The Sesim-LEV model. Lisa Brouwers Pontus Johansson Karin Mossler Nils Janlöv Anders Ekholm. Ministry of Health and Social Affairs

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1 The Sesim-LEV model Lisa Brouwers Pontus Johansson Karin Mossler Nils Janlöv Anders Ekholm Ministry of Health and Social Affairs

2 Outline Project aims Model Data Health in SESIM-LEV Results Impact Most recent work: tobacco smoking in SESIM- LEV

3 ( LEV ) Overall aim of project Analyze the long term development of demand and costs for welfare services Welfare = healthcare and elderly care Identify factors that are central to the development Construct different scenarios for how demand and costs may develop under different assumptions Is it possible to change the development? Time horizon: from now until 2050 Project duration: 2007 until 2009 Ministry of Health and Social Affairs

4 The SESIM model First implementation in 1997 Focus of simulations and forecasts has shifted over time The LEV-work extends the work of the BabyBoom project (-07) Conventional micro-simulation model, variables are updated in a sequence with a time step of one year...not linked to a macro model, macro economic assumptions are instead fed into the model (rate of inflation etc.) Simulations start year1999 Programmed in VisualBasic 6, one year takes 40 seconds to run Ministry of Health and Social Affairs

5 Data Main source of information is LINDA, a panel data set ~3.5 % of the population, family members added Personal unique identifier makes it possible to link registers on the level of the individual Data from different registers are collected: HEK (sample survey household economy) ULF/HILDA (sample survey w panel part living conditions including health) Patient registers, the Kungsholmen study, the SHARE study Population size in SESIM-LEV is and is entirely created from administrative registers Ministry of Health and Social Affairs

6 Health status Imputed to the model individuals based on individual attributes already present in Sesim Estimated on ULF/HILDA. Questions over four health-dimensions were combined into four states: full health (4), not full health (3), some illness (2) and severe illness (1) Dimensions: self-assessed health, self-assessed mobility, longstading illness and working capacity. Ministry of Health and Social Affairs

7 Health related features Health care consumption Elderly care and dependency (ADL) Focus diseases introduced: Cancer (5 types), AMI, stroke, diabetes and dementia and now COPD Death risks Health status influences directly Dementia Elderly care and ADL-level Tobacco smoking

8 Tobacco smoking Daily smoker, previous smoker, never smoker Affects health status Directly affects risk for lung cancer and COPD (Chronic Obstructive Pulmonary Disease) excess risk for smokers, previous smokers We limitied negative impact of previous smoking to 10 years Dynamic model: start, quit, relapse

9 After implementation and validation What did the simulations show?

10 Sesim forecasts a smaller population than forecasts from statistics sweden Gap to be filled with health Referensscenario SESIM-LEV SCB:s prognos Statistics Sweden

11 We will live longer, but how healthy will the last years be? Three scenarios in Sesim-LEV:

12 Some project results The LEV-project is finished and the results were presented to our politicians and to many stakeholders through presentions and seminars

13 Difference in individual consumption Percentage difference in age-standardised consumption of care and social services per individual in 2030 and 2050 in relation to Age standardisation means that the population in the three different scenarios has the same size and age composition.

14 Increase of costs, health care and elderly care Percentage trend in costs of consumption of health care due to demographic change in 2010 to 2050 in different scenarios fixed prices. Percentage increase in costs of consumption of care for the elderly due to demographic factors in in different scenarios.

15 Adding increased technology/ambition Cost for health and elderly care as share of GDP. (1) Demographic factors only (three bottom lines) and (2) trend including increased level of ambition/technology effect (the top three lines).

16 Health and elderly care share of GDP in 2050 Rising from 13% to 16% Currently costs about 330 billion SEK

17 Conclusion: the future needs can be met! With a productivity of 0.6 to 0.7 percent per year in health care can the increased need be met without increasing the sector's share of GDP It is possible to combine steps to meeting the growing ne eds: Better health and functional capacity reduces the need for health and social care A more efficient health care can mean better results and / or reduced costs Innovation, Development and Research

18 Impact - our experiences Anders Ekholm, chief analyst, has presented the SESIM- LEV results to ~6000 persons since 2010 Politicians, civil servants, administrators and professionals within health and elderly care agree that they are working ineffecientlyand should work with imrovements in a more systematic manner Increased productivity in elderly care on the political agenda Large impact on politics

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20 Most recent addition: tobacco smoking ( ) Kristian Bolin (Lund) Lisa Brouwers (Stockholm) Gustav Kjellson (Lund) Björn Lindgren (Lund)

21 Goals Modify and extend the SESIM-LEV model in order to allow for simulations showing the consequences if people - quit smoking - never take up smoking The primary goal was to make it possible to analyze the effects on the overall costs for - health- and elderlycare - sickness benefit - early retirement Two focus diseases directly affected by smoke status: - Chronic Obstructive Pulmonary Disorder (COPD) [KOL], lung cancer

22 Realisation New estimations of smoking behaviour and the health effects (Lund) Implementation in the SESIM-LEV model (Stockholm) Iterative process

23 New estimation (1): tobacco smoking Estimations performed on ULF-data (linked w data from the Patient register (SoS) and data from Statistics Sweden. Database: HILDA in Lund. Daily smoker < 25 years Quit Previous smoker Assignment of smoke status Daily smoker >= 25 y Non smoker < 25 Relapse Start Non smoker >= 25

24 Ny estimation (2): health status Health estimations were also performed on linked ULF-data (Hilda database). Additional expl. variables: smoke status Daily smoker (yes/no) and Previous smoker (yes/no) Static and dynamic model

25 Further additions based on literature and data from SoS The negative effect of previous smoking was in SESIM limited to 10 years (linear decrease of negative impact) Risk for relapse depending on years since cessation: < 10 y ago: 4,8 % >= 10 y ago: 1,2 % Prevalence of COPD and lung cancer was based on smoke status, over risks from literature

26 Validation Simulated prevalence were compared with ULF-data

27 Impact on target variables

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35 Conclusions SESIM-LEV is now suited for policy experiments on effects of tobacco smoking measures The model generates (reproduces) - observed smoking patterns - health differences due to smoking behaviour - expected differences in care consumtions based on differences in health status The model overestimates - total number of days of work absence (sick benefit) - total number of early retired

36 Some preliminary policy experiments performed by KI student 2012 Geronimo Salomon, student at KI (public health) implemented a tobacco endgame strategy in Sweden using the extended SESIM-LEV model. Policy: smoking prevalence = 0 in 2025 (decreasing prevalence over 10 years, from 2015) Results: Life expectancy at birth increases with 0,34 years in a tobacco-free society and the amount of quality adjusted life years are more an average year , compared to the baseline. Health improvements lead to a lower health care consumption, leading to savings of 1 billion Swedish kronor an average year An healthier population leads to fewer sick days per year and fewer early retirees from work (younger than 65 years). This would reduce production losses and thereby give the society revenues of 6 billion Swedish kronor (excluding production losses due to premature deaths).