Yoxas/statistical_literacy huggingface dataset:
Environmental exposures, such as ambient air pollution and household fuel use affect health and under-5 mortality (U5M) but there is a paucity of data in the Global South. This study examined early-life exposure to ambient particulate matter with a diameter of 2.5 µm or less (PM2.5), alongside household characteristics (including self-reported household fuel use), and their relationship with U5M in the Navrongo Health and Demographic Surveillance Site (HDSS) in northern Ghana. We employed Satellite-based spatiotemporal models to estimate the annual average PM2.5 concentrations with the Navrongo HDSS area (1998 to 2016). Early-life exposure levels were determined by pollution estimates at birth year. Socio-demographic and household data, including cooking fuel, were gathered during routine surveillance. Cox proportional hazards models were applied to assess the link between early-life PM2.5 exposure and U5M, accounting for child, maternal, and household factors. We retrospectively studied 48,352 children born between 2007 and 2017, with 1872 recorded deaths, primarily due to malaria, sepsis, and acute respiratory infection. Mean early-life PM2.5 was 39.3 µg/m3, and no significant association with U5M was observed. However, Children from households using "unclean" cooking fuels (wood, charcoal, dung, and agricultural waste) faced a 73 % higher risk of death compared to those using clean fuels (adjusted HR = 1.73; 95 % CI: 1.29, 2.33). Being born female or to mothers aged 20-34 years were linked to increased survival probabilities. The use of "unclean" cooking fuel in the Navrongo HDSS was associated with under-5 mortality, highlighting the need to improve indoor air quality by introducing cleaner fuels.
Environmental factors contribute to more than 25% of all global disease but as medical student, we consider that environmental health is insufficiently taught. We are not armed for facing patient's questions, prevent diseases linked to environmental exposure and adapt care in susceptible population. We conducted a national French survey, using a web-based questionnaire from October to November 2021. All medical students were interrogated to assess their knowledge on environmental health and their desire to obtain a specific curriculum on this topic. Two hundred and thirty-two students responded to the survey and most (62.7%) had never had any teaching on environmental health during their medical studies. The majority claimed to have no knowledge on environmental health (63.6%), and most had never been read or seen media content about it (59.2%). Those who had knowledge on environmental health, had been informed through social media, questioning the reliability of this content. Most (87.3%) were ready to take a specific course on environmental health issues during their medical studies in order to better inform their patients with evidence-based medicine and identify at risk population. Through a survey, we identified the need and the emergency of implanting a curriculum on environmental medicine in our medical education.
Environmental health literacy (EHL) involves understanding and using environmental information to make decisions about health. This study developed a validated survey instrument with four scales for assessing media-specific (i.e., air, food, water) and general EHL. The four scales were created as follows: 1) item generation: environmental health scientists and statisticians developed an initial set of items in three domains: knowledge, attitudes, and behaviors; 2) item review: items were reviewed for face validity; 3) validation: 174 public health students, the exploratory sample, and 98 community members, the test sample, validated the scales. The scales' factor structure was based on exploratory factor analysis (EFA) and model fit was assessed through confirmatory factor analysis (CFA). For each scale, the final EFA resulted in an independent three-factor solution for knowledge, attitudes, and behaviors. Good fit for the three-factor structure was observed. Model fit for CFA was generally confirmed with fit indices. The scales showed internal consistency with Cronbach's alpha from 0.63 to 0.70. The 42-item instrument represents an important contribution towards assessing EHL and is designed to enable meaningful engagement between researchers and community members about environmental health. The intended outcome is sustained community-academic partnerships benefiting research design, implementation, translation, dissemination, and community action.
Environmentally sustainable food initiatives accompanying nutrition education, such as the Food Education and Sustainability Training (FEAST) program, have gained traction in school settings. The aim of this trial was to conduct an impact and process evaluation of FEAST, to evaluate its effect on children's fruit and vegetable (F&V) intakes, and secondary outcomes: F&V variety consumed, nutrition knowledge, food preparation/cooking skills, self-efficacy and behaviours, food waste knowledge and behaviours, and food production knowledge. FEAST was a 10-week curriculum-aligned program, designed to educate children about healthy eating, food waste, and sustainability, while teaching cooking skills. It was implemented by classroom teachers, face-to-face and online, during COVID-19 school closures, in Australia in 2021. A custom designed survey was used to collect baseline and post-intervention data from students. Generalised linear mixed models (GLMM) estimated group differences in pre-post changes for primary and secondary outcomes. Surveys were also administered to students and teachers to evaluate intervention implementation. Twenty schools participated and self-selected to be either intervention schools (n = 10) or wait-list control (WLC) schools (n = 10). A total of 977, 5th and 6th grade children participated in the trial with a mean age of 11.1 years (SD ± 0.7). The FEAST intervention, compared to WLC, did not result in significant increases in primary outcomes nor secondary outcomes. The process evaluation revealed FEAST was well-received by students and teachers, but COVID-19 school closures hindered implementation fidelity with a less intense program delivered under the constraints of pandemic lockdowns. This is the first cluster non-randomized controlled trial designed to independently evaluate FEAST in the primary-school setting. No evidence was found for improved F&V intakes in children, nor secondary outcomes. However, the positive process evaluation results suggest that further trials of the program are warranted. If implemented as originally designed (pre-pandemic), with increased duration and complemented by supporting school policies, such programs have the potential to improve children's daily F&V intakes, cooking skills and food waste behaviours. This would support the Australian curriculum and contribute to: health promotion within schools and sustainable schools initiatives, the national agenda to reduce food waste and sustainable development goals. AUSTRALIAN AND NEW ZEALAND CLINICAL TRIALS REGISTRY: [ACTRN12620001347954]- Registered prospectively on 14/12/2020.
Enzymes are nature's catalysts, mediating chemical processes in living systems. The study of enzyme function and mechanism includes defining the maximum catalytic rate and affinity for substrate/s (among other factors), referred to as enzyme kinetics. Enzyme kinetics is a staple of biochemistry curricula and other disciplines, from molecular and cellular biology to pharmacology. However, because enzyme kinetics involves concepts rarely employed in other areas of biology, it can be challenging for students and researchers. Traditional graphical analysis was replaced by computational analysis, requiring another skill not core to many life sciences curricula. Computational analysis can be time-consuming and difficult in free software (e.g., R) or require costly software (e.g., GraphPad Prism). We present Enzyme Kinetics Analysis (EKA), a web-tool to augment teaching and learning and streamline EKA. EKA is an interactive and free tool for analyzing enzyme kinetic data and improving student learning through simulation, built using R and RStudio's ShinyApps. EKA provides kinetic models (Michaelis-Menten, Hill, simple reversible inhibition models, ternary-complex, and ping-pong) for users to fit experimental data, providing graphical results and statistics. Additionally, EKA enables users to input parameters and create data and graphs, to visualize changes to parameters (e.g., KM or number of measurements). This function is designed for students learning kinetics but also for researchers to design experiments. EKA (enzyme-kinetics.shinyapps.io/enzkinet_webpage/) provides a simple, interactive interface for teachers, students, and researchers to explore enzyme kinetics. It gives researchers the ability to design experiments and analyze data without specific software requirements.
Enzymes synthesize complex natural products effortlessly by catalyzing chemo-, regio-, and enantio-selective transformations. Further, biocatalytic processes are increasingly replacing conventional organic synthesis steps because they use mild solvents, avoid the use of metals, and reduce overall non-biodegradable waste. Here, we present a single-step retrosynthesis search algorithm to facilitate enzymatic synthesis of natural product analogs. First, we develop a tool, RDEnzyme, capable of extracting and applying stereochemically consistent enzymatic reaction templates, i.e., subgraph patterns that describe the changes in connectivity between a product molecule and its corresponding reactant(s). Using RDEnzyme, we demonstrate that molecular similarity is an effective metric to propose retrosynthetic disconnections based on analogy to precedent enzymatic reactions in UniProt/RHEA. Using ∼5500 reactions from RHEA as a knowledge base, the recorded reactants to the product are among the top 10 proposed suggestions in 71% of ∼700 test reactions. Second, we trained a statistical model capable of discriminating between reaction pairs belonging to homologous enzymes and evolutionarily distant enzymes using ∼30 000 reaction pairs from SwissProt as a knowledge base. This model is capable of understanding patterns in enzyme promiscuity to evaluate the likelihood of experimental evolution success. By recursively applying the similarity-based single-step retrosynthesis and evolution prediction workflow, we successfully plan the enzymatic synthesis routes for both active pharmaceutical ingredients (e.g. Islatravir, Molnupiravir) and commodity chemicals (e.g. 1,4-butanediol, branched-chain higher alcohols/biofuels), in a retrospective fashion. Through the development and demonstration of the single-step enzymatic retrosynthesis strategy using natural transformations, our approach provides a first step towards solving the challenging problem of incorporating both enzyme- and organic-chemistry based transformations into a computer aided synthesis planning workflow.
Epidemic diseases (EDs) present a significant but challenging risk endangering public health, evidenced by the outbreak of COVID-19. Compared to other risks affecting public health such as flooding, EDs attract little attention in terms of risk assessment in the current literature. It does not well respond to the high practical demand for advanced techniques capable of tackling ED risks. To bridge this gap, an adapted fuzzy evidence reasoning method is proposed to realize the quantitative analysis of ED outbreak risk assessment (EDRA) with high uncertainty in risk data. The novelty of this article lies in (1) taking the lead to establish the outbreak risk evaluation system of epidemics covering the whole epidemic developing process, (2) combining quantitative and qualitative analysis in the fields of epidemic risk evaluation, (3) collecting substantial first-hand data by reviewing transaction data and interviewing the frontier experts and policymakers from Chinese Centers for Disease Control and Chinese National Medical Products Administration. This work provides useful insights for the regulatory bodies to (1) understand the risk levels of different EDs in a quantitative manner and (2) the sensitivity of different EDs to the identified risk factors for their effective control. For instance, in the case study, we use real data to disclose that influenza has the highest breakout risk level in Beijing. The proposed method also provides a potential tool for evaluating the outbreak risk of COVID-19.
Epidemic propagation on networks represents an important departure from traditional mass-action models. However, the high-dimensionality of the exact models poses a challenge to both mathematical analysis and parameter inference. By using mean-field models, such as the pairwise model (PWM), the high-dimensionality becomes tractable. While such models have been used extensively for model analysis, there is limited work in the context of statistical inference. In this paper, we explore the extent to which the PWM with the susceptible-infected-recovered (SIR) epidemic can be used to infer disease- and network-related parameters. Data from an epidemics can be loosely categorised as being population level, e.g., daily new cases, or individual level, e.g., recovery times. To understand if and how network inference is influenced by the type of data, we employed the widely-used MLE approach for population-level data and dynamical survival analysis (DSA) for individual-level data. For scenarios in which there is no model mismatch, such as when data are generated via simulations, both methods perform well despite strong dependence between parameters. In contrast, for real-world data, such as foot-and-mouth, H1N1 and COVID19, whereas the DSA method appears fairly robust to potential model mismatch and produces parameter estimates that are epidemiologically plausible, our results with the MLE method revealed several issues pertaining to parameter unidentifiability and a lack of robustness to exact knowledge about key quantities such as population size and/or proportion of under reporting. Taken together, however, our findings suggest that network-based mean-field models can be used to formulate approximate likelihoods which, coupled with an efficient inference scheme, make it possible to not only learn about the parameters of the disease dynamics but also that of the underlying network.
Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically-unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.
Epidemiologic research is an exercise in measurement. Observational epidemiologic results usually include a point estimate, a measure of random error such as a frequentist confidence interval, and a qualitative discussion of study limitations. Without randomization of study subjects to exposure groups, inference from study results requires an educated guess about the strength of the systematic errors compared with the strength of the exposure effects. Although quantitative methods to make these educated guesses exist, the conventional approach is qualitative, which reduces the educated guessing to a problem of reasoning under uncertainty. In circumstances such as these, humans predictably reason poorly. Heuristics and resulting biases that simplify the judgmental tasks tend to underestimate the systematic error, underestimate the uncertainty, and focus the inference on the study's specific evidence while excluding countervailing external information. Common warnings to interpret results with trepidation are an ineffective solution. The methods that quantify systematic error and uncertainty challenge the analyst to specify the alternative explanations for associations that are otherwise too readily judged causal.
Epidemiological and clinical cancer research is essential to understanding tumour behaviour and developing new therapies in oncology. However, several countries including Brazil as well as many other regions of the world have limited participation in cancer research. Despite 625,000 new cancer cases recorded in Brazil in 2022, only 2.2% of ongoing cancer clinical trials are available in the country. We conducted an online survey to describe physician engagement with research and to identify the main barriers precluding participation in and conduct of clinical cancer research in the country. An anonymous online survey of 23 objective questions was sent by e-mail to Brazilian members of the Latin American Cooperative Oncology Group and the Brazilian Society of Clinical Oncology. The first 13 questions addressed demographic information, medical training and previous research participation. In the second part, the main barriers to engagement and participation in clinical trials in Brazil were addressed. Continuous variables were measured by median and range. Analyses were performed using SAS statistical software (version 9.4; SAS Institute, Inc. Cary, NC). 109 physicians answered the survey. Most participants were oncologists (N = 98, 89.9%), living in capital cities (N = 84, 77.1%), were from the Southeast region of Brazil (N = 63, 57.8%) and worked at institutions providing exclusively private healthcare (N = 59, 54.1%). Of the 109 respondents, 83 (76.1%) reported working in research centres (as investigators or sub-investigators). Surprisingly, 31.2% of physicians recognised they invite less than 1% of their patients to participate in clinical trials, even though 98 (89.9%) considered the participation of patients in clinical trials extremely relevant. The main barriers compromising the conduct of research in the country were the low number of available trials (48.2%) and the lack of qualified human resources to staff research sites (22.9%). Other reported barriers were the lengthy regulatory approval process (42.2%), followed by a lack of awareness of clinical research by patients resulting in low recruitment rates (24.1%). Of the 26 (23.8%) respondents not working with research, 25 (96.1%) reported interest in being involved, 31.8% have tried participating in research and 62.4% reported limited knowledge of trial procedures. These results suggest a clear need to further engage physicians in clinical research activities in Brazil. Patient education strategies should improve the low recruitment rates and secondarily increase the number of proposed trials in the country.
epidemiological estimates from the 2021 Joint United Nations Program on HIV/AIDS (UNAIDS) emphasize the existing gender disparities, where women face a higher risk of HIV/AIDS exposure. In Mozambique, as of 2021, the HIV prevalence rate among the adult population stood at 12.5%, with an even more concerning rate of 15.4% among women of reproductive age. a cross-sectional study was carried out with secondary data from the Survey on National Indicators of Vaccination, Malaria, and HIV/AIDS (IMASIDA 2015), where we included married women, both civil marriage and common law marriage of reproductive age 15-49 years. Statistical analyses, including chi-squared tests and logistic regression models, accounting for survey design, were employed to assess associations. the study findings showed that HIV prevalence was higher among married women aged 35-49 years (aOR=2.5; 95% CI: 1.3-4.6; p=0.005), those without formal education (aOR=7.7; 95% CI: 1.1-52.9; p=0.038) and those with primary education (aOR=9.8; 95% CI: 1.6-60.1; p=0.014), those who experienced domestic violence (aOR=1.8; 95% CI: 1.0-3.2; p=0.04), had an uncircumcised partner (aOR=1.9; 95% CI: 1.2-3.1; p=0.008), and had three or more lifetime sex partners (aOR=3.6; 95% CI: 2.9-7.3; p<0.001). Women who were in one lifelong union had a lower risk of HIV positivity (aOR=0.5; 96%CI: 0.3-0.8, p=0.005). the findings of this study highlight sociodemographic, behavioral, and violent factors associated with HIV prevalence among women. These findings underscore the importance of targeted interventions and education programs aimed at reducing HIV transmission among females and promoting safer sexual practices.
Epidemiological observational studies have elucidated a correlation between rheumatoid arthritis (RA) and bronchiectasis. However, the causal nature of this association remains ambiguous. To clarify this potential causal linkage, we conducted a two-sample Mendelian randomization (MR) analysis to explore the bidirectional causality between RA and bronchiectasis. Summary statistics for RA and bronchiectasis were obtained from the IEU OpenGWAS database We employed various methods, including inverse variance weighting (IVW), MR-Egger, weighted median, weighted mode, and simple mode, to explore potential causal links between RA and bronchiectasis. Additionally, a series of sensitivity studies, such as Cochran's Q test, MR Egger intercept test, and leave-one-out analysis, were conducted to assess the MR analysis's accuracy further. In the forward MR analysis, the primary analysis indicated that a genetic predisposition to RA correlated with an increased risk of bronchiectasis in European populations (IVW odds ratio (OR): 1.28, 95% confidence interval (CI): 1.20-1.37, p = 1.18E-13). Comparable results were noted in the East Asian subjects (IVW OR: 1.55, 95% CI: 1.30-1.34, p = 8.33E-07). The OR estimates from the other four methods were consistent with those obtained from the IVW method. Sensitivity analysis detected no evidence of horizontal pleiotropy or heterogeneity. Conversely, in the reverse MR analysis, we found no evidence to support a genetic causality between bronchiectasis and RA in either European or East Asian populations. This study indicates that genetic predisposition to RA correlates with a heightened risk of bronchiectasis in both European and East Asian populations. These results imply that routine screening for bronchiectasis in RA patients could be beneficial, and effective management of RA may contribute to a reduced risk of bronchiectasis. Future research should aim to clarify the underlying mechanisms linking these two conditions.