Population Health Model (POHEM) Coronary Heart Disease: Acute Myocardial Infarction (AMI)

POpulation HEalth Model (POHEM) Case-by-case, Monte Carlo microsimulation Models competing risks, comorbidity, interventions Generates plausible health biographies for synthetic individuals population initialized in 2001 from CCHS cycle 1.1 subject to cohort-specific mortality hazards (by age, sex, YOB) generates births and immigrants based on Census projections Projects basic counts and distributions population prevalence of risk factors cases eligible for primary interventions disease incidence and progression (e.g. first AMI, readmissions, deaths) Tabulates all these by age, sex, calendar year, geography, etc.

POHEM example Starting Population: Canadian Community Health Survey 2001 (CCHS) cross-sectional representation of the Canadian population aged 18+ 2001 …….. VARIABLE age sex province health region immigration status education level income quartile body mass index smoking status diabetic status total cholesterol* HDL* blood pressure* HUI *imputed from Canadian Heart Health Surveys (1986-1992) VALUE 44 male Ontario York non-immigrant post-secondary Q4 (richest) 32.2 kg/m2 (obese) smoker non-diabetic high low 0.96 Every year on birthday, evaluate the hazard of developing disease (AMI, diabetes, cancer, osteoarthritis,...)  no disease events in 2001 >Data Source: CCHS cycle 1.1 2001 Master file >Database records: Start N=131535; final N=105908 after exclusions including those less than 18 * variables imputed from Canadian Heart Health Surveys (1986-1992) Included for printing – variables in blue indicate they are updated (initialize) in year shown OTHER VARIABLES NOT included yet heart disease status alcohol level nutrition level ethnicity

POHEM example Starting Population: Canadian Community Health Survey 2001 (CCHS) cross-sectional representation of the Canadian population aged 18+ 2001 …….. age sex province health region immigration status education level income quartile body mass index smoking status diabetic status total cholesterol HDL blood pressure HUI Every year on birthday, evaluate the hazard of developing disease (AMI, diabetes, cancer, osteoarthritis,...)  AMI in 0.3 years 2002 …….. AMI variables in blue indicate they are updated (initialize) in year shown illustrates case-by-case way of proceeding AMI at age 45.3 Now at risk of 2nd AMI, CHF, UA, ...

Congestive Heart Failue (comorbid with Congestive Heart Failure) POHEM example Starting Population: Canadian Community Health Survey 2001 (CCHS) cross-sectional representation of the Canadian population aged 18+ 2001 …….. age sex province health region immigration status education level income quartile body mass index smoking status diabetic status total cholesterol HDL blood pressure HUI apply Δ BMI model (function of age, sex education, income, region and BMI in 2001)  Remains Obese 2002 …….. 2003 …….. … …….. Death at age 71.2 CHF OA AMI Congestive Heart Failue at age 66.1 in year 2023 OA at age 69.4 in year 2028 (comorbid with Congestive Heart Failure) Form of current BMI model (from NPHS): Δ BMI(2002,2004)= α + ß1 Δ BMI(2000,2002) + ß2 Δ BMI(1998,2000) +ß3 Δ BMI(1996,1998) + ß4BMI(1996) + ß5IncQuartile(1996) + ß6Educ_cat(1996) + ß7Region(1996) for 28 Sex-Age-BMI_categories defined in NPHS 1996

POHEM example Starting Population: Canadian Community Health Survey 2001 (CCHS) cross-sectional representation of the Canadian population aged 18+ 2001 …….. 2002 …….. 2003 …….. … …….. death >100,000 records on CCHS representing ~24 million Canadians (4 hours on a PC)

Coronary Heart Disease: Acute Myocardial Infarction (AMI)

Data Analysis and Input to POHEM-AMI Incidence rates (I) estimated from administrative data by age group, sex and province Incidence-risk equation obtained from the literature: Framingham risk function (Wilson, 1998) α = baseline risk (by age, sex, province)  = coefficients for cholesterol, HDL, diabetes and smoking Baseline risk (α) is calibrated such that the incidence-risk equation (F) implemented in POHEM reproduces the observed incidence rates by age, sex and province Values and models of change in risk factors based on data and trends from national surveys In 2002 at age 45, the risk profile for this simulated person would remain unchanged but the random number this time generates a waiting time less than 1 year. t = -ln(u) / h = -ln(0.5333) / 2.09 = 0.3 years AMI would occur at age 45.3 in year 2002 *the alpha term is estimated by age group the target rates are shown here, but actually we calibrate with the risk and distribution of BMI in the population in 2001 to obtain baseline rates (b). The baseline rates (by age and sex) are assumed to remain constant over time; the individual’s age and BMI changes over time, which changes the risk as they are projected forward in the simulation 160 200 240 280

How POHEM Generates an Incident Case of AMI POHEM selects a record from CCHS in simulation year 2001: male, age 44, smoker, non-diabetic, high total cholesterol, low HDL, medium blood pressure... Lookup baseline risk and risk factor coefficients from input parameter table. Evaluate the probability (p) of AMI using the Framingham risk function (with rescaling): p = F/(1+F) = 0.877 Convert to annualized hazard (h): h = -ln(1-p) = 2.09 Generate a a random number (u) between 0 and 1 u = 0.025 Transform the hazard to a waiting time: t = -ln(u) / h = 1.76 years AMI does not occur at age 44 in year 2001. The risk of AMI will be re-evaluated at the next birthday. POHEM ages the person forward to next birthday updates the person’s risk factors profile re-evaluates risk of AMI (steps 2-7 repeated every year until AMI occurs or death) other events are evaluated Steps 1 to 8 are repeated for every record on CCHS α = 0.00138 βsmoking= 0.523 βdiab = 0 βchol = 0.657 βHDL = 0.497 βBP = 0.283 In 2002 at age 45, the risk profile for this simulated person would remain unchanged but the random number this time generates a waiting time less than 1 year. t = -ln(u) / h = -ln(0.5333) / 2.09 = 0.3 years AMI would occur at age 45.3 in year 2002 *the alpha term is estimated by age group the target rates are shown here, but actually we calibrate with the risk and distribution of BMI in the population in 2001 to obtain baseline rates (b). The baseline rates (by age and sex) are assumed to remain constant over time; the individual’s age and BMI changes over time, which changes the risk as they are projected forward in the simulation 160 200 240 280

Data Sources Canadian Community Health Survey (2001) starting population for POHEM (initialize age, sex, geography, BMI, smoking, diabetes) National Population Health Survey (1994-2004) models of change in BMI and smoking Canadian Heart Health Survey (1986 to 1992) joint distribution of other cardiac risk factors cholesterol, diabetes, blood pressure HDL imputed Health Person-Oriented Information (1992/93 to 2001/02) hospital separations by province rate of index AMI (5-yr wash-out) by province Registered Person database (1988/89 to 2001/02) Ontario hospital separations linked to vital statistics Survival time from AMI event to subsequent AMI event or death

Model Input: Smoking transitions non-smoker 96 and 98 smoker 96 and non-smoker 98 non-smoker 96 and smoker 98 smoker 96 and 98 Never smokers Successful quitter in1996 Quit in1998 Smokers in all 3 years 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% non-smoker 94 smoker 94 4th order hierarchical Markov chain Based on NPHS 1994 - 2002 (after that sample size too small) Example: Smoking transitions capture in a 4th order hierarchical Markov chain by age group and sex based on NPHS from 1994 to 2002 (after which time sample size is too small)

Model Input: HDL distribution example HDL distribution by total cholesterol for male aged 55-59, overweight and non-diabetic HDL (5 categories) is imputed from CHHS data based on the current values of sex, age group, total cholesterol level, diabetic status (Y/N), and BMI category as the simulation progresses. 2x12x2x4x5=960 combinations x 5 possible HDL levels. So a person falls into 1 of the 960 possible configurations based on their sex, age, bmi category, diabetic status and total cholesterol. A random number is used to then choose their HDL level from the distribution.

Modeling Risk Factor Transitions Initialization 2001 CCHS 2001 age = 55 sex = male income education region BMI diabetes hypertension (y/n) smoker (y/n) CHHS (86-92) total cholesterol HDL Blood pressure Year 2001 2003 2005 2007 2009 2011 t=0 t+5 t+10 Smokt+2 Smokt+4 Smokt+6 Smokt+8 Smokt+10 Smokingt BMIt+2 BMIt+4 BMIt+6 BMIt+8 BMIt+10 BMIt The transitions are activated when the person reaches the next age group (5 year age groups). The joint transition modeled BMI (4 categories), diabetes (2 categories), cholesterol (5 categories), and blood pressure (5 categories) from one age group to the next (5 year intervals), by sex. HDL (5 categories) is imputed from CHHS data based on the current values of sex, age group, total cholesterol level, diabetic status (Y/N), and BMI category as the simulation progresses. Initial values for cholesterol level and blood pressure status imputed from CHHS by sex, age group, diabetic status (Y/N), and BMI category. Smoking (not shown here) was modeled from NPHS and was based on sex, age group, and past history of smoking (1st, 2nd, and 3rd order Markov chains). It is updated every two years. Diabt+5 Cholt+5 Hyptt+5 Diabt+10 Cholt+10 Hyptt+10 Diabt Cholt Hyptt transitions derived from NPHS (1996-2002) transitions derived from CHHS

Preliminary Results Acute Myocardial Infarction in Canada: Projection of risk factors, incidence and progression from 2001 to 2021

Objectives Project the prevalence of risk factors most commonly associated with acute myocardial infarction (AMI) between 2001 and 2021 Project the number of resulting AMI events over that period Estimate the contribution of each risk factor to AMI outcomes in future years

Summary of preliminary results Number of AMI cases projected to increase, principally due to aging of the population Smoking projected to decline, reduces the overall increase in AMI Proportion of persons with diabetes projected to rise Approximately 10% of new index AMI cases attributed to diabetes In males, 24% of new AMI cases attributed to elevated blood pressure In females, 17% of new AMI cases attributed to elevated total cholesterol ** note ** Jack Tu is noticing a decline in AMI incidence in the last few years in their admin data **

Model Projection: Prevalence of Smoking Proportion of Pop 0.35 0.3 0.25 Proportion smokers declining for all age groups. 0.2 0.15 0.1 0.05 < 30 30-39 40-49 50-59 60-69 70-79 80+ All Age 2001 2006 2011

Illustrative “What-if ?” Scenarios Statins: given to people at high risk according to guidelines from working group on dyslipidemias; reduces their AMI risk by 31% (La Rosa, 99). BMI: 10% reduction for everyone overweight or obese (BMI ≥ 25) at baseline in 2001 Smoking: 20% of smokers permanently quit smoking at baseline in 2001 Cholesterol: 5% reduction of total cholesterol value for everyone at baseline in 2001 Note: interactions in RF dynamics  change in one at baseline affects subsequent levels of others Statin given to people according to guidelines on previous page from previous slide (except for Canada).

AMIs avoided by by “what-if” scenarios 10,000 20,000 30,000 40,000 50,000 60,000 70,000 2001 2003 2005 2007 2009 2011 Year Cumulative number of index AMIs avoided Cholesterol -5% Statins to medium and high risk Statins to high risk BMI -10% Smoking -20% ~40,000 new AMI cases in 2001, ~500,000 prevalence AMI cases after by 2011 Limitations: Statin coverage at baseline not modeled: the graph overestimates benefit No side-effects of statins were modeled

Future Work Revision and validation Improvements to data foundations understand the causes of overprojected AMI cases in Ontario: revise Framingham equation analyse treatment data (statins) model AMI prevalence at the start of simulation recreate 1994 – 2004 history of incidence and mortality Improvements to data foundations update index AMI rates with most recent data (from 2001 to 2004) update to CCHS cycle 3.1 (or pooled) to initialize POHEM update with measured risk factor prevalence from CHMS (when available) update survival with cause-specific mortality data (HPOI linked to vital stats) Model extensions more robust model of diabetes (Rosella and Manual, ICES) add procedures (CABG, PCI, catheterizations) as consequence of AMI relate procedures to survival outcomes add models of stroke, peripheral vascular disease and other CVD Health-related Quality of Life (HALE) LIMITATIONS: Only models AMI identified with hospital admissions – misses sudden death Risk factor prevalence and trends based on household population Only have all cause of death after AMI means cannot model impact of treatment Models only index AMI and consequences. Other updates: update models of smoking and weight change as more recent data becomes available from new cycles of NPHS prevalence estimates to warm-up the simulation health care utilization - physician visits, days of hospital stays, direct costs health care access - additional variables from CCHS Note: diabetes risk algorithm and complication algorithm being build by by Laura Rosella and Doug Manual at ICES