http://www.epi-perspectives.com The latest research articles published by Epidemiologic Perspectives & Innovations 2010-08-31T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ ObjectiveThe purpose of this paper was to compare two mathematical procedures to estimate the annual attributable number of deaths (the Allison et al procedure and the Mokdad et al procedure), and derive a new procedure that combines the best aspects of both procedures. The new procedure calculates attributable number of deaths along a continuum (i.e. for each unit of exposure), and allows for one or more neutral (neither exposed nor nonexposed) exposure categories. Methods: Mathematical derivations and real datasets were used to demonstrate the theoretical relationship and practical differences between the two procedures. Results of the comparison were used to develop a new procedure that combines the best features of both.FindingsThe Allison procedure is complex because it directly estimates the number of attributable deaths. This necessitates calculation of probabilities of death. The Mokdad procedure is simpler because it estimates the number of attributable deaths indirectly through population attributable fractions. The probabilities of death cancel out in the numerator and denominator of the fractions. However, the Mokdad procedure is not applicable when a neutral exposure category exists. Conclusion: By combining the innovation of the Allison procedure (allowing for a neutral category) and the simplicity of the Mokdad procedure (using population attributable fractions), this paper proposes a new procedure to calculate attributable numbers of death. http://www.epi-perspectives.com/content/7/1/8 Bernard Choi Epidemiologic Perspectives & Innovations 2010, 7:8 2010-08-31T00:00:00Z doi:10.1186/1742-5573-7-8 Epidemiologic Perspectives & Innovations 1742-5573 7 8 2010-08-31T00:00:00Z PDF The authors respond to the comments of Poole (2010) http://www.epi-perspectives.com/content/7/1/7 Nicolle Gatto Ulka Campbell Sharon Schwartz Epidemiologic Perspectives & Innovations 2010, 7:7 2010-08-26T00:00:00Z doi:10.1186/1742-5573-7-7 Epidemiologic Perspectives & Innovations 1742-5573 7 7 2010-08-26T00:00:00Z PDF A person can experience an effect on the occurrence of an outcome in a defined follow-up period without experiencing an effect on the risk of that outcome over the same period. Sufficient causes are sometimes used to deepen potential-outcome explanations of this phenomenon. In doing so, care should be taken to avoid tipping the balance between simplification and realism too far toward simplification. Death and other competing risks should not be assumed away. The time scale should be explicit, with specific times for the occurrence of specified component causes and for the completion of each sufficient cause. Component causes that affect risk should occur no later than the start of the risk period. Sufficient causes should be allowed to have component causes in common. When individuals experience all components of two or more sufficient causes, the outcome must be recurrent. In addition to effects on rates and risks, effects on incidence time itself should be considered. http://www.epi-perspectives.com/content/7/1/6 Charles Poole Epidemiologic Perspectives & Innovations 2010, 7:6 2010-08-24T00:00:00Z doi:10.1186/1742-5573-7-6 Epidemiologic Perspectives & Innovations 1742-5573 7 6 2010-08-24T00:00:00Z PDF Sufficient causes of disease are redundant when an individual acquires the components of two or more sufficient causes. In this circumstance, the individual still would have become diseased even if one of the sufficient causes had not been acquired. In the context of a study, when any individuals acquire components of more than one sufficient cause over the observation period, the etiologic effect of the exposure (defined as the absolute or relative difference between the proportion of the exposed who develop the disease by the end of the study period and the proportion of those individuals who would have developed the disease at the moment they did even in the absence of the exposure) may be underestimated. Even in the absence of confounding and bias, the observed effect estimate represents only a subset of the etiologic effect. This underestimation occurs regardless of the measure of effect used.To some extent, redundancy of sufficient causes is always present, and under some circumstances, it may make a true cause of disease appear to be not causal. This problem is particularly relevant when the researcher's goal is to characterize the universe of sufficient causes of the disease, identify risk factors for targeted interventions, or construct causal diagrams. In this paper, we use the sufficient component cause model and the disease response type framework to show how redundant causation arises and the factors that determine the extent of its impact on epidemiologic effect measures. http://www.epi-perspectives.com/content/7/1/5 Nicolle Gatto Ulka Campbell Epidemiologic Perspectives & Innovations 2010, 7:5 2010-08-02T00:00:00Z doi:10.1186/1742-5573-7-5 Epidemiologic Perspectives & Innovations 1742-5573 7 5 2010-08-02T00:00:00Z XML This paper describes how to fit an additive Poisson model using standard software. It is illustrated with SAS code, but can be similarly used for other software packages. http://www.epi-perspectives.com/content/7/1/4 Hendriek Boshuizen Edith Feskens Epidemiologic Perspectives & Innovations 2010, 7:4 2010-07-20T00:00:00Z doi:10.1186/1742-5573-7-4 Epidemiologic Perspectives & Innovations 1742-5573 7 4 2010-07-20T00:00:00Z XML Background: Schistosomiasis infection, contracted through contact with contaminated water, is a global public health concern. In this paper we analyze data from a retrospective study reporting water contact and schistosomiasis infection status among 1011 individuals in rural China. We present semi-parametric methods for identifying risk factors through a comparison of three analysis approaches: a prediction-focused machine learning algorithm, a simple main-effects multivariable regression, and a semi-parametric variable importance (VI) estimate inspired by a causal population intervention parameter. Results: The multivariable regression found only tool washing to be associated with the outcome, with a relative risk of 1.03 and a 95% confidence interval (CI) of 1.01-1.05. Three types of water contact were found to be associated with the outcome in the semi-parametric VI analysis: July water contact (VI estimate 0.16, 95% CI 0.11-0.22), water contact from tool washing (VI estimate 0.88, 95% CI 0.80-0.97), and water contact from rice planting (VI estimate 0.71, 95% CI 0.53-0.96). The July VI result, in particular, indicated a strong association with infection status - its causal interpretation implies that eliminating water contact in July would reduce the prevalence of schistosomiasis in our study population by 84%, or from 0.3 to 0.05 (95% CI 78%-89%). Conclusions: The July VI estimate suggests possible within-season variability in schistosomiasis infection risk, an association not detected by the regression analysis. Though there are many limitations to this study that temper the potential for causal interpretations, if a high-risk time period could be detected in something close to real time, new prevention options would be opened. Most importantly, we emphasize that traditional regression approaches are usually based on arbitrary pre-specified models, making their parameters difficult to interpret in the context of real-world applications. Our results support the practical application of analysis approaches that, in contrast, do not require arbitrary model pre-specification, estimate parameters that have simple public health interpretations, and apply inference that considers model selection as a source of variation. http://www.epi-perspectives.com/content/7/1/3 Sylvia Sudat Elizabeth Carlton Edmund Seto Robert Spear Alan Hubbard Epidemiologic Perspectives & Innovations 2010, 7:3 2010-07-14T00:00:00Z doi:10.1186/1742-5573-7-3 Epidemiologic Perspectives & Innovations 1742-5573 7 3 2010-07-14T00:00:00Z XML Identifying diet-disease relationships in nutritional cohort studies is plagued by the measurement error in self-reported intakes.The authors propose using biomarkers known to be correlated with dietary intake, so as to strengthen analyses of diet-disease hypotheses. The authors consider combining self-reported intakes and biomarker levels using principal components, Howe's method, or a joint statistical test of effects in a bivariate model. They compared the statistical power of these methods with that of conventional univariate analyses of self-reported intake or of biomarker level. They used computer simulation of different disease risk models, with input parameters based on data from the literature on the relationship between lutein intake and age-related macular degeneration.The results showed that if the dietary effect on disease was fully mediated through the biomarker level, then the univariate analysis of the biomarker was the most powerful approach. However, combination methods, particularly principal components and Howe's method, were not greatly inferior in this situation, and were as good as, or better than, univariate biomarker analysis if mediation was only partial or non-existent. In some circumstances sample size requirements were reduced to 20-50% of those required for conventional analyses of self-reported intake.The authors conclude that (i) including biomarker data in addition to the usual dietary data in a cohort could greatly strengthen the investigation of diet-disease relationships, and (ii) when the extent of mediation through the biomarker is unknown, use of principal components or Howe's method appears a good strategy. http://www.epi-perspectives.com/content/7/1/2 Laurence Freedman Victor Kipnis Arthur Schatzkin Natasa Tasevska Nancy Potischman Epidemiologic Perspectives & Innovations 2010, 7:2 2010-01-20T00:00:00Z doi:10.1186/1742-5573-7-2 Epidemiologic Perspectives & Innovations 1742-5573 7 2 2010-01-20T00:00:00Z XML Background: In epidemiologic studies, cancer stage is an important predictor of outcomes. However, cancer stage is typically unavailable in medical insurance claims datasets, thus limiting the usefulness of such data for epidemiologic studies. Therefore, we sought to develop an algorithm to predict cancer stage based on covariates available from claims-based data. Methods: We identified a cohort of 77,306 women age ≥ 66 years with stage I-IV breast cancer, using the Surveillence Epidemiology and End Results (SEER)-Medicare database. We formulated an algorithm to predict cancer stage using covariates (demographic, tumor, and treatment characteristics) obtained from claims. Logistic regression models derived prediction equations in a training set, and equations' test characteristics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value [NPV]) were calculated in a validation set. Results: Of the entire sample of women diagnosed with invasive breast cancer, 51% had stage I; 26% stage II; 11% stage III; and 4% stage IV disease. The equation predicting stage IV disease achieved sensitivity of 81%, specificity 89%, positive predictive value (PPV) 24%, and negative predictive value (NPV) 99%, while the equation distinguishing stage I/II from stage III disease achieved sensitivity 83%, specificity 78%, PPV 98%, and NPV 31%. Combined, the equations most accurately identified early stage disease and ascertained a sample in which 98% of patients were stage I or II. Conclusions: A claims-based algorithm was utilized to predict breast cancer stage, and was particularly successful when used to identify early stage disease. These prediction equations may be applied in future studies of breast cancer patients, substantially improving the utility of claims-based studies in this group. This method may similarly be employed to develop algorithms permitting claims-based epidemiologic studies of patients with other cancers. http://www.epi-perspectives.com/content/7/1/1 Grace Smith Ya-Chen Shih Sharon Giordano Benjamin Smith Thomas Buchholz Epidemiologic Perspectives & Innovations 2010, 7:1 2010-01-15T00:00:00Z doi:10.1186/1742-5573-7-1 Epidemiologic Perspectives & Innovations 1742-5573 7 1 2010-01-15T00:00:00Z XML Regression adjustment for the propensity score is a statistical method that reduces confounding from measured variables in observational data. A Bayesian propensity score analysis extends this idea by using simultaneous estimation of the propensity scores and the treatment effect. In this article, we conduct an empirical investigation of the performance of Bayesian propensity scores in the context of an observational study of the effectiveness of beta-blocker therapy in heart failure patients. We study the balancing properties of the estimated propensity scores. Traditional Frequentist propensity scores focus attention on balancing covariates that are strongly associated with treatment. In contrast, we demonstrate that Bayesian propensity scores can be used to balance the association between covariates and the outcome. This balancing property has the effect of reducing confounding bias because it reduces the degree to which covariates are outcome risk factors. http://www.epi-perspectives.com/content/6/1/5 Lawrence McCandless Paul Gustafson Peter Austin Adrian Levy Epidemiologic Perspectives & Innovations 2009, 6:5 2009-09-10T00:00:00Z doi:10.1186/1742-5573-6-5 Epidemiologic Perspectives & Innovations 1742-5573 6 5 2009-09-10T00:00:00Z XML In 1986 the International Journal of Epidemiology published "Identifiability, Exchangeability and Epidemiological Confounding". We review the article from the perspective of a quarter century after it was first drafted and relate it to subsequent developments on confounding, ignorability, and collapsibility. http://www.epi-perspectives.com/content/6/1/4 Sander Greenland James Robins Epidemiologic Perspectives & Innovations 2009, 6:4 2009-09-04T00:00:00Z doi:10.1186/1742-5573-6-4 Epidemiologic Perspectives & Innovations 1742-5573 6 4 2009-09-04T00:00:00Z XML