Emerging Themes in Epidemiology - Latest Articles

Assessment of methods for prediction of human West Nile virus (WNV) disease from WNV-infected dead birds

Anna Veksler — 2009-06-05T00:00:00Z

Background: West Nile virus (WNV) is currently the leading cause of arboviral-associated encephalitis in the U.S., and can lead to long-term neurologic sequelae. Improvements in dead bird specimen processing time, including the availability of rapid field laboratory tests, allows reassessment of the effectiveness of using WNV-positive birds in forecasting human WNV disease. Methods: Using New York State integrated WNV surveillance data from transmissions seasons in 2001–2003, this study determined which factors associated with WNV-positive dead birds are most closely associated with human disease. The study also addressed the 'delay' period between the distribution of the dead bird variable and the distribution of the human cases. In the last step, the study assessed the relative risk of contracting WNV disease for people who lived in counties with a 'signal' value of the predictor variable versus people who lived in counties with no 'signal' value of the predictor variable. Results: The variable based on WNV-positive dead birds [(Positive/Tested)*(Population/Area)] was identified as the optimum variable for predicting WNV human disease at a county level. The delay period between distribution of the variable and human cases was determined to be approximately two weeks. For all 3 years combined, the risk of becoming a WNV case for people who lived in 'exposed' counties (those with levels of the positive dead bird variable above the signal value) was about 2 times higher than the risk for people who lived in 'unexposed' counties, but risk varied by year. Conclusion: This analysis develops a new variable based on WNV-positive dead birds, [(Positive/Tested)*(Population/Area)] to be assessed in future real-time studies for forecasting the number of human cases in a county. A delay period of approximately two weeks between increases in this variable and the human case onset was identified. Several threshold 'signal' values were assessed and found effective at indicating human case risk, although specific thresholds are likely to vary by region and surveillance system differences.

Can we apply the Mendelian randomization methodology without considering epigenetic effects?

Ikechukwu Ogbuanu — 2009-05-11T00:00:00Z

IntroductionInstrumental variable (IV) methods have been used in econometrics for several decades now, but have only recently been introduced into the epidemiologic research frameworks. Similarly, Mendelian randomization studies, which use the IV methodology for analysis and inference in epidemiology, were introduced into the epidemiologist's toolbox only in the last decade.AnalysisMendelian randomization studies using instrumental variables (IVs) have the potential to avoid some of the limitations of observational epidemiology (confounding, reverse causality, regression dilution bias) for making causal inferences. Certain limitations of randomized controlled trials, such as problems with generalizability, feasibility and ethics for some exposures, and high costs, also make the use of Mendelian randomization in observational studies attractive. Unlike conventional randomized controlled trials (RCTs), Mendelian randomization studies can be conducted in a representative sample without imposing any exclusion criteria or requiring volunteers to be amenable to random treatment allocation.Within the last decade, epigenetics has gained recognition as an independent field of study, and appears to be the new direction for future research into the genetics of complex diseases. Although previous articles have addressed some of the limitations of Mendelian randomization (such as the lack of suitable genetic variants, unreliable associations, population stratification, linkage disequilibrium (LD), pleiotropy, developmental canalization, the need for large sample sizes and some potential problems with binary outcomes), none has directly characterized the impact of epigenetics on Mendelian randomization. The possibility of epigenetic effects (non-Mendelian, heritable changes in gene expression not accompanied by alterations in DNA sequence) could alter the core instrumental variable assumptions of Mendelian randomization.This paper applies conceptual considerations, algebraic derivations and data simulations to question the appropriateness of Mendelian randomization methods when epigenetic modifications are present. Conclusion: Given an inheritance of gene expression from parents, Mendelian randomization studies not only need to assume a random distribution of alleles in the offspring, but also a random distribution of epigenetic changes (e.g. gene expression) at conception, in order for the core assumptions of the Mendelian randomization methodology to remain valid. As an increasing number of epidemiologists employ Mendelian randomization methods in their research, caution is therefore needed in drawing conclusions from these studies if these assumptions are not met.

Methodological issues in estimating survival in patients with multiple primary cancers: an application to women with breast cancer as a first tumour.

Stefano Rosso — 2009-02-27T00:00:00Z

Background: Comparing survival of patients with a single tumour and patients with multiple primaries poses different methodological problems. In population based studies, where we cannot rely on detailed clinical information, the issue is disentangling the share of survival probability from the first and second cancer, and their compounded effect. We examined three hypotheses: A) the survival probability since the first tumour does not change with the occurrence of a second tumour; B) the probability of surviving a tumour does not change with the presence of a previous primary; C) the probabilities of surviving two subsequent primary tumours are independent (additivity hypothesis on mortality rates). Methods: We studied the survival probabilities modelling mortality rates according to hypotheses A), B) and C). Mortality rates were calculated using Aalen-Johansen estimators which allowed to discount for the lag-time survival before developing a second tumour. We applied this approach to a cohort of 436 women with breast cancer (BC) and a subsequent tumour in the resident population of Turin, Italy, between 1985 and 2002. Results: We presented our results in term of a Standardised Mortality Ratio calculated (SMRAJ) after 10 years of follow-up. For hypothesis A we observed a significant excess mortality of 2.21 (95% C.I. 1.94 – 2.45). Concerning hypothesis B we found a not significant SMRAJ of 0.98 (95% C.I. 0.87 – 1.10). The additivity hypothesis (C) was not confirmed as it overestimated the risk of death, in fact SMRsAJ were all below 1: 0.75 (95% C.I. 0.66 – 0.84) for BC and all subsequent cancers, 0.72 (95% C.I. 0.55 – 0.94) for BC and colon-rectum cancer, 0.76 (95% C.I. 0.48 – 1.14) for BC and corpus uteri cancer (not significant). Conclusion: This method proved to be useful in disentangling the effect of different subsequent cancers on mortality. In our application it shows a worse long-term mortality for women with two cancers than that with BC only. However, the increase in mortality was lower than expected under the additivity assumption.

Revisiting the relationship between baseline risk and risk under treatment

Hao Wang — 2009-02-17T00:00:00Z

Background: In medical practice, it is generally accepted that the 'effect model' describing the relationship between baseline risk and risk under treatment is linear, i.e. 'relative risk' is constant. Absolute benefit is then proportional to a patient's baseline risk and the treatment is most effective among high-risk patients. Alternatively, the 'effect model' becomes curvilinear when 'odds ratio' is considered to be constant. However these two models are based on purely empirical considerations, and there is still no theoretical approach to support either the linear or the non-linear relation.Presentation of the hypothesisFrom logistic and sigmoidal Emax (Hill) models, we derived a phenomenological model which includes the possibility of integrating both beneficial and harmful effects. Instead of a linear relation, our model suggests that the relationship is curvilinear i.e. the moderate-risk patients gain most from the treatment in opposition to those with low or high risk.Testing the hypothesisTwo approaches can be proposed to investigate in practice such a model. The retrospective one is to perform a meta-analysis of clinical trials with subgroups of patients including a great range of baseline risks. The prospective one is to perform a large clinical trial in which patients are recruited according to several prestratified diverse and high risk groups.Implications of the hypothesisFor the quantification of the treatment effect and considering such a model, the discrepancy between odds ratio and relative risk may be related not only to the level of risk under control conditions, but also to the characteristics of the dose-effect relation and the amount of dose administered. In the proposed approach, OR may be considered as constant in the whole range of Rc, and depending only on the intrinsic characteristics of the treatment. Therefore, OR should be preferred rather than RR to summarize information on treatment efficacy.

Revised estimates of influenza-associated excess mortality, United States, 1995 through 2005

Ivo Foppa — 2008-12-30T00:00:00Z

Background: Excess mortality due to seasonal influenza is thought to be substantial. However, influenza may often not be recognized as cause of death. Imputation methods are therefore required to assess the public health impact of influenza. The purpose of this study was to obtain estimates of monthly excess mortality due to influenza that are based on an epidemiologically meaningful model.Methods and ResultsU.S. monthly all-cause mortality, 1995 through 2005, was hierarchically modeled as Poisson variable with a mean that linearly depends both on seasonal covariates and on influenza-certified mortality. It also allowed for overdispersion to account for extra variation that is not captured by the Poisson error. The coefficient associated with influenza-certified mortality was interpreted as ratio of total influenza mortality to influenza-certified mortality. Separate models were fitted for four age categories (<18, 18–49, 50–64, 65+). Bayesian parameter estimation was performed using Markov Chain Monte Carlo methods. For the eleven year study period, a total of 260,814 (95% CI: 201,011–290,556) deaths was attributed to influenza, corresponding to an annual average of 23,710, or 0.91% of all deaths. Conclusion: Annual estimates for influenza mortality were highly variable from year to year, but they were systematically lower than previously published estimates. The excellent fit of our model with the data suggest validity of our estimates.

Interpreting results of cluster surveys in emergency settings: is the LQAS test the best option?

Oleg Bilukha — 2008-12-09T00:00:00Z

Cluster surveys are commonly used in humanitarian emergencies to measure health and nutrition indicators. Deitchler et al. have proposed to use Lot Quality Assurance Sampling (LQAS) hypothesis testing in cluster surveys to classify the prevalence of global acute malnutrition as exceeding or not exceeding the pre-established thresholds. Field practitioners and decision-makers must clearly understand the meaning and implications of using this test in interpreting survey results to make programmatic decisions. We demonstrate that the LQAS test–as proposed by Deitchler et al. – is prone to producing false-positive results and thus is likely to suggest interventions in situations where interventions may not be needed. As an alternative, to provide more useful information for decision-making, we suggest reporting the probability of an indicator's exceeding the threshold as a direct measure of "risk". Such probability can be easily determined in field settings by using a simple spreadsheet calculator. The "risk" of exceeding the threshold can then be considered in the context of other aggravating and protective factors to make informed programmatic decisions.

Sample size requirements to detect the effect of a group of genetic variants in case-control studies

Ramal Moonesinghe — 2008-12-03T00:00:00Z

Background: Because common diseases are caused by complex interactions among many genetic variants along with environmental risk factors, very large sample sizes are usually needed to detect such effects in case-control studies. Nevertheless, many genetic variants act in well defined biologic systems or metabolic pathways. Therefore, a reasonable first step may be to detect the effect of a group of genetic variants before assessing specific variants. Methods: We present a simple method for determining approximate sample sizes required to detect the average joint effect of a group of genetic variants in a case-control study for multiplicative models. Results: For a range of reasonable numbers of genetic variants, the sample size requirements for the test statistic proposed here are generally not larger than those needed for assessing marginal effects of individual variants and actually decline with increasing number of genetic variants in many situations considered in the group. Conclusion: When a significant effect of the group of genetic variants is detected, subsequent multiple tests could be conducted to detect which individual genetic variants and their combinations are associated with disease risk. When testing for an effect size in a group of genetic variants, one can use our global test described in this paper, because the sample size required to detect an effect size in the group is comparatively small. Our method could be viewed as a screening tool for assessing groups of genetic variants involved in pathogenesis and etiology of common complex human diseases.

Development of a Quality Assessment Tool for Systematic Reviews of Observational Studies (QATSO) of HIV prevalence in men having sex with men and associated risk behaviours

William Wong — 2008-11-17T00:00:00Z

Background: Systematic reviews based on the critical appraisal of observational and analytic studies on HIV prevalence and risk factors for HIV transmission among men having sex with men are very useful for health care decisions and planning. Such appraisal is particularly difficult, however, as the quality assessment tools available for use with observational and analytic studies are poorly established. Methods: We reviewed the existing quality assessment tools for systematic reviews of observational studies and developed a concise quality assessment checklist to help standardise decisions regarding the quality of studies, with careful consideration of issues such as external and internal validity. Results: A pilot version of the checklist was developed based on epidemiological principles, reviews of study designs, and existing checklists for the assessment of observational studies. The Quality Assessment Tool for Systematic Reviews of Observational Studies (QATSO) Score consists of five items: External validity (1 item), reporting (2 items), bias (1 item) and confounding factors (1 item). Expert opinions were sought and it was tested on manuscripts that fulfil the inclusion criteria of a systematic review. Like all assessment scales, QATSO may oversimplify and generalise information yet it is inclusive, simple and practical to use, and allows comparability between papers. Conclusion: A specific tool that allows researchers to appraise and guide study quality of observational studies is developed and can be modified for similar studies in the future.

Questions on causality and responsibility arising from an outbreak of Pseudomonas aeruginosa infections in Norway

Bjorn Iversen — 2008-10-23T00:00:00Z

In 2002, Norway experienced a large outbreak of Pseudomonas aeruginosa infections in hospitals with 231 confirmed cases. This fuelled intense public and professional debates on what were the causes and who were responsible. In epidemiology, other sciences, in philosophy and in law there is a long tradition of discussing the concept of causality. We use this outbreak as a case; apply various theories of causality from different disciplines to discuss the roles and responsibilities of some of the parties involved. Mackie's concept of INUS conditions, Hill's nine viewpoints to study association for claiming causation, deterministic and probabilistic ways of reasoning, all shed light on the issues of causality in this outbreak. Moreover, applying legal theories of causation (counterfactual reasoning and the "but-for" test and the NESS test) proved especially useful, but the case also illustrated the weaknesses of the various theories of causation.We conclude that many factors contributed to causing the outbreak, but that contamination of a medical device in the production facility was the major necessary condition. The reuse of the medical device in hospitals contributed primarily to the size of the outbreak. The unintended error by its producer – and to a minor extent by the hospital practice – was mainly due to non-application of relevant knowledge and skills, and appears to constitute professional negligence. Due to criminal procedure laws and other factors outside the discourse of causality, no one was criminally charged for the outbreak which caused much suffering and shortening the life of at least 34 people.

Seek, and ye shall find: Accessing the global epidemiological literature in different languages

Isaac Fung — 2008-09-30T00:00:00Z

The thematic series 'Beyond English: Accessing the global epidemiological literature' in Emerging Themes in Epidemiology highlights the wealth of epidemiological and public health literature in the major languages of the world, and the bibliographic databases through which they can be searched and accessed. This editorial suggests that all systematic reviews in epidemiology and public health should include literature published in the major languages of the world and that the use of regional and non-English bibliographic databases should become routine.