Variability In Risk Estimates Research Articles

Cancer incidence and mortality in the Norwegian Fire Departments cohort: Are incidence versus mortality outcome measures contributing to variability in risk estimates?

Cancer incidence and mortality in the Norwegian Fire Departments cohort: Are incidence versus mortality outcome measures contributing to variability in risk estimates?

Study protocol for the multicentre cohorts of Zika virus infection in pregnant women, infants, and acute clinical cases in Latin America and the Caribbean: the ZIKAlliance consortium

BackgroundThe European Commission (EC) Horizon 2020 (H2020)-funded ZIKAlliance Consortium designed a multicentre study including pregnant women (PW), children (CH) and natural history (NH) cohorts. Clinical sites were selected over a wide geographic range within Latin America and the Caribbean, taking into account the dynamic course of the ZIKV epidemic.MethodsRecruitment to the PW cohort will take place in antenatal care clinics. PW will be enrolled regardless of symptoms and followed over the course of pregnancy, approximately every 4 weeks. PW will be revisited at delivery (or after miscarriage/abortion) to assess birth outcomes, including microcephaly and other congenital abnormalities according to the evolving definition of congenital Zika syndrome (CZS).After birth, children will be followed for 2 years in the CH cohort. Follow-up visits are scheduled at ages 1–3, 4–6, 12, and 24 months to assess neurocognitive and developmental milestones. In addition, a NH cohort for the characterization of symptomatic rash/fever illness was designed, including follow-up to capture persisting health problems.Blood, urine, and other biological materials will be collected, and tested for ZIKV and other relevant arboviral diseases (dengue, chikungunya, yellow fever) using RT-PCR or serological methods. A virtual, decentralized biobank will be created. Reciprocal clinical monitoring has been established between partner sites.Substudies of ZIKV seroprevalence, transmission clustering, disabilities and health economics, viral kinetics, the potential role of antibody enhancement, and co-infections will be linked to the cohort studies.DiscussionResults of these large cohort studies will provide better risk estimates for birth defects and other developmental abnormalities associated with ZIKV infection including possible co-factors for the variability of risk estimates between other countries and regions. Additional outcomes include incidence and transmission estimates of ZIKV during and after pregnancy, characterization of short and long-term clinical course following infection and viral kinetics of ZIKV.Study registrationsclinicaltrials.gov NCT03188731 (PW cohort), June 15, 2017; clinicaltrials.gov NCT03393286 (CH cohort), January 8, 2018; clinicaltrials.gov NCT03204409 (NH cohort), July 2, 2017.

Inhaled corticosteroids for chronic obstructive pulmonary disease: what is their role in therapy?

Inhaled corticosteroids (ICSs) are a mainstay of COPD treatment for patients with a history of exacerbations. Initial studies evaluating their use as monotherapy failed to show an effect on rate of pulmonary function decline in COPD, despite improvements in symptoms and reductions in exacerbations. Subsequently, ICS use in combination with long-acting β2-agonists (LABAs) was shown to provide improved reductions in exacerbations, lung function, and health status. ICS-LABA combination therapy is currently recommended for patients with a history of exacerbations despite treatment with long-acting bronchodilators alone. The presence of eosinophilic bronchial inflammation, detected by high blood eosinophil levels or a history of asthma or asthma–COPD overlap, may define a population of patients in whom ICSs may be of particular benefit. Prospective clinical studies to determine an appropriate threshold of eosinophil levels for predicting the beneficial effects of ICSs are needed. Further study is also required in COPD patients who continue to smoke to assess the impact of cell- and tissue-specific changes on ICS responsiveness. The safety profile of ICSs in COPD patients is confounded by comorbidities, age, and prior use of systemic corticosteroids. The risk of pneumonia in patients with COPD is increased, particularly with more advanced age and worse disease severity. ICS-containing therapy also has been shown to increase pneumonia risk; however, differences in study design and the definition of pneumonia events have led to substantial variability in risk estimates, and some data indicate that pneumonia risk may differ by the specific ICS used. In summary, treatment with ICSs has a role in dual and triple therapy for COPD to reduce exacerbations and improve symptoms. Careful assessment of COPD phenotypes related to risk factors, triggers, and comorbidities may assist in individualizing treatment while maximizing the benefit-to-risk ratio of ICS-containing COPD treatment.

Erratum to: Bridging the etiologic and prognostic outlooks in individualized assessment of absolute risk of an illness: application in lung cancer.

Assessment of individual risk of illness is an important activity in preventive medicine. Development of risk-assessment models has heretofore relied predominantly on studies involving follow-up of cohort-type populations, while case–control studies have generally been considered unfit for this purpose. To present a method for individualized assessment of absolute risk of an illness (as illustrated by lung cancer) based on data from a ‘non-nested’ case–control study. We used data from a case–control study conducted in Montreal, Canada in 1996–2001. Individuals diagnosed with lung cancer (n = 920) and age- and sex-matched lung-cancer-free subjects (n = 1288) completed questionnaires documenting life-time cigarette-smoking history and occupational, medical, and family history. Unweighted and weighted logistic models were fitted. Model overfitting was assessed using bootstrap-based cross-validation and ‘shrinkage.’ The discriminating ability was assessed by the c-statistic, and the risk-stratifying performance was assessed by examination of the variability in risk estimates over hypothetical risk-profiles. In the logistic models, the logarithm of incidence-density of lung cancer was expressed as a function of age, sex, cigarette-smoking history, history of respiratory conditions and exposure to occupational carcinogens, and family history of lung cancer. The models entailed a minimal degree of overfitting (‘shrinkage’ factor: 0.97 for both unweighted and weighted models) and moderately high discriminating ability (c-statistic: 0.82 for the unweighted model and 0.66 for the weighted model). The method’s risk-stratifying performance was quite high. The presented method allows for individualized assessment of risk of lung cancer and can be used for development of risk-assessment models for other illnesses.

Bridging the etiologic and prognostic outlooks in individualized assessment of absolute risk of an illness: application in lung cancer.

Assessment of individual risk of illness is an important activity in preventive medicine. Development of risk-assessment models has heretofore relied predominantly on studies involving follow-up of cohort-type populations, while case-control studies have generally been considered unfit for this purpose. To present a method for individualized assessment of absolute risk of an illness (as illustrated by lung cancer) based on data from a 'non-nested' case-control study. We used data from a case-control study conducted in Montreal, Canada in 1996-2001. Individuals diagnosed with lung cancer (n=920) and age- and sex-matched lung-cancer-free subjects (n=1288) completed questionnaires documenting life-time cigarette-smoking history and occupational, medical, and family history. Unweighted and weighted logistic models were fitted. Model overfitting was assessed using bootstrap-based cross-validation and 'shrinkage.' The discriminating ability was assessed by the c-statistic, and the risk-stratifying performance was assessed by examination of the variability in risk estimates over hypothetical risk-profiles. In the logistic models, the logarithm of incidence-density of lung cancer was expressed as a function of age, sex, cigarette-smoking history, history of respiratory conditions and exposure to occupational carcinogens, and family history of lung cancer. The models entailed a minimal degree of overfitting ('shrinkage' factor: 0.97 for both unweighted and weighted models) and moderately high discriminating ability (c-statistic: 0.82 for the unweighted model and 0.66 for the weighted model). The method's risk-stratifying performance was quite high. The presented method allows for individualized assessment of risk of lung cancer and can be used for development of risk-assessment models for other illnesses.

Average spectral acceleration as an intensity measure for collapse risk assessment

SummaryThis paper investigates the performance of spectral acceleration averaged over a period range (Saavg) as an intensity measure (IM) for estimating the collapse risk of structures subjected to earthquake loading. The performance of Saavg is evaluated using the following criteria: efficiency, sufficiency, the availability or ease of developing probabilistic seismic hazard information in terms of the IM and the variability of collapse risk estimates produced by the IM. Comparisons are also made between Saavg and the more traditional IM: spectral acceleration at the first‐mode period of the structure (Sa(T1)). Though most previous studies have evaluated IMs using a relatively limited set of structures, this paper considers nearly 700 moment‐resisting frame and shear wall structures of various heights to compare the efficiency and sufficiency of the IMs. The collapse risk estimates produced by Saavg and Sa(T1) are also compared, and the variability of the risk estimates is evaluated when different ground motion sets are used to assess the structural response. The results of this paper suggest that Saavg, when computed using an appropriate period range, is generally more efficient, more likely to be sufficient and provides more stable collapse risk estimates than Sa(T1). Copyright © 2015 John Wiley & Sons, Ltd.

Circulation : Clinical Summaries

<i>Circulation</i> : Clinical Summaries

Sex differences in long-term mortality after myocardial infarction: a systematic review.

Studies of sex differences in long-term mortality after acute myocardial infarction have reported mixed results. A systematic review is needed to characterize what is known about sex differences in long-term outcomes and to define gaps in knowledge. We searched the Medline database from 1966 to December 2012 to identify all studies that provided sex-based comparisons of mortality after acute myocardial infarction. Only studies with at least 5 years of follow-up were reviewed. Of the 1877 identified abstracts, 52 studies met the inclusion criteria, of which 39 were included in this review. Most studies included fewer than one-third women. There was significant heterogeneity across studies in patient populations, methodology, and risk adjustment, which produced substantial variability in risk estimates. In general, most studies reported higher unadjusted mortality for women compared with men at both 5 and 10 years after acute myocardial infarction; however, many of the differences in mortality became attenuated after adjustment for age. Multivariable models varied between studies; however, most reported a further reduction in sex differences after adjustment for covariates other than age. Few studies examined sex-by-age interactions; however, several studies reported interactions between sex and treatment whereby women have similar mortality risk as men after revascularization. Sex differences in long-term mortality after acute myocardial infarction are largely explained by differences in age, comorbidities, and treatment use between women and men. Future research should aim to clarify how these differences in risk factors and presentation contribute to the sex gap in mortality.

Mapping ecological risks with a portfolio‐based technique: incorporating uncertainty and decision‐making preferences

AbstractAimGeographic mapping of risks is a useful analytical step in ecological risk assessments and in particular, in analyses aimed to estimate risks associated with introductions of invasive organisms. In this paper, we approach invasive species risk mapping as a portfolio allocation problem and apply techniques from decision theory to build an invasion risk map that combines risk and uncertainty in a single map product.LocationCanada.MethodsWe divide the study area into a set of spatial domains and treat each domain as an individual ‘portfolio’ with a unique distribution of the expected impacts of invasion. The risk of invasion is then mapped by finding nested ‘efficient’ portfolio sets that identify the geographic areas exhibiting the worst combinations of the estimated risk of invasion and the uncertainty in that estimate. For Canadian municipalities, we apply the approach to quantify the risk that a given location will receive invasive forest pests with commercial freight transported via the North American road network. We compare risk allocation techniques that employ the concepts of nested mean‐variance (M‐V) frontiers and second‐degree stochastic dominance.ResultsWhile both methods based on M‐V and the stochastic dominance principles identified similar areas of highest risk, they differed in how they demarcated moderate‐risk areas. Furthermore, they address uncertainty in different ways, treating it as a risk premium (in the case of nested M‐V frontiers) or producing risk‐averse delineations (in the case of stochastic dominance).Main conclusionsThe portfolio‐based approach offers a viable strategy for dealing with the typically wide variability in risk estimates caused by a lack of knowledge about a new invader. The methodology also provides a tractable way of incorporating decision‐making preferences into the final risk estimates and thus better aligns risk assessments with particular decision‐making scenarios about the organism of concern.

A Role for the DRD4 Exon III VNTR in Modifying the Association Between Nicotine Dependence and Neuroticism

Neurotic psychopathology has been extensively examined as a risk factor for nicotine dependence (ND). Genetic stratification may partially explain variability in risk estimates. Genetic variants that compromise dopaminergic neurotransmission may motivate exposure to dopamine-stimulating agents, including nicotine. The 7-repeat allele of a Variable Number Tandem Repeat (VNTR) polymorphism in DRD4 (and evolutionary derivatives 5, 6, and 8 repeats; 7R+) is associated with reduced dopamine receptor function. The purpose of this study was to examine association between both smoking initiation (SI) and progression to ND by young adulthood and (a) history of neuroticism during adolescence, (b) DRD4 7R+, and (c) interaction between neuroticism and DRD4 7R+. Participants were drawn from the Victorian Adolescent Health Cohort Study, a longitudinal study of the health and well-being of young Australians across 8 waves (14-24 years). Neuroticism was measured at Waves 3 and 6 (mean 15.9 and 17.4 years). SI was defined as any smoking at any wave. ND was measured at Wave 8 (mean 24.1 years). Genotype data for the DRD4 VNTR were available for 839 participants. While adolescent neuroticism was associated with SI, evidence for association with progression to ND was weak. However, there was evidence of interaction between neuroticism and DRD4 7R+: The odds of progression to ND among those with a history of neuroticism were more than 3.5-fold higher among 7R+ carriers. Without considering stratification by 7R+, the association between progression to ND and neuroticism would have been assumed barely significant. However, among those carrying DRD4 7R+, risk of progression was considerably intensified.

A bayesian approach to parameter estimation for a crayfish (Procambarus spp.) bioaccumulation model.

Bioaccumulation models are used to describe chemical uptake and clearances by organisms. Averaged input parameter values are traditionally used and yield point estimates of model outputs. Hence, the uncertainty and variability of model predictions are ignored. Probabilistic modeling approaches, such as Monte Carlo simulation and the Bayesian method, have been recommended by the U.S. Environmental Protection Agency to provide a quantitative description of the degree of uncertainty and/or variability in risk estimates in ecological hazards and human health effects. In this study, a Bayesian analysis was conducted to account for the combined uncertainty and variability of model parameters in a crayfish bioaccumulation model. After a 5-d exposure in the LaBranche Wetlands (LA, USA), crayfish were analyzed for polycyclic aromatic hydrocarbon concentrations and lipid fractions. The posterior distribution of model parameters were derived from the joint posterior parameter distributions using a Markov chain Monte Carlo approach and the experimental data. The results were then used to predict the distribution of chrysene concentration versus time in the crayfish to compare the predicted ranges at the different study sites.

Setting microbiological water quality standards for sea bathing - a critical evaluation

The World Health Organization (WHO) recently produced draft guidelines for safe recreational water environments. The microbiological standards proposed in the guidelines are expected to overestimate the degree of water quality required to provide given levels of public health protection. The WHO standards were obtained by means of a risk assessment which featured a dose-response model derived from a series of randomised controlled trials. The trials have many strengths but biases and problems with statistical analysis are likely to have led to over-estimation of the risks from bathing in the dose-response model. In addition, the WHO risk assessment failed to consider the effects of uncertainty and variability in risk estimates and sensitivity to model assumptions. Improved standards could be obtained by extending the risk assessment to examine these effects and by incorporating a suitably revised dose-response model.

Design of animal carcinogenicity studies for goodness-of-fit of multistage models

The effect of bioassay design changes on the variability of risk estimates in the experimental dose region is investigated. Three-dose designs with a control group and other “usual” designs utilizing 200 animals are studied in detail. Constraints on the minimum power of the linear trend test in proportions are used to eliminate designs with unacceptable power. An acceptable design region is compared to designs aimed at improving the low dose extrapolation. A group of designs acceptable for a range of objectives is proposed.