Methodological aspects in valuing loss of production and informal care in cost-of-illness studies

Estimating costs of illness in social terms requires the consideration of the loss of production costs due to absence from work. Costs related to informal care should also be counted. Costs due to the loss of production are valued through the human capital method and the frictional costs method. Opportunity cost and proxy good methods are utilized for the informal care. Our study highlights practical issues about the use of a variety of methods and points out their main limits

Bayesian approach to the cost-effectiveness evaluation of healthcare technologies

The development of Bayesian statistical methods for the assessment of the cost-effectiveness of health care technologies is reviewed. Although many studies adopt a frequentist approach, several authors have advocated the use of Bayesian methods in health economics. Emphasis has been placed on the advantages of the Bayesian approach, which include: (i) the ability to make more intuitive and meaningful inferences; (ii) the ability to tackle complex problems, such as allowing for the inclusion of patients who generate no cost, thanks to the availability of powerful computational algorithms; (iii) the importance of a full use of quantitative and structural prior information to produce realistic inferences. Much literature comparing the cost-effectiveness of two treatments is based on the incremental cost-effectiveness ratio. However, new methods are arising with the purpose of decision making. These methods are based on a net benefits approach. In the present context, the cost-effectiveness acceptability curves have been pointed out to be intrinsically Bayesian in their formulation. They plot the probability of a positive net benefit against the threshold cost of a unit increase in efficacy.A case study is presented in order to illustrate the Bayesian statistics in the cost-effectiveness analysis. Emphasis is placed on the cost-effectiveness acceptability curves. Advantages and disadvantages of the method described in this paper have been compared to frequentist methods and discussed

Factors affecting hospitalization costs in Type 2 diabetic patients

OBJECTIVE: To evaluate Type 2 diabetes hospitalization costs and their determinants by applying a proper methodological approach, taking into account the presence of several observations with zero costs. METHODS: A cohort study using per-patient hospital discharge abstracts in a period of 4.5 years of follow-up (from 1/1/1996 to 30/6/2000). Potential cost predictors were age, sex, body max index, hypertension, diabetes duration, hemoglobin A1c levels, insulin treatment, retinopathy, coronary artery disease, peripheral artery disease, nephropathy, death and presence of comorbidity (cancer, chronic liver disease, chronic obstructive pulmonary disease, and psychiatric disease). Among risk factors, total cholesterol, HDL cholesterol and smoking were considered. A two-part model has been adopted in order to take into account the presence of patients with zero hospital costs: the probability of any hospitalization has been modeled via a standard logit generalized linear model (GLM); the actual level of total costs has been modeled via a GLM, with a gamma cost distribution and a LOG link function. RESULTS: In 4.5 years the median total cost per hospitalized person was $4404 (mean$8180). In line with existing evidence, diabetes complications showed a high impact on average costs. In particular, peripheral and coronary artery diseases determined more than $1000 increase in the median costs. Chronic comorbidity were responsible for the highest incremental hospitalization costs ($1771). CONCLUSIONS: Hospitalization costs were significantly increased by the presence of diabetes complications and chronic conditions. The adoption of a two-part model has allowed to obtain estimates not neglecting the effect of covariates on the probability of having no hospital care

Normal Glucose Values Are Associated With a Lower Risk of Mortality in Hospitalized Patients

OBJECTIVE: Hyperglycemia is a common condition in hospitalized patients. The aim of this study was to investigate the relationships between glycemia upon admission and mortality in a heterogeneous group of adult patients. RESEARCH DESIGN AND METHODS: The 3-year records released from a general hospital were associated with a plasma glucose dataset of its general laboratory. A matched case-control study was implemented (3,338 case-control subject pairs). All-patient refined diagnosis-related groups and the relative risk of death were the matching criteria. A multivariate conditional logistic regression model was used to evaluate the associations between death and glycemia. RESULTS: Higher in-hospital mortality was associated with hyperglycemia or hypoglycemia, whereas lower risk was observed for values between 78 and 101 mg/dl. CONCLUSIONS: Our data confirm the relation between glycemia upon admission and mortality and suggest that slightly increased or decreased plasma glucose can be linked with increased mortality risk

Survival models for cost data: The forgotten additive approach

The usage of the Aalen additive approach is proposed to model cost data. Using a Monte Carlo simulation, in a wide set of scenarios, we showed that the Aalen model is performing well and can be a reasonable alternative to the standard Gamma regression models. In addition, with reference to the COSTAMI trial data, we highlighted the ability of the Aalen model to offer additional information about the relationships between costs and specific covariates, as compared with standard regression techniques