Determining the best population-level alcohol consumption model and its impact on estimates of alcohol-attributable harms.

BACKGROUND: The goals of our study are to determine the most appropriate model for alcohol consumption as an exposure for burden of disease, to analyze the effect of the chosen alcohol consumption distribution on the estimation of the alcohol Population- Attributable Fractions (PAFs), and to characterize the chosen alcohol consumption distribution by exploring if there is a global relationship within the distribution. METHODS: To identify the best model, the Log-Normal, Gamma, and Weibull prevalence distributions were examined using data from 41 surveys from Gender, Alcohol and Culture: An International Study (GENACIS) and from the European Comparative Alcohol Study. To assess the effect of these distributions on the estimated alcohol PAFs, we calculated the alcohol PAF for diabetes, breast cancer, and pancreatitis using the three above-named distributions and using the more traditional approach based on categories. The relationship between the mean and the standard deviation from the Gamma distribution was estimated using data from 851 datasets for 66 countries from GENACIS and from the STEPwise approach to Surveillance from the World Health Organization. RESULTS: The Log-Normal distribution provided a poor fit for the survey data, with Gamma and Weibull distributions providing better fits. Additionally, our analyses showed that there were no marked differences for the alcohol PAF estimates based on the Gamma or Weibull distributions compared to PAFs based on categorical alcohol consumption estimates. The standard deviation of the alcohol distribution was highly dependent on the mean, with a unit increase in alcohol consumption associated with a unit increase in the mean of 1.258 (95% CI: 1.223 to 1.293) (R2 = 0.9207) for women and 1.171 (95% CI: 1.144 to 1.197) (R2 = 0. 9474) for men. CONCLUSIONS: Although the Gamma distribution and the Weibull distribution provided similar results, the Gamma distribution is recommended to model alcohol consumption from population surveys due to its fit, flexibility, and the ease with which it can be modified. The results showed that a large degree of variance of the standard deviation of the alcohol consumption Gamma distribution was explained by the mean alcohol consumption, allowing for alcohol consumption to be modeled through a Gamma distribution using only average consumption.

The truncated mean of an asymmetric distribution

This paper investigates a simple procedure to estimate robustly the mean of an asymmetric distribution. The procedure removes the observations which are larger or smaller than certain limits and takes the arithmetic mean of the remaining observations, the limits being determined with the help of a parametric model, e.g., the Gamma, the Weibull or the Lognormal distribution. The breakdown point, the influence function, the (asymptotic) variance, and the contamination bias of this estimator are explored and compared numerically with those of competing estimates.

Economics of dialysis dependence following renal replacement therapy for critically ill acute kidney injury patients.

BACKGROUND: The obective of this study was to perform a cost-effectiveness analysis comparing intermittent with continuous renal replacement therapy (IRRT versus CRRT) as initial therapy for acute kidney injury (AKI) in the intensive care unit (ICU). METHODS: Assuming some patients would potentially be eligible for either modality, we modeled life year gained, the quality-adjusted life years (QALYs) and healthcare costs for a cohort of 1000 IRRT patients and a cohort of 1000 CRRT patients. We used a 1-year, 5-year and a lifetime horizon. A Markov model with two health states for AKI survivors was designed: dialysis dependence and dialysis independence. We applied Weibull regression from published estimates to fit survival curves for CRRT and IRRT patients and to fit the proportion of dialysis dependence among CRRT and IRRT survivors. We then applied a risk ratio reported in a large retrospective cohort study to the fitted CRRT estimates in order to determine the proportion of dialysis dependence for IRRT survivors. We conducted sensitivity analyses based on a range of differences for daily implementation cost between CRRT and IRRT (base case: CRRT day $632 more expensive than IRRT day; range from $200 to $1000) and a range of risk ratios for dialysis dependence for CRRT as compared with IRRT (from 0.65 to 0.95; base case: 0.80). RESULTS: Continuous renal replacement therapy was associated with a marginally greater gain in QALY as compared with IRRT (1.093 versus 1.078). Despite higher upfront costs for CRRT in the ICU ($4046 for CRRT versus $1423 for IRRT in average), the 5-year total cost including the cost of dialysis dependence was lower for CRRT ($37 780 for CRRT versus $39 448 for IRRT on average). The base case incremental cost-effectiveness analysis showed that CRRT dominated IRRT. This dominance was confirmed by extensive sensitivity analysis. CONCLUSIONS: Initial CRRT is cost-effective compared with initial IRRT by reducing the rate of long-term dialysis dependence among critically ill AKI survivors.

Evaluation des différentes formules de la loi de Gumbel pour l'estimation des précipitations journalières extrêmes en Suisse

Les précipitations journalières extrêmes centennales ont été estimées à partir d'analyses de Gumbel et de sept formule empiriques effectuées sur des séries de mesures pluviométriques à 151 endroits de la Suisse pour deux périodes de 50 ans. Ces estimations ont été comparées avec les valeurs journalières maximales mesurées durant les 100 dernières années (1911-2010) afin de tester l'efficacité de ces sept formules. Cette comparaison révèle que la formule de Weibull serait la meilleure pour estimer les précipitations journalières centennales à partir de la série de mesures pluviométriques 1961-2010, mais la moins bonne pour la série de mesures 1911-1960. La formule de Hazen serait la plus efficace pour cette dernière période. Ces différences de performances entre les formules empiriques pour les deux périodes étudiées résultent de l'augmentation des précipitations journalières maximales mesurées de 1911 à 2010 pour 90% des stations en Suisse. Mais les différences entre les pluies extrêmes estimées à partir des sept formules empiriques ne dépassent pas 6% en moyenne.

Population specific and up to date cardiovascular risk charts can be efficiently obtained with record linkage of routine and observational data.

BACKGROUND: Only few countries have cohorts enabling specific and up-to-date cardiovascular disease (CVD) risk estimation. Individual risk assessment based on study samples that differ too much from the target population could jeopardize the benefit of risk charts in general practice. Our aim was to provide up-to-date and valid CVD risk estimation for a Swiss population using a novel record linkage approach. METHODS: Anonymous record linkage was used to follow-up (for mortality, until 2008) 9,853 men and women aged 25-74 years who participated in the Swiss MONICA (MONItoring of trends and determinants in CVD) study of 1983-92. The linkage success was 97.8%, loss to follow-up 1990-2000 was 4.7%. Based on the ESC SCORE methodology (Weibull regression), we used age, sex, blood pressure, smoking, and cholesterol to generate three models. We compared the 1) original SCORE model with a 2) recalibrated and a 3) new model using the Brier score (BS) and cross-validation. RESULTS: Based on the cross-validated BS, the new model (BS = 14107×10(-6)) was somewhat more appropriate for risk estimation than the original (BS = 14190×10(-6)) and the recalibrated (BS = 14172×10(-6)) model. Particularly at younger age, derived absolute risks were consistently lower than those from the original and the recalibrated model which was mainly due to a smaller impact of total cholesterol. CONCLUSION: Using record linkage of observational and routine data is an efficient procedure to obtain valid and up-to-date CVD risk estimates for a specific population.

Does evolution lead to maximizing behavior?

A long-standing question in biology and economics is whether individual organisms evolve to behave as if they were striving to maximize some goal function. We here formalize this "as if" question in a patch-structured population in which individuals obtain material payoffs from (perhaps very complex multimove) social interactions. These material payoffs determine personal fitness and, ultimately, invasion fitness. We ask whether individuals in uninvadable population states will appear to be maximizing conventional goal functions (with population-structure coefficients exogenous to the individual's behavior), when what is really being maximized is invasion fitness at the genetic level. We reach two broad conclusions. First, no simple and general individual-centered goal function emerges from the analysis. This stems from the fact that invasion fitness is a gene-centered multigenerational measure of evolutionary success. Second, when selection is weak, all multigenerational effects of selection can be summarized in a neutral type-distribution quantifying identity-by-descent between individuals within patches. Individuals then behave as if they were striving to maximize a weighted sum of material payoffs (own and others). At an uninvadable state it is as if individuals would freely choose their actions and play a Nash equilibrium of a game with a goal function that combines self-interest (own material payoff), group interest (group material payoff if everyone does the same), and local rivalry (material payoff differences).