Yoxas/statistical_literacy huggingface dataset:
Little is known on the association between cross-shift changes in pulmonary function and personal inhalation exposure to particulate matter (PM) among informal electronic-waste (e-waste) recovery workers who have substantial occupational exposure to airborne pollutants from burning e-waste. Using a cross-shift design, pre- and post-shift pulmonary function assessments and accompanying personal inhalation exposure to PM (sizes <1, <2.5 μm, and the coarse fraction, 2.5-10 μm in aerodynamic diameter) were measured among e-waste workers (n = 142) at the Agbogbloshie e-waste site and a comparison population (n = 65) in Accra, Ghana during 2017 and 2018. Linear mixed models estimated associations between percent changes in pulmonary function and personal PM. Declines in forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) per hour were not significantly associated with increases in PM (all sizes) among either study population, despite breathing zone concentrations of PM (all sizes) that exceeded health-based guidelines in both populations. E-waste workers who worked "yesterday" did, however, have larger cross-shift declines in FVC [-2.4% (95%CI: -4.04%, -0.81%)] in comparison to those who did not work "yesterday," suggesting a possible role of cumulative exposure. Overall, short-term respiratory-related health effects related to PM exposure among e-waste workers were not seen in this sample. Selection bias due to the "healthy worker" effect, short shift duration, and inability to capture a true "pre-shift" pulmonary function test among workers who live at the worksite may explain results and suggest the need to adapt cross-shift studies for informal settings.
Little is known whether patients with lower health literacy could retain the practice and knowledge of home blood pressure monitoring (HBPM) after an educational programme. A cluster randomised controlled trial in five primary care clinics recruited participants with uncontrolled hypertension. Clinics were randomised either to a HBPM group education (Risk Assessment and Management Programme (RAMP-group), or individual counselling of self-management (RAMP-individual). Health literacy was assessed by the Chinese Health Literacy Scale for Chronic Care. Practice and knowledge of HBPM were surveyed by a 10-item HBPM knowledge checklist and patient record review 6 months after interventions. Predictors for regular HBPM and good HBPM knowledge were assessed by multivariate logistic regression models. 287 participants (RAMP-group: 151; RAMP-individual: 136) were follow-up for 6 months. 272 participants completed the knowledge questionnaires (response rate 94.8%). 67.8% of the participants performed HBPM regularly, and there was no statistical difference between both interventions. Age more than 65 (adjusted odds ratios (aOR) 2.58, 95% CI 1.37 to 4.86, p=0.003), not working (aOR 2.34, 95% CI 1.10 to 4.97, p=0.027)and adequate health literacy (aOR 2.25, 95% CI 1.28 to 3.95, p=0.005) predicted regular HBPM. Participants in RAMP-group demonstrated a significant lower body weight than those in RAMP-individual (-0.3±2.0 kg vs +0.7 ±1.7 kg, p<0.001).The RAMP-group participants were eight times more likely to have full HBPM knowledge score than the RAMP-individual participants (aOR 8.46, 95% CI 4.68 to 15.28, p<0.001). Patients could retain HBPM knowledge better after RAMP-group than RAMP-individual. Older, retired and patients with adequate health literacy were more likely to continue weekly HBPM 6 months after education. NCT02551393.
Little research has investigated factors that determine whether people falter in the face of an obstacle or successfully follow through on an initial commitment to act. We integrated multiple theories (the Reasoned Action Approach [RAA], Prototype Willingness Model, and anticipated regret theory) to test which factors predict initial commitment to register as an organ donor and to discover whether different factors predict initial commitment vs. following through with registration. Participants from a nationally representative UK sample (N = 1,008) reported their beliefs about organ donation and indicated their decision to register. An obstacle that participants could not foresee was that they had to complete registration in a second survey 3 days after making their initial commitment. Findings showed that 14.8% of participants followed through, 19.7% demonstrated initial commitment, and 65.5% declined to register. Linear discriminant function analysis derived two functions that distinguished these registration patterns. The first function discriminated participants who declined to register from the other groups. The declined group had lower scores on RAA variables compared to their counterparts. The second function distinguished participants who made an initial commitment to register from those who followed through. Follow-through was associated with less anticipated negative affect, more favorable descriptive norms, and stronger identification with organ donors. The present findings indicate that even modest friction leads to a large reduction in follow-through. Moreover, different factors influence initial commitment vs. following through. Whereas RAA variables predicted initial commitment, following through was a function of anticipated negative affect and social processes.
Liver cancer is one of the major death factors in the world. Transplantation and tumor removal are two main therapies in common clinical practice. Both tasks need image assisted planning and quantitative evaluations. Automatic liver segmentation is required for corresponding quantitative evaluations. Conventional approaches in liver segmentation consist of finding the initial liver border followed by tuning the border to the final mask. Finding the liver initial border is of great importance as the latter step largely depends on the initial step. In the previous works, the liver initial border was determined by applying thresholding and morphological filters. In order to estimate the liver initial boundary, we have proposed a technique based on anatomical knowledge of liver, its surrounding tissues as well as the approach that a clinician follows in screening liver in a CT dataset. Based on the above reasoning, we developed a multi-step heuristic technique to segment liver from other tissues in multi-slice CT images. The proposed technique can deal with various shapes, locations, and liver sizes. The method was evaluated in the presence of 50 actual liver data sets and the results were encouraging.
Liver disease is a major cause of mortality and morbidity worldwide and its epidemiology depends on the genetic background, exposure to risk factors, access to healthcare and other sociodemographic characteristics. Brazil is a large country with diverse multicultural and ethnic heritages and important socioeconomic inequalities. The burden of liver disease in Brazil, its regions and population is unknown. We retrieved data from the Unified Health System regarding liver diseases and analyzed the mortality and morbidity from 1996 to 2022 by gender, race/ethnicity, age, region and overall. We calculated the age-specific risk of deaths by liver disease, age-standardization of the data, mean hospitalization and liver transplant-associated costs. Malignant neoplasm of the liver and intrahepatic bile ducts, alcohol-associated liver disease, fibrosis, and cirrhosis of the liver, other diseases of the liver, hepatic failure, chronic viral hepatitis were identified as the major causes of death and morbidity in Brazil in the period analyzed. The epidemiology of these diseases was diverse, with variations according to geographic regions, gender and race/ethnicity. The major economic burden of liver disease is related to liver transplants, a common outcome of the progression of these diseases. Liver disease in Brazil is a serious issue for the public health system due to the high number of deaths and increasing mortality rate. Our study contributes as a necessary prerequisite for the development of tailored public health policies aimed at mitigating the increasing burden of liver diseases in specific populations and regions. CNPq, INCT, CAPES, FAPEMIG.
Livestock cause many fatal and non-fatal agricultural accidents. It is crucial to understand how farmers perceive and manage different risks associated with livestock handling to devise better solutions for accident reduction. The current study investigated farmers' perception and management of four types of livestock handling risks related to self, animal, environment, and equipment. Additionally, farmers' and agricultural stakeholders' perspectives were compared.Two samples comprising 56 farmers and 55 stakeholders from the UK and Ireland completed the online study. Participants were presented with eight short livestock handling vignettes, two per risk type, and were asked to decide whether they would proceed with the task, to report their reasoning, and to detail their risk management strategies. Likert-scale responses across scenarios were compared. Thematic analysis was used to identify qualitative data patterns.Stress and fatigue were perceived as low risk by both samples based on quantitative and qualitative results. The thematic analysis revealed that risk was evaluated in terms of broader aspects, including animal welfare and duty. Participants reported the use of cognitive non-technical skills when mitigating risks associated with handling livestock alone.By changing safety messages to capture farmer priorities, agricultural organisations could encourage risk avoidance, especially in situations involving stress or fatigue. Furthermore, the cognitive non-technical skills identified could be trained within existing courses for farmers.
Livestock manure is one of the most important pools of antibiotic resistance genes (ARGs) in the environment. Aerobic composting can effectively reduce the spread of antibiotic resistance risk in livestock manure. Understanding the effect of aerobic composting process parameters on manure-sourced ARGs is important to control their spreading risk. In this study, the effects of process parameters on ARGs during aerobic composting of pig manure were explored through data mining based on 191 valid data collected from literature. Machine learning (ML) models (XGBoost and Random Forest) were utilized to predict the rate of ARGs changes during pig manure composting. The model evaluation index of the XGBoost model (R2 = 0.651) was higher than that of the Random Forest (R2 = 0.490), indicating that XGBoost had better prediction performance. Feature importance was further calculated for the XGBoost model, and the XGBoost black box model was interpreted by Shapley additive explanations analysis. Results indicated that the influencing factors on the ARGs variation in pig manure were sequentially divided into thermophilic period, total composting period, composting real time, and thermophilic stage average temperature. The findings gave an insight into the application of ML models to predict and decipher the ARG changes during manure composting and provided suggestions for better composting manipulation and optimization of process parameters.
Living with type 1 diabetes is challenging, and to support self-management, repeated consultations in specialist outpatient care are often required. The emergence of new digital solutions has revolutionized how health care services can be patient centered, providing unprecedented opportunities for flexible, high-quality care. However, there is a lack of studies exploring how the use of digital patient-reported outcome measures (PROMs) for flexible specialist care affects diabetes self-management. To provide new knowledge on the relevance of using PROMs in standard care, we have designed a multimethod prospective study. The overall aim of this protocol is to describe our prospective multimethod observational study designed to investigate digital PROMs in a routine specialist outpatient setting for flexible patient-centered diabetes care (DigiDiaS). This protocol outlines the design of a multimethod prospective observational cohort study that includes data from electronic health records, self-reported questionnaires, clinical consultation field observations, and individual in-depth interviews with patients and diabetes health care personnel. All patients with type 1 diabetes at a designated outpatient clinic were invited to participate and use the digital PROM implemented in clinical care. Both users and nonusers of the digital PROM were eligible for the prospective study, allowing for a comparison of the two groups. Data were collected at baseline and after 12 months, including self-management as the primary outcome assessed using the Patient Activation Measure, along with the secondary outcomes of digital health literacy, quality of life, health economy, and clinical variables such as glycated hemoglobin. The digital solution was implemented for routine clinical care in the department in November 2021, and data collection for the prospective study started in October 2022. As of September 6, 2023, 84.6% (186/220) of patients among those in the digital PROM and 15.5% (34/220) of patients among the nonusers have consented to participate. We expect the study to have enough participants by the autumn of 2023. With 1 year of follow-up, the results are expected by spring 2025. In conclusion, a multimethod prospective observational cohort study can offer valuable insights into the relevance, effectiveness, and acceptability of digital tools using PROMs in diabetes specialist care. Such knowledge is crucial for achieving broad and successful implementation and use of these tools in a large diabetes outpatient clinic. DERR1-10.2196/52766.
Loneliness among mothers of infants is a serious problem that leads to increased stress and depression. Social networking sites (SNS) are platforms commonly used by mothers to gain information and socialize; however, the sites can also lead to social comparison. This study aimed to identify and compare the types of parental social comparisons on SNS among mothers with infants and examine their differences regarding maternal loneliness. A random sampling cross-sectional survey of 233 Japanese mothers with infants aged 6-11 months old was conducted. The questionnaire considered the frequency of parental social comparisons on SNS and the associated positive/negative emotions, loneliness, perceived social support, SNS use, and participant characteristics. Hierarchical cluster analysis and analysis of covariance were used to examine differences in loneliness by social comparison type. Identified types of parental social comparisons on SNS included: "Negative-leaning comparisons (n = 40)," "Ambivalent comparisons (n = 53)," "Heavy comparisons (n = 39)," and "Positive-leaning comparisons (n = 67)." The "non-comparative group (n = 34)" made no comparisons. The loneliness scores of the "Negative-leaning comparisons" group were significantly higher than those of the "Ambivalent comparisons," "Positive-leaning comparisons," and "Non-comparative" groups (p = 0.019, p = 0.017, and p < 0.001, respectively). Additionally, the loneliness scores of the "Heavy comparisons" group were higher than those of the "Non-comparative" group (p = 0.005). Interventions aimed at enhancing digital literacy among mothers and providing tailored support based on their social comparison types are crucial for mitigating the negative effects of parental social comparisons on SNS.
Long COVID is a major problem affecting patient health, the health service, and the workforce. To optimise the design of future interventions against COVID-19, and to better plan and allocate health resources, it is critical to quantify the health and economic burden of this novel condition. We aimed to evaluate and estimate the differences in health impacts of long COVID across sociodemographic categories and quantify this in Quality-Adjusted Life-Years (QALYs), widely used measures across health systems. With the approval of NHS England, we utilised OpenPROMPT, a UK cohort study measuring the impact of long COVID on health-related quality-of-life (HRQoL). OpenPROMPT invited responses to Patient Reported Outcome Measures (PROMs) using a smartphone application and recruited between November 2022 and October 2023. We used the validated EuroQol EQ-5D questionnaire with the UK Value Set to develop disutility scores (1-utility) for respondents with and without Long COVID using linear mixed models, and we calculated subsequent Quality-Adjusted Life-Months (QALMs) for long COVID. The total OpenPROMPT cohort consisted of 7575 individuals who consented to data collection, with which we used data from 6070 participants who completed a baseline research questionnaire where 24.6% self-reported long COVID. In multivariable regressions, long COVID had a consistent impact on HRQoL, showing a higher likelihood or odds of reporting loss in quality-of-life (Odds Ratio (OR): 4.7, 95% CI: 3.72-5.93) compared with people who did not report long COVID. Reporting a disability was the largest predictor of losses of HRQoL (OR: 17.7, 95% CI: 10.37-30.33) across survey responses. Self-reported long COVID was associated with an 0.37 QALM loss. We found substantial impacts on quality-of-life due to long COVID, representing a major burden on patients and the health service. We highlight the need for continued support and research for long COVID, as HRQoL scores compared unfavourably to patients with conditions such as multiple sclerosis, heart failure, and renal disease. This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).
Long COVID is the patient-coined term for the persistent symptoms of COVID-19 illness for weeks, months or years following the acute infection. There is a large burden of long COVID globally from self-reported data, but the epidemiology, causes and treatments remain poorly understood. Primary care is used to help identify and treat patients with long COVID and therefore Electronic Health Records (EHRs) of past COVID-19 patients could be used to help fill these knowledge gaps. We aimed to describe the incidence and differences in demographic and clinical characteristics in recorded long COVID in primary care records in England. With the approval of NHS England we used routine clinical data from over 19 million adults in England linked to SARS-COV-2 test result, hospitalisation and vaccination data to describe trends in the recording of 16 clinical codes related to long COVID between November 2020 and January 2023. Using OpenSAFELY, we calculated rates per 100,000 person-years and plotted how these changed over time. We compared crude and adjusted (for age, sex, 9 NHS regions of England, and the dominant variant circulating) rates of recorded long COVID in patient records between different key demographic and vaccination characteristics using negative binomial models. We identified a total of 55,465 people recorded to have long COVID over the study period, which included 20,025 diagnoses codes and 35,440 codes for further assessment. The incidence of new long COVID records increased steadily over 2021, and declined over 2022. The overall rate per 100,000 person-years was 177.5 cases in women (95% CI: 175.5-179) and 100.5 in men (99.5-102). The majority of those with a long COVID record did not have a recorded positive SARS-COV-2 test 12 or more weeks before the long COVID record. In this descriptive study, EHR recorded long COVID was very low between 2020 and 2023, and incident records of long COVID declined over 2022. Using EHR diagnostic or referral codes unfortunately has major limitations in identifying and ascertaining true cases and timing of long COVID. This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).
Long-acting reversible contraceptive (LARC) method use is an ideal strategy for longer protection against unintended pregnancies, unsafe abortions, maternal morbidities, and mortalities related to pregnancies and childbirth. Despite low utilization of LARC methods in Ethiopia, early discontinuation remains a problem. This study aimed to assess prevalence of early discontinuation of LARC methods and associated factors in Hossana town. A community-based cross-sectional study was conducted among 433 adult women of reproductive age who had a history of LARC use. Logistic regression model was considered for the analysis. Proportion of LARC methods discontinuation within one year was 24.5%, 95% CI (20.6, 26.8%). Women whose age ≥ 30 years (AOR = 3.16, 95% CI: 1.27, 7.89), who had < 3 live children (AOR = 5.17, 95% CI 2.30, 11.61), who had a desire for pregnancy (AOR = 2.35, 95% CI 1.14, 4.85), who did not get pre-insertion counseling on the benefits of LARC methods (AOR = 1.79, 95% CI 1.01, 3.21) and who experienced side effects (AOR = 3.63, 95% CI 2.07, 6.38) were more likely to discontinue LARC methods early than their counterparts. Nearly one-fourth of clients discontinued using the LARC methods within the first year of insertion, highlighting the need to promote longer use for improved protection and success of family planning programs.
Long-range correlated temporal fluctuations in the beats of musical rhythms are an inevitable consequence of human action. According to recent studies, such fluctuations also lead to a favored listening experience. The scaling laws of amplitude variations in rhythms, however, are widely unknown. Here we use highly sensitive onset detection and time series analysis to study the amplitude and temporal fluctuations of Jeff Porcaro's one-handed hi-hat pattern in "I Keep Forgettin'"-one of the most renowned 16th note patterns in modern drumming. We show that fluctuations of hi-hat amplitudes and interbeat intervals (times between hits) have clear long-range correlations and short-range anticorrelations separated by a characteristic time scale. In addition, we detect subtle features in Porcaro's drumming such as small drifts in the 16th note pulse and non-trivial periodic two-bar patterns in both hi-hat amplitudes and intervals. Through this investigation we introduce a step towards statistical studies of the 20th and 21st century music recordings in the framework of complex systems. Our analysis has direct applications to the development of drum machines and to drumming pedagogy.
Long-read whole genome sequencing like Oxford Nanopore Technology, is increasingly being introduced in clinical settings. With its ability to simultaneously call sequence variation and DNA modifications including 5-methylcytosine, nanopore is a promising technology to improve diagnostics of imprinting disorders. Currently, no tools to analyze DNA methylation patterns at known clinically relevant imprinted regions are available. Here we present NanoImprint, which generates an easily interpretable report, based on long-read nanopore sequencing, to use for identifying clinical relevant abnormalities in methylation levels at 14 imprinted regions and diagnosis of common imprinting disorders. NanoImprint outputs a summarizing table and visualization plots displays methylation frequency (%) and chromosomal positions for all regions, with phased data color-coded for the two alleles. We demonstrate the utility of NanoImprint using three imprinting disorder samples from patients with Beckwith-Wiedemann syndrome (BWS), Angelman syndrome (AS) and Prader-Willi syndrome (PWS). NanoImprint script is available from https://github.com/carolinehey/NanoImprint.