Precision Health Economics, Not Just Genetics

Transcription

1 Precision Health Economics, Not Just Genetics The Impact of Real-World User Behavior and Treatment Adherence Patterns on Health and Economic Outcomes Janne Martikainen Professor (pharmacoeconomics) Deputy Head of the UEF House of Effectiveness University of Eastern Finland

2 What are the determinants of health (outcome)?

3 Social circumstances 15 40% Behavior/lifestyle 30-50% Genetics 20 30% An individual s health Health care 10 20% Environmental & physical influences 5 20% (McGovern et al. Health Affairs 2014) 3

4 From personalised medicine to precision medicine an approach to disease treatment and prevention that seeks to maximize effectiveness by taking into account individual variability in genes, environment, and lifestyle - President Obama s 2015 Precision Medicine Initiative treatments targeted to the needs of individual patients on the basis of genetic, biomarker, phenotypic, or psychosocial characteristics that distinguish a given patient from other patients with similar clinical presentations - Jameson & Longo, NEJM 2015 however, no mentions about costs, costeffectiveness, preferences, or value 4

5 Toward data-driven precision health economics and outcomes research (HEOR) Traditional HEOR has focused on the typical patients and often in terms of the mean (i.e. mean treatment response) This traditional focus fails to recognize the real-world heterogeneity in (patient) populations to identify (patient) subgroups that potentially could have significantly more benefit than the general (patient) population Precision HEOR with access to multiple RWD sources From variability to heterogeneity From typical patient analysis to detailed patient cluster analyses Precision medicine & HEOR Individual patients receive the best possible medical care for their condition Overall societal healthcare resource allocation will achieve the best outcomes for the money spent

6 Patient Profiling Approach (PPA) as a potential tool for precision HEOR The right care, to the right person, at the right time Concept of mass customization Markets of a few (e.g. Starbucks) Main steps of Patient Profiling Approach 1) Analyzing longitudinal trajectory modelling of relevant outcomes Statistical group-based trajectory modelling etc. 2) Exploring the predictors of trajectory memberships Multivariable logistic models, machine learning techniques etc. 3) The adaption of care/ intervention per an identified patient profile groups (Dekkers & Hertroijs. Adv Ther 2018) 6

7 Practical example I: - Improving the cost-effectiveness of statin therapy with the PPA 7

8 Impact of real-world adherence behavior on the cost-effectiveness of statin therapy (Aarnio E, Korhonen M, Huupponen R, Martikainen J. Atherosclerosis 2015)

9 Profiling of (register-based) real-world adherence behavior among statin users n= (Aarnio E, Martikainen J, Winn A, Huupponen R, Vahtera J, Korhonen MJ. Circulation - Cardiovascular Quality and Outcomes 2016) 9

10 Examples of register-based factors associating with adherence trajectories Older age Unemployment (only in men) Diabetes diagnosis Low socioeconomic status (only in men) Use of other CV medications Better adherence Poorer adherence Depression diagnosis Hypertension diagnosis (only in men) Retirement (only in women) Higher statin outof-pocket costs Hospital days in the year preceding statin therapy

11 Practical example II: - Optimizing the effectiveness of a mobile app supporting the weight management in persons at elevated risk of T2D 11

12 Stop Diabetes Project The Finnish Stop Diabetes Study ( Lead by Prof. Jussi Pihlajamäki (UEF) Type 2 diabetes (T2D) is a worldwide health-problem affected by overweight and unhealthy lifestyle Current key challenges Identification of individuals at T2D risk Low adherence to lifestyle interventions Failure to change lifestyle behaviors permanently The aim is to study the effectiveness of digital recruitment and a mobile app (BitHabit) supporting permanent change in lifestyle behavior The study is currently funded by The Strategic Research Council (SRC) at the Academy of Finland ( ) Digitally screened (% of population)

13 Outcome-based targeting of the BitHabit app by applying data-driven methods User profiling based on StopDia follow-up data Changes in glucose tolerance, weight and lifestyle, etc. BitHabit app log data Selected and performed habits, user logins Currently ~ 2 million performed habits Outcome-based targeting Dose-response (e.g. user activity <-> weight loss) Long-term outcomes Diabetes medications, reimbursement status User profiling with machine learning techniques 13

14 Practical example III: - The optimization of T2D care pathways with the PPA applying RWD

15 Real World Data analytics based on EMR data New project: The aim of this collaboration project is to build RWD analytics for patient profiling and outcome prediction in patients with T2D The dynamic cohort of patients (n>10 000) with diagnosed T2D (ICD-10 E11) from all North Karelia municipalities (N= ) Data sources Laboratory values (HbA1c, BMI, BP, LDL, etc.) Primary care use Secondary care use Medical procedures Hospital admissions Prescriptions Reimbursed medicines and their costs (from the national Prescription Register) Spatial data sources (socioeconomic status, etc.) Collaborators IMPRO project ( SiunSote ( StopDia project ( The Finnish Innovation Fund Sitra (

16 Optimizing the treatment of T2D patients Proportion of T2D patients on treatment target (HbA1c < 53 mmol/mol) T2D Patient Profile Groups (HbA1c levels) T2D-related complications (Walraven et al. 2015) T2D-related costs Data-driven tailored chronic care management 16

17 Conclusions Precision medicine & HEOR (combined with RWD) has a significant potential to improve patient experience to improve population health and quality of life to reduce the cost of care, by reducing the overuse, underuse and misuse of healthcare services But, we have also some challenges: - Privacy concerns (incl. GDPR) & limits to access to RWD - Uneven quality of RWD sources - Integrating data from multiple sources - Collaborating with numerous stakeholders holding the data - Ever-expanding data sources (e.g. non-traditional, health-related digital data sources, IoT, wearables, etc) - Developing statistical and AI competence - Lack of standardization of RWD analytics - Need for standardized RWD platforms (e.g. for data lakes, etc.)? 17

18 Contacts Professor Janne Martikainen School of Pharmacy