Proper modeling strategies selection for the assessment of post-infarction costs

Patient-level costs of major cardiovascular conditions: a review of the international literature

OBJECTIVE

Robust cost estimates of cardiovascular (CV) events are required for assessing health care interventions aimed at reducing the economic burden of major adverse CV events. This review synthesizes international cost estimates of CV events.

METHODS

MEDLINE database was searched electronically for English language studies published during 2007-2012, with cost estimates for CV events of interest - unstable angina, myocardial infarction, heart failure, stroke, and CV revascularization. Included studies provided at least one estimate of patient-level direct costs in adults for any identified country. Information on study characteristics and cost estimates were collected. All costs were adjusted for inflation to 2013 values.

RESULTS

Across the 114 studies included, the average cost was US $6,466 for unstable angina, $11,664 for acute myocardial infarction, $11,686 for acute heart failure, $11,635 for acute ischemic stroke, $37,611 for coronary artery bypass graft, and $13,501 for percutaneous coronary intervention. The ranges for cost estimates varied widely across countries with US cost estimate being at least twice as high as European Union costs for some conditions. Few studies were found on populations outside the US and European Union.

CONCLUSION

This review showed wide variation in the cost of CV events within and across countries, while showcasing the continuing economic burden of CV disease. The variability in costs was primarily attributable to differences in study population, costing methodologies, and reporting differences. Reliable cost estimates for assessing economic value of interventions in CV disease are needed.

Is the choice of the statistical model relevant in the cost estimation of patients with chronic diseases? An empirical approach by the Piedmont Diabetes Registry

BACKGROUND

Chronic diseases impose large economic burdens. Cost analysis is not straightforward, particularly when the goal is to relate costs to specific patterns of covariates, and to compare costs between diseased and healthy populations. Using different statistical methods this study describes the impact on results and conclusions of analyzing health care costs in a population with diabetes.

METHODS

Direct health care costs of people living in Turin were estimated from administrative databases of the Regional Health System. Patients with diabetes were identified through the Piedmont Diabetes Registry. The effect of diabetes on mean annual expenditure was analyzed using the following multivariable models: 1) an ordinary least squares regression (OLS); 2) a lognormal linear regression model; 3) a generalized linear model (GLM) with gamma distribution. Presence of zero cost observation was handled by means of a two part model.

RESULTS

The OLS provides the effect of covariates in terms of absolute additive costs due to the presence of diabetes (€ 1,832). Lognormal and GLM provide relative estimates of the effect: the cost for diabetes would be six fold that for non diabetes patients calculated with the lognormal. The same data give a 2.6-fold increase if calculated with the GLM. Different methods provide quite different estimated costs for patients with and without diabetes, and different costs ratios between them, ranging from 3.2 to 5.6.

CONCLUSIONS

Costs estimates of a chronic disease vary considerably depending on the statistical method employed; therefore a careful choice of methods to analyze data is required before inferring results.

Regression models for analyzing costs and their determinants in health care: an introductory review

OBJECTIVE

This article aims to describe the various approaches in multivariable modelling of healthcare costs data and to synthesize the respective criticisms as proposed in the literature.

METHODS

We present regression methods suitable for the analysis of healthcare costs and then apply them to an experimental setting in cardiovascular treatment (COSTAMI study) and an observational setting in diabetes hospital care.

RESULTS

We show how methods can produce different results depending on the degree of matching between the underlying assumptions of each method and the specific characteristics of the healthcare problem.

CONCLUSIONS

The matching of healthcare cost models to the analytic objectives and characteristics of the data available to a study requires caution. The study results and interpretation can be heavily dependent on the choice of model with a real risk of spurious results and conclusions.