bims-librar Biomed News
on Biomedical librarianship
Issue of 2021‒01‒24
seventeen papers selected by
Thomas Krichel
Open Library Society


  1. J Am Med Inform Assoc. 2021 Jan 23. pii: ocaa232. [Epub ahead of print]
    Kavanagh PL, Frater F, Navarro T, LaVita P, Parrish R, Iorio A.
      OBJECTIVE: Our aim was to develop an efficient search strategy for prognostic studies and clinical prediction guides (CPGs), optimally balancing sensitivity and precision while independent of MeSH terms, as relying on them may miss the most current literature.MATERIALS AND METHODS: We combined 2 Hedges-based search strategies, modified to remove MeSH terms for overall prognostic studies and CPGs, and ran the search on 269 journals. We read abstracts from a random subset of retrieved references until ≥ 20 per journal were reviewed and classified them as positive when fulfilling standardized quality criteria, thereby assembling a standard dataset used to calibrate the search strategy. We determined performance characteristics of our new search strategy against the Hedges standard and performance characteristics of published search strategies against the standard dataset.
    RESULTS: Our search strategy retrieved 16 089 references from 269 journals during our study period. One hundred fifty-four journals yielded ≥ 20 references and ≥ 1 prognostic study or CPG. Against the Hedges standard, the new search strategy had sensitivity/specificity/precision/accuracy of 84%/80%/2%/80%, respectively. Existing published strategies tested against our standard dataset had sensitivities of 36%-94% and precision of 5%-10%.
    DISCUSSION: We developed a new search strategy to identify overall prognosis studies and CPGs independent of MeSH terms. These studies are important for medical decision-making, as they identify specific populations and individuals who may benefit from interventions.
    CONCLUSION: Our results may benefit literature surveillance and clinical guideline efforts, as our search strategy performs as well as published search strategies while capturing literature at the time of publication.
    Keywords:  literature search; prognosis; search strategy; sensitivity; specificity; updating
    DOI:  https://doi.org/10.1093/jamia/ocaa232
  2. Econ Policy. 2020 Apr;35(102): 269-304
    Staudt J.
      In April 2008, the National Institutes of Health (NIH) implemented the Public Access Policy (PAP), which mandated that the full text of NIH-supported articles be made freely available on PubMed Central - the NIH's repository of biomedical research. This paper uses 600,000 NIH articles and a matched comparison sample to examine how the PAP impacted researcher access to the biomedical literature and publishing patterns in biomedicine. Though some estimates allow for large citation increases after the PAP, the most credible estimates suggest that the PAP had a relatively modest effect on citations, which is consistent with most researchers having widespread access to the biomedical literature prior to the PAP, leaving little room to increase access. I also find that NIH articles are more likely to be published in traditional subscription-based journals (as opposed to 'open access' journals) after the PAP. This indicates that any discrimination the PAP induced, by subscription-based journals against NIH articles, was offset by other factors - possibly the decisions of editors and submission behaviour of authors.
    Keywords:  031; 034; 038
    DOI:  https://doi.org/10.1093/epolic/eiaa015
  3. Ugeskr Laeger. 2020 Dec 28. pii: V10200722. [Epub ahead of print]182(53):
    Frandsen TF, Eriksen MB.
      The planning of a systematic search is of great importance to the results. The systematic searches are planned as part of a protocol, which is developed to minimise bias. The development of the search strategy uses elements from a conceptualisation model. The information sources are selected based on the review question and should include more than one bibliographic database and possibly grey literature. Search terms are defined, and the thesauri of the databases should be used if possible. Any alternative search strategies may be considered. Finally, the searches must be reported in detail. Those points are discussed in this review.
  4. J Racial Ethn Health Disparities. 2021 Jan 19.
    Mohammed M, Sha'aban A, Jatau AI, Yunusa I, Isa AM, Wada AS, Obamiro K, Zainal H, Ibrahim B.
      BACKGROUND: A relentless flood of information accompanied the novel coronavirus 2019 (COVID-19) pandemic. False news, conspiracy theories, and magical cures were shared with the general public at an alarming rate, which may lead to increased anxiety and stress levels and associated debilitating consequences.OBJECTIVES: To measure the level of COVID-19 information overload (COVIO) and assess the association between COVIO and sociodemographic characteristics among the general public.
    METHODS: A cross-sectional online survey was conducted between April and May 2020 using a modified Cancer Information Overload scale. The survey was developed and posted on four social media platforms. The data were only collected from those who consented to participate. COVIO score was classified into high vs. low using the asymmetrical distribution as a guide and conducted a binary logistic regression to examine the factors associated with COVIO.
    RESULTS: A total number of 584 respondents participated in this study. The mean COVIO score of the respondents was 19.4 (± 4.0). Sources and frequency of receiving COVID-19 information were found to be significant predictors of COVIO. Participants who received information via the broadcast media were more likely to have high COVIO than those who received information via the social media (adjusted odds ratio ([aOR],14.599; 95% confidence interval [CI], 1.608-132.559; p = 0.017). Also, participants who received COVID-19 information every minute (aOR, 3.892; 95% CI, 1.124-13.480; p = 0.032) were more likely to have high COVIO than those who received information every week.
    CONCLUSION: The source of information and the frequency of receiving COVID-19 information were significantly associated with COVIO. The COVID-19 information is often conflicting, leading to confusion and overload of information in the general population. This can have unfavorable effects on the measures taken to control the transmission and management of COVID-19 infection.
    Keywords:  COVID-19; General public; Information overload
    DOI:  https://doi.org/10.1007/s40615-020-00942-0
  5. Aust N Z J Public Health. 2021 Jan 18.
    Ferguson C, Merga M, Winn S.
      OBJECTIVE: Government communications in a crisis can influence public health outcomes. This research aimed to investigate if written communications of the most commonly sought sources of COVID-19 information available on the internet have readability levels commensurate with those of the general public.METHODS: Online documents from the World Health Organization (WHO), and the governments of Australia, the UK and the US were assessed for readability using an online instrument that calculated scores for the Flesch Reading Ease Score, the SMOG Index and the Readability Consensus Grade Level.
    RESULTS: Similar to the previous research, most documents assessed had a readability standard that was at or above the recommended grade level, and as such inaccessible to substantial portions of the general public. A one-way ANOVA with post hoc tests revealed significant differences among the data, with Australian documents significantly more difficult to read than those from the UK and US.
    CONCLUSIONS: Government departments need to consider their audience and monitor readability of the documents they produce to ensure that readers can understand them. Implications for public health: Health communications need to be written at a level appropriate for the targeted population in order to be fit for purpose.
    Keywords:  COVID-19; communication; readability; written word
    DOI:  https://doi.org/10.1111/1753-6405.13066
  6. PLoS One. 2021 ;16(1): e0240664
    Pleasants E, Guendelman S, Weidert K, Prata N.
      BACKGROUND: In the United States, the internet is widely used to seek health information. Despite an estimated 18 million Google searches on abortion per year and the demonstrated importance of the abortion pill as an option for pregnancy termination, the top webpage search results for abortion pill searches, as well as the content and quality of those webpages, are not well understood.METHODS: We used Google's Custom Search Application Programming Interface (API) to identify the top 10 webpages presented for "abortion pill" searches on August 06, 2018. We developed a comprehensive, evidence-based Family Planning Webpage Quality Assessment Tool (FPWQAT), which was used to assess webpage quality for the five top webpages presenting text-based educational content.
    RESULTS: Of the top webpages for "abortion pill" searches, a plannedparenthood.com page was the top result and scored highest on our assessment (81%), providing high-quality and useable information. The other four webpages, a Wikipedia.com page and three anti-abortion information webpages, scored much lower on our assessment (14%-43%). These four webpages had lower quality of information in less useable formats. The anti-abortion pages also presented a variety of disinformation about the abortion pill.
    CONCLUSIONS: Both the lack of accurate clinical content on the majority of top webpages and the concerning disinformation they contained raise concerns about the quality of online abortion pill information, while underlining challenges posed by Google search results to informed choice for consumers. Healthcare providers and consumers must be informed of online abortion pill content that is not based in current clinical evidence, while advocates and policymakers should push for online information that is credible and useable. These changes are imperative given the importance of sound abortion pill information for reproductive decision-making at a time when in-person abortion services are further challenged in the US.
    DOI:  https://doi.org/10.1371/journal.pone.0240664
  7. Death Stud. 2021 Jan 16. 1-8
    Bojanić L, Razum J, Gorski I.
      Google search trends have shown promise for predicting suicide deaths. We examined the relationship between search trends data for suicide-related search terms and monthly suicide deaths (2014-2018) in Croatia. We identified two suicide prevention search terms, samoubojstvo and suicid (engl. suicide), where an increase in searches preceded a decrease in suicides, and one suicide risk term, kako se ubiti (engl. how to kill yourself), where an increase in searches preceded an increase in suicides. On webpages elicited by suicide-related search terms, factual information about suicide was most common. Results imply the need for a comprehensive online suicide prevention strategy.
    DOI:  https://doi.org/10.1080/07481187.2021.1873458
  8. Bioinformatics. 2021 Jan 20. pii: btab019. [Epub ahead of print]
    Luo L, Yan S, Lai PT, Veltri D, Oler A, Xirasagar S, Ghosh R, Similuk M, Robinson PN, Lu Z.
      MOTIVATION: Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dictionary-based matching methods, which can achieve high precision but suffer from lower recall. Recently, machine learning-based methods have been proposed to identify biomedical concepts, which can recognize more unseen concept synonyms by automatic feature learning. However, most methods require large corpora of manually annotated data for model training, which is difficult to obtain due to the high cost of human annotation.RESULTS: In this paper, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text. We first use all concepts and synonyms in HPO to construct a dictionary, which is then used to automatically build a distantly supervised training dataset for machine learning. Next, a cutting-edge deep learning model is trained to classify each candidate phrase (n-gram from input sentence) into a corresponding concept label. Finally, the dictionary and machine learning-based prediction results are combined for improved performance. Our method is validated with two HPO corpora, and the results show that PhenoTagger compares favorably to previous methods. In addition, to demonstrate the generalizability of our method, we retrained PhenoTagger using the disease ontology MEDIC for disease concept recognition to investigate the effect of training on different ontologies. Experimental results on the NCBI disease corpus show that PhenoTagger without requiring manually annotated training data achieves competitive performance as compared with state-of-the-art supervised methods.
    AVAILABILITY: The source code, API information and data for PhenoTagger are freely available at https://github.com/ncbi-nlp/PhenoTagger.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btab019
  9. J Med Internet Res. 2021 Jan 19. 23(1): e14794
    Suzuki R, Suzuki T, Tsuji S, Fujiwara K, Yamashina H, Endoh A, Ogasawara K.
      BACKGROUND: An increasing number of people are visiting hospital websites to seek better services and treatments compared to the past. It is therefore important for hospitals to develop websites to meet the needs of their patients. However, few studies have investigated whether and how the current hospital websites meet the patient's needs. Above all, in radiation departments, it may be difficult for patients to obtain the desired information regarding modality and diagnosis because such information is subdivided when described on a website.OBJECTIVE: The purpose of this study is to suggest a hospital website search behavior model by analyzing the browsing behavior model using a Bayesian network from the perspective of one-to-one marketing.
    METHODS: First, we followed the website access log of Hokkaido University Hospital, which was collected from September 1, 2016, to August 31, 2017, and analyzed the access log using Google Analytics. Second, we specified the access records related to radiology from visitor browsing pages and keywords. Third, using these resources, we structured 3 Bayesian network models based on specific patient needs: radiotherapy, nuclear medicine examination, and radiological diagnosis. Analyzing each model, this study considered why some visitors could not reach their desired page and improvements to meet the needs of visitors seeking radiology-related information.
    RESULTS: The radiotherapy model showed that 74% (67/90) of the target visitors could reach their requested page, but only 2% (2/90) could reach the Center page where inspection information, one of their requested pages, is posted. By analyzing the behavior of the visitors, we clarified that connecting with the radiotherapy and radiological diagnosis pages is useful for increasing the proportion of patients reaching their requested page.
    CONCLUSIONS: We proposed solutions for patient web-browsing accessibility based on a Bayesian network. Further analysis is necessary to verify the accuracy of the proposed model in comparison to other models. It is expected that information provided on hospital websites will be improved using this method.
    Keywords:  hospitals; information-seeking behavior; internet; radiology; web marketing
    DOI:  https://doi.org/10.2196/14794
  10. JMIR Res Protoc. 2021 Jan 20. 10(1): e25474
    Lander J, Curbach J, von Sommoggy J, Bitzer EM, Dierks ML.
      BACKGROUND: In early childhood allergy prevention (ECAP), parents act on behalf of their children. Parental health literacy and the availability of high-quality information, both online and offline, are crucial for effective ECAP. Recent research highlights three main points. First, parents need sufficient health literacy to discriminate between high-quality and low-quality information. Second, ECAP information behaviors may vary between phases of childhood development and according to individual circumstances. Third, to strengthen user-centeredness of available services, a better overview of parents' information practices and needs and how they handle uncertainties is required.OBJECTIVE: This study aims to explore why, how, and when parents search for and apply ECAP-specific health information and which individual (eg, understanding of advice) and organizational challenges (eg, information services, information complexity, and changing recommendations) they perceive and how they handle them. This study also aims to assess the needs and preferences that parents express for future information formats and contents. The findings should inform the practical design of ECAP information as well as formats and channels specific to different parent groups.
    METHODS: The above-named issues will be explored with parents in four German cities as one element in our efforts to cover the spectrum of perspectives. Based on a mixed methods design, including qualitative and quantitative assessments, the first year serves to prepare focus groups, a piloted focus group guide, a short standardized survey adapted from the European Health Literacy Project, recruitment channels, and the recruitment of participants. After conducting 20 focus groups in the second year, data will be analyzed via a constant comparison method in the third year. Based on this, practice implications on channels (ie, Where?), formats (ie, How?), and contents (ie, What?) of ECAP-specific information will be derived and discussed with parents and associated project partners before its dissemination to relevant ECAP actors (eg, childcare institutions and pediatricians).
    RESULTS: The study began with preselection of recruitment channels, drafting of recruitment and study information for potential participants, and agreement on a first full version of the guideline. Then, a detailed contact list was compiled of health professionals, administrative and social institutions, and relevant social media channels (N=386) to be approached for assistance in contacting parents. The recruitment was postponed due to COVID-19 and will start in January 2021.
    CONCLUSIONS: ECAP is a relevant example for assessing how users (ie, parents) handle not only health information but the various and continuous changes, uncertainties, and controversies attached to it. So far, it is unclear how parents implement the respective scientific recommendations and expert advice, which is why this study aims to inform those who communicate with parents about ECAP information.
    INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/25474.
    Keywords:  allergy prevention; children; health information; health literacy; parents
    DOI:  https://doi.org/10.2196/25474
  11. Adv Rheumatol. 2021 Jan 19. 61(1): 6
    Ng JY, Vacca A, Jain T.
      BACKGROUND: Complementary and alternative medicine (CAM) use is prevalent among patients living with arthritis. Such patients often seek information online, for the purpose of gaining a second opinion to their healthcare provider or even self-medication. Little is known about the quality of web-based consumer health information at the intersection of CAM and arthritis; thus, investigating the quality of websites containing this information was the purpose of this study.METHODS: Four unique search terms were searched on Google across four English-speaking countries. We assessed the first 20 results of each search, including them if they contained CAM consumer health information for the treatment and/or management of arthritis. Eligible websites were assessed in duplicate using the DISCERN instrument, which consists of 16-items designed to assess quality.
    RESULTS: Of total of 320 webpages, 239 were duplicates, and a total of 38 unique websites were deemed eligible and assessed using the DISCERN instrument. The mean summed  DISCERN scores across all websites was 55.53 (SD = 9.37). The mean score of the overall quality of each website was 3.71 (SD = 0.63), thus the majority of websites are ranked as slightly above 'fair' quality.
    CONCLUSION: Eligible websites generally received scores better than 'moderate' in terms of overall quality. Several shortcomings included a lack of transparency surrounding references used and underreporting of risks associated with treatment options. These results suggest that health providers should be vigilant of the variable quality of information their patients may be accessing online and educate them on how to identify high quality resources.
    Keywords:  Arthritis; Complementary and alternative medicine; Consumer health information; DISCERN; Information assessment; Quality of information
    DOI:  https://doi.org/10.1186/s42358-021-00162-y
  12. BMC Public Health. 2021 01 18. 21(1): 151
    Halboub E, Al-Ak'hali MS, Al-Mekhlafi HM, Alhajj MN.
      BACKGROUND: This study sought to assess the quality and readability of web-based Arabic health information on COVID-19.METHODS: Three search engines were searched on 13 April 2020 for specific Arabic terms on COVID-19. The first 100 consecutive websites from each engine were analyzed for eligibility, which resulted in a sample of 36 websites. These websites were subjected to quality assessments using the Journal of the American Medical Association (JAMA) benchmarks tool, the DISCERN tool, and Health on the Net Foundation Code of Conduct (HONcode) certification. The readability of the websites was assessed using an online readability calculator.
    RESULTS: Among the 36 eligible websites, only one (2.7%) was HONcode certified. No website attained a high score based on the criteria of the DISCERN tool; the mean score of all websites was 31.5 ± 12.55. As regards the JAMA benchmarks results, a mean score of 2.08 ± 1.05 was achieved by the websites; however, only four (11.1%) met all the JAMA criteria. The average grade levels for readability were 7.2 ± 7.5, 3.3 ± 0.6 and 93.5 ± 19.4 for the Flesch Kincaid Grade Level, Simple Measure of Gobbledygook, and Flesch Reading Ease scales, respectively.
    CONCLUSION: Almost all of the most easily accessible web-based Arabic health information on COVID-19 does not meet recognized quality standards regardless of the level of readability and ability to be understood by the general population of Arabic speakers.
    Keywords:  COVID-19; Health information; Infodemiology; Misinformation; Public health; Quality
    DOI:  https://doi.org/10.1186/s12889-021-10218-9
  13. J Med Internet Res. 2021 Jan 18. 23(1): e17680
    Sun F, Yang F, Zheng S.
      BACKGROUND: The internet has changed the way of people acquiring health information. Previous studies have shown that Wikipedia is a reasonably reliable medical resource, and it has been ranked higher than other general websites in various search engines. Baidu Encyclopedia is one of the most popular encyclopedia websites in China. However, no studies have shown the quality of the content provided in the Baidu Encyclopedia.OBJECTIVE: This study aimed to evaluate the quality of liver disease information provided by Wikipedia (in English) and Baidu Encyclopedia (in Chinese) and to perform a comparison of the quality and timeliness of the articles published in these two encyclopedias. Moreover, a 3-year follow-up study was conducted to compare if the information in both these websites was updated regularly over this period.
    METHODS: We searched for information on liver diseases by using the International Statistical Classification of Diseases and Related Health Problems 10th Revision Version 2016 codes on Wikipedia (in English) and Baidu Encyclopedia (in Chinese). The quality of the articles was assessed using the DISCERN instrument, which consists of 3 sections. We recorded the latest editing date of the webpages and calculated the date interval to evaluate the update timeliness of these websites.
    RESULTS: We found 22 entries on liver diseases in Baidu Encyclopedia and 15 articles in Wikipedia between September 15, 2016, and September 30, 2016, and we found 25 entries in Baidu Encyclopedia and 16 articles in Wikipedia between September 15, 2019, and September 30, 2019. In section 1 of the DISCERN instrument, the mean (SE) scores of Baidu Encyclopedia entries were significantly lower than those of Wikipedia articles. In section 2 and section 3 of the DISCERN instrument, the DISCERN scores of Baidu Encyclopedia entries were lower than those of Wikipedia articles, but the differences were not statistically significant. The total DISCERN scores of Baidu Encyclopedia entries were significantly lower than those of Wikipedia articles. The update interval of the entries in Baidu Encyclopedia was found to be significantly longer than that of the articles in Wikipedia.
    CONCLUSIONS: This study shows that the quality of articles and the reliability of the research content on liver diseases in Wikipedia are better than those of the entries in Baidu Encyclopedia. However, the quality of the treatment choices provided in both Wikipedia and Baidu Encyclopedia is not satisfactory. Wikipedia is updated more frequently than Baidu Encyclopedia, thereby ensuring that the information presented has the most recent research findings. The findings of our study suggest that in order to find accurate health information, it is important to seek the help of medical professionals instead of looking for a prescription amid the confusing information provided on the internet.
    Keywords:  Baidu Encyclopedia; DISCERN instrument; Wikipedia; health information; internet; liver disease; timeliness; website
    DOI:  https://doi.org/10.2196/17680
  14. J Med Internet Res. 2021 01 20. 23(1): e19151
    Yeong JL, Thomas P, Buller J, Moosajee M.
      BACKGROUND: Despite the introduction of the Web Content Accessibility Guidelines and legislations, many websites remain poorly accessible to users with disability, especially those with visual impairment, as the internet has become a more visually complex environment. With increasing reliance on the internet and almost 2 million people in the United Kingdom being affected by vision loss, it is important that they are not overlooked when developing web-based materials. A significant proportion of those affected have irreversible vision loss due to rare genetic eye disorders, and many of them use the internet as a primary source of information for their conditions. However, access to high-quality web-based health information with an inclusive design remains a challenge for many. We have developed a new web-based resource for genetic eye disorders called Gene.Vision that aims to provide a holistic guide for patients, relatives, and health care professionals. by sight loss, it is important that they are not overlooked when developing web-based materials. A significant proportion of those affected have irreversible sight loss due to rare genetic eye disorders, and many of them use the internet as a primary source of information for their conditions. However, access to high-quality web-based health information with an inclusive design remains a challenge for many.OBJECTIVE: Through a usability testing session of our website prototype, this study aims to identify key web-based accessibility features for internet users with vision impairment and to explore whether the contents provided in Gene.Vision are relevant and comprehensible.
    METHODS: A face-to-face testing session with 8 participants (5 patients, 2 family members, and 1 member of the public) and 8 facilitators was conducted on a prototype website. Remote testing was performed with another patient due to COVID-19 restrictions. Home page design, navigation, content layout and quality, language, and readability were explored through direct observation and task completion using the think-aloud method. A patient focus group was organized to elicit further feedback. Qualitative data were gathered and analyzed to identify core themes through open and axial coding.
    RESULTS: All participants had good computer literacy; 6 patients with visual impairment used visual aid software including iOS VoiceOver and Speak Screen, iOS Classic Invert, ZoomText 2020, Job Access With Speech, and Nonvisual Desktop Access. The features identified by the participants that will enhance accessibility and usability for users with visual impairment were a consistent website layout, a structured information hierarchy with a clear description of links, good chromatic and luminance contrast, a simple home page with predictable and easy navigation, adaptability to various assistive software, and readable and relevant content. They reported that dynamic content (such as carousels) and large empty spaces reduced accessibility. Information on research, support available, practical advice, and links to charities were incentives for repeated website visits.
    CONCLUSIONS: We demonstrated the importance of developing a website with a user-based approach. Through end user testing, we identified several key web-based accessibility features for people with visual impairment. Target end users should always be involved early and throughout the design process to ensure their needs are met. Many of these steps can be implemented easily and will aid in search engine optimization.
    Keywords:  blindness; consumer health information; eye disease; genetic diseases; internet access; internet-based intervention; mobile phone; qualitative research; usability testing
    DOI:  https://doi.org/10.2196/19151
  15. iScience. 2021 Jan 22. 24(1): 101922
    Shetty P, Ramprasad R.
      Materials science literature has grown exponentially in recent years making it difficult for individuals to master all of this information. This constrains the formulation of new hypotheses that scientists can come up with. In this work, we explore whether materials science knowledge can be automatically inferred from textual information contained in journal papers. Using a data set of 0.5 million polymer papers, we show, using natural language processing methods that vector representations trained for every word in our corpus can indeed capture this knowledge in a completely unsupervised manner. We perform time-based studies through which we track popularity of various polymers for different applications and predict new polymers for novel applications based solely on the domain knowledge contained in our data set. Using co-relations detected automatically from literature in this manner thus, opens up a new paradigm for materials discovery.
    Keywords:  Artificial Intelligence; Computer Science; Materials Science; Polymers
    DOI:  https://doi.org/10.1016/j.isci.2020.101922