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Objective: The overall goal of this work is to produce a set of recommendations (SoNHR–Social Networks in Health Research) that will improve the reporting and dissemination of social network concepts, methods, data, and analytic results within health sciences research. Methods: This study used a modified-Delphi approach for recommendation development consistent with best practices suggested by the EQUATOR health sciences reporting guidelines network. An initial set of 28 reporting recommendations was developed by the author team. A group of 67 (of 147 surveyed) experienced network and health scientists participated in an online feedback survey. They rated the clarity and importance of the individual recommendations, and provided qualitative feedback on the coverage, usability, and dissemination opportunities of the full set of recommendations. After examining the feedback, a final set of 18 recommendations was produced. Results: The final SoNHR reporting guidelines are comprised of 18 recommendations organized within five domains: conceptualization (how study research questions are linked to network conceptions or theories), operationalization (how network science portions of the study are defined and operationalized), data collection & management (how network data are collected and managed), analyses & results (how network results are analyzed, visualized, and reported), and ethics & equity (how network-specific human subjects, equity, and social justice concerns are reported). We also present a set of exemplar published network studies which can be helpful for seeing how to apply the SoNHR recommendations in research papers. Finally, we discuss how different audiences can use these reporting guidelines. Conclusions: These are the first set of formal reporting recommendations of network methods in the health sciences. Consistent with EQUATOR goals, these network reporting recommendations may in time improve the quality, consistency, and replicability of network science across a wide variety of important health research areas. [ABSTRACT FROM AUTHOR]

Many high-throughput sequencing datasets can be represented as objects with coordinates along a reference genome. Currently, biological investigations often involve a large number of such datasets, for example representing different cell types or epigenetic factors. Drawing overall conclusions from a large collection of results for individual datasets may be challenging and time-consuming. Meaningful interpretation often requires the results to be aggregated according to metadata that represents biological characteristics of interest. In this light, we here propose the hierarchical Genomic Suite HyperBrowser (hGSuite), an open-source extension to the GSuite HyperBrowser platform, which aims to provide a means for extracting key results from an aggregated collection of high-throughput DNA sequencing data. The hGSuite utilizes a metadata-informed data cube to calculate various statistics across the multiple dimensions of the datasets. With this work, we show that the hGSuite and its associated data cube methodology offers a quick and accessible way for exploratory analysis of large genomic datasets. The web-based toolkit named hGsuite Hyperbrowser is available at https://hyperbrowser.uio.no/hgsuite under a GPLv3 license. [ABSTRACT FROM AUTHOR]

Variation in the electrical conductivity (EC) of water can reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. Broader application of open source (OS) EC sensors could provide an inexpensive method to measure water quality. While studies show that other water quality parameters can be robustly measured with sensors, a similar effort is needed to evaluate the performance of OS EC sensors. To address this need, we evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. We found a significant difference between OS sensor mean accuracy (3.08%) and all other sensors combined (9.23%). Our study also found that EC sensor precision decreased across all sensor configurations with increasing calibration standard EC. There was also a significant difference between OS sensor mean precision (2.85 μS/cm) and the mean precision of all other sensors combined (9.12 μS/cm). Cable length did not affect OS sensor precision. Furthermore, our results suggest that future research should include evaluating how performance is impacted by combining OS sensors with commercial data loggers as this study found significantly decreased performance in OS/commercial-hybrid sensor configurations. To increase confidence in the reliability of OS sensor data, more studies such as ours are needed to further quantify OS sensor performance in terms of accuracy and precision across different settings and OS sensor and data collection platform configurations. [ABSTRACT FROM AUTHOR]

Background: In Ethiopia, child malnutrition is a significant public health problem. To address the problem, Nutrition-Sensitive Agriculture (NSA) program was introduced. However, there is a paucity of evidence about the prevalence of child undernutrition in NSA-implemented districts. Therefore, this study aimed to assess the prevalence of undernutrition among children aged 6–59 months in NSA-implemented districts. Method: A community-based cross-sectional study was conducted by enrolling 422 children aged 6–59 months paired with their mothers. A systematic sampling technique was used to select respondents. Data were collected by Open Data Kit (ODK) data collection platform, and Stata version 16 was used for analysis. The multivariable logistic analysis model was fitted to assess the association between variables, and 95% CI was estimated to measure the strength of the association. The level of statistical significance was declared at a p-value of less than 0.05 in the multivariable model. Result: Overall, 406 respondents participated in the study, and a response rate of 96.2% was obtained. The prevalence of stunting, wasting, and underweight was 24.1% (95% CI: 19.9–28.4), 8.87% (95% CI: 6.3–12.1) and 19.95% (95% CI: 16.2–24.2), respectively. Household food insecurity was significantly associated with being underweight (AOR: 3.31, 95% CI (1.7–6.3). Child dietary diversity (AOR: 0.06, 95% CI: 0.01–0.48) and being a beneficiary of the NSA (AOR: 0.12, 95% CI: 0.02–0.96) program were associated with wasting. Lack of ANC visits and diarrhea in the past two weeks was associated with stunting and wasting, respectively. Conclusion: The prevalence of malnutrition was a moderate public health problem. Wasting was more prevalent than the recent national and Amhara region averages. However, the prevalence of stunting and underweight was lower than the national average and other studies conducted in Ethiopia. Healthcare providers should work to increase dietary diversity, ANC visits, and reduce diarrheal disease. [ABSTRACT FROM AUTHOR]

Dental caries and periodontal disease are very common chronic diseases closely linked to inadequate removal of dental plaque. Powered toothbrushes are viewed as more effective at removing plaque; however, the conflicting evidence and considerable unexplained heterogeneity in their clinical outcomes does not corroborate the relative merits of powered tooth brushing. To explain the heterogeneity of brushing patterns with powered toothbrushes, we conducted a observational study of tooth brushing practices of 12 participants in their naturalistic setting. Integrated brush sensors and a digital data collection platform allowed unobtrusive and accurate capture of habitual brushing patterns. Annotated brushing data from 10 sessions per participant was chosen for scrutiny of brushing patterns. Analysis of brushing patterns from the total 120 sessions revealed substantial between- and within-participant variability in brushing patterns and efficiency. Most participants (91.67%) brushed for less than the generally prescribed two minutes; individual participants were also inconsistent in brushing duration across sessions. The time devoted to brushing different dental regions was also quite unequal. Participants generally brushed their buccal tooth surfaces more than twice as long as the occlusal (2.18 times longer (95% CI 1.42, 3.35; p < 0.001)) and lingual surfaces (2.22 times longer (95% CI 1.62, 3.10; p < 0.001); the lingual surfaces of the maxillary molars were often neglected (p < 0.001). Participants also varied in the epochs of excessive brushing pressure and the regions to which they were applied. In general, the occlusal surfaces were more likely to be brushed with excessive pressure (95% CI 0.10, 0.98; p = 0.015). Our study reveals that users of powered toothbrushes vary substantially in their use of the toothbrushes and diverge from recommended brushing practices. The inconsistent brushing patterns, between and within individuals, can affect effective plaque removal. Our findings underscore the limited uptake of generic oral self-care recommendations and emphasize the need for personalized brushing recommendations that derive from the objective sensor data provided by powered toothbrushes. [ABSTRACT FROM AUTHOR]

Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients' lives. Prior work has trained models using data from single longitudinal studies, collected from demographically homogeneous populations, over short time periods, using a single data collection platform or mobile application. The generalizability of model performance across studies has not been assessed. This study presents a first analysis to understand if models trained using combined longitudinal study data to predict mental health symptoms generalize across current publicly available data. We combined data from the CrossCheck (individuals living with schizophrenia) and StudentLife (university students) studies. In addition to assessing generalizability, we explored if personalizing models to align mobile sensing data, and oversampling less-represented severe symptoms, improved model performance. Leave-one-subject-out cross-validation (LOSO-CV) results were reported. Two symptoms (sleep quality and stress) had similar question-response structures across studies and were used as outcomes to explore cross-dataset prediction. Models trained with combined data were more likely to be predictive (significant improvement over predicting training data mean) than models trained with single-study data. Expected model performance improved if the distance between training and validation feature distributions decreased using combined versus single-study data. Personalization aligned each LOSO-CV participant with training data, but only improved predicting CrossCheck stress. Oversampling significantly improved severe symptom classification sensitivity and positive predictive value, but decreased model specificity. Taken together, these results show that machine learning models trained on combined longitudinal study data may generalize across heterogeneous datasets. We encourage researchers to disseminate collected de-identified mobile sensing and mental health symptom data, and further standardize data types collected across studies to enable better assessment of model generalizability. [ABSTRACT FROM AUTHOR]

In mountainous and high latitude regions, migratory animals exploit green waves of emerging vegetation coinciding with rising daily mean temperatures initiating snowmelt across the landscape. Snowmelt also causes rivers and streams draining these regions to swell, a process referred to as to as the 'spring pulse.' Networks of streamgages measuring streamflow in these regions often have long-term and continuous periods of record available in real-time and at the daily time step, and thus produce data with potential to predict temporal migration patterns for species exploiting green waves. We tested the potential of models informed by streamflow data to predict timing of spring migration of mule deer (Odocoileus hemionus) herds in a headwater basin of the Colorado River. Models using streamflow data were compared with those informed by traditional temperature-derived measures of the onset of spring. Non-parametric linear-regression techniques were used to test for temporal stationarity in each variable, and logistic-regression models were used to produce probabilities of migration initiation. Our analysis indicates that models using daily streamflow data can perform as well as those using temperature-derived data to predict past-migration patterns, and nearly as well in potential to forecast future migrations. The best performing model was used to generate probabilities of onset of migration for mule deer herds over the 69-year period-of-record from a streamgage. That model indicated spring migration has been trending toward earlier initiations, with modeled median initiations shifting from a Julian day of 123 in the mid 20th century to Julian day 115 over the most recent two decades. The period of 1960 to 1979 had the latest modeled median initiations with Julian day of 128. The analyses demonstrate promise for merging existing hydrologic and biological data collection platforms in these regions to explore timing of past migration patterns and predict migration onsets in real-time. [ABSTRACT FROM AUTHOR]

Along with the major impact on public health, the COVID-19 outbreak has caused unprecedented concerns ranging from sudden loss of employment to mental stress and anxiety. We implemented a survey-based data collection platform to characterize how the COVID-19 pandemic has affected the socio-economic, physical and mental health conditions of individuals. We focused on three broad areas, namely, changes in social interaction during home confinement, economic impact and their health status. We identified a substantial increase in virtual interaction among individuals, which might be a way to alleviate the sudden unprecedented mental health burden, exacerbated by general awareness about viral infections or other manifestations associated with them. The majority of participants (85%) lived with one or more companions and unemployment issues did not affect 91% of the total survey takers, which was one of the crucial consequences of the pandemic. Nevertheless, measures such as an increased frequency of technology-aided distant social interaction, focus on physical fitness and leisure activities were adopted as coping mechanisms during this period of home isolation. Collectively, these metrics provide a succinct and informative summary of the socio-economic and health impact of the COVID-19 pandemic on the individuals. Findings from our study reflect that continuous surveillance of the psychological consequences for outbreaks should become routine as part of preparedness efforts worldwide. Given the limitations of analyzing the large number of variables, we have made the raw data publicly available on the OMF ME/CFS Data Center server to facilitate further analyses (https://igenomed.stanford.edu/dataset/survey-study-on-lifestyle-changes-during-covid-19-pandemic). [ABSTRACT FROM AUTHOR]

Clinical tuberculosis research, both within research groups and across research ecosystems, is often undertaken in isolation using bespoke data collection platforms and applying differing data conventions. This failure to harmonise clinical phenotype data or apply standardised data collection and storage standards in turn limits the opportunity to undertake meta-analyses using data generated across multiple research projects for the same research domain. We have developed the Tuberculosis DataBase Template (TBDBT), a template for the well-supported, free and commonly deployed clinical databasing platform, REDCap. This template can be used to set up a new tuberculosis research database with a built-in set of standardised data conventions, to ensure standardised data capture across research projects and programs. A modular design enables researchers to implement only the modules of the database template that are appropriate for their particular study. The template includes core modules for informed consent data, participant demographics, clinical symptoms and presentation, diagnostic imaging and laboratory tests. Optional modules have been designed for visit scheduling and calendar functionality, clinical trial randomisation, study logistics and operations, and pharmacokinetic data. Additional fields can be added as needed. This REDCap template can facilitate collection of high-quality data for tuberculosis research, providing a tool to ensure better data harmonisation, analysis and meta-analysis. [ABSTRACT FROM AUTHOR]

Inertial measurement units (IMUs) have been increasingly popular in rehabilitation research. However, despite their accessibility and potential advantages, their uptake and acceptance by health professionals remain a big challenge. The development of an IMU-based clinical tool must bring together engineers, researchers and clinicians. This study is part of a developmental process with the investigation of clinicians' perspectives about IMUs. Clinicians from four rehabilitation centers were invited to a 30-minute presentation on IMUs. Then, two one-hour focus groups were conducted with volunteer clinicians in each rehabilitation center on: 1) IMUs and their clinical usefulness, and 2) IMUs data analysis and visualization interface. Fifteen clinicians took part in the first focus groups. They expressed their thoughts on: 1) categories of variables that would be useful to measure with IMUs in clinical practice, and 2) desired characteristics of the IMUs. Twenty-three clinicians participated to the second focus groups, discussing: 1) functionalities, 2) display options, 3) clinical data reported and associated information, and 4) data collection duration. Potential influence of IMUs on clinical practice and added value were discussed in both focus groups. Clinicians expressed positive opinions about the use of IMUs, but their expectations were high before considering using IMUs in their practice. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF) is a major cause of morbidity and mortality. However, much of the clinical data is unstructured in the form of radiology reports, while the process of data collection and curation is arduous and time-consuming. Purpose: We utilized a machine learning (ML)-based natural language processing (NLP) approach to extract clinical terms from unstructured radiology reports. Additionally, we investigate the prognostic value of the extracted data in predicting all-cause mortality (ACM) in HF patients. Materials and methods: This observational cohort study utilized 122,025 thoracoabdominal computed tomography (CT) reports from 11,808 HF patients obtained between 2008 and 2018. 1,560 CT reports were manually annotated for the presence or absence of 14 radiographic findings, in addition to age and gender. Thereafter, a Convolutional Neural Network (CNN) was trained, validated and tested to determine the presence or absence of these features. Further, the ability of CNN to predict ACM was evaluated using Cox regression analysis on the extracted features. Results: 11,808 CT reports were analyzed from 11,808 patients (mean age 72.8 ± 14.8 years; 52.7% (6,217/11,808) male) from whom 3,107 died during the 10.6-year follow-up. The CNN demonstrated excellent accuracy for retrieval of the 14 radiographic findings with area-under-the-curve (AUC) ranging between 0.83–1.00 (F1 score 0.84–0.97). Cox model showed the time-dependent AUC for predicting ACM was 0.747 (95% confidence interval [CI] of 0.704–0.790) at 30 days. Conclusion: An ML-based NLP approach to unstructured CT reports demonstrates excellent accuracy for the extraction of predetermined radiographic findings, and provides prognostic value in HF patients. [ABSTRACT FROM AUTHOR]

Inadequate sanitation can lead to exposure to fecal contamination through multiple environmental pathways and can result in adverse health outcomes. By understanding the relative importance of multiple exposure pathways, sanitation interventions can be tailored to those pathways with greatest potential public health impact. The SaniPath Exposure Assessment Tool allows users to identify and quantify human exposure to fecal contamination in low-resource urban settings through a systematic yet customizable process. The Tool includes: a project management platform; mobile data collection and a data repository; protocols for primary data collection; and automated exposure assessment analysis. The data collection protocols detail the process of conducting behavioral surveys with households, school children, and community groups to quantify contact with fecal exposure pathways and of collecting and analyzing environmental samples for E. coli as an indicator of fecal contamination. Bayesian analyses are used to estimate the percentage of the population exposed and the mean dose of fecal exposure from microbiological and behavioral data. Fecal exposure from nine pathways (drinking water, bathing water, surface water, ocean water, open drains, floodwater, raw produce, street food, and public or shared toilets) can be compared through a common metric–estimated ingestion of E. coli units (MPN or CFU) per month. The Tool generates data visualizations and recommendations for interventions designed for both scientific and lay audiences. When piloted in Accra, Ghana, the results of the Tool were comparable with that of an in-depth study conducted in the same neighborhoods and highlighted consumption of raw produce as a dominant exposure pathway. The Tool has been deployed in nine cities to date, and the results are being used by local authorities to design and prioritize programming and policy. The SaniPath Tool is a novel approach to support public-health evidence-based decision-making for urban sanitation policies and investments. [ABSTRACT FROM AUTHOR]

The teaching of professional roles in medical education is an interdisciplinary concern. However, surgeons require specific standards of professionalism for certain context-based situations. In addition to communication, studies require collaboration, leadership, error-/conflict-management, patient-safety and decision-making as essential competencies for surgeons. Standards for corresponding competencies are defined in special chapters of the German National Competency-based Learning Objectives for Undergraduate Medical Education (NKLM; chapter 8, 10). The current study asks whether these chapters are adequately taught in surgical curricula. Eight German faculties contributed to analysing mapping data considering surgical courses of undergraduate programs. All faculties used the MERlin mapping platform and agreed on procedures for data collection and processing. Sub-competency and objective coverage, as well as the achievement of the competency level were mapped. Overall counts of explicit citations were used for analysis. Collaboration within the medical team is a strongly represented topic. In contrast, interprofessional cooperation, particularly in healthcare sector issues is less represented. Patient safety and dealing with errors and complications is most emphasized for the Manager/Leader, while time management, career planning and leadership are not addressed. Overall, the involvement of surgery in teaching the competencies of the Collaborator and Manager/Leader is currently low. However, there are indications of a curricular development towards explicit teaching of these roles in surgery. Moreover, implicitly taught roles are numerous, which indicates a beginning awareness of professional roles. [ABSTRACT FROM AUTHOR]

Background: The utilization of routine health information is an essential factor of the structural capacity of health departments and public health performance depends on the effectiveness of information use for routine and programed decisions. Considerable research has been conducted in health data collection and ways to improve data quality, but little is known about utilization of routine health information among health workers in Ethiopia in general and in the study area in particular. Objectives: The aim of this study was to assess level of utilization of routine health information and associated factors among health workers in Hadiya zone, Southern Ethiopia, 2019. Methods and materials: Facility-based cross-sectional study design with both quantitative and qualitative data collection methods was employed at the Hadiya zone from March 10–25, 2019. A total of 480 health workers were included in the study and systematic random sampling was employed to select the health care workers in the study. The results were analyzed and presented in tables and graphs. Finally, the binary logistic regression was used to examine independent predictors. Result: Good level of utilization of routine health information was reported by 301 (62.7%, 95% CI: 58.5%-66.9%) of the health workers. Training [AOR = 8.12; 95% CI: (4.33–15.23)], supportive supervision [AOR = 2.34; 95% CI:(1.40–3.92)], good perceived culture of health information [AOR = 5.05; 95% CI: (2.99–8.50], having a standard set of indicators (AOR = 2.05 95%; CI: (1.23–3.41) and competence on routine health information tasks [AOR = 5.85; 95% CI: (3.41–10.02)] were independent predictors. Conclusion: Good level of utilization of routine health information was noted in less than two-third of the study participants. Further training, supportive supervision, perceived culture of health information, having standard set of indicators and competence on routine health information task were factors that improve routine health information utilization. [ABSTRACT FROM AUTHOR]

The prevalence of social media platforms that share photos and videos could prove useful for wildlife research and conservation programs. When social media users post pictures and videos of animals, near real-time data like individual identification, sex, location, or other information are made accessible to scientists. These data can help inform researchers about animal occurrence, behavior, or threats to survival. The endangered Hawaiian monk seal (Neomonachus schauinslandi) population has only 1,400 seals remaining in the wild. A small but growing population of seals has recently reestablished itself in the human-populated main Hawaiian Islands. While this population growth raises concerns about human-seal interactions it also provides the opportunity to capitalize on human observations to enhance research and conservation activities. We measured the potential utility of non-traditional data sources, in this case Instagram, to supplement current population monitoring of monk seals in the main Hawaiian Islands. We tracked all Instagram posts with the identifier #monkseal for a one-year period and assessed the photos for biological and geographical information, behavioral concerns, human disturbance and public perceptions. Social media posts were less likely to provide images suitable for individual seal identification (16.5%) than traditional sighting reports (79.9%). However, social media enhanced the ability to detect human-seal interactions or animal disturbances: 22.1%, of the 2,392 Instagram posts examined showed people within 3 meters of a seal, and 17.8% indicated a disturbance to the animal, meanwhile only 0.64% of traditional reports noted a disturbance to the animal. This project demonstrated that data obtained through social media posts have value to monk seal research and management strategies beyond traditional data collection, and further development of social media platforms as data resources is warranted. Many conservation programs may benefit from similar work using social media to supplement the research and conservation activities they are undertaking. [ABSTRACT FROM AUTHOR]

In the past scientists reported summaries of their findings; they did not provide their original data collections. Many stakeholders (e.g., funding agencies) are now requesting that such data be made publicly available. This mandate is being adopted to facilitate further discovery, and to mitigate waste and deficits in the research process. At the same time, the necessary infrastructure for data curation (e.g., repositories) has been evolving. The current target is to make research products FAIR (Findable, Accessible, Interoperable, Reusable), resulting in data that are curated and archived to be both human and machine compatible. However, most scientists have little training in data curation. Specifically, they are ill-equipped to annotate their data collections at a level that facilitates discoverability, aggregation, and broad reuse in a context separate from their creation or sub-field. To circumvent these deficits data architects may collaborate with scientists to transform and curate data. This paper's example of a data collection describes the electrical properties of outer hair cells isolated from the mammalian cochlea. The data is expressed with a variant of The Ontology for Biomedical Investigations (OBI), mirrored to provide the metadata and nested data architecture used within the Hierarchical Data Format version 5 (HDF5) format. Each digital specimen is displayed in a tree configuration (like directories in a computer) and consists of six main branches based on the ontology classes. The data collections, scripts, and ontological OWL file (OBI based Inner Ear Electrophysiology (OBI_IEE)) are deposited in three repositories. We discuss the impediments to producing such data collections for public use, and the tools and processes required for effective implementation. This work illustrates the impact that small collaborations can have on the curation of our publicly-funded collections, and is particularly salient for fields where data is sparse, throughput is low, and sacrifice of animals is required for discovery. [ABSTRACT FROM AUTHOR]

The article focuses on the evolution of the Android-, web-based mobile application, called LINKS, and its key elements for greater efficiency in electronic data capture for global health programs. Information regarding the development of LINKS system and launch of the open source project Open Data Kit (ODK) is discussed. Among the benefits of LINKS include cost savings, data quality, and data collection and implementation of results.

Objective: Pharmaceuticals play an important role in clinical care. However, in community-based research, medication data are commonly collected as unstructured free-text, which is prohibitively expensive to code for large-scale studies. The ASPirin in Reducing Events in the Elderly (ASPREE) study developed a two-pronged framework to collect structured medication data for 19,114 individuals. ASPREE provides an opportunity to determine whether medication data can be cost-effectively collected and coded, en masse from the community using this framework. Methods: The ASPREE framework of type-to-search box with automated coding and linked free text entry was compared to traditional method of free-text only collection and post hoc coding. Reported medications were classified according to their method of collection and analysed by Anatomical Therapeutic Chemical (ATC) group. Relative cost of collecting medications was determined by calculating the time required for database set up and medication coding. Results: Overall, 122,910 participant structured medication reports were entered using the type-to-search box and 5,983 were entered as free-text. Free-text data contributed 211 unique medications not present in the type-to-search box. Spelling errors and unnecessary provision of additional information were among the top reasons why medications were reported as free-text. The cost per medication using the ASPREE method was approximately USD $0.03 compared with USD $0.20 per medication for the traditional method. Conclusion: Implementation of this two-pronged framework is a cost-effective alternative to free-text only data collection in community-based research. Higher initial set-up costs of this combined method are justified by long term cost effectiveness and the scientific potential for analysis and discovery gained through collection of detailed, structured medication data. [ABSTRACT FROM AUTHOR]