Morgan R, George A, Ssali S, et al. How to do (or not to do)...gender analysis in health system research. Health Policy and Planning 2016;1-10
(doi: 10.1093/heapol/czw037)
The article outlines what gender analysis is and how gender analysis can be incorporated into health system research (HSR) content, process and outcomes. It recommends exploring whether and how gender power relations affect females and males in health systems through the use of sex disaggregated data, gender frameworks and questions. It also examines gender in HSR process by reflecting on how the research process itself is imbued with power relations, and in HSR outcomes by supporting how power relations can be transformed progressively or at least not exacerbated.
http://heapol.oxfordjournals.org/content/early/2016/04/26/heapol.czw037.abstract
Showing posts with label SCIENCE. Show all posts
Showing posts with label SCIENCE. Show all posts
Thursday, June 16, 2016
Friday, April 1, 2016
B - Health-related data protection
Dove ES, Thompson B, Knoppers BM. A step forward for data protection and biomedical research. The Lancet 2016;387(10026):1374-1375
A European General Data Protection Regulation that is favourable for research was agreed by Member States and Parliament in December 2015. The Regulation deems health-related data and genetic data as so-called special categories of sensitive data, subject to increased restrictions. However, researchers can use these data without consent as long as it is permitted under EU or Member State law and appropriate safeguards are in place. The Regulation facilitates the reuse of data for research, even where the data were collected for another purpose.
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)30078-2/abstract
A European General Data Protection Regulation that is favourable for research was agreed by Member States and Parliament in December 2015. The Regulation deems health-related data and genetic data as so-called special categories of sensitive data, subject to increased restrictions. However, researchers can use these data without consent as long as it is permitted under EU or Member State law and appropriate safeguards are in place. The Regulation facilitates the reuse of data for research, even where the data were collected for another purpose.
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)30078-2/abstract
Wednesday, March 30, 2016
B - Personalized medicine
Schork NJ. Personalized medicine: time for one-person trials. Nature 2015;520:609-611
Every day, millions of people are taking medications that will not help them. The top ten highest-grossing drugs in the United States help between 1 in 25 and 1 in 4 of the people who take them. Recognition that physicians need to take individual variability into account is driving huge interest in 'precision' medicine, that requires a different type of clinical trial focusing on individual, not average, responses to therapy. But regulatory agencies, researchers and clinicians are rightfully wary of moving away from classical clinical trials, and pharmaceutical companies tend to focus on drugs that are likely to be used by thousands or millions of people.
http://www.nature.com/news/personalized-medicine-time-for-one-person-trials-1.17411
(doi:10.1038/520609a)
Every day, millions of people are taking medications that will not help them. The top ten highest-grossing drugs in the United States help between 1 in 25 and 1 in 4 of the people who take them. Recognition that physicians need to take individual variability into account is driving huge interest in 'precision' medicine, that requires a different type of clinical trial focusing on individual, not average, responses to therapy. But regulatory agencies, researchers and clinicians are rightfully wary of moving away from classical clinical trials, and pharmaceutical companies tend to focus on drugs that are likely to be used by thousands or millions of people.
http://www.nature.com/news/personalized-medicine-time-for-one-person-trials-1.17411
Tuesday, January 5, 2016
B - Interdisciplinary research
Van Noorden R. Interdisciplinary research by the numbers. Nature 2015 September 16;525(7569):306-7
(doi: 10.1038/525306a)
Interdisciplinary work is considered crucial by scientists, policy-makers and funders. This study reveals the extent and impact of research that bridges disciplines.The fraction of paper references that point to work in other disciplines is increasing in both the natural and the social sciences. And although papers that combine very disparate fields tend to have fewer citations, interdisciplinary work can have broad societal and economic impacts that are not captured by citations.
http://www.nature.com/news/interdisciplinary-research-by-the-numbers-1.18349
(doi: 10.1038/525306a)
Interdisciplinary work is considered crucial by scientists, policy-makers and funders. This study reveals the extent and impact of research that bridges disciplines.The fraction of paper references that point to work in other disciplines is increasing in both the natural and the social sciences. And although papers that combine very disparate fields tend to have fewer citations, interdisciplinary work can have broad societal and economic impacts that are not captured by citations.
http://www.nature.com/news/interdisciplinary-research-by-the-numbers-1.18349
Wednesday, December 23, 2015
B - Storing and accessing biomedical big data
Bourne PE, Lorsch JR. Green ED. Sustaining the big-data ecosystem. Nature Nov 5 2015;527
(doi: 10.1038/527S16a)
Biomedical big data offer tremendous potential for making discoveries, but the cost of sustaining these digital assets and the resources needed to make them useful have received relatively little attention. Funders should encourage the development of new metrics to ascertain the usage and value of data and when we have a better understanding of data usage, we can develop business models for storing, organizing and accessing them. Tools and rewards that incentivize researchers to submit their data to data resources in ways that maximize both quality and ease of access, are also needed.
http://www.nature.com/nature/journal/v527/n7576_supp/full/527S16a.html
(doi: 10.1038/527S16a)
Biomedical big data offer tremendous potential for making discoveries, but the cost of sustaining these digital assets and the resources needed to make them useful have received relatively little attention. Funders should encourage the development of new metrics to ascertain the usage and value of data and when we have a better understanding of data usage, we can develop business models for storing, organizing and accessing them. Tools and rewards that incentivize researchers to submit their data to data resources in ways that maximize both quality and ease of access, are also needed.
http://www.nature.com/nature/journal/v527/n7576_supp/full/527S16a.html
Tuesday, October 6, 2015
B - Increasing value and reduce waste
Moher D, Glasziou P, Chalmers I, et al. Increasing value and reducing waste in biomedical research: who's listening? The Lancet Sept. 28, 2015
(doi: 10.1016/S0140-6736(15)00307-4)
Published online during the REWARD/EQUATOR Conference in Edinburgh (September 28-30), this review provides some initial observations on the possible effects of The Lancet 2014 series of five reviews showing how dividends from the investment in research might be increased from the relevance and priorities of the questions being asked, to how the research is designed, conducted, and reported. 17 recommendations were addressed to five main stakeholders—funders, regulators, journals, academic institutions, and researchers, Some examples of individual initiatives show ways to reduce waste and increase value in biomedical research.
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)00307-4/fulltext?rss%3Dyes
(doi: 10.1016/S0140-6736(15)00307-4)
Published online during the REWARD/EQUATOR Conference in Edinburgh (September 28-30), this review provides some initial observations on the possible effects of The Lancet 2014 series of five reviews showing how dividends from the investment in research might be increased from the relevance and priorities of the questions being asked, to how the research is designed, conducted, and reported. 17 recommendations were addressed to five main stakeholders—funders, regulators, journals, academic institutions, and researchers, Some examples of individual initiatives show ways to reduce waste and increase value in biomedical research.
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)00307-4/fulltext?rss%3Dyes
Monday, September 21, 2015
B - Scientometric analysis on big data
Vivek KS, Sumit KB, Khushboo S, et al. Scientometric mapping of research on "Big Data". Scientometrics e-pub 9 Sept. 2015
(doi: 10.1007/s11192-015-1729-9)
This paper presents a scientometric analysis of research work done on the emerging area of "Big Data" in the years 2010-2014. The analysis maps comprehensively the parameters of total output, growth of output, authorship and country-level collaboration patterns, major contributors (countries, institutions and individuals), top publication sources, thematic trends and emerging themes in the field.
http://rd.springer.com/article/10.1007%2Fs11192-015-1729-9
(doi: 10.1007/s11192-015-1729-9)
This paper presents a scientometric analysis of research work done on the emerging area of "Big Data" in the years 2010-2014. The analysis maps comprehensively the parameters of total output, growth of output, authorship and country-level collaboration patterns, major contributors (countries, institutions and individuals), top publication sources, thematic trends and emerging themes in the field.
http://rd.springer.com/article/10.1007%2Fs11192-015-1729-9
B - Cluster randomized trials
Meurer WJ, Lewis RJ. Cluster randomized trials evaluating treatments applied to groups. JAMA 2015;313(20):2068-2069
(doi: 10.1001/jama.2015.5199)
Sometimes a new treatment is best introduced to an entire group of patients rather than to individual patients. One approach to evaluate the efficacy of such treatments is to conduct a cluster randomized trial. According to the authors, four points are to be considered when evaluating a cluster randomized trial. Among them, was the use of clustering well justified? Was the intracluster correlation appropriately accounted for in the design, analysis, and interpretationm of the trial?
http://jama.jamanetwork.com/article.aspx?articleid=2297149
(doi: 10.1001/jama.2015.5199)
Sometimes a new treatment is best introduced to an entire group of patients rather than to individual patients. One approach to evaluate the efficacy of such treatments is to conduct a cluster randomized trial. According to the authors, four points are to be considered when evaluating a cluster randomized trial. Among them, was the use of clustering well justified? Was the intracluster correlation appropriately accounted for in the design, analysis, and interpretationm of the trial?
http://jama.jamanetwork.com/article.aspx?articleid=2297149
Wednesday, July 22, 2015
B - Reasons for abandoning clinical trials
Couzin-Frankel J. Researchers seek clear reasons when clinical trials end early. Science July 2015;349(6245):222
(doi: 10.1126/science.349.6245.222)
About 12% of clinical trials are reported to shut down prematurely. Knowing why could help minimize the number of terminated trials going forward. A team of three computational biologists began exploring why clinical trials end prematurely. They looked at all 3122 terminated trials on the ClinicalTrials.gov registry at the time their study began, and divided the reasons for ending early into "buckets," such as funding, ethical reasons, or business decisions, so they could see the breakdown by category. but those explanations were often hazy.
https://www.sciencemag.org/content/349/6245/222.full
(doi: 10.1126/science.349.6245.222)
About 12% of clinical trials are reported to shut down prematurely. Knowing why could help minimize the number of terminated trials going forward. A team of three computational biologists began exploring why clinical trials end prematurely. They looked at all 3122 terminated trials on the ClinicalTrials.gov registry at the time their study began, and divided the reasons for ending early into "buckets," such as funding, ethical reasons, or business decisions, so they could see the breakdown by category. but those explanations were often hazy.
https://www.sciencemag.org/content/349/6245/222.full
Friday, June 26, 2015
B - Antibody problem
Baker M. Reproducibility crisis: Blame it on the antibodies. Nature 21 May 2015;521:274-276
Antibodies are among the most commonly used tools in biological experiments, but they are littering the field with false findings. A few scientists are pushing for change and are calling for the creation of standards by which antibodies should be made, used and described. Several journals (including Nature) ask authors to make clear that antibodies used in their papers have been profiled for that particular application.
http://www.nature.com/news/reproducibility-crisis-blame-it-on-the-antibodies-1.17586
Antibodies are among the most commonly used tools in biological experiments, but they are littering the field with false findings. A few scientists are pushing for change and are calling for the creation of standards by which antibodies should be made, used and described. Several journals (including Nature) ask authors to make clear that antibodies used in their papers have been profiled for that particular application.
http://www.nature.com/news/reproducibility-crisis-blame-it-on-the-antibodies-1.17586
B - SciDetect: a hoax-detecting software
Bohannon J. Hoax-detecting software spots fake papers. Science 3 April 2015;348(6230):18-19
Springer announced the creation of SciDetect, a freely available programme to automatically detect automatically generated papers. The tool uses a statistical technique similar to those used by email spam filters to automatically detect papers created with SCIgen and similar programmes.
http://www.johnbohannon.org/NewFiles/SciDetect.pdf
Springer announced the creation of SciDetect, a freely available programme to automatically detect automatically generated papers. The tool uses a statistical technique similar to those used by email spam filters to automatically detect papers created with SCIgen and similar programmes.
http://www.johnbohannon.org/NewFiles/SciDetect.pdf
B - Misidentified cell lines
Announcement: Time to tackle cells' mistaken identity. Nature 16 April 2015;520:264
This Editorial sets out new ways to tackle the problem of misidentified cell lines. Changes apply to all Nature journals from 1st May 2015. Authors of papers involving cell lines are asked to check their cell lines against publicly available lists of known misidentified cell lines. If the authors use a known problematic cell line, they are asked to provide a scientific justification for its use, and clearly state its identity in the Methods section.
Authors must report on a cell line's source, authentication testing, and Mycoplasma testing status. For authentication testing, authors are asked to state the test method, test result, and when testing was last performed.
http://www.nature.com/news/announcement-time-to-tackle-cells-mistaken-identity-1.17316
This Editorial sets out new ways to tackle the problem of misidentified cell lines. Changes apply to all Nature journals from 1st May 2015. Authors of papers involving cell lines are asked to check their cell lines against publicly available lists of known misidentified cell lines. If the authors use a known problematic cell line, they are asked to provide a scientific justification for its use, and clearly state its identity in the Methods section.
Authors must report on a cell line's source, authentication testing, and Mycoplasma testing status. For authentication testing, authors are asked to state the test method, test result, and when testing was last performed.
http://www.nature.com/news/announcement-time-to-tackle-cells-mistaken-identity-1.17316
B - Head-to-head RCTs and industry sponsor
Flacco ME, Manzoli L, Boccia S, et al. Head-to-head randomized trials are mostly industry sponsored and almost always favor the industry sponsor. Journal of Clinical Epidemiology 2015;68(7):811-820
(doi: 10.1016/j.jclinepi.2014.12.016)
To map the current status of head-to-head comparative evidence, the authors analyzed a large sample of recently published head-to-head randomized clinical trials (RCTs) covering a wide range of clinical conditions. They evaluated trials with at least 100 participants and specifically focused on the sponsoring of these trials. The literature of head-to-head RCTs is dominated by the industry. Industry-sponsored comparative assessments systematically yield favorable results for the sponsors, even more so when noninferiority designs are involved.
http://www.sciencedirect.com/science/article/pii/S089543561500058X
(doi: 10.1016/j.jclinepi.2014.12.016)
To map the current status of head-to-head comparative evidence, the authors analyzed a large sample of recently published head-to-head randomized clinical trials (RCTs) covering a wide range of clinical conditions. They evaluated trials with at least 100 participants and specifically focused on the sponsoring of these trials. The literature of head-to-head RCTs is dominated by the industry. Industry-sponsored comparative assessments systematically yield favorable results for the sponsors, even more so when noninferiority designs are involved.
http://www.sciencedirect.com/science/article/pii/S089543561500058X
Thursday, March 26, 2015
B - Health care's big data
White SE. A review of big data in health care: challenges and opportunities. Open Access Bioinformatics 2014;6:13-18
(doi: 10.2147/OAB.S50519)
Health care is a high-data volume industry. A literature review was conducted to identify recent articles about the use of big data in health care. These data has the potential to revamp the process of health care delivery in the US and inform providers about the most efficient and effective treatment pathways. The biggest challenge is determining the proper balance between protecting the patient's information and maintaining the integrity and usability of data.
http://www.dovepress.com/a-review-of-big-data-in-health-care-challenges-and-opportunities-peer-reviewed-article-OAB
(doi: 10.2147/OAB.S50519)
Health care is a high-data volume industry. A literature review was conducted to identify recent articles about the use of big data in health care. These data has the potential to revamp the process of health care delivery in the US and inform providers about the most efficient and effective treatment pathways. The biggest challenge is determining the proper balance between protecting the patient's information and maintaining the integrity and usability of data.
http://www.dovepress.com/a-review-of-big-data-in-health-care-challenges-and-opportunities-peer-reviewed-article-OAB
Monday, March 23, 2015
B - Academic career
Austin J. What it takes. Science 2014;344(6190):1422
(doi: 10.1126/science.344.6190.1422)
Science Careers posted a widget that lets early-career scientists calculate the probability that they will someday become principal investigators. Four factors are indicated as the most important ingredients of academic career success: be male, be selfish, be elite, publish in journals with high impact factors. They are linked to rigorous, serious, and significant research, and demonstrate the wide gap between science's ideals and incentives. Discovery and solutions to society's most compelling problems should really matter and be rewarded consequently.
http://www.sciencemag.org/content/344/6190/1422.long
(doi: 10.1126/science.344.6190.1422)
Science Careers posted a widget that lets early-career scientists calculate the probability that they will someday become principal investigators. Four factors are indicated as the most important ingredients of academic career success: be male, be selfish, be elite, publish in journals with high impact factors. They are linked to rigorous, serious, and significant research, and demonstrate the wide gap between science's ideals and incentives. Discovery and solutions to society's most compelling problems should really matter and be rewarded consequently.
http://www.sciencemag.org/content/344/6190/1422.long
Wednesday, March 11, 2015
B - NIH plans to enhance reproducibility
Collins FS, Tabak LA. Policy: NIH plans to enhance reproducibility. Nature 2014 Jan. 27
The authors discuss the significant initiatives that the US National Institutes of Health is exploring to restore the self-correcting nature of preclinical research. They share the concern that the complex system for ensuring the reproducibility of biomedical research is failing. This has compromised the ability of today’s researchers to reproduce others’ findings demanding immediate and substantive action. The NIH is firmly committed to making systematic changes that should reduce the frequency and severity of this problem.
The authors discuss the significant initiatives that the US National Institutes of Health is exploring to restore the self-correcting nature of preclinical research. They share the concern that the complex system for ensuring the reproducibility of biomedical research is failing. This has compromised the ability of today’s researchers to reproduce others’ findings demanding immediate and substantive action. The NIH is firmly committed to making systematic changes that should reduce the frequency and severity of this problem.
Wednesday, January 21, 2015
B - Big data and public health
Khoury MJ, Ioannidis JPA. Big data meets public health. Science 346(6213):1054
(doi: 10.1126/science.aaa2709)
The term “Big Data” refers to volumes of large, complex, linkable information. Beyond genomics and other “omic” fields, Big Data includes medical, environmental, financial, geographic, and social media information. This swell of data will continue to grow. Big Data can improve health by providing insights into the causes and outcomes of disease, better drug targets for precision medicine, and enhanced disease prediction and prevention. But "Big Error" can plague Big Data. The combination of a strong epidemiologic foundation, robust knowledge integration, principles of evidence-based medicine, and an expanded translation research agenda can put Big Data on the right course.
http://www.sciencemag.org/content/346/6213/1054.full
(doi: 10.1126/science.aaa2709)
The term “Big Data” refers to volumes of large, complex, linkable information. Beyond genomics and other “omic” fields, Big Data includes medical, environmental, financial, geographic, and social media information. This swell of data will continue to grow. Big Data can improve health by providing insights into the causes and outcomes of disease, better drug targets for precision medicine, and enhanced disease prediction and prevention. But "Big Error" can plague Big Data. The combination of a strong epidemiologic foundation, robust knowledge integration, principles of evidence-based medicine, and an expanded translation research agenda can put Big Data on the right course.
http://www.sciencemag.org/content/346/6213/1054.full
Friday, December 19, 2014
B - Evidence-based medicine in crisis?
Greenhalgh T, Howick J, Maskrey N. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
(doi: 10.1136/bmj.g3725)
The authors argue that, although evidence based medicine has had many benefits, it has also had some negative unintended consequences. They offer a preliminary agenda for the movement’s renaissance, refocusing on providing useable evidence that can be combined with context and professional expertise so that individual patients get optimal treatment.
http://www.bmj.com/content/348/bmj.g3725
(doi: 10.1136/bmj.g3725)
The authors argue that, although evidence based medicine has had many benefits, it has also had some negative unintended consequences. They offer a preliminary agenda for the movement’s renaissance, refocusing on providing useable evidence that can be combined with context and professional expertise so that individual patients get optimal treatment.
http://www.bmj.com/content/348/bmj.g3725
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