The cost of obesity and overweight in 2005: a case study of Alberta, Canada.

OBJECTIVE: The aim of this paper is to highlight the potential impact of costs associated with overweight and obesity for provincial policy and prevention initiatives. METHOD: Prevalence-based cost-of-illness methodology was used to estimate the direct costs (hospital care, drugs, physician care, institutional care, additional costs) and indirect costs (short- and long-term disability, premature mortality) associated with excess weight for 22 health conditions. Total costs for each health condition were estimated using the Public Health Agency of Canada's Economic Burden of Illness database. Population attributable fractions (PAF) were also estimated using 2004 and 2005 CCHS data and current literature reviews. RESULTS: In 2005, the cost of excess weight in Alberta totaled $1.27 billion. The direct cost of excess weight was $630.1M (49.5%), the indirect cost $643.8M (50.5%). Excluding costs associated with premature mortality and caregiving, obesity accounted for 69.5% ($500.8M) of costs and overweight the remaining 30.5% ($220.2M). Among the 22 health conditions, coronary heart disease had the highest costs attributable to excess weight ($307.1 M), followed by osteoarthritis ($167.7M) and type 2 diabetes ($161.5M). The total cost of excess weight equated to 5.6% of the province's annual health care expenditures for 2005. CONCLUSION: While obesity costing research often focuses on the direct health care costs, this study reveals that the indirect costs of excess weight are also significant and can account for over half of the total costs. Interventions to reduce excess weight among Canadians have the potential to improve the health of the population while reducing provincial and national health care costs.

Does the timing of comorbidity affect colorectal cancer survival? A population based study.

OBJECTIVES: Comorbid conditions in colorectal cancer patients can influence both clinical eligibility for treatment and survival. We aimed to evaluate the effect of comorbidity on 1 year survival from colorectal cancer, and to assess whether this effect varied with the timing of the comorbidity in relation to the cancer diagnosis. STUDY DESIGN AND SETTING: A population based cohort of 29,563 colorectal cancer patients diagnosed between 1997 and 2004 in the North West of England was evaluated. The excess hazard of death up to 1 year after diagnosis was estimated using deprivation and region specific life tables to adjust for background mortality. Results were adjusted for age and stage at diagnosis. RESULTS: Comorbid conditions diagnosed during the period 18 to 6 months before the diagnosis of colorectal cancer were strongly associated with lower survival at 1 year. Stage and age remained the strongest predictors of cancer related mortality even after adjustment for comorbidity. CONCLUSIONS: Administrative data provide a good estimate of the prevalence of most comorbid conditions but may be biased for some comorbid conditions that can be contra-indicators for cancer treatment. The time window in which a comorbid condition occurs in relation to the cancer diagnosis should be taken into account. Adjustment should be carried out, where possible, to provide more robust and clinically appropriate comparisons of population based cancer patient survival.

Modelling relative survival in the presence of incomplete data: a tutorial.

BACKGROUND: Missing data frequently create problems in the analysis of population-based data sets, such as those collected by cancer registries. Restriction of analysis to records with complete data may yield inferences that are substantially different from those that would have been obtained had no data been missing. 'Naive' methods for handling missing data, such as restriction of the analysis to complete records or creation of a 'missing' category, have drawbacks that can invalidate the conclusions from the analysis. We offer a tutorial on modern methods for handling missing data in relative survival analysis. METHODS: We estimated relative survival for 29 563 colorectal cancer patients who were diagnosed between 1997 and 2004 and registered in the North West Cancer Intelligence Service. The method of multiple imputation (MI) was applied to account for the common example of incomplete stage at diagnosis, under the missing at random (MAR) assumption. Multivariable regression with a generalized linear model and Poisson error structure was then used to estimate the excess hazard of death of the colorectal cancer patients, over and above the background mortality, adjusting for significant predictors of mortality. RESULTS: Incomplete information on stage, morphology and grade meant that only 55% of the data could be included in the 'complete-case' analysis. All cases could be included after indicator method (IM) or MI method. Handling missing data by MI produced a significantly lower estimate of the excess mortality for stage, morphology and grade, with the largest reductions occurring for late-stage and high-grade tumours, when compared with the results of complete-case analysis. CONCLUSION: In complete-case analysis, almost 50% of the information could not be included, and with the IM, all records with missing values for stage were combined into a single 'missing' category. We show that MI methods greatly improved the results by exploiting all the information in the incomplete records. This method also helped to ensure efficient inferences about survival were made from the multivariate regression analyses.