Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya.
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ABSTRACT:
SUBMITTER: Muchene L
PROVIDER: S-EPMC7790388 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe COVID-19 pandemic has spread at an alarming speed, and effective treatment for the disease is still lacking. The body of evidence on COVID-19 has been increasing at an impressive pace, creating the need for a method to rapidly assess the current knowledge and identify key information. Gold standard methods such as systematic reviews and meta-analyses are regarded unsuitable because they have a narrow scope and are very time consuming.ObjectiveThis study aimed to explore the published scientific literature on COVID-19 and map the research evolution during the early phase of the COVID-19 pandemic.MethodsWe performed a PubMed search to analyze the titles, keywords, and abstracts of published papers on COVID-19. We used latent Dirichlet allocation modeling to extract topics and conducted a trend analysis to understand the temporal changes in research for each topic, journal impact factor (JIF), and geographic origin.ResultsBased on our search, we identified 16,670 relevant articles dated between February 14, 2020, and June 1, 2020. Of these, 6 articles were reports from peer-reviewed randomized trials on patients with COVID-19. We identified 14 main research topics, of which the most common topics were health care responses (2812/16,670, 16.86%) and clinical manifestations (1828/16,670, 10.91%). We found an increasing trend for research on clinical manifestations and protective measures and a decreasing trend for research on disease transmission, epidemiology, health care response, and radiology. Publications on protective measures, immunology, and clinical manifestations were associated with the highest JIF. The overall median JIF was 3.7 (IQR 2.6-5.9), and we found that the JIF for these publications declined over time. The top countries producing research were the United States, China, Italy, and the United Kingdom.ConclusionsIn less than 6 months since the novel coronavirus was first detected, a remarkably high number of research articles on COVID-19 have been published. Here, we discuss and present the temporal changes in the available COVID-19 research during the early phase of the pandemic. Our findings may aid researchers and policy makers to form a structured view of the current COVID-19 evidence base and provide further research directions.
| S-EPMC7674137 | biostudies-literature
Project description:BackgroundWHO estimates that more than 50 million people worldwide have epilepsy and 80% of cases are in low-income and middle-income countries. Most studies in Africa have focused on active convulsive epilepsy in rural areas, but there are few data in urban settings. We aimed to estimate the prevalence and spatial distribution of all epilepsies in two urban informal settlements in Nairobi, Kenya.MethodsWe did a two-stage population-based cross-sectional study of residents in a demographic surveillance system covering two informal settlements in Nairobi, Kenya (Korogocho and Viwandani). Stage 1 screened all household members using a validated epilepsy screening questionnaire to detect possible cases. In stage 2, those identified with possible seizures and a proportion of those screening negative were invited to local clinics for clinical and neurological assessments by a neurologist. Seizures were classified following the International League Against Epilepsy recommendations. We adjusted for attrition between the two stages using multiple imputations and for sensitivity by dividing estimates by the sensitivity value of the screening tool. Complementary log-log regression was used to assess prevalence differences by participant socio-demographics.FindingsA total of 56 425 individuals were screened during stage 1 (between Sept 17 and Dec 23, 2021) during which 1126 were classified as potential epilepsy cases. A total of 873 were assessed by a neurologist in stage 2 (between April 12 and Aug 6, 2022) during which 528 were confirmed as epilepsy cases. 253 potential cases were not assessed by a neurologist due to attrition. 30 179 (53·5%) of the 56 425 individuals were male and 26 246 (46·5%) were female. The median age was 24 years (IQR 11-35). Attrition-adjusted and sensitivity-adjusted prevalence for all types of epilepsy was 11·9 cases per 1000 people (95% CI 11·0-12·8), convulsive epilepsy was 8·7 cases per 1000 people (8·0-9·6), and non-convulsive epilepsy was 3·2 cases per 1000 people (2·7-3·7). Overall prevalence was highest among separated or divorced individuals at 20·3 cases per 1000 people (95% CI 15·9-24·7), unemployed people at 18·8 cases per 1000 people (16·2-21·4), those with no formal education at 18·5 cases per 1000 people (16·3-20·7), and adolescents aged 13-18 years at 15·2 cases per 1000 people (12·0-18·5). The epilepsy diagnostic gap was 80%.InterpretationEpilepsy is common in urban informal settlements of Nairobi, with large diagnostic gaps. Targeted interventions are needed to increase early epilepsy detection, particularly among vulnerable groups, to enable prompt treatment and prevention of adverse social consequences.FundingNational Institute for Health Research using Official Development Assistance.
| S-EPMC11254782 | biostudies-literature
Project description:ObjectiveIncrease in alcohol and substance use among college students is a global public health concern. It is associated with the risk of alcohol and substance use disorders to the individual concerned and public health problems to their family and society. Among students there is also the risk of poor academic performance, taking longer to complete their studies or dropping out of university. This study determined the prevalence and patterns of alcohol and substance use of students at the entry to the university.MethodA total of 406 (50.7% male) students were interviewed using the Assessment of Smoking and Substance Involvement Test (ASSIST) and the Alcohol Use Disorder Identification Tool (AUDIT). Bivariate logistic regression analyses were used to examine associations between substance use and students' socio-demographic characteristics. Multivariate logistic regression analysis was conducted to examine the predictors of the lifetime and current alcohol and substance use.ResultsLifetime and current alcohol and substance use prevalence were 103 (25%) and 83 (20%) respectively. Currently frequently used substances were alcohol 69 (22%), cannabis 33 (8%) and tobacco 28 (7%). Poly-substance use was reported by 48 (13%) respondents, the main combinations being cannabis, tobacco, and alcohol. Students living in private hostels were four times more likely to be current substance users compared with those living on campus (OR = 4.7, 95% CI: 2.0, 10.9).ConclusionA quarter of the study respondents consumed alcohol and/or substances at the entry to university pushing the case for early intervention strategies to delay initiation of alcohol and substance use and to reduce the associated harmful consequences.
| S-EPMC7454962 | biostudies-literature
Project description:In sub-Saharan Africa, where the effects of human immunodeficiency virus type 1 (HIV-1) have been most devastating, there are multiple subtypes of this virus. The distribution of different subtypes within African populations is generally not linked to particular risk behaviors. Thus, Africa is an ideal setting in which to examine the diversity and mixing of viruses from different subtypes on a population basis. In this setting, it is also possible to address whether infection with a particular subtype is associated with differences in disease stage. To address these questions, we analyzed the HIV-1 subtype, plasma viral loads, and CD4 lymphocyte levels in 320 women from Nairobi, Kenya. Subtype was determined by a combination of heteroduplex mobility assays and sequence analyses of envelope genes, using geographically diverse subtype reference sequences as well as envelope sequences of known subtype from Kenya. The distribution of subtypes in this population was as follows: subtype A, 225 (70.3%); subtype D, 65 (20.5%); subtype C, 22 (6.9%); and subtype G, 1 (0.3%). Intersubtype recombinant envelope genes were detected in 2.2% of the sequences analyzed. Given that the sequences analyzed represented only a small fraction of the proviral genome, this suggests that intersubtype recombinant viral genomes may be very common in Kenya and in other parts of Africa where there are multiple subtypes. The plasma viral RNA levels were highest in women infected with subtype C virus, and women infected with subtype C virus had significantly lower CD4 lymphocyte levels than women infected with the other subtypes. Together, these data suggest that women in Kenya who are infected with subtype C viruses are at more advanced stages of immunosuppression than women infected with subtype A or D. There are at least two models to explain the data from this cross-sectional study; one is that infection with subtype C is associated with a more rapid disease progression, and the second is that subtype C represents an older epidemic in Kenya. Discriminating between these possibilities in a longitudinal study will be important for increasing our understanding of the role of specific subtypes in the transmission and pathogenesis of HIV-1.
| S-EPMC104220 | biostudies-literature
Project description:PNAS is one of world's most cited multidisciplinary scientific journals. The PNAS official classification structure of subjects is reflected in topic labels submitted by the authors of articles, largely related to traditionally established disciplines. These include broad field classifications into physical sciences, biological sciences, social sciences, and further subtopic classifications within the fields. Focusing on biological sciences, we explore an internal soft-classification structure of articles based only on semantic decompositions of abstracts and bibliographies and compare it with the formal discipline classifications. Our model assumes that there is a fixed number of internal categories, each characterized by multinomial distributions over words (in abstracts) and references (in bibliographies). Soft classification for each article is based on proportions of the article's content coming from each category. We discuss the appropriateness of the model for the PNAS database as well as other features of the data relevant to soft classification.
| S-EPMC387299 | biostudies-literature
Project description:We propose a new method to assess the merit of any set of scientific papers in a given field based on the citations they receive. Given a field and a citation impact indicator, such as the mean citation or the [Formula: see text]-index, the merit of a given set of [Formula: see text] articles is identified with the probability that a randomly drawn set of [Formula: see text] articles from a given pool of articles in that field has a lower citation impact according to the indicator in question. The method allows for comparisons between sets of articles of different sizes and fields. Using a dataset acquired from Thomson Scientific that contains the articles published in the periodical literature in the period 1998-2007, we show that the novel approach yields rankings of research units different from those obtained by a direct application of the mean citation or the [Formula: see text]-index.
| S-EPMC3496740 | biostudies-literature
Project description:Background: To date, there are few studies carried out on low back pain (LBP) among university teaching staff in developing countries despite academics being a high-risk group for LBP. In Kenya, to the best of our knowledge, there are no published studies that have investigated risk factors for LBP among teaching staff. The objectives of this study were to estimate the prevalence of LBP among teaching staff of the University of Nairobi (UoN), during the period June 2016 - May 2017, and to identify its socio-demographic and work-related risk factors. Methods: An analytical cross-sectional study design was used to estimate the prevalence and investigate the risk factors for LBP among 136 teaching staff of UoN. A semi-structured questionnaire was used to collect data on LBP history, work-related and socio-demographic characteristics of the study participants. The 12-month prevalence of LBP and its associated 95% exact binomial confidence interval were estimated. A mixed-effects logistic regression model was used to evaluate the relationship between the predictors and LBP. Results: The estimated 12-month prevalence of LBP was 64% (95% CI: 55.3%-72.0%). From the multivariable analysis, physical inactivity (aOR: 6.0; 95% CI: 1.2-29.6), office chairs without lumbar supports (aOR: 3.3; 95% CI: 0.1-0.9) and high workplace stress (aOR: 4.4; 95% CI: 1.1-17.5) were identified as significant risk factors for LBP among the respondents. Conclusions: This study has revealed a high burden of LBP among teaching staff of the UoN and undoubtedly mimics the situation in other higher learning institutions in Kenya. Physical inactivity, sitting on chairs without lumbar supports and workplace stress have been identified as modifiable risk factors for LBP among teaching staff. This suggests a need to strengthen advocacy for regular physical activity, team-building activities and investment in office infrastructure to mitigate the effects of LBP within learning institutions.
| S-EPMC7531051 | biostudies-literature
Project description:Publications in scientific journals contain a considerable fraction of our scientific knowledge. Analyzing data from publication databases helps us understand how this knowledge is obtained and how it changes over time. In this study, we present a mathematical model for the temporal dynamics of data on the scientific content of publications. Our data set consists of references to thousands of genes in the >15 million publications listed in PubMed. We show that the observed dynamics may result from a simple process: Researchers predominantly publish on genes that already appear in many publications. This might be a rewarding strategy for researchers, because there is a positive correlation between the frequency of a gene in scientific publications and the journal impact of the publications. By comparing the empirical data with model predictions, we are able to detect unusual publication patterns that often correspond to major achievements in the field. We identify interactions between yeast genes from PubMed and show that the frequency differences of genes in publications lead to a biased picture of the resulting interaction network.
| S-EPMC1924584 | biostudies-literature
Project description:Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.
| S-EPMC4200399 | biostudies-literature
Project description: Not available
| S-EPMC6119233 | biostudies-literature