Clinical and Health Workforce Implications of Improving Population - PowerPoint PPT Presentation

Clinical and Health Workforce Implications of Improving Population Health Annual Research Meeting June 25, 2017 Tim Dall, tim.dall@ihsmarkit.com Will Iacobucci Ritashree Chakrabarti Frank Chen Terry West

2 Research Question • How will improvements in population health affect aggregate demand for health care services and providers? > Will demand decline because people are healthier thus requiring fewer services in hospitals or other settings? > Will demand increase because people live longer?

3 Population Health Improvement Scenario Modeled • Modeling assumptions > Sustained 5% body weight loss for overweight and obese adults > Improved blood pressure, cholesterol, and blood glucose levels for adults with elevated levels – 34.42 mg/dL (CI, 22.04-46.40) reduce total blood cholesterol 1 – 14.5 mm Hg reduction in systolic blood pressure by and 10.7 mm Hg reduction diastolic blood pressure 2 – 1 percentage point annual reduction in hemoglobin A1c until diabetes control reached at 7.5% 3 > Smoking cessation • Hypothetical scenario covers portion of population health/preventive care goals 1 Taylor et al. Statins for the primary prevention of cardiovascular disease . Cochrane Database Syst Rev 2013;1:CD004816. 2 Baguet et al. Updated meta-analytical approach to the efficacy of antihypertensive drugs in reducing blood pressure . Clin Drug Investig . 2007;27(11):735-753. 3 Sherifali et al. The effect of oral antidiabetic agents on A1C levels: a systematic review and meta-analysis . Diabetes Care . 2010;33(8):1859-1864.

4 Healthcare Simulation Model • Four integrated components: demand, supply, disease, expenditures • Supports work with governments, associations, hospital systems, health plans • National, state, and local projections incorporating population health risk factors • Microsimulation: individuals are the unit of analysis Disease Healthcare Publications Using The Model Prevention Demand Microsimulation Microsimulation Model (DPMM) Model (HDMM) Healthcare Simulation Model Health Workforce Medical Supply Model Expenditure (HWSM) Model (MEM)

5 The DPMM is designed to simulate disease onset and economic outcomes under disease prevention/health promotion scenarios MODEL CHARACTERISTICS • Microsimulation model: individual people are modeled • Markov approach: current characteristics and status used to predict next year’s status • Monte Carlo simulation: each person is modeled multiple times, and outcomes can differ each time • Prediction equations come from published clinical trials and observational studies, and original empirical analysis PROJECTION HORIZON • Annual projections until the end of the projection horizon or death (have modeled lifetime,10- and 20- year projections) PERSPECTIVE • Can be societal, health-system, employer, individual/household, or payer OUTCOMES • Disease incidence/prevalence, medical expenditures, mortality, economic outcomes (employment, productivity, earnings, taxes, social security), and quality adjusted life years 6

6 The Disease Prevention Microsimulation Model was developed to answer the following questions • For each person in a given population and over a specified period of time: Starting health profile > What is the likelihood and timing of disease onset and severity? > How will health affect: Outcomes Change in – Health care use? • Disease incidence health risk • Medical costs factors • Productivity – Medical expenditures? • Quality of life • Mortality – Employment and productivity? – Quality of life? – Mortality? Change in disease health • If an intervention changes one states or more health risk factors, how will this affect the above questions?

7 The DPMM projects 50+ clinical and economic outcomes Cardiovascular Musculoskeletal • • Hypertension Stroke • • • Osteoporosis Osteoarthritis Chronic back pain • • Ischemic heart disease Congestive heart failure Neoplasms • • Myocardial infarction Dyslipidemia • • • Breast Kidney Ovarian Diabetes & sequelae • • • Cervical Leukemia Pancreatic • Diabetes incidence and prevalence • • • Colorectal Liver Prostate • Prediabetes incidence and prevalence • • • Endometrial Lung Stomach • Annual progression rate from prediabetes to diabetes • • • Esophageal Multiple myeloma Thyroid • Amputation (diabetes-related only) • • Gallbladder Non-Hodgkin's lym. • Retinopathy (diabetes-related only) Pulmonary Gastroenterology • • Pneumonia Asthma • Gallbladder disease • • Pulmonary embolism COPD • Gastroesophageal Reflux Disease (GERD) Socioeconomic • Non-alcoholic fatty liver disease (NAFLD) • • Medical expenditure Absenteeism Mental & Cognitive • • Household/personal income Life years/death • • Depression Bipolar disorder • • Probability of employment QALY • • • Alzheimer’s Disease Schizophrenia Social security cost • • • Others Obesity CKD/ESRD Obstructive sleep apnea Constructed initial population file from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) 6

8 BMI as a key model driver Direct Effect Disease States Indirect Effect Disease States BMI as a key model driver has direct, secondary, and tertiary Endocrine CKD impact on many outcomes. Renal failure Diabetes (HbA1c) For example: Prediabetes (HbA1c) Amputation Blindness BMI Cardiovascular Stroke CHF Hypertension PVD (SBP, DBP) Body weight IHD (BMI) Myocardial infarction Dyslipidemia (HDL, Atrial fibrillation Total cholesterol) LVH HbA1c Cancers Breast NHL Cervical Multiple Myeloma Note: Connecting lines show Endometrial Ovarian the items in the model that are linked Esophageal Pancreatic Gallbladder Prostate Diabetes Kidney Stomach Abbreviations: BMI=body mass index, CHF=congestive heart Leukemia Thyroid failure, CKD=chronic kidney Liver Colorectal disease, DBP=diastolic blood pressure, GERD= gastroesophageal reflux disease, Respiratory HbA1c=hemoglobin A1c, HDL=high-density lipoprotein, Pulmonary IHD=ischemic heart disease, Pneumonia embolism LVH=left ventricular IHD hypertrophy, NAFLD=non- Other alcoholic fatty liver disease, Osteoarthritis OSA=obstructive sleep apnea, Gallstones & PVD=peripheral vascular disease, Chronic back pain gallbladder SBP=systolic blood pressure. GERD Major depression NAFLD OSA

Cardiologist Cardiology-related Primary Diagnosis Office Outpatient Emergency Hospital- Inpatient Parameter Visits 1 Visits 1 Visits 2 ization 2 Days 1 Example: Use of 1.00 1.00 1.00 1.00 1.00 Non-Hispanic White Ethnicity 0.79** 0.97 1.36** 1.32** 1.14** Race- Non-Hispanic Black Cardiology Services 0.90** 0.75** 0.86 0.94 1.10** Non-Hispanic Other 1 Rate ratios from Poisson 0.79** 0.68** 0.93 0.84** 1.07** Hispanic 1.13** 1.59** 0.89* 1.11 0.97** regression analysis using Male 0.11** 0.24** 0.66** 0.40** 0.84** 18-34 years 2009-2013 MEPS/2013 NIS. 0.22** 0.63** 0.95 0.76** 0.80** 2 Odds ratios from logistic 35-44 years Age 0.50** 0.86** 1.05 1.10 0.86** 45-64 years regression analysis using 65-74 years 0.83** 1.21** 1.11 1.50** 0.93** 2009-2013 MEPS. 75+ years 1.00** 1.00** 1.00** 1.00** 1.00 Statistically significant at the 0.73** 0.84** 1.22** 1.11 Smoker 0.05 (*) or 0.01 (**) level. 1.55** 1.13** 3.86** 2.66** Hypertension 8.50** 10.73** 2.93** 3.84** Heart disease Diagnosed with History of heart attack 1.63** 1.36** 2.36** 2.60** Demographics 1.08** 1.26** 2.92** 3.04** History of stroke 1.15** 1.34** 1.01 1.19** 1.02** Diabetes 1.10** 1.24** 0.96 0.96 Arthritis 1.04* 1.08** 1.00 1.07 Asthma 1.06** 1.11** 1.01 0.99 History of cancer Health Risk & Normal 1.00** 1.00** 1.00** 1.00** Weight Body 1.04** 1.09** 0.87** 0.82** Overweight Behavior 1.11** 1.18** 1.01 1.02 Obese 2.61** 2.09** 0.92 1.09 0.99* Has insurance Insured 1.36** 1.30** 1.59** 1.71** 1.23** In Medicaid Economic & 1.00 1.24** 0.99 0.99 In managed care plan Policy 0.90** 0.97 1.23** 1.19** <$10,000 0.92** 0.91** 1.16* 1.20** $10,000 to <$15,000 Household Income 0.93** 0.93* 0.82 0.99 $15,000 to < $20,000 Care Delivery 0.89** 0.73** 1.15 1.06 $20,000 to < $25,000 0.92** 0.96 1.16* 1.05 $25,000 to < $35,000 0.88** 1.07* 0.91 0.93 $35,000 to < $50,000 0.96* 1.17** 0.93 0.82** $50,000 to < $75,000 1.00 1.00 1.00 1.00 $75,000 or higher 1.31** 1.09** 1.07 0.91 1.03** Metro Area

10 Population Health Improvement • National outcomes cumulative 2015 to 2030 > 10.2 million fewer people with heart disease > 3.2 million fewer strokes > 3 million fewer heart attacks > Reduced incidence of cancer and other diseases, e.g., – 2.7 million fewer cases of prostate cancer – 460,000 fewer cases of thyroid cancer – But, more cases of ovarian cancer, stomach cancer, Alzheimer, osteoporosis and other conditions associated with an older, living population • Initially, improved population health means fewer hospitalizations and less demand for care • National, per capita utilization declines by 1-2%