Economic evaluation of Cardio inCode®, a clinical-genetic function for coronary heart disease risk assessment

Genetic-Guided Pharmacotherapy for Coronary Artery Disease: A Systematic and Critical Review of Economic Evaluations

BACKGROUND

Genetic-guided pharmacotherapy (PGx) is not recommended in clinical guidelines for coronary artery disease (CAD). We aimed to examine the extent and quality of evidence from economic evaluations of PGx in CAD and to identify variables influential in changing conclusions on cost-effectiveness.

METHODS AND RESULTS

From systematic searches across 6 databases, 2 independent reviewers screened, included, and rated the methodological quality of economic evaluations of PGx testing to guide pharmacotherapy for patients with CAD. Of 35 economic evaluations included, most were model-based cost-utility analyses alone, or alongside cost-effectiveness analyses of PGx testing to stratify patients into antiplatelets (25/35), statins (2/35), pain killers (1/35), or angiotensin-converting enzyme inhibitors (1/35) to predict CAD risk (8/35) or to determine the coumadin doses (1/35). To stratify patients into antiplatelets (96/151 comparisons with complete findings of PGx versus non-PGx), PGx was more effective and more costly than non-PGx clopidogrel (28/43) but less costly than non-PGx prasugrel (10/15) and less costly and less effective than non-PGx ticagrelor (22/25). To predict CAD risk (51/151 comparisons), PGx using genetic risk scores was more effective and less costly than clinical risk score (13/17) but more costly than no risk score (16/19) or no treatment (9/9). The remaining comparisons were too few to observe any trend. Mortality risk was the most common variable (47/294) changing conclusions.

CONCLUSIONS

Economic evaluations to date found PGx to stratify patients with CAD into antiplatelets or to predict CAD risk to be cost-effective, but findings varied based on the non-PGx comparators, underscoring the importance of considering local practice in deciding whether to adopt PGx.

Methodology of the health economic evaluation of the Feel4Diabetes-study

BACKGROUND

The clinical and economic burden of type 2 diabetes mellitus on society is rising. Effective and efficient preventive measures may stop the increasing prevalence, given that type 2 diabetes mellitus is mainly a lifestyle-driven disease. The Feel4Diabetes-study aimed to tackle unhealthy lifestyle (unhealthy diet, lack of physical activity, sedentary behaviour, and excess weight) of families with a child in the first grades of elementary school. These schools were located in regions with a relatively low socio-economic status in Belgium, Bulgaria, Finland, Greece, Hungary and Spain. Special attention was paid to families with a high risk of developing type 2 diabetes mellitus.

METHODS

The aim of this paper is to describe the detailed methodology of the intervention's cost-effectiveness analysis. Based on the health economic evaluation of the Toybox-study, both a decision analytic part and a Markov model have been designed to assess the long-term (time horizon of 70 year with one-year cycles) intervention's value for money. Data sources used for the calculation of health state incidences, transition probabilities between health states, health state costs, and health state utilities are listed. Intervention-related costs were collected by questionnaires and diaries, and attributed to either all families or high risk families only.

CONCLUSIONS

The optimal use of limited resources is pivotal. The future results of the health economic evaluation of the Feel4Diabetes-study will contribute to the efficient use of those resources.

Prediction of leprosy in the Chinese population based on a weighted genetic risk score

Genome wide association studies (GWASs) have revealed multiple genetic variants associated with leprosy in the Chinese population. The aim of our study was to utilize the genetic variants to construct a risk prediction model through a weighted genetic risk score (GRS) in a Chinese set and to further assess the performance of the model in identifying higher-risk contact individuals in an independent set. The highest prediction accuracy, with an area under the curve (AUC) of 0.743 (95% confidence interval (CI): 0.729-0.757), was achieved with a GRS encompassing 25 GWAS variants in a discovery set that included 2,144 people affected by leprosy and 2,671 controls. Individuals in the high-risk group, based on genetic factors (GRS > 28.06), have a 24.65 higher odds ratio (OR) for developing leprosy relative to those in the low-risk group (GRS≤18.17). The model was then applied to a validation set consisting of 1,385 people affected by leprosy and 7,541 individuals in contact with leprosy, which yielded a discriminatory ability with an AUC of 0.707 (95% CI: 0.691-0.723). When a GRS cut-off value of 22.38 was selected with the optimal sensitivity and specificity, it was found that 39.31% of high risk contact individuals should be screened in order to detect leprosy in 64.9% of those people affected by leprosy. In summary, we developed and validated a risk model for the prediction of leprosy that showed good discrimination capabilities, which may help physicians in the identification of patients coming into contact with leprosy and are at a higher-risk of developing this condition.

Estimates of costs for modelling return on investment from smoking cessation interventions

BACKGROUND AND AIMS

Modelling return on investment (ROI) from smoking cessation interventions requires estimates of their costs and benefits. This paper describes a standardized method developed to source both economic costs of tobacco smoking and costs of implementing cessation interventions for a Europe-wide ROI model [European study on Quantifying Utility of Investment in Protection from Tobacco model (EQUIPTMOD)].

DESIGN

Focused search of administrative and published data. A standardized checklist was developed in order to ensure consistency in methods of data collection.

SETTING AND PARTICIPANTS

Adult population (15+ years) in Hungary, Netherlands, Germany, Spain and England. For passive smoking-related costs, child population (0-15 years) was also included.

MEASUREMENTS

Costs of treating smoking-attributable diseases; productivity losses due to smoking-attributable absenteeism; and costs of implementing smoking cessation interventions.

FINDINGS

Annual costs (per case) of treating smoking attributable lung cancer were between €5074 (Hungary) and €52 106 (Germany); coronary heart disease between €1521 (Spain) and €3955 (Netherlands); chronic obstructive pulmonary disease between €1280 (England) and €4199 (Spain); stroke between €1829 (Hungary) and €14 880 (Netherlands). Costs (per recipient) of smoking cessation medications were estimated to be: for standard duration of varenicline between €225 (England) and €465 (Hungary); for bupropion between €25 (Hungary) and €220 (Germany). Costs (per recipient) of providing behavioural support were also wide-ranging: one-to-one behavioural support between €34 (Hungary) and €474 (Netherlands); and group-based behavioural support between €12 (Hungary) and €257 (Germany). The costs (per recipient) of delivering brief physician advice were: €24 (England); €9 (Germany); €4 (Hungary); €33 (Netherlands); and €27 (Spain).

CONCLUSIONS

Costs of treating smoking-attributable diseases as well as the costs of implementing smoking cessation interventions vary substantially across Hungary, Netherlands, Germany, Spain and England. Estimates for the costs of these diseases and interventions can contribute to return on investment estimates in support of national or regional policy decisions.

Development and application of an economic model (EQUIPTMOD) to assess the impact of smoking cessation

BACKGROUND AND AIMS

Although clear benefits are associated with reducing smoking, there is increasing pressure on public health providers to justify investment in tobacco control measures. Decision-makers need tools to assess the Return on Investment (ROI)/cost-effectiveness of programmes. The EQUIPT project adapted an ROI tool for England to four European countries (Germany, the Netherlands, Spain and Hungary). EQUIPTMOD, the economic model at the core of the ROI tool, is designed to assess the efficiency of packages of smoking cessation interventions. The objective of this paper is to describe the methods for EQUIPTMOD and identify key outcomes associated with continued and cessation of smoking.

METHODS

EQUIPTMOD uses a Markov model to estimate life-time costs, quality-adjusted life years (QALYs) and life years associated with a current and former smoker. It uses population data on smoking prevalence, disease prevalence, mortality and the impact of smoking combined with associated costs and utility effects of disease. To illustrate the tool's potential, costs, QALYs and life expectancy were estimated for the average current smoker for five countries based on the assumptions that they continue and that they cease smoking over the next 12 months. Costs and effects were discounted at country-specific rates.

RESULTS

For illustration, over a life-time horizon, not quitting smoking within the next 12 months in England will reduce life expectancy by 0.66, reduce QALYs by 1.09 and result in £4961 higher disease-related health care costs than if the smoker ceased smoking in the next 12 months. For all age-sex categories, costs were lower and QALYs higher for those who quit smoking in the 12 months than those who continued.

CONCLUSIONS

EQUIPTMOD facilitates assessment of the cost effectiveness of smoking cessation strategies. The demonstrated results indicate large potential benefits from smoking cessation at both an individual and population level.

Cost-effectiveness of alternative smoking cessation scenarios in Spain: results from the EQUIPTMOD

AIMS

To assess the cost-effectiveness of alternative smoking cessation scenarios from the perspective of the Spanish National Health Service (NHS).

DESIGN

We used the European study on Quantifying Utility of Investment in Protection from Tobacco model (EQUIPTMOD), a Markov-based state transition economic model, to estimate the return on investment (ROI) of: (a) the current provision of smoking cessation services (brief physician advice and printed self-helped material + smoking ban and tobacco duty at current levels); and (b) four alternative scenarios to complement the current provision: coverage of proactive telephone calls; nicotine replacement therapy (mono and combo) [prescription nicotine replacement therapy (Rx NRT)]; varenicline (standard duration); or bupropion. A rate of 3% was used to discount life-time costs and benefits.

SETTING

Spain.

PARTICIPANTS

Adult smoking population (16+ years).

MEASUREMENTS

Health-care costs associated with treatment of smoking attributable diseases (lung cancer, coronary heart disease, chronic obstructive pulmonary infection and stroke); intervention costs; quality-adjusted life years (QALYs). Costs and outcomes were summarized using various ROI estimates.

FINDINGS

The cost of implementing the current provision of smoking cessation services is approximately €61 million in the current year. This translates to 18 quitters per 1000 smokers and a life-time benefit-cost ratio of 5, compared with no such provision. All alternative scenarios were dominant (cost-saving: less expensive to run and generated more QALYs) from the life-time perspective, compared with the current provision. The life-time benefit-cost ratios were: 1.87 (proactive telephone calls); 1.17 (Rx NRT); 2.40 (varenicline-standard duration); and bupropion (2.18). The results remained robust in the sensitivity analysis.

CONCLUSIONS

According to the EQUIPTMOD modelling tool it would be cost-effective for the Spanish authorities to expand the reach of existing GP brief interventions for smoking cessation, provide pro-active telephone support, and reimburse smoking cessation medication to smokers trying to stop. Such policies would more than pay for themselves in the long run.

Cardiovascular Risk Assessment

Cardiovascular risk assessment is fundamental to prevention of cardiovascular disease, because it helps determine the size of the potential benefits that might accrue to individual patients from use of statins, aspirin, and other preventive interventions. Current guidelines recommend specific algorithms for cardiovascular risk assessment that combine information from traditional risk factors including blood pressure, lipids, and smoking, along with age and sex and other factors. These algorithms are the subject of active research and controversy. This article addresses the rationale, current guidelines and use, and potential future directions of cardiovascular risk assessment.

Clinical Utility of Multimarker Genetic Risk Scores for Prediction of Incident Coronary Heart Disease: A Cohort Study Among Over 51 000 Individuals of European Ancestry

BACKGROUND

We evaluated whether including multilocus genetic risk scores (GRSs) into the Framingham Risk Equation improves the predictive capacity, discrimination, and reclassification of asymptomatic individuals with respect to coronary heart disease (CHD) risk.

METHODS AND RESULTS

We performed a cohort study among 51 954 European-ancestry members of a Northern California integrated healthcare system (67% female; mean age 59) free of CHD at baseline (2007-2008). Four GRSs were constructed using between 8 and 51 previously identified genetic variants. After a mean (±SD) follow-up of 5.9 (±1.5) years, 1864 incident CHD events were documented. All GRSs were linearly associated with CHD in a model adjusted by individual risk factors: hazard ratio (95% confidence interval) per SD unit: 1.21 (1.15-1.26) for GRS_8, 1.20 (1.15-1.26) for GRS_12, 1.23 (1.17-1.28) for GRS_36, and 1.23 (1.17-1.28) for GRS_51. Inclusion of the GRSs improved the C statistic (ΔC statistic =0.008 for GRS_8 and GRS_36; 0.007 for GRS_12; and 0.009 for GRS_51; all P<0.001). The net reclassification improvement was 5% for GRS_8, GRS_12, and GRS_36 and 4% for GRS_51 in the entire cohort and was (after correcting for bias) 9% for GRS_8 and GRS_12 and 7% for GRS_36 and GRS_51 when analyzing those classified as intermediate Framingham risk (10%-20%). The number required to treat to prevent 1 CHD after selectively treating with statins up-reclassified subjects on the basis of genetic information was 36 for GRS_8 and GRS_12, 41 for GRS_36, and 43 for GRS_51.

CONCLUSIONS

Our results demonstrate significant and clinically relevant incremental discriminative/predictive capability of 4 multilocus GRSs for incident CHD among subjects of European ancestry.