) Predicting the costs of managing patients with chronic obstructive pulmonary disease. Respiratory Medicine, 99,

14 Healthcare expenditures SIMPHS2 Validation Workshop Brussels, 31 Jan Euros Average Costs per Hospital Patient Cluster A Cluster D Euros for Diabetes EU27 Healthcare expenditure: 1,179,625 Mill EUR Healthcare Expenditure per capita: 2,079 EUR In-patient expenditure: 432,900 Mill EUR Euros DK FI Cluster B SE AT BE FR EU27: CHF: 5,049 Diabetes: 42,677 All Diseases: 5,112 BU CZ EE EL Cluster C LT LU DE PL RO SK NL SI Euros for Diabetes In-patient expenditure per in-patient: 5,112 CVD expenditure: 109,689 (59,102 in-patient) Mill EUR As % of healthcare exp: 9.3 CY HU IE IT LV MT PT ES UK CHF All Diseases Diabetes Source:Authors'elaboration from different databases. CHF costs are assumed equal to CVD costs Diabetes costs are calculated by assuming that in-patient costs are a 35% of total diabetes costs. Diabetes expenditure: 112,353 Mill EUR As % healthcare exp: 9.5

15 Healthcare expenditures SIMPHS2 Validation Workshop Brussels, 31 Jan Euros Average Costs per Hospital Bed Day Cluster A Cluster D Diabetes Euros CY HU IE IT LV MT PT ES UK DK FI SE AT FR DE NL Cluster B BE BU CZ EE EL Cluster C LT EU27: CHF: 564 Diabetes: 4,057 All Diseases: 649 LU PL RO SK SI Diabetes CHF All Diseases Diabetes Source:Authors'elaboration from different databases. CHF costs per bed day are assumed equal to CVD costs COPD costs per bed day are taken equal to costs per bed day for All Diseases (US data analysis by Mapel, 2005 estimates at 602 Euros approx.). Diabetes costs are calculated by assuming that in-patient costs are a 35% of total diabetes costs.

16 Translate Objectives into Indicators SIMPHS2 Validation Workshop Brussels, 31 Jan Objectives of the extrapolation exercise: Potential market: costs of the ITC ehealth component Impact: benefits translated to: - reduction of healthcare costs, in particular hospitalization costs - improvement of quality of life, in particular delaying mortality one year

17 Link input data to Indicators SIMPHS2 Validation Workshop Brussels, 31 Jan

18 Link input data to Indicators SIMPHS2 Validation Workshop Brussels, 31 Jan Assumptions on ehealth deployment and its costs: Telehealth - deployment level of 20% - cost per monitored patient per day at 4 which corresponds to the tender costs of a telehealth project in Lombardy (Abadie, F. et al., 2011) Telecare - deployment level of 16% over the 65 and older population - cost of 0.5 per patient per day. It has been found that the monitoring costs of 1st generation telecare ranges between 10 and 30 Euros per month (DG INFSO: ICT & Ageing: European Study on Users, Markets and Technologies, 2010)

19 Link input data into Indicators SIMPHS2 Validation Workshop Brussels, 31 Jan

20 Link input data to Indicators SIMPHS2 Validation Workshop Brussels, 31 Jan Impact Assumptions on reduced hospitalization: - Σ is defined as a 25% reduction in bed days for patients treated by telehealth, according to a Care Coordination/Home Telehealth" program introduced by the Veterans Health Administration in the US (See Abadie, F. et al. 2011).

21 Link input data to Indicators SIMPHS2 Validation Workshop Brussels, 31 Jan

22 Link input data to Indicators SIMPHS2 Validation Workshop Brussels, 31 Jan Impact Assumptions on reduced mortality: The assumed monetary value of an extra year of life (Ω) is 30,000 Euros. The parameter of reduced deaths Μ : - 35% of the number of deaths of telehealth monitored patients - 20% of the number of deaths of telecare monitored patients. - This benchmark for reduced death at 35% is higher than other benchmarks found in the literature at 20%, but lower than the one estimated from the Whole System Demonstrator programme in UK which reaches a 45% (Headline Findings Department of Health. UK).

23 Link input data into Indicators SIMPHS2 Validation Workshop Brussels, 31 Jan

24 Expected Results: Potential Market SIMPHS2 Validation Workshop Brussels, 31 Jan Projections at EU19 level obtained in SIMPHS1 (Abadie et al. 2011)

25 Expected Results: Potential Market SIMPHS2 Validation Workshop Brussels, 31 Jan Mill. Euros ,000 4,000 6,000 8,000 Telehealth and Telecare Market Value EU27(Billion EUR) CHF:2.843 COPD:4.860 Diabetes:9.612 Telecare:2.543 Cluster D Cluster B Cluster C Cluster A CHF COPD Diabetes Telecare Source:Authors'elaboration from different databases. Assumed deployment level: 20% for telehealth and 16% for telecare. Assumed costs per monitored patient per day: 4 for telehealth and 0.5 for telecare. Cluster A: DK,SE,FI Cluster B: CY,HU,IE,IT,LV,MT,PT,ES,UK Cluster C: BE,BU,CZ,EE,EL,LI,LU,PO,RO,SK,SI Cluster D: AT,DE,FR,NL

26 Expected Results: Savings in Hospitalization SIMPHS2 Validation Workshop Brussels, 31 Jan Mill. Euros ,000 1,500 Savings from Reduced Hospitalization EU27(Billion EUR) CHF:0.438 COPD:0.356 Diabetes:1,966 Cluster D Cluster B Cluster C Cluster A CHF COPD Diabetes Source:Authors'elaboration from different databases. Assumed telehealth deployment level: 20 percent. Impact on reduced hospitalization assumed at 25 percent of bed days Cluster A: DK,SE,FI Cluster B: CY,HU,IE,IT,LV,MT,PT,ES,UK Cluster C: BE,BU,CZ,EE,EL,LI,LU,PO,RO,SK,SI Cluster D: AT,DE,FR,NL

27 Expected Results: Value of Reduced Mortality SIMPHS2 Validation Workshop Brussels, 31 Jan Mill. Euros ,000 1,500 2,000 Value of Reduced Mortality EU27 (Billion EUR) CHF:0.582 COPD:0.150 Diabetes:0.584 Telecare:3.729 Cluster D Cluster B Cluster C Cluster A CHF COPD Diabetes Telecare Source:Authors'elaboration from different databases. Assumed deployment level: 20% for telehealth and 16% for telecare. Impact on reduced mortality: 35 percent for telehealth over chronic patients and 20% for telecare over 65 and older population Cluster A: DK,SE,FI Cluster B: CY,HU,IE,IT,LV,MT,PT,ES,UK Cluster C: BE,BU,CZ,EE,EL,LI,LU,PO,RO,SK,SI Cluster D: AT,DE,FR,NL

28 Expected results: cost-benefit SIMPHS2 Validation Workshop Brussels, 31 Jan Break-even: COPD Telehealth Market Break-even: Chronic Hearth Failure Telehealth Market UK NL DE LU SE FR BE DK IE PT FI AT IT EL SI CZ ES PL SK LT LV EE MT CY HU RO BU Euros per patient-day Break-even on savings hospitalization Value reduced mortality Euros per patient-day MT LU BE IE NL EL DK AT ES UK SI IT FR CY SE DE FI PT RO EE BU SK CZ HU LT LV PL Break-even on savings hospitalization Value reduced mortality Break-even: Diabetes Telehealth Market LU DK NL SE AT FR BE DE FI UK IT ES EL CY IE SI PT MT SK CZ HU PL EE LT LV BU RO Euros per patient-day Break-even on savings hospitalization Value reduced mortality

29 Expected Results: cost-benefit SIMPHS2 Validation Workshop Brussels, 31 Jan Break-even: Total Telehealth Market LU UK NL DK BE MT DE SE FR AT IE IT FI EL ES PT SI CY CZ SK EE PL HU LT LV RO BU Euros per patient-day Break-even for CHF Break-even for COPD Break-even for Diabetes

30 Toolkit extrapolation framework SIMPHS2 Validation Workshop Brussels, 31 Jan Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Please, select countries/regions Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain EU27 Cluster A Cluster D Cluster B Telehealth Correlation deployment level Deployment level 20 0 With: (1 to 100) Overall ehealth readiness index 1 Cost per patient-day 4 (Euros) Cluster C Reduction Mortality 35 (1 to 100) A: DK, FI, SE D: AT,DE,FR,NL Reduction hospital use 25 B: CY,HU,IE,IT,LV,MT,PT,ES,UK (1 to 100) C: BE,BU,CZ,EE,EL,LI,LU,PO,RO, SK,SI LOAD Please, input parameters ICT healthcare expenditure index Seeking health information in internet index Hospital ehealth deployment index Chronic Illness COPD Telecare Correlation deployment level Deployment level 16 0 With: (1 to 100) Overall ehealth readiness index ICT healthcare expenditure index Seeking health information in internet index Cost per patient-day Hospital ehealth deployment index (Euros) CHF Diabetes Hungary Ireland Sweden United Kingdom Reduction Mortality 20 (1 to 100) Italy

31 Next step: Modelling Impact SIMPHS2 Validation Workshop Brussels, 31 Jan We can do better. Can we have a Break-even depending on the ehealth Deployment level? - Different growth rate of monitored total patients than monitored hospitalized patients - Adding an effect of deployment level on average length of stay in hospitals Break-even increasing with deployment level? Yes, if the effect on reduced hospitalization increases at higher rate than the monitored patients. Can we estimate feedback effects of deployment level on prevalence and mortality rates?

32 And finally. SIMPHS2 Validation Workshop Brussels, 31 Jan Discussion Any post-workshop issues: