Exploring non-iterative time trade-off methods for valuation of EQ-5D-5L health states

INTRODUCTION

The composite time trade-off (cTTO) method is used as the primary method for valuing EQ-5D-5L health states, but it requires intensive interviewer training and stringent quality control, which increases the burden of conducting cTTO studies. In this study, two non-iterative variants of the TTO method, non-stopping TTO (nTTO) and open-ended TTO (oTTO), were tested head-to-head with the cTTO method aiming to reduce the administration burden.

METHODS

31 EQ-5D-5L health states from an orthogonal array was selected and valued by a general public sample in China. Data were collected by 7 interviewers with all interviewers performed an equal number of interviews using all three TTO methods. We compared the value distribution, logical consistency, administration burden, and modeling performance of these three TTO methods.

RESULTS

In total, 422 participants participated in the valuation interviews, with 139 using the nTTO method, 140 using the oTTO method, and 143 using the cTTO method. Both oTTO and nTTO methods saved around 10 min for conducting an interview. The mean values of three methods were similar with each method showed different characteristics in their value distributions. cTTO outperformed the other two methods in terms of modeling performance.

DISCUSSION

Both non-iterative TTO methods showed potential for valuing EQ-5D health states, although their data distributions and modeling performance were inferior to the cTTO method. The results of this study showed the potential of these two alternative non-iterative TTO methods.

Surgical patient registries: scoping study of challenges and solutions

Patient surgical registries are essential tools for public health specialists, creating research opportunities through linkage of registry data with healthcare outcomes. However, little is known regarding data error sources in the management of surgical registries. In June 2022, we undertook a scoping study of the empirical literature including publications selected from the PUBMED and EMBASE databases. We selected 48 studies focussing on shared experiences centred around developing surgical patient registries. We identified seven types of data specific challenges, grouped in three categories- data capture, data analysis and result dissemination. Most studies underlined the risk for a high volume of missing data, non-uniform geographic representation, inclusion biases, inappropriate coding, as well as variations in analysis reporting and limitations related to the statistical analysis. Finally, to expand data usability, we discussed cost-effective ways of addressing these limitations, by citing aspects from the protocols followed by established exemplary registries.

Equality, diversity, and inclusion in oncology clinical trials: an audit of essential documents and data collection against INCLUDE under-served groups in a UK academic trial setting

BACKGROUND

Clinical trials should be as inclusive as possible to facilitate equitable access to research and better reflect the population towards which any intervention is aimed. Informed by the UK's National Institute for Health and Care Research (NIHR) Innovations in Clinical Trial Design and Delivery for the Under-served (INCLUDE) guidance, we audited oncology trials conducted by the Clinical Trials and Statistics Unit at The Institute of Cancer Research, London (ICR-CTSU) to identify whether essential documents were overtly excluding any groups and whether sufficient data were collected to assess diversity of trial participants from groups suggested by INCLUDE as under-served by research in the UK.

METHODS

Thirty cancer clinical trials managed by ICR-CTSU and approved between 2011-2021 were audited. The first ethics approved version of each trial's protocol, patient information sheet, and patient completed questionnaire, together with the first case report forms (CRFs) version were reviewed. A range of items aligned with the INCLUDE under-served groups were assessed, including age, sex and gender, socio-economic and health factors. The scope did not cover trial processes in participating hospitals.

RESULTS

Data relating to participants' age, ethnic group and health status were well collected and no upper age limit was specified in any trials' eligibility criteria. 23/30 (77%) information sheets used at least one gendered term to address patients. Most CRFs did not specify whether they were collecting sex or gender and only included male or female categories. The median reading age for information sheets was 15-16 years (IQR: 14-15 - 16-17). Socio-economic factors were not routinely collected and not commonly mentioned in trial protocols.

CONCLUSIONS

No systemic issues were identified in protocols which would explicitly prevent any under-served group from participating. Areas for improvement include reducing use of gendered words and improving readability of patient information. The challenge of fully assessing adequate inclusion of under-served populations remains, as socio-economic factors are not routinely collected because they fall beyond the data generally required for protocol-specified trial endpoint assessments. This audit has highlighted the need to agree and standardise demographic data collection to permit adequate monitoring of the under-served groups identified by the NIHR.

Database quality assessment in research in paramedicine: a scoping review

BACKGROUND

Research in paramedicine faces challenges in developing research capacity, including access to high-quality data. A variety of unique factors in the paramedic work environment influence data quality. In other fields of healthcare, data quality assessment (DQA) frameworks provide common methods of quality assessment as well as standards of transparent reporting. No similar DQA frameworks exist for paramedicine, and practices related to DQA are sporadically reported. This scoping review aims to describe the range, extent, and nature of DQA practices within research in paramedicine.

METHODS

This review followed a registered and published protocol. In consultation with a professional librarian, a search strategy was developed and applied to MEDLINE (National Library of Medicine), EMBASE (Elsevier), Scopus (Elsevier), and CINAHL (EBSCO) to identify studies published from 2011 through 2021 that assess paramedic data quality as a stated goal. Studies that reported quantitative results of DQA using data that relate primarily to the paramedic practice environment were included. Protocols, commentaries, and similar study types were excluded. Title/abstract screening was conducted by two reviewers; full-text screening was conducted by two, with a third participating to resolve disagreements. Data were extracted using a piloted data-charting form.

RESULTS

Searching yielded 10,105 unique articles. After title and abstract screening, 199 remained for full-text review; 97 were included in the analysis. Included studies varied widely in many characteristics. Majorities were conducted in the United States (51%), assessed data containing between 100 and 9,999 records (61%), or assessed one of three topic areas: data, trauma, or out-of-hospital cardiac arrest (61%). All data-quality domains assessed could be grouped under 5 summary domains: completeness, linkage, accuracy, reliability, and representativeness.

CONCLUSIONS

There are few common standards in terms of variables, domains, methods, or quality thresholds for DQA in paramedic research. Terminology used to describe quality domains varied among included studies and frequently overlapped. The included studies showed no evidence of assessing some domains and emerging topics seen in other areas of healthcare. Research in paramedicine would benefit from a standardized framework for DQA that allows for local variation while establishing common methods, terminology, and reporting standards.

Lay health worker research personnel for home-based data collection in clinical and translational research: Qualitative and quantitative findings from two trials in hard-to-reach populations

Aims

The role of lay health workers in data collection for clinical and translational research studies is not well described. We explored lay health workers as data collectors in clinical and translational research studies. We also present several methods for examining their work, i.e., qualitative interviews, fidelity checklists, and rates of unusable/missing data.

Methods

We conducted 2 randomized, controlled trials that employed lay health research personnel (LHR) who were employed by community-based organizations. In one study, n = 3 Latina LHRs worked with n = 107 Latino diabetic participants. In another study, n = 6 LHR worked with n = 188 Cambodian American refugees with depression. We investigated proficiency in biological, behavioral, and psychosocial home-based data collection conducted by LHR. We also conducted in-depth interviews with lay LHR to explore their experience in this research role. Finally, we described the training, supervision, and collaboration for LHR to be successful in their research role.

Results

Independent observers reported a very high degree of fidelity to technical data collection protocols (>95%) and low rates of missing/unusable data (1.5%-11%). Qualitative results show that trust, training, communication, and supervision are key and that LHR report feeling empowered by their role. LHR training included various content areas over several weeks with special attention to LHR and participant safety. Training and supervision from both the academic researchers and the staff at the community-based organizations were necessary and had to be well-coordinated.

Conclusions

Carefully selected, trained, and supervised LHRs can collect sophisticated data for community-based clinical and translational research.

Inter-hospital variation in data collection, radiotherapy treatment, and survival in patients with head and neck cancer: A multisite study

BACKGROUND AND PURPOSE

Inter-hospital inequalities in head and neck cancer (HNC) survival may exist due to variation in radiotherapy treatment-related factors. This study investigated inter-hospital variation in data collection, primary radiotherapy treatment, and survival in HNC patients from an Australian setting.

MATERIALS AND METHODS

Data collected in oncology information systems (OIS) from seven Australian hospitals was extracted for 3,182 adults treated with curative radiotherapy, with or without surgery or chemotherapy, for primary, non-metastatic squamous cell carcinoma of the head and neck (2000-2017). Death data was sourced from the National Death Index using record linkage. Multivariable Cox regression was used to assess the association between survival and hospital.

RESULTS

Inter-hospital variation in data collection, primary radiotherapy dose, and five-year HNC-related death was detected. Completion of eleven fields ranged from 66%-98%. Primary radiotherapy treated Tis-T1N0 glottic and any stage oral cavity and oropharynx cancers received significantly different time-corrected biologically equivalent dose in two gray fractions (EQD2T) by hospital, with observed deviation from Australian radiotherapy guidelines. Increased EQD2T dose was associated with a reduced risk of five-year HNC-related death in all patients and those treated with primary radiotherapy. Hospital, tumour site, and T and N classification were also identified as independent prognostic factors for five-year HNC-related death in all patients treated with radiotherapy.

CONCLUSION

Unexplained variation exists in HNC-related death in patients treated at Australian hospitals. Available routinely collected data in OIS are insufficient to explain variation in survival. Innovative data collection, extraction, and classification practices are needed to inform clinical practice.

Quest markup for developing FAIR questionnaire modules for epidemiologic studies

BACKGROUND

Online questionnaires are commonly used to collect information from participants in epidemiological studies. This requires building questionnaires using machine-readable formats that can be delivered to study participants using web-based technologies such as progressive web applications. However, the paucity of open-source markup standards with support for complex logic make collaborative development of web-based questionnaire modules difficult. This often prevents interoperability and reusability of questionnaire modules across epidemiological studies.

RESULTS

We developed an open-source markup language for presentation of questionnaire content and logic, Quest, within a real-time renderer that enables the user to test logic (e.g., skip patterns) and view the structure of data collection. We provide the Quest markup language, an in-browser markup rendering tool, questionnaire development tool and an example web application that embeds the renderer, developed for The Connect for Cancer Prevention Study.

CONCLUSION

A markup language can specify both the content and logic of a questionnaire as plain text. Questionnaire markup, such as Quest, can become a standard format for storing questionnaires or sharing questionnaires across the web. Quest is a step towards generation of FAIR data in epidemiological studies by facilitating reusability of questionnaires and data interoperability using open-source tools.

Exploring the perspectives of selectors and collecters of trial outcome data: an international qualitative study

INTRODUCTION

Selecting and collecting data to support appropriate primary and secondary outcomes is a critical step in designing trials that can change clinical practice. In this study, we aimed to investigate who contributes to the process of selecting and collecting trial outcomes, and how these people are involved. This work serves two main purposes: (1) it provides the trials community with evidence to demonstrate how outcomes are currently selected and collected, and (2) it allows people involved in trial design and conduct to pick apart these processes to consider how efficiencies and improvements can be made.

METHODS

One-with-one semi-structured interviews, supported by a topic guide to ensure coverage of key content. The Framework approach was used for thematic analysis of data, and themes were linked through constant comparison of data both within and across participant groups. Interviews took place between July 2020 and January 2021. Participants were twenty-nine international trialists from various contributor groups, working primarily on designing and/or delivering phase III pragmatic effectiveness trials. Their experience spanned various funders, trial settings, clinical specialties, intervention types, and participant populations.

RESULTS

We identified three descriptive themes encompassing the process of primary and secondary outcome selection, collection, and the publication of outcome data. Within these themes, participants raised issues around the following: 1) Outcome selection: clarity of the research question; confidence in selecting trial outcomes and how confidence decreases with increased experience; interplay between different interested parties; how patients and the public are involved in outcome selection; perceived impact of poor outcome selection including poor recruitment and/or retention; and use of core outcome sets. 2) Outcome collection: disconnect between decisions made by outcome selectors and the practical work done by outcome collectors; potential impact of outcome measures on trial participants; potential impact on trial staff workload; and use of routinely collected data. 3) Publication of outcome data: difficulties in finding time to write and revise manuscripts for publication due to time and funding constraints. Participants overwhelmingly focused on the process of outcome selection, a topic they talked about unprompted. When prompted, participants do discuss outcome collection, but poor communication between selectors and collectors at the trial design stage means that outcome selection is rarely linked with the data collection workload it generates.

DISCUSSION

People involved in the design and conduct of trials fail to connect decisions around outcome selection with data collection workload. Publication of outcome data and effective dissemination of trial results are hindered due to the project-based culture of some academic clinical trial research.

Internet of Things-based sustainable environment management for large indoor facilities

Due to global warming and climate change, the poultry industry is heavily impacted, especially the broiler industry, due to the sensitive immune system of broiler chickens. However, the continuous monitoring and controlling of the farm's environmental parameters can help to curtail the negative impacts of the environment on chickens' health, leading to increased meat production. This article presents smart solutions to such issues, which are practically implemented, and have low production and operational costs. In this article, an Internet of Things (IoT) based environmental parameters monitoring has been demonstrated for the poultry farmhouse. This system enables the collection and visualization of crucially sensed data automatically and reliably, and at a low cost to efficiently manage and operate a poultry farm. The proposed IoT-based remote monitoring system collects and visualizes environmental parameters, such as air temperature, relative humidity (RH), oxygen level (O2), carbon dioxide (CO2), carbon monoxide (CO), and ammonia (NH3) gas concentrations. The wireless sensor nodes have been designed and deployed for efficient data collection of the essential environmental parameters that are key for monitoring and decision-making process. The hardware is implemented and deployed successfully at a site within the control shed of the poultry farmhouse. The results revealed important findings related to the environmental conditions within the poultry farm. The temperature inside the control sheds remained within the desired range throughout the monitoring period, with daily average values ranging from 32 °C to 34 °C. The RH showed slight variations monitoring period, ranging from 65% to 75%, with a daily average of 70%. The O2 concentration exhibited an average value of 17% to 18.5% throughout the monitoring period. The CO2 levels showed occasional increases, reaching a maximum value of 1,100 ppm. However, this value was below the maximum permissible level of 2,500 ppm, indicating that the ventilation system was effective in maintaining acceptable CO2 levels within the control sheds. The NH3 gas concentration remained consistently low throughout the duration, with an average value of 50 parts per million (ppm).

A Small Randomized Controlled Trial of Three Remote Methods to Collect Mental Health Data from Migrant Farmworker Adults

There has been limited longitudinal study of the health of migrant farmworkers due to their migratory lifestyles and there are opportunities to test new remote data collection methods in this subpopulation. A small randomized controlled trial was conducted with 75 migrant farmworker families who were randomly assigned to one of three groups that participated by (1) telephone interview, (2) online survey, or (3) mobile app between June 2021-April 2022. Of 50 farmworker adults who completed the baseline survey, there was differential attrition with 21% of the telephone interview group, 18% of the online survey group, and 3.2% of the online app group completing the 2-month follow-up. Over this period, migrant farmworkers reported relatively few mental health problems but notable alcohol use problems. Online apps were less effective than traditional methods for remote data collection. Alcohol use problems among migrant farmworkers in the U.S. may be an issue that deserves further study.

Clinical validation of in-hospital stroke diagnosis

OBJECTIVE

In-hospital stroke cases occur during hospitalization for another diagnosis and reflect a clinically distinct cohort from community-onset stroke. The objective was to validate the diagnostic accuracy of in-hospital stroke identification in stroke audit data at a large teaching hospital.

METHODS

A retrospective clinical validation of in-hospital stroke diagnoses from two linked data sources was completed for a 2-year period from January 1st 2020 to December 31st 2021. The linked data sources include the Hospital Inpatient Enquiry system which assigns coded stroke diagnoses at discharge and/or the local stroke audit coordinators who work clinically in stroke teams and input additional specific clinical data. Diagnostic sensitivity, specificity and the level of agreement using an unweighted Cohen's Kappa were calculated.

RESULTS

There were 597 strokes admitted during the 2-year period. The median age was 72 years and 55% occurred in males. In total, 88 cases of in-hospital stroke were clinically validated yielding an in-hospital stroke rate of 15%. The clinical audit coordinator identified in-hospital stroke with higher sensitivity (86%; 95% CI 77%-93%) whereas the coding process was more specific at 96% (95% CI 85% to 99%). Levels of agreement with the clinically validated gold standard sample were moderate for the audit coordinator and coding process with κ = 0.57 and K = 0.42 respectively. When both data sources were combined the level of agreement was substantial (κ = 0.65; p < .000).

CONCLUSIONS

Clinical validation studies are required to reinforce data quality within stroke registers. Combining clinical and administrative data sources improves diagnostic accuracy for in-hospital stroke identification.

Generation of traffic input for flexible pavement design

Layered elastic theory (LET) was performed by Burmister. It helped to build mechanistic - empirical (M-E) pavement design. In this study, three different approaches were used to predict Cumulative Equivalent single axle load (C-ESAL) over the design period. Two were based on M-E and one was empirical. In each of these cases, standard axle loads were used as well as weight limits and vehicle classification, according to their axle configurations (single, tandem, tridem). Traffic data came from annual traffic census campaigns over the past ten years. Gross vehicle weight (GVW) and axle weight (AW) data came from a fixed weighing station performed during 31 days in 2020. Two road axis were considered: One having a weighing station (reference road) and one under technical studies (specific road). Traffic road data were used to perform regression analyses and predictions. AW and GVW helped to calculate Axle load equivalency factors (ALEF) and Truck equivalency factors (TEF) on the reference road. These values were projected on the specific road. Frequency distribution, gross vehicle weight distribution, axle load distribution of heavy vehicles are applied on the reference road. We performed overload AW and overload GVW analyses. Comparisons were done for the three approaches and an evaluation of technical studies was proposed, including traffic and AW monitoring and management systems. This work came as a basis for the transposition of M-E calculation of traffic inputs, more accurate and used over the passed fifty years, in Higher Income countries, called AASHTO method for USA, LCPC-SETRA method for France, to Cameroon and Sub-sahara African countries, that have been using empirical generation of traffic inputs over the same period, called CEBTP method.

Do animation videos increase participation in national health surveys? A randomised controlled trial

BACKGROUND

Declining response proportions in surveys have been observed internationally. Improving response proportions is important for the generalizability of the outcome. The aim of this study was to examine the potential of animation videos to improve response proportions and sample composition in health surveys.

METHODS

A randomized trial was embedded in the Danish National Health Survey 2021 (n = 186,113) where the use of animation videos in the digital invitation letter was tested as a mean to increase response proportion. The effect of both demographic-targeted videos and a general video was tested. The sample was stratified into four subsamples; (1) individuals with non-western background and a non-Danish citizenship (n = 9,956), (2) men aged 16-24 years (n = 12,481), (3) women aged 75 years or older (n = 7,815) and (4) the remaining individuals (n = 155,861). The fourth subsample was randomized into two equal sized groups; a group receiving the general video and a control group receiving no video. Each of the first three subsamples was subsequently randomized into three subgroups with 25% receiving the target group video, 25% receiving the general video and 50% receiving no video. A total of four reminders (one digital and three postal) were sent to the eligible population.

RESULTS

The use of animation videos resulted in similar or slightly lower overall response proportion compared to the control group. The different animation videos were found to have heterogeneous effects on response proportions. A positive effect was found among men aged 16-24 years before the delivery of the postal reminder for the targeted animation video compared to no video (odds ratio: 1.13; 95% confidence interval: 1.02-1.26). Overall, the targeted animation videos tended to produce higher response proportions than the general animation video.

CONCLUSIONS

The heterogeneous effects of the videos suggest that there is some potential for the use of animation videos to improve response proportions and sample composition. The content, target group and timing of evaluation seem to be important for the animation videos to be successful. This warrants further research to better identify in which contexts, in which subgroups and under which circumstances, animation videos are useful to increase response proportions.

TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT05520242, registered 08/26/2022.

End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a Danish Breast Cancer Group cohort

Large Digital Imaging and Communications in Medicine (DICOM) datasets are key to support research and the development of machine learning technology in radiotherapy (RT). However, the tools for multi-centre data collection, curation and standardisation are not readily available. Automated batch DICOM export solutions were demonstrated for a multicentre setup. A Python solution, Collaborative DICOM analysis for RT (CORDIAL-RT) was developed for curation, standardisation, and analysis of the collected data. The setup was demonstrated in the DBCG RT-Nation study, where 86% (n = 7748) of treatments in the inclusion period were collected and quality assured, supporting the applicability of the end-to-end framework.

Automatic Profiles Collection from Twitter Users with Depressive Symptoms

Mental illness is a pressing issue that needs urgent attention, as the number of people suffering from mental disorders continues to increase. Diagnosing mental health disorders can be challenging, and gathering information about a patient's medical history and symptoms is crucial for an accurate diagnosis. Self-disclosure on social media can provide valuable insights into whether users may be suffering from a mental illness. This paper proposes a method for automatically collecting data from social media users who disclosed their depression. The proposed approach yielded a 97% accuracy rate with a majority of 95%.

Accuracy of Manual Intracranial Pressure Recording Compared to a Computerized High-Resolution System: A CENTER-TBI Analysis

BACKGROUND

Monitoring intracranial pressure (ICP) and cerebral perfusion pressure (CPP) is crucial in the management of the patient with severe traumatic brain injury (TBI). In several institutions ICP and CPP are summarized hourly and entered manually on bedside charts; these data have been used in large observational and interventional trials. However, ICP and CPP may change rapidly and frequently, so data recorded in medical charts might underestimate actual ICP and CPP shifts. The aim of this study was to evaluate the accuracy of manual data annotation for proper capturing of ICP and CPP. For this aim, we (1) compared end-hour ICP and CPP values manually recorded (MR) with values recorded continuously by computerized high-resolution (HR) systems and (2) analyzed whether MR ICP and MR CPP are reliable indicators of the burden of intracranial hypertension and low CPP.

METHODS

One hundred patients were included. First, we compared the MR data with the values stored in the computerized system during the first 7 days after admission. For this point-to-point analysis, we calculated the difference between end-hour MR and HR ICP and CPP. Then we analyzed the burden of high ICP (> 20 mm Hg) and low CPP (< 60 mm Hg) measured by the computerized system, in which continuous data were stored, compared with the pressure-time dose based on end-hour measurements.

RESULTS

The mean difference between MR and HR end-hour values was 0.02 mm Hg for ICP (SD 3.86 mm Hg) and 1.54 mm Hg for CPP (SD 8.81 mm Hg). ICP > 20 mm Hg and CPP < 60 mm Hg were not detected by MR in 1.6% and 5.8% of synchronized measurements, respectively. Analysis of the pathological ICP and CPP throughout the recording, however, indicated that calculations based on manual recording seriously underestimated the ICP and CPP burden (in 42% and 28% of patients, respectively).

CONCLUSIONS

Manual entries fairly represent end-hour HR ICP and CPP. However, compared with a computerized system, they may prove inadequate, with a serious risk of underestimation of the ICP and CPP burden.

Concerns and frustrations about the public reporting of device-related healthcare-associated infections: Perspectives of hospital leaders and staff

BACKGROUND

Public reporting of healthcare-associated infections (HAIs) aims to incentivize improvement in infection prevention. The motivation and mechanisms of public reporting have raised concerns about the reliability of this data, but little is known about the specific concerns of hospital leaders and staff. This study sought to better understand perspectives of individuals in these roles regarding the identification and public reporting of HAIs.

METHODS

We conducted interviews with 471 participants including hospitals leaders (eg, administrative and clinical leaders) and hospital staff (eg, physicians and nurses) between 2017 and 2019 across 18 US hospitals. A semistructured interview guide was used to explore perspectives about the use of HAI data within the context of management strategies used to support infection prevention.

RESULTS

Interviewees described concerns about public reporting of HAI data, including a lack of trust in the data and inadvertent consequences of its public reporting, as well as specific frustrations related to the identification and accountability for publicly-reported HAIs.

CONCLUSION

Concerns and frustrations related to public reporting of HAI data highlight the need for improved guidelines, transparency, and incentives. Efforts to build trust in publicly-reported HAI data can help ensure this information is used effectively to improve infection prevention practices.

Smart scan of medical device displays to integrate with a mHealth application

Background

The daily monitoring of the physiological parameters is essential for monitoring health condition and to prevent health problems. This is possible due to the democratization of numerous types of medical devices and promoted by the interconnection between these and smartphones. Nevertheless, medical devices that connect to smartphones are typically limited to manufacturers applications.

Objectives

This paper proposes an intelligent scanning system to simplify the collection of data displayed on different medical devices screens, recognizing the values, and optionally integrating them, through open protocols, with centralized databases.

Methods

To develop this system, a dataset comprising 1614 images of medical devices was created, obtained from manufacturer catalogs, photographs and other public datasets. Then, three object detector algorithms (yolov3, Single-Shot Detector [SSD] 320 × 320 and SSD 640 × 640) were trained to detect digits and acronyms/units of measurements presented by medical devices. These models were tested under 3 different conditions to detect digits and acronyms/units as a single object (single label), digits and acronyms/units as independent objects (two labels), and digits and acronyms/units individually (fifteen labels). Models trained for single and two labels were completed with a convolutional neural network (CNN) to identify the detected objects. To group the recognized digits, a condition-tree based strategy on density spatial clustering was used.

Results

The most promising approach was the use of the SSD 640 × 640 for fifteen labels.

Conclusion

Lastly, as future work, it is intended to convert this system to a mobile environment to accelerate and streamline the process of inserting data into mobile health (mhealth) applications.

A proposal to embed patient and public involvement within qualitative data collection and analysis phases of a primary care based implementation study

BACKGROUND

Patient and public involvement (PPI) is increasingly seen as essential to health service research. There are strong moral and ethical arguments for good quality PPI. Despite the development of guidance aimed at addressing the inconsistent reporting of PPI activities within research, little progress has been made in documenting the steps taken to undertake PPI and how it influences the direction of a study. Without this information, there are minimal opportunities to share learnings across projects and strengthen future PPI practices. The aim of this paper is to present details on the processes and activities planned to integrate PPI into the qualitative research component of a mixed-methods, multi-site study evaluating the implementation of a smart template to promote personalised primary care for patients with multiple long-term conditions.

METHODS

This proposal describes the processes and activities planned to integrate PPI into the development and piloting of qualitative data collection tools (topic guides for both practice staff and patients) and a tailored data analysis package developed for PPI members incorporating broad concepts and specific methods of qualitative data analysis.

DISCUSSION

Outputs relating to PPI activity may include clear, concise and suitably worded topic guides for qualitative interviews. Piloting of the topic guides via mock interviews will further develop researchers' skills including sensitisation to the experiences of participants being interviewed. Working with PPI members when analysing the qualitative data aims to provide reciprocal learning opportunities and may contribute to improving the overall rigour of the data analysis. The intent of publishing proposed PPI activities within this project is to inform the future delivery of high quality PPI.

[The shoulder and elbow register of the DVSE-trend monitoring or early warning system? : A literature-based analysis]

The endoprosthesis register (SEPR) of the D‑A-CH Association for Shoulder and Elbow Surgery e. V. (DVSE) collects data on the implantation of shoulder and elbow endoprostheses. The question arises as to whether the data is only used to monitor trends in arthroplasty, or whether it can also be used as an early warning system for risks and possible complications. The existing literature on the SEPR was analyzed and compared with other national endoprosthesis registries. The SEPR of the DVSE enables the collection and analysis of epidemiological data on primary implantation, follow-up and revision in shoulder and elbow endoprosthetics. It is an instrument of quality control and contributes to ensuring the greatest possible patient safety. It is used for the early detection of risks and potential requirements associated with shoulder and elbow arthroplasty.

Definition, Composition, and Harmonization of Core Datasets Within the German Center for Lung Research

Core datasets are the composition of essential data items for a certain research scope. As they state commonalities between heterogeneous data collections, they serve as a basis for cross-site and cross-disease research. Therefore, researchers at the national and international levels have addressed the problem of missing core datasets. The German Center for Lung Research (DZL) comprises five sites and eight disease areas and aims to gain further scientific knowledge by continuously promoting collaborations. In this study, we elaborated a methodology for defining core datasets in the field of lung health science. Additionally, through support of domain experts, we have utilized our method and compiled core datasets for each DZL disease area and a general core dataset for lung research. All included data items were annotated with metadata and where possible they were assigned references to international classification systems. Our findings will support future scientific collaborations and meaningful data collections.

Antimicrobial use in animals: a journey towards integrated surveillance

In 2015, the World Organisation for Animal Health (WOAH, founded as OIE) initiated the annual collection of data on antimicrobials intended for use in animals using a Microsoft Excel questionnaire. In 2022, WOAH initiated the migration to a customised interactive online system: ANIMUSE Global Database. This system enables national Veterinary Services not only to monitor and report data more easily and more accurately, but also to visualise, analyse and use data for surveillance purposes to their own benefit in the implementation of national action plans on antimicrobial resistance. This journey started seven years ago, with progressive improvements in the way data are collected, analysed and reported and continuous adaptations to overcome various challenges encountered (e.g. data confidentiality, training of civil servants, calculation of active ingredients, standardisation to enable fair comparisons and trend analyses, and data interoperability). Technical developments have been key in the success of this endeavour. However, it is important not to underestimate the importance of the human element: to listen to WOAH Members and their needs, and to exchange to solve issues, adapt tools, and gain and maintain trust. The journey is not over yet, and more developments are foreseen, such as to complement current data sources with data collected directly at the farm level; strengthen interoperability and integrated analysis with cross-sectoral databases; and facilitate institutionalisation of data collection and systematic use in monitoring, evaluation, lesson learning, reporting and, eventually, surveillance of antimicrobial use and antimicrobial resistance when implementing and updating national action plans. This paper describes how all these challenges were overcome and how future challenges will be addressed.

Economic evaluation of antimicrobial usage surveillance in livestock

There is increased pressure by governments and industry to develop national surveillance programmes to evaluate antimicrobial usage (AMU) in animals. This article presents a methodological approach to cost-effectiveness analysis of such programmes. Seven objectives are proposed for AMU surveillance in animals: quantifying use, finding trends, detecting hotspots, identifying risk factors, encouraging research, evaluating the impact of policies and diseases, and demonstrating compliance with regulations. Achieving these objectives would assist in making decisions about potential interventions, help to generate trust, incentivise the reduction of AMU and decrease the risk of antimicrobial resistance. The cost-effectiveness of each objective can be found by dividing the cost of the programme by the performance indicators of the surveillance required to meet the objective concerned. The precision and accuracy of surveillance outputs are suggested here as useful performance indicators. Precision depends on the level of surveillance coverage (SC) and surveillance representativeness (SR). Accuracy is influenced by the quality of farm records and SR. The authors argue that there is an increase in marginal cost for each unit increase of SC, SR and data quality. This is caused by the increasing difficulty of recruiting farmers due to potential barriers such as staff capacity, capital availability, computing literacy and availability, and geographical differences, among other factors. A simulation model was conducted to test the approach, using the quantification of AMU as the primary objective, and to provide evidence of the application of the law of diminishing returns. Cost-effectiveness analysis can be used to support decisions on the level of coverage, representativeness and data quality required in such AMU programmes.

Collecting health-related research data using consumer-based wireless smart scales

BACKGROUND

Serious public-health concerns such as overweight and obesity are in many cases caused by excess intake of food combined with decreases in physical activity. Smart scales with wireless data transfer can, together with smart watches and trackers, observe changes in the population's health. They can present us with a picture of our metabolism, body health, and disease risks. Combining body composition data with physical activity measurements from devices such as smart watches could contribute to building a human digital twin.

OBJECTIVE

The objectives of this study were to (1) investigate the evolution of smart scales in the last decade, (2) map status and supported sensors of smart scales, (3) get an overview of how smart scales have been used in research, and (4) identify smart scales for current and future research.

METHOD

We searched for devices through web shops and smart scale tests/reviews, extracting data from the manufacturer's official website, user manuals when available, and data from web shops. We also searched scientific literature databases for smart scale usage in scientific papers.

RESULT

We identified 165 smart scales with a wireless connection from 72 different manufacturers, released between 2009 and end of 2021. Of these devices, 49 (28%) had been discontinued by end of 2021. We found that the use of major variables such as fat and muscle mass have been as good as constant over the years, and that minor variables such as visceral fat and protein mass have increased since 2015. The main contribution is a representative overview of consumer grade smart scales between 2009 and 2021.

CONCLUSION

The last six years have seen a distinct increase of these devices in the marketplace, measuring body composition with bone mass, muscle mass, fat mass, and water mass, in addition to weight. Still, the number of research projects featuring connected smart scales are few. One reason could be the lack of professionally accurate measurements, though trend analysis might be a more feasible usage scenario.

Business-centric data solutions for safeguarding animal agriculture in the United States of America

Business-centric solutions to data-related problems often yield the greatest positive impacts and improvements for private enterprises but are challenging to design and implement at scale within government agencies. The core mission of the Veterinary Services of the United States Department of Agriculture (USDA) Animal Plant Health Inspection Service is to safeguard animal agriculture in the United States of America, and effective data management underpins these efforts. As this agency works to assist data-driven decision-making in animal health management, it continues to use a blend of best practices from Federal Data Strategy initiatives and the International Data Management Association framework. This paper describes three case studies that focus on strategies to improve animal health data collection, integration, reporting and governance for animal health authorities. These strategies have enhanced the way USDA's Veterinary Services execute their mission and core operational activities for prevention, detection and early response to support disease containment and control.