Costs of heart disease and risk behaviour: implications for expenditure on prevention

Particle Swarm Optimisation-Support Vector Machine Optimised by Association Rules for Detecting Factors Inducing Heart Diseases

Existing systems for diagnosing heart diseases are time consuming, expensive, and error prone. Aiming at this, a detection algorithm for factors inducing heart diseases based on a particle swarm optimisation-support vector machine (PSO-SVM) optimised by association rules (ARs) was proposed. Firstly, AR was used to select features from a disease data set so as to train feature sets. Then, PSO-SVM was used to classify training and testing sets, and then the factors inducing heart diseases were analysed. Finally, the effectiveness and reliability of the proposed algorithm was verified by experiments on the UCI Cleveland data set with confidence as the index. The experimental results showed that females have less risk of having a heart attack than males. Irrespective of gender, once diagnosed with chest pain without symptoms and angina caused by exercise, people are more likely to suffer from heart disease. Moreover, compared with another two advanced classification algorithms, the proposed algorithm showed better classification performance and therefore can be used as a powerful tool to help doctors diagnose and treat heart diseases.

Costs of traffic injuries

OBJECTIVE

The aim of this study was to analyse the socioeconomic costs of traffic injuries in Denmark, notably the healthcare costs and the productivity costs related to traffic injuries, in a bottom-up, register-based perspective.

METHOD

Traffic injury victims were identified using national emergency room data and police records. Victims were matched with five controls per case by means of propensity score, nearest-neighbour matching. In the cohort, consisting of the 52 526 individuals that experienced a traffic injury in 2000 and 262 630 matched controls, attributable healthcare costs were assessed using Danish national healthcare registers. Productivity costs were computed using duration analysis (Cox regression models). In a subanalysis, cost per severe traffic injury was computed for the 12 995 individuals that experienced a severe injury.

RESULTS

The socioeconomic cost of a traffic injury was €1406 (2009 price level) in the first year, and €8950 over a 10-year period. Per 100 000 population, the 10-year cost was €6 565 668. A severe traffic injury costs €4969 per person in the first year, and €4 006 685 per 100 000 population over a 10-year period. Victims of traffic injuries are younger and generally worse off, compared to the general population.

CONCLUSIONS

Prevention of traffic injuries could result in societal savings. The bottom-up, register-based approach renders more precise figures for these savings. The socioeconomic profile of injury victims differs from that of the general population on most parameters.

A method to assess the potential effects of air pollution mitigation on healthcare costs

Objective. The aim of this study was to develop a method to assess the potential effects of air pollution mitigation on healthcare costs and to apply this method to assess the potential savings related to a reduction in fine particle matter in Denmark. Methods. The effects of air pollution on health were used to identify “exposed” individuals (i.e., cases). Coronary heart disease, stroke, chronic obstructive pulmonary disease, and lung cancer were considered to be associated with air pollution. We used propensity score matching, two-part estimation, and Lin's method to estimate healthcare costs. Subsequently, we multiplied the number of saved cases due to mitigation with the healthcare costs to arrive to an expression for healthcare cost savings. Results. The potential cost saving in the healthcare system arising from a modelled reduction in air pollution was estimated at €0.1–2.6 million per 100,000 inhabitants for the four diseases. Conclusion. We have illustrated an application of a method to assess the potential changes in healthcare costs due to a reduction in air pollution. The method relies on a large volume of administrative data and combines a number of established methods for epidemiological analysis.

Register-based studies of healthcare costs

INTRODUCTION

The aim of this paper is to provide an overview and a few examples of how national registers are used in analyses of healthcare costs in Denmark.

RESEARCH TOPICS

The paper focuses on health economic analyses based on register data. For the sake of simplicity, the studies are divided into three main categories: economic evaluations of healthcare interventions, cost-of-illness analyses, and other analyses such as assessments of healthcare productivity.

CONCLUSION

We examined a number of studies using register-based data on healthcare costs. Use of register-based data renders a comprehensive data material, often in the form of time series, which is very useful in health economic analyses. The disadvantage of register-based data is the use of tariffs, charges, or market prices as proxies for costs in the computation of healthcare costs.

Cost estimation of cardiovascular disease events in the US

BACKGROUND

In this study, we developed cost prediction equations that facilitate estimation of the costs of various cardiovascular events for patients of specific demographic and clinical characteristics over varying time horizons.

METHODS

We used administrative claims data and generalized linear models to develop cost prediction equations for selected cardiovascular events, including myocardial infarction (MI), angina, strokes and revascularization procedures. Separate equations were estimated for patients with events and for their propensity score-matched controls. Attributable costs were estimated on a monthly basis for the first 36 months after each event and annually thereafter, with differences in survival between cases and controls factored into the longitudinal cost calculations. The regression models were used to estimate event costs ($US, year 2007 values) for the 'average' patient in each event group, over various time periods ranging from 1 month to lifetime.

RESULTS

When the equations are run for the average patient in each event group, attributable costs of each event in the acute phase (i.e. first 3 years) are substantial (e.g. MI $US 73 300; hospitalization for angina $US 36 000; non-fatal haemorrhagic stroke $US 71 600). Furthermore, for most events, cumulative costs remain substantially higher among cases than among controls over the remaining lifetime of the patients.

CONCLUSIONS

This study provides updated estimates of medical care costs of cardiovascular events among a managed care population over various time horizons. Results suggest that the economic burden of cardiovascular disease is substantial, both in the acute phase as well as over the longer term.

The Danish National Cohort Study (DANCOS)

Introduction: The Danish National Cohort Study (DANCOS) is a nationally representative public health survey based on linkage of information in the repeated Danish Health Interview surveys, 1986—2005, to the national Danish registers on health and welfare. It facilitates studies of self-reported health behaviour and utilisation of healthcare services by subgroups and analysis of non-response bias. Research topics: DANCOS data are utilised in a variety of analyses presented here by a few examples that emphasise the impact of modifiable risk factors on public health, description of non-response bias, and the epidemiology of chronic pain and of osteoarthritis. Examples of DANCOS-based results are shown for each of the four topics. Smoking results in 24% of all deaths and, compared to other risk factors for public health, smoking accounts for the highest number of years of life lost. For non-response the mortality is higher among non-respondents than among respondents, but no significant bias on healthcare estimates can be seen. On average individuals with chronic pain had 12.8 contacts per year to the primary healthcare sector compared with 7.3 for individuals without. For osteoarthritis it is estimated that in 2020 there will be 22,600 incident cases. Conclusion: DANCOS is a public health survey linked with registers with many research possibilities. With this article we hope to stimulate further interest in the survey.