Impact of HbA1c testing on cardiovascular morbidity and mortality: A Regression Discontinuity Design study

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1 Impact of HbA1c testing on cardiovascular morbidity and mortality: A Regression Discontinuity Design study

2 PROJECT GROUP Irene Petersen (Department of Primary Care & Population Health, University College London and Dept. of Clinical Epidemiology, Aarhus University, Denmark) Federico Ricciardi, Gianluca Baio (Department of Statistical Science, University College London) Sia Kromann Nicolaisen, Reimar W Thomsen and Lars Pedersen (Department of Clinical Epidemiology, Aarhus University, Denmark)

3 ABOUT Young, modern university from ,500 students, 10% international students A graduate university. More than 50% of students study at master or PhD level

5 REGRESSION DISCONTINUITY DESIGN (RDD) In epidemiology, there is a great need for designs that can support generation of causal evidence To date, RDD has not been implemented much in epidemiological research using real world data Therefore, we aimed to explore the potential for RDD to generate causal evidence using existing healthcare data in Denmark

6 AIM In particular, our aims were: To review the necessary RDD design assumptions in relation to a real study on HbA1c testing and cardiovascular morbidity and mortality in Denmark. To run simple intention-to-treat (ITT) analyses and explore the robustness of the effect estimates for different bandwidths. If possible, incorporate treatment in the analyses and derive causal complier estimates (CCHR), and compare the findings against the existing literature.

7 HBA1C The term HbA1c refers to glycated haemoglobin HbA1c reflects the average blood glucose level over the last 8-12 weeks In Denmark, HbA1c is used to diagnose patients with type2 diabetes

8 DISTRIBUTION OF HBA1C MEASUREMENTS IN DENMARK AMONG PEOPLE WITH NO PREVIOUS DIABETES DIAGNOSIS Percent PROFESSOR, 65 MSC, 70 PHD 75 HbA1c, mmol/mol

9 EPIDEMIOLOGICAL QUESTION We want to evaluate the effect (ITT) of treatment eligibility (HbA1c 48 mmol/mol) on allcause mortality and cardiovascular events Subsequently, we want to estimate the effect (CCHR) of early metformin initiation versus deferred or no metformin initiation Note: According to Danish guidelines, metformin treatment initiation is recommended for patients newly diagnosed with diabetes, i.e., patients should be treated with metformin if their HbA1c level exceeds 48 mmol/mol

10 THE RDD TIME TO EVENT MODEL Y = Mortality and cardiovascular events Z = Value of the first HbA1c measurement (LABKA) c = 48 mmol/mol Threshold indicator (0 if Z < c, 1 if Z c) T = Treatment indicator (T = 0 if the patient does not receive metformin, T = 1 otherwise) Z c = Centered assignment variable

11 DATA AND POPULATION Data from North and Central Denmark Regions Total population ~1.8M Complete coverage Free, tax-supported healthcare for everyone in Denmark Record-linkage at an individual level

12 LABKA The clinical laboratory information system (LABKA) Holds test results from every blood sample analyzed in clinical chemistry department laboratories in the North and Central Denmark regions (pop of ~1.8M) Includes results from any patient visiting a hospital or a general practitioner Few exceptions are some results from small, rapid point-of-care devices used by medical staff or patients themselves Data available since 1992 (only selected departments). Since late 1990s, full geographical coverage Data are updated annually

14 STUDY POPULATION We included individuals aged years at the time of their first HbA1c measurement between 2006 and 2014 (N = 525,266) We excluded individuals with a previous diagnosis of type I and type II diabetes, individuals treated with metformin or other glucose lowering drugs prior to the initial HbA1c measurement, and individuals with a cardiovascular event before the initial HbA1c measurement After exclusions, we had 290,333 patients

15 HISTOGRAM OF THE ASSIGNMENT VARIABLE Percent PROFESSOR, 65 MSC, 70 PHD 75 HbA1c, mmol/mol

16 PROBABILITY OF METFORMIN INITIATION 1.0 Cumulative incidence HbA1c, mmol/mol 1 month 3 months

17 BASELINE CHARACTERISTICS Range 1 Range 2 Range Std diff Std diff Std diff Total 76,031 8,011 36,360 6,710 14,216 5,416 Male 47.6% 52.5% % 52% % 51.6% Median age Heart failure 861 (1.1%) 202 (2.5%) (1.5%) 179 (2.7%) (1.8%) 134 (2.5%) Peripheral vascular disease 1,418 (1.9%) 197 (2.5%) (2.2%) 173 (2.6%) (2.4%) 145 (2.7%) Obesity 2,274 (3.0%) 405 (5.1%) ,257 (3.5%) 337 (5.0%) (3.9%) 257 (4.7%) Cancer 2,211 (2.9%) 350 (4.4%) ,201 (3.3%) 285 (4.2%) (3.6%) 214 (4.0%) -0.02

18 BASELINE CHARACTERISTICS Range 1 Range 2 Range Std diff Std diff Std diff Total 76,031 8,011 36,360 6,710 14,216 5,416 Male 47.6% 52.5% % 52% % 51.6% Median age Heart failure 861 (1.1%) 202 (2.5%) (1.5%) 179 (2.7%) (1.8%) 134 (2.5%) Peripheral vascular disease 1,418 (1.9%) 197 (2.5%) (2.2%) 173 (2.6%) (2.4%) 145 (2.7%) Obesity 2,274 (3.0%) 405 (5.1%) ,257 (3.5%) 337 (5.0%) (3.9%) 257 (4.7%) Cancer 2,211 (2.9%) 350 (4.4%) ,201 (3.3%) 285 (4.2%) (3.6%) 214 (4.0%) -0.02

19 BASELINE CHARACTERISTICS Range 1 Range 2 Range Std diff Std diff Std diff Total 76,031 8,011 36,360 6,710 14,216 5,416 Male 47.6% 52.5% % 52% % 51.6% Median age Heart failure 861 (1.1%) 202 (2.5%) (1.5%) 179 (2.7%) (1.8%) 134 (2.5%) Peripheral vascular disease 1,418 (1.9%) 197 (2.5%) (2.2%) 173 (2.6%) (2.4%) 145 (2.7%) Obesity 2,274 (3.0%) 405 (5.1%) ,257 (3.5%) 337 (5.0%) (3.9%) 257 (4.7%) Cancer 2,211 (2.9%) 350 (4.4%) ,201 (3.3%) 285 (4.2%) (3.6%) 214 (4.0%) -0.02

20 BASELINE CHARACTERISTICS (MEDICATION) Range 1 Range 2 Range Std diff Std diff Std diff Total 76,031 8,011 36,360 6,710 14,216 5,416 Statins 17,039 (22.4%) 1,969 (24.6%) ,924 (24.5%) 1,683 (25.1%) ,647 (25.7%) 1,389 (25.6%) 0.00 NSAIDS 19,726 (25.9%) 2,363 (29.5%) ,803 (27.0%) 1,945 (29.0%) ,948 (27.8%) 1,556 (28.7%) Glucocorticoids 5,461 (7.2%) 859 (10.7%) ,003 (8.3%) 721 (10.7%) ,354 (9.5%) 582 (10.7%) Diuretics 16,045 (21.1%) 2,610 (32.6%) ,965 (24.7%) 2,186 (32.6%) ,959 (27.8%) 1,760 (32.5%) Antidepressants 10,438 (13.7%) 1,277 (15.9%) ,199 (14.3%) 1,082 (16.1%) ,108 (14.8%) 871 (16.1%) Antibiotics 25,094 (33.0%) 2,951 (36.8%) ,654 (34.8%) 2,496 (37.2%) ,185 (36.5%) 1,991 (36.8%) -0.01

21 BASELINE CHARACTERISTICS (MEDICATION) Range 1 Range 2 Range Std diff Std diff Std diff Total 76,031 8,011 36,360 6,710 14,216 5,416 Statins 17,039 (22.4%) 1,969 (24.6%) ,924 (24.5%) 1,683 (25.1%) ,647 (25.7%) 1,389 (25.6%) 0.00 NSAIDS 19,726 (25.9%) 2,363 (29.5%) ,803 (27.0%) 1,945 (29.0%) ,948 (27.8%) 1,556 (28.7%) Glucocorticoids 5,461 (7.2%) 859 (10.7%) ,003 (8.3%) 721 (10.7%) ,354 (9.5%) 582 (10.7%) Diuretics 16,045 (21.1%) 2,610 (32.6%) ,965 (24.7%) 2,186 (32.6%) ,959 (27.8%) 1,760 (32.5%) Antidepressants 10,438 (13.7%) 1,277 (15.9%) ,199 (14.3%) 1,082 (16.1%) ,108 (14.8%) 871 (16.1%) Antibiotics 25,094 (33.0%) 2,951 (36.8%) ,654 (34.8%) 2,496 (37.2%) ,185 (36.5%) 1,991 (36.8%) -0.01

22 FIRST HBA1C MEASUREMENT AND THE EVENT RATE (PER PY) Rate HbA1c, mmol/mol

23 FIRST HBA1C MEASUREMENT AND THE EVENT RATE (PER PY) N~7,000 N~3,000 Rate 0.04 N~ 290, N~36, HbA1c, mmol/mol

24 RDD MODEL FOR THE EVENT RATE where log(hazard(y i Z i )) = β 0 + β 1 Z i c + β 2 1 Z i < c + β 3 Z i c 1(Z i c) β 1 is the slope and β 0 +β 2 the intercept below the threshold. β 1 +β 3 is the slope and β 0 the intercept above the threshold. β 2 is the difference in intercepts in the threshold.

25 MODEL FIT RANGE MMOL/MOL VERSUS MMOL/MOL Rate HbA1c, mmol/mol

26 INTENTION-TO-TREAT ESTIMATES EFFECT OF TREATMENT ELIGIBILITY ON CARDIOVASCULAR OUTCOMES AND MORTALITY Strata Bandwidth Mortality Stroke and AMI Combined All ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; )

27 INTENTION-TO-TREAT ESTIMATES STABLE ESTIMATES EVEN IN SMALLER BANDWIDTHS Strata Bandwidth Mortality Stroke and AMI Combined All ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; )

28 INTENTION-TO-TREAT ESTIMATES ALL ESTIMATES POINT IN THE SAME DIRECTION Strata Bandwidth Mortality Stroke and AMI Combined All ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; )

29 INTENTION-TO-TREAT ESTIMATES (STRATIFIED) EFFECT OF TREATMENT ELIGIBILITY ON CARDIOVASCULAR OUTCOMES AND MORTALITY Strata Bandwidth Mortality Stroke and AMI Combined Women ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) Men ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) Age <60y ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) Age >=60y ( ; ) ( ; ) ( ; ) ( ; ) ( PROFESSOR, ; ) MSC, PHD ( JUNE ; ) ( ; ) ( ; ) ( ; )

30 INTENTION-TO-TREAT ESTIMATES (STRATIFIED) EFFECT OF TREATMENT ELIGIBILITY ON CARDIOVASCULAR OUTCOMES AND MORTALITY Strata Bandwidth Mortality Stroke and AMI Combined Women ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) Men ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) Age <60y ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) ( ; ) Age >=60y ( ; ) ( ; ) ( ; ) ( ; ) ( PROFESSOR, ; ) MSC, PHD ( JUNE ; ) ( ; ) ( ; ) ( ; )

31 CSAL TREATMENT EFFECT ESTIMATES (BOR ET AL., EPIDEMIOLOGY, 25(5), PP ) RDD analysis on the combined endpoint Bandwidth Paramter Interpretation Estimate h_above Hazard among eligibles 0.04 h_below Hazard among non-eligibles 0.07 h_above_treat Hazard in always-takers and treated compliers 0.04 h_below_treat Hazard in always-takers 0.00 TreatProb_above Treatment probability among eligibles 0.19 TreatProb_below Treatment probability among non-eligibles 0.15 Prob_complier Difference in treatment probability in the threshold 0.03 ITT_frd Difference in hazards in the threshold (ITT) CACE_frd Complier causal difference in the threshold (CACE) h_above_complier Treated complier hazard 1.24 h_below_complier Control complier hazard 1.97 CCHR_frd Complier causal hazard ratio (CCHR) 0.63

32 INTENTION-TO-TREAT (ITT) AND COMPLIER CSAL HAZARD RATIOS (CCHR) Strata Bandwidth Combined event ITT Combined event CCHR All ( ; ) ( ; ) ( ; ) 0.44

33 SUMMARY The effect (ITT) of treatment eligibility showed a 20-30% reduced risk of cardiovascular events and mortality. The reduced risk was mainly driven by mortality. The reduced risk was also observed across age, gender and calendar year. The effect (CCHR) of early metformin treatment showed a more than 20% reduced risk of cardiovascular events and mortality. The estimated difference in treatment probabilities at the threshold is small, i.e., the complier effects (CCHR) are unstable and should be interpreted with caution.

34 STRENGTH OF THE STUDY Data collection independent of research question A large study population and complete follow-up on everyone High validity and completeness of endpoints

35 LIMITATIONS Fuzzy treatment assignment No data on primary care diagnoses We did not use information on treatment or HbA1c measurements during follow-up

36 EVIDENCE FROM CLINICAL TRIALS In a meta-analysis of twelve RCTs among metformin initiators (n = 5,455 ) and comparators (n=8,996), metformin was associated with a significant reduction in cardiovascular events compared to placebo or no therapy (OR 0.79, 95% CI ) In a small subgroup of overweight patients in the UKPDS trial, metformin-treated patients had a 30% reduced risk of macrovascular complications and a 36% reduced all-cause mortality

37 CONCLUSION We were able to evaluate the RDD assumption on routinely recorded healthcare data The data showed acceptable balance in observed covariates around the threshold There was no evidence of bunching around the threshold Below the threshold, around 95% were untreated within 3 months Above the threshold, less than 20% were treated within 3 months

38 CONCLUSION (CON T) Simple ITT analyses showed acceptable robustness in estimates across all bandwidths. The analyses showed that patients eligible for treatment seem to have a reduced risk of cardiovascular events and mortality Complier estimates (CCHR) showed a beneficial effect of early metformin treatment. The CCHR estimates should be interpreted with caution. However, estimates were comparable with the existing literature

Regression Discontinuity Designs: An Approach to Causal Inference Using Observational Data

Regression Discontinuity Designs: An Approach to Causal Inference Using Observational Data Aidan O Keeffe Department of Statistical Science University College London 18th September 2014 Aidan O Keeffe