Dataset Information

Invited commentary: How big is that interaction (in my community)--and in which direction?

ABSTRACT: In an accompanying article, Turner et al. (Am J Epidemiol. 2014;180(12):1145-1149) compare the joint effects of smoking and air pollution to make inferences about the reduction in lung cancer mortality achieved when reducing each exposure separately and when reducing both together. In this commentary, we use first principles to quantify the difference between the risk or mortality reduction obtained from reducing each of 2 exposures together and the sum of the risk differences obtained from reducing the 2 exposures separately. Metrics of the impact of joint effects or comparisons of joint effects presented in units of absolute risk, such as Rothman's I, can provide more meaningful quantitative measures of public health impact than unitless metrics (e.g., ratios) and standardized metrics (e.g., the population attributable fraction) of potential interventions for reducing smoking and air pollution exposure. In particular, the venerable attributable community risk metric can provide an estimate of the community impact of such interventions in units of absolute risk. A spreadsheet we provide demonstrates the calculation of the various metrics for hypothetical data similar to those reported by Turner et al. Using algebra, graphics, and examples, we show that positive interaction, or synergy, on the additive scale implies that the impact on risk reduction from a program that applies both interventions will be lesser than the sum of the impacts of the separate interventions.

SUBMITTER: Panagiotou OA

PROVIDER: S-EPMC4262440 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Invited commentary: How big is that interaction (in my community)--and in which direction?

Panagiotou Orestis A OA Wacholder Sholom S

American journal of epidemiology 20141113 12

In an accompanying article, Turner et al. (Am J Epidemiol. 2014;180(12):1145-1149) compare the joint effects of smoking and air pollution to make inferences about the reduction in lung cancer mortality achieved when reducing each exposure separately and when reducing both together. In this commentary, we use first principles to quantify the difference between the risk or mortality reduction obtained from reducing each of 2 exposures together and the sum of the risk differences obtained from redu ...[more]

PMID: 25395027

Similar Datasets

Project description:Importance:In peer-reviewed medical journals, authoring an invited commentary on an original article is a recognition of expertise. It has been documented that women author fewer invited publications than men do. However, it is unknown whether this disparity is due to gender differences in characteristics that are associated with invitations, such as field of expertise, seniority, and scientific output. Objective:To estimate the odds ratio (OR) of authoring an invited commentary for women compared with men who had similar expertise, seniority, and publication metrics. Design, Setting, and Participants:This matched case-control study included all medical invited commentaries published from January 1, 2013, through December 31, 2017, in English-language medical journals and multidisciplinary journals. Invited commentaries were defined as publications that cite another publication within the same journal volume and issue. Bibliometric data were obtained from Scopus. Cases were defined as corresponding authors of invited commentaries in a given journal during the study period. Controls were matched to cases based on scientific expertise by calculating a similarity index for abstracts published during the same period using natural language processing. Data analyses were conducted from March 13, 2019, through May 3, 2019. Exposure:Corresponding or sole author gender was predicted from author first name and country of origin using genderize.io. Main Outcomes and Measures:The OR for gender was estimated after adjusting for field of expertise, publication output, citation impact, and years active (ie, years since first publication), with an interaction between gender and years active. Results:The final data set included 43 235 cases across 2549 journals; there were 34 047 unique intraciting commentary authors, among whom 9072 (26.6%) were women. For researchers who had been active for the median of 19 years, the odds of invited commentary authorship were 21% lower for women (OR, 0.79 [95% CI, 0.77-0.81]; P < .001) compared with men who had similar scientific expertise, number of publications, and citation impact. For every decile increase in years active, the OR decreased by a factor of 0.97 (95% CI, 0.96-0.98; P < .001). Conclusions and Relevance:In this case-control study, women had lower odds of authoring invited commentaries than their male peers. This disparity was larger for senior researchers. Journal editors could use natural language processing of published research to widen and diversify the pool of experts considered for commentary invitations.

Project description:Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V's) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V's) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.

Project description:Vision is the most powerful sense guiding our interaction with the environment. Its process starts with the retinal image as input and results in visually guided behaviors as output. This paper summarizes insights I gained over >40 years dealing with clinical ophthalmology, visual science, and vision rehabilitation, disciplines that all involve vision, but from different points of view. The retinal image contains 2-dimensional forms that have no inherent meaning. The brain matches this input to stored concepts, to create a Mental Model that is filled with 3-dimensional objects that are meaningful and linked to other senses. Ultimately this leads to the output of goal-directed visually guided behavior. The processes involved are too complex to be covered by a single practitioner. Optimal vision rehabilitation requires teamwork that includes contributions from various professions. It also requires an understanding, as well as possible, of the cerebral processes involved. The visual sciences study mostly the input-driven process from retinal image to visual percepts. Their studies deal mostly with groups and group averages and only occasionally with individual disease conditions. Clinical ophthalmology deals mostly with individuals, rather than group averages. The motto of the American Academy of Ophthalmology reminds us that the end point of patient care goes beyond "preserving sight." It also includes "empowering lives" by creating the conditions for goal-directed interaction with the environment through visually directed behavior. Traditionally, the study of vision has mainly involved the conscious part of vision, handled mostly in the ventral stream. However, the subconscious part of vision, handled mostly in the dorsal stream must also be considered. This is further stimulated by the demands of computer vision, image processing, and artificial intelligence. Vision rehabilitation traditionally deals with the input side through better illumination and various magnification devices. This is the domain of low vision aids. Increasingly, however, it must also address the output side, and the involvement of other senses (braille, long cane, and talking books). This requires better understanding of the goal-directed higher visual processes. The supplemental material covers the development of numerical scales to quantify not only visual acuity but also visual abilities, and the use of different tests.Financial disclosuresThe author(s) have no proprietary or commercial interest in any materials discussed in this article.

Project description:Personalized medicine (PM) operates with biological data to optimize therapy or prevention and to achieve cost reduction. Associated data may consist of large variations of informational subtypes e.g. genetic characteristics and their epigenetic modifications, biomarkers or even individual lifestyle factors. Present innovations in the field of information technology have already enabled the procession of increasingly large amounts of such data ('volume') from various sources ('variety') and varying quality in terms of data accuracy ('veracity') to facilitate the generation and analyzation of messy data sets within a short and highly efficient time period ('velocity') to provide insights into previously unknown connections and correlations between different items ('value'). As such developments are characteristics of Big Data approaches, Big Data itself has become an important catchphrase that is closely linked to the emerging foundations and approaches of PM. However, as ethical concerns have been pointed out by experts in the debate already, moral concerns by stakeholders such as patient organizations (POs) need to be reflected in this context as well. We used an empirical-ethical approach including a website-analysis and 27 telephone-interviews for gaining in-depth insight into German POs' perspectives on PM and Big Data. Our results show that not all POs are stakeholders in the same way. Comparing the perspectives and political engagement of the minority of POs that is currently actively involved in research around PM and Big Data-driven research led to four stakeholder sub-classifications: 'mediators' support research projects through facilitating researcher's access to the patient community while simultaneously selecting projects they preferably support while 'cooperators' tend to contribute more directly to research projects by providing and implemeting patient perspectives. 'Financers' provide financial resources. 'Independents' keep control over their collected samples and associated patient-related information with a strong interest in making autonomous decisions about its scientific use. A more detailed terminology for the involvement of POs as stakeholders facilitates the adressing of their aims and goals. Based on our results, the 'independents' subgroup is a promising candidate for future collaborations in scientific research. Additionally, we identified gaps in PO's knowledge about PM and Big Data. Based on these findings, approaches can be developed to increase data and statistical literacy. This way, the full potential of stakeholder involvement of POs can be made accessible in discourses around PM and Big Data.

Dataset Information

Invited commentary: How big is that interaction (in my community)--and in which direction?

Publications

Invited commentary: How big is that interaction (in my community)--and in which direction?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets