Comparison and combination of three data sources from patient medical records to determine optimal quantification of postoperative morbidity according to the Clavien Dindo Classification and the Comprehensive Complication Index. A prospective study

INTRODUCTION

It is currently unknown which data sources from the clinical history, or combination thereof, should be evaluated to achieve the most complete calculation of postoperative complications (PC). The objectives of this study were: to analyze the morbidity and mortality of 200 consecutive patients undergoing major surgery, to determine which data sources or combination collect the maximum morbidity, and to determine the accuracy of the morbidity reflected in the discharge report.

METHODS

Observational and prospective cohort study. The sum of all PC found in the combined review of medical notes, nursing notes, and a specific form was considered the gold standard. PC were classified according to the Clavien Dindo Classification and the Comprehensive Complication Index (CCI).

RESULTS

The percentage of patients who presented PC according to the gold standard, medical notes, nursing notes and form were: 43.5%, 37.5%, 35% and 18.7% respectively. The combination of sources improved CCI agreement by 8%-40% in the overall series and 39.1-89.7 % in patients with PC. The correct recording of PC was inversely proportional to the complexity of the surgery, and the combination of sources increased the degree of agreement with the gold standard by 35 %-67.5% in operations of greater complexity. The CDC and CCI of the discharge report coincided with the gold-standard values in patients with PC by 46.8% and 18.2%, respectively.

CONCLUSIONS

The combination of data sources, particularly medical and nursing notes, considerably increases the quantification of PC in general, most notably in complex interventions.

Ambient air concentrations of plant protection products: Data collection for the combined air concentration database and associated risk assessment

CropLife Europe collected literature values from monitoring studies measuring air concentrations of Plant Protection Products (PPPs) that may be inhaled by humans located in rural areas but not immediately adjacent to PPP applications. The resulting "Combined Air Concentration Database" (CACD) was used to determine whether air concentrations of PPPs reported by the French "Agency for Food, Environmental and Occupational Health & Safety" (ANSES) are consistent with those measured by others to increase confidence in values of exposure to humans. The results were put into risk assessment context. Results show that 25-90% of samples do not contain measurable PPP concentrations. Measured respirable fractions were below EU default air concentrations used for risk assessment for resident exposure by the European Food Safety Authority. All measured exposures in the CACD were also below established toxicological endpoints, even when considering the highest maximum average reported concentrations and very conservative inhalation rates. The highest recorded air concentration was for prosulfocarb (0.696 μg/m³ measured over 48 h) which is below the EFSA default limit of 1 μg/m³ for low volatility substances. In conclusion, based on the CACD, measured air concentrations of PPPs are significantly lower than EFSA default limits and relevant toxicological reference values.

A deep dive into green infrastructure failures using fault tree analysis

Green Infrastructure has transformed traditional urban stormwater management systems by fostering a wide range of service functions. Despite their popularity, green infrastructure's performance can deteriorate over their lifecycle, leading to operational failures. The operation of green infrastructure has predominantly relied on reactive maintenance strategies. To anticipate malfunctions and enhance the performance of green infrastructure in the long run, failure data needs to be recorded so that deterioration processes and component vulnerabilities can be recognized, modelled and included in predictive maintenance schemes. This study investigates possible failures in representative GIs and provides insights into the most important events that should be prioritized in the data collection process. A method for qualitative Fault Tree Analysis using minimal cut sets are introduced, aiming to identify potential failures with the minimum number of events. To identify events of interest fault trees were constructed for bioswales, rain gardens and green roofs, for three groups of service function failures, namely runoff quantity control, runoff quality control and additional service functions. The resulting fault trees consisted of 45 intermediate and 54 basic events. The minimal cut set analysis identified recurring basic events that could affect operation among all three green infrastructure instances. These events are 'trash accumulation', 'clogging due to sediment accumulation', and 'overly dense vegetation'. Among all the possible cut sets, events such as 'plants not thriving', 'invasive plants taking over', and 'deterioration caused by external influences' could potentially disrupt most of the service functions green infrastructure provides. Furthermore, the analysis of interactions between component failures shows vegetation and filter media layer failures have the highest influence over other components. The constructed fault trees and identified basic events could be potentially employed for additional research on data collection processes and calculating the failure rates of green infrastructure and as a result, contribute to a shift toward their proactive operation and maintenance.

Challenges to conducting research on oral health with older adults living in long-term care facilities

BACKGROUND

The challenges to conducting oral health studies involving older people in long-term care facilities (LTCFs) must be debated.

OBJECTIVE

This study aimed to investigate researchers' perceptions and experiences while conducting an epidemiological survey on oral health among older individuals residing in LTCFs.

METHODS

A qualitative study was conducted involving six researchers who utilized field diaries to record their impressions during data collection through interviews (older individuals (or their proxies), caregivers, and LTCF coordinators) and oral examinations of the older people participants. Additionally, researchers responded to open-ended questions about their experiences. The collected material was subjected to content analysis by two researchers.

RESULTS

The themes that emerged from the analysis were institutional context, aspects affecting the operationalization of the study, and data collection oriented by the clinical-functional profile of the older people. According to the researchers' perceptions, LTCF coordinators demonstrated concern for the study's benefits for older adults and the preservation of institutional routines during the research process. Caregivers emerged as vital sources of information, guiding researchers in navigating the challenges posed by the physical and mental complexities of the older people participants, necessitating empathy, sensitivity, and attentive listening from the researchers. The organization of materials and a streamlined data collection process proved essential for optimizing time efficiency and reducing stress for participants and researchers.

CONCLUSION

The researchers recognized the important role played by LTCF coordinators and formal caregivers, underscoring the significance of empathetic methodologies and streamlined data collection processes in mitigating the challenges inherent to research conducted within LTCFs.

Handling missing data in research

Missing data are an inevitable part of research and lead to a decrease in the size of the analyzable population, and biased and imprecise estimates. In this article, we discuss the types of missing data, methods to handle missing data and suggest ways in which missing data can be minimized.

Evaluating registry-based trial economics: Results from the STRESS clinical trial

Background

Registry-based trials have the potential to reduce randomized clinical trial (RCT) costs. However, observed cost differences also may be achieved through pragmatic trial designs. A systematic comparison of trial costs across different designs has not been previously performed.

Methods

We conducted a study to compare the current Steroids to Reduce Systemic inflammation after infant heart surgery (STRESS) registry-based RCT vs. two established designs: pragmatic RCT and explanatory RCT. The primary outcome was total RCT design costs. Secondary outcomes included: RCT duration and personnel hours. Costs were estimated using the Duke Clinical Research Institute's pricing model.

Results

The Registry-Based RCT estimated duration was 31.9 weeks greater than the other designs (259.5 vs. 227.6 weeks). This delay was caused by the Registry-Based design's periodic data harvesting that delayed site closing and statistical reporting. Total personnel hours were greatest for the Explanatory design followed by the Pragmatic design and the Registry-Based design (52,488 vs 29,763 vs. 24,480 h, respectively). Total costs were greatest for the Explanatory design followed by the Pragmatic design and the Registry-Based design ($10,140,263 vs. $4,164,863 vs. $3,268,504, respectively). Thus, Registry-Based total costs were 32 % of the Explanatory and 78 % of the Pragmatic design.

Conclusion

Total costs for the STRESS RCT with a registry-based design were less than those for a pragmatic design and much less than an explanatory design. Cost savings reflect design elements and leveraging of registry resources to improve cost efficiency, but delays to trial completion should be considered.

Establishment of a registry of clinical data and bioresources for rare nervous system diseases

Rare diseases are predominantly genetic or inherited, and patients with these conditions frequently exhibit neurological symptoms. Diagnosing and treating many rare diseases is a complex challenge, and their low prevalence complicates the performance of research, which in turn hinders the advancement of therapeutic options. One strategy to address this issue is the creation of national or international registries for rare diseases, which can help researchers monitor and investigate their natural progression. In the Republic of Korea, we established a registry across 5 centers that focuses on 3 rare diseases, all of which are characterized by gait disturbances resulting from motor system dysfunction. The registry will collect clinical information and human bioresources from patients with amyotrophic lateral sclerosis, spinocerebellar ataxia, and hereditary spastic paraplegia. These resources will be stored at ICreaT and the National Biobank of Korea. Once the registry is complete, the data will be made publicly available for further research. Through this registry, our research team is dedicated to identifying genetic variants that are specific to Korean patients, uncovering biomarkers that show a strong correlation with clinical symptoms, and leveraging this information for early diagnosis and the development of treatments.

Exception Reporting: A Novel Approach to Tracking of Multiple Behavioral Health Outcomes

Federally funded medical and behavioral healthcare programs often have substantial evaluation outcome tracking and reporting requirements, which can become burdensome to program staff resulting in decreased buy-in, increased chance of staff burnout and turnover, and less rigorous and consistent data collection efforts. To address this issue, a novel data collection approach, "exception reporting," was implemented to supplement and support the required data collection for a federally funded Assertive Outpatient Treatment (AOT) program. This work details the process and outcomes related to exception reporting for this comprehensive behavioral health treatment program that serves justice involved clients with serious mental illness (SMI). Results indicate that exception reporting was easily integrated into clinician's normal workflows and resulted in a number of benefits. Specifically, results indicated that exception reporting decreased the data collection burden for program staff while allowing them to efficiently track program outcomes required by the funder. Additional research into which practice settings exception reporting can most easily be integrated into, and which client outcomes may be best tracked using this methodology, is indicated.

Positive affect and heart rate variability: a dynamic analysis

Traditional survey methods can provide noisy data arising from recall, memory and other biases. Technological advances (particularly in neuroscience) are opening new ways of monitoring physiological processes through non-intrusive means. Such dense continuous data provide new and fruitful avenues for complementing self-reported data with a better understanding of human dynamics and human interactions. In this study, we use a survey to collect positive affect (feelings) data from more than 300 individuals over a period of 24 h, and at the same time, map their core activities (5000 recorded activities in total) with measurements of their heart rate variability (HRV). Our results indicate a robust correlation between the HRV measurements and self-reported affect. By drawing on the neuroscience and wellbeing literature we show that dynamic HRV results are what we expect for positive affect, particularly when performing activities like sleep, travel, work, exercise and eating. This research provides new insights into how to collect HRV data, model and interpret it.

Joint trajectory, transmission time and power optimization for multi-UAV data collecting system

Unmanned aerial vehicles (UAVs) have been generally applied in the field of communication due to their small size, flexible mobility, and convenient deployment. As a mobile base station, the UAV node can quickly establish a line-of-sight link with the ground node, thereby improving communication performance. In this paper, we study a multi-UAV assisted data collecting system. Specifically, in the case of limited system energy consumption, UAV flight energy consumption and ground node data transmission energy consumption are considered as an general limitation, and considering the channel interference between nodes, a multi-UAV assisted data collection model is studied. An non-convex problem that maximizes the minimum amount of data collected from ground nodes is further formulated. Since the original optimization problem is non-convex that difficult to solve directly, the problem is first decomposed into four sub-problems, and then the solution of each sub-problem is obtained by using successive convex approximation and block coordinate descent method. Finally, based on the solution of the four subproblems, an iterative algorithm for joint optimization of data transmission planning, transmission power, UAV trajectory and mission time is proposed. Simulation experiments show that the proposed algorithm can obtain more transmission data than the baseline algorithms.

Institution-Wide Retreats Foster Organizational Learning and Action at a Comprehensive Cancer Center

Providing safe and informed healthcare for sexual and gender minority (SGM) individuals with cancer is stymied by the lack of sexual orientation and gender identity (SOGI) data reliably available in health records and by insufficient training for staff. Approaches that support institutional learning, especially around sensitive topics, are essential for hospitals seeking to improve practices impacting patient safety and research. We engineered annual institutional retreats to identify and unify stakeholders, promote awareness of gaps and needs, identify initiatives, minimize redundant projects, and coordinate efforts that promote improvements in SGM cancer care, education, and research. The 2022 and 2023 retreats employed a 4-h hybrid format allowing virtual and in-person engagement. Retreat organizers facilitated small-group discussions for brainstorming among participants. We performed descriptive statistics from retreat evaluations. The retreats engaged 104 attendees from distinct departments and roles. Participants expressed robust satisfaction, commending the retreat organization and content quality. Notably, the first retreat yielded leadership endorsement and funding for a Quality Improvement pilot to standardize SOGI data collection and clinical staff training. The second retreat provided a platform for updates on focused efforts across the institution and for receiving direction regarding national best practices for SGM care and research. We report the processes and outcomes of institution-wide retreats, which served as a platform for identifying gaps in organizational healthcare practices and research for SGM individuals with cancer. The strategies described herein may be readily scaled at other cancer hospitals seeking to learn and enact system-wide practice changes that support the needs of SGM patients and families.

Patient Reported Indicator Surveys (Paris): Methodological Considerations of a Field Trial in Slovenia

Introduction

Healthcare systems collect little information about the experiences and outcomes of care from the perspectives of patients. Patient Reported Indicator Surveys (PaRIS) is an OECD initiative to measure the outcomes and experiences of people living with chronic conditions, who are managed in primary care.

Objectives

To evaluate the feasibility of the methodology employed in the Field Trial of the PaRIS survey in Slovenia and propose adjustments to enhance sampling in the Main Survey.

Methods

In 2022, we conducted a cross-sectional observational study in 50 family medicine practices in Slovenia with a target of recruiting 70 patients per practice. We used the Slovenian version of the PaRIS questionnaires, and evaluated sampling and data collection.

Results

The sample contained 21 providers (42.0% response rate) and 454 patients (50.7% response rate). The provider sample did not differ from the population characteristics, while the patient sample differed significantly from the patient population. All providers completed the survey online, in 20.9±11.1 minutes and had 1.5±1.5 restarts. Most patients (74.9%) completed the survey online and needed 36.0±22.6 minutes, and the mean number of restarts was 1.4±2.2.

Conclusion

Based on the results, we recommend conducting a methodology test for quality assessment studies before initiating the main survey. Legal issues should be addressed and considered early when developing the methodology. It is also necessary to be aware of the feasibility of the study in practice, to avoid a low participation rate.

A Mixed-Methods Assessment of the Feasibility of Conducting Neurosurgical Clinical Research in Uganda

BACKGROUND

Clinical research is necessary to evaluate neurosurgical interventions, yet clinical trials are conducted less frequently in low- and middle-income countries. Because specific barriers, facilitating factors, and strategies for neurosurgical clinical research in Uganda have not been previously identified, this study evaluated neurosurgical providers' perspectives on clinical research and documentation patterns of neurosurgical variables at Mulago National Referral Hospital.

METHODS

Retrospective review of 166 neurosurgical patient charts assessed the frequency of documentation of key variables. Twenty-two providers working in neurosurgery participated in 6 focus group discussions with qualitative analysis utilizing the framework method.

RESULTS

Chart review showed that primary diagnosis (99.4%), pupil light response (97.6%), and computed tomography scan results (93.3%) were documented for most patients. Cranial nerve exam (61.5%), pupil size (69.9%), and time to neurosurgical intervention (45%) were documented less frequently. On average, Glasgow Coma Scale was documented for 86.6% of days hospitalized, while vital signs were documented for 12.3%. In most focus group discussions, participants identified follow-up, financing, recruitment, time, approval, and sociocultural factors as research barriers. Participants described how the current health workforce facilitates successful research. To improve research capacity, suggested strategies focused on research networks, data collection, leadership, participant recruitment, infrastructure, and implementation.

CONCLUSIONS

At Mulago National Referral Hospital, there was variability in the frequency of documentation of neurosurgical variables, which may impact data collection for future studies. While multiple barriers were identified, sociocultural, financing, and time barriers greatly impacted neurosurgical clinical research. Despite that, identified facilitating factors and strategies could be utilized to support neurosurgical research capacity growth.

How do quantitative studies involving people with dementia report experiences of standardised data collection? A narrative synthesis of NIHR published studies

BACKGROUND

People with dementia are routinely included as research participants in trials and other quantitative studies in which they are invited to respond to standardised measures. This paper reviews the reporting of standardised data collection from people with dementia in reports published in the National Institute for Health and Care Research (NIHR) Journals Library. The aim was to understand how the administration of standardised, self-report measures with people with dementia is reported in NIHR monographs and what could be learnt from this about the feasibility and acceptability of data collection approaches for future studies.

METHODS

This was a systematic review with narrative synthesis. Broad search terms (Dementia OR Alzheimer*) were used to search the NIHR Journals Library website in December 2021. All studies that used (or intended to use) standardised measures to collect research data directly from people with dementia were eligible for inclusion. Information was extracted (where reported) on the process of data collection, dementia severity, levels of missing data and the experiences and reflections of those involved.

RESULTS

Searches returned 42 records, from which 17 reports were assessed as eligible for inclusion, containing 22 studies. Response rates from participants with dementia in these studies varied considerably and appeared to be related to dementia severity and place of residence. Little information was reported on the process of data collection or the reasons for missing data, and most studies did not report the experiences of participants or those administering the measures. However, there was an indication from two studies that standardised data collection could provoke emotional distress in some participants with dementia.

CONCLUSIONS

Through this review we identified both variation in levels of missing data and gaps in reporting which make it difficult to ascertain the reasons for this variation. We also identified potential risks to the well-being of participants with dementia which may be associated with the content of standardised measures and the context of data collection. Open reporting of and reflection upon data collection processes and the experiences of people involved is essential to ensure both the success of future data collection and the wellbeing of study participants.

TRIAL REGISTRATION

Registered with Research on Research https://ror-hub.org/study/2905/ .

A nationwide mobile phone survey for tobacco use in Tanzania: Sample quality and representativeness compared to a household survey

We investigated the feasibility of an interactive voice response (IVR) survey in Tanzania and compared its prevalence estimates for tobacco use to the estimates of the 'Global Adult Tobacco Survey (GATS) 2018'. IVR participants were enrolled by random digit dialing. Quota sampling was employed to achieve the required sample sizes of age-sex strata: sex (male/female) and age (18-29-, 30-44-, 45-59-, and ≥60-year-olds). GATS was a nationally representative survey and used a multistage stratified cluster sampling design. The IVR sample's weights were generated using the inverse proportional weighting (IPW) method with a logit model and the standard age-sex distribution of Tanzania. The IVR and GATS had 2362 and 4555 participants, respectively. Compared to GATS, the unweighted IVR sample had a higher proportion of males (58.7 % vs. 43.2 %), educated people (secondary/above education: 43.3 % vs. 21.1 %), and urban residents (56.5 % vs. 40 %). The weighted prevalence (95 % confidence interval (CI)) of current smoking was 4.99 % (4.11-6.04), 5.22 % (4.36-6.24), and 7.36 % (6.51-8.31) among IVR (IPW), IVR (age-sex standard), and GATS samples, respectively; the weighted prevalence (95 % CI) of smokeless tobacco use was similar: 3.54 % (2.73-4.57), 3.58 % (2.80-4.56), and 2.43 % (1.98-2.98), respectively. Most differences in point estimates for tobacco indicators were small (<2%). Overall, the odds of tobacco smoking indicators were lower in IVR than in GATS; however, the odds of smokeless tobacco use were reversed. Although we found under-/over-estimation of the prevalence of tobacco use in IVR than GATS, the estimates were close. Further research is required to increase the representativeness of IVR.

Data of the subatomic resolution structure of glucose isomerase complexed with xylitol inhibitor

Glucose isomerase (GI) is a crucial enzyme in industrial processes, including the production of high-fructose corn syrup, biofuels, and other renewable chemicals. Understanding the mechanisms of GI inhibition by GI inhibitors can offer valuable insights into enhancing production efficiency. We previously reported the subatomic resolution structure of Streptomyces rubiginosus GI (SruGI) complexed with a xylitol inhibitor, determined at 0.99 Å resolution, was reported. Structural analysis showed that the xylitol inhibitor is partially bound to the M1 binding site at the SruGI active site, enabling it to distinguish the xylitol-bound and -free state of SruGI. This structural information demonstrates that xylitol binding to the M1 site causes a conformational change in the metal binding site and the substrate binding channel of SruGI. Herein, detailed information on data collection and processing procedures of the subatomic resolution structure of the SruGI complexed with xylitol was reported.

Reflections on the design and application of 'Surveypura': a simulation-based pedagogical tool for quantitative research methods in public health and social sciences

It has been reported from various contexts that learning quantitative methods for public health and social research is challenging for students. Based on our observations of these challenges, we designed a simulation-based pedagogical tool called Surveypura to support classroom-based learning of quantitative research methods. The tool includes a large illustration of a fictional village with 155 houses, alongside data for each of the households. The features of the houses, household characteristics, and the village have been carefully designed to give the visual feel of an actual village and better assist the pedagogical process. The tool was used by five facilitators with their masters' students at our university in courses on social research and epidemiology. Our observations of the sessions and interactions with facilitators and students suggested that the tool supported more engaged learning of quantitative research methods in a non-intimidating manner. We believe that Surveypura can be a useful simulation-based pedagogical tool to teach quantitative research methods in epidemiology and social sciences even in other contexts.

Does survey mode matter? Comparing in-person and phone agricultural surveys in India

Ubiquitous mobile phone ownership makes phone surveying an attractive method of low-cost data collection. We explore differences between in-person and phone survey measures of agricultural production collected for an impact evaluation in India. Phone responses have greater mean and variance, a difference that persists even within a subset of respondents that answered the same question over both modes. Treatment effect estimation remains stable across survey mode, but estimates are less precise when using phone data. These patterns are informative for cost and sample size considerations in study design and for aggregating evidence across study sites or time periods.

Determining Protein Structures Using X-Ray Crystallography

X-ray crystallography is a robust and widely used technique that facilitates the three-dimensional structure determination of proteins at an atomic scale. This methodology entails the growth of protein crystals under controlled conditions followed by their exposure to X-ray beams and the subsequent analysis of the resulting diffraction patterns via computational tools to determine the three-dimensional architecture of the protein. However, achieving high-resolution structures through X-ray crystallography can be quite challenging due to complexities associated with protein purity, crystallization efficiency, and crystal quality.In this chapter, we provide a detailed overview of the gene to structure determination pipeline used in X-ray crystallography, a crucial tool for understanding protein structures. The chapter covers the steps in protein crystallization, along with the processes of data collection, processing, structure determination, and refinement. The most commonly faced challenges throughout this procedure are also addressed. Finally, the importance of standardized protocols for reproducibility and accuracy is emphasized, as they are crucial for advancing the understanding of protein structure and function.

Opportunities to improve the adoption of health-related quality of life evidence as part of the French Health Technology Assessment process

OBJECTIVES

Patient's health-related quality of life (HRQoL) is an important outcome measure that is considered by many payers and health technology assessment (HTA) bodies in the evaluation of treatments. We aimed to identify opportunities for HRQoL to be further incorporated into the assessment of the French HTA by comparing three health systems. We put forward recommendations that could bring further innovations to French patients.

METHODS

We reviewed methodologies by the French, German and British HTA, and conducted a systematic review of all French (n = 312) and German (n = 175) HTA appraisals from 01 January 2019 to 31 December 2021. We also setup an advisory board of 11 ex-HTA leaders, payers, methodologists, healthcare providers and patient advocates, from France, Britain and Germany, to discuss opportunities to improve acceptance and adoption of HRQoL evidence in France.

RESULTS

Our systematic review of HTA appraisals showed a higher HRQoL data rejection rate in France: in > 75% of cases the HRQoL evidence submitted was not accepted for the assessment (usually for methodological reasons, for example, data being considered exploratory; 16-75% of the appraisals mentioned HRQoL evidence, varying by therapeutic area). Overall, we found the French HTA to be more restrictive in its approach than IQWiG.

CONCLUSIONS

Based on these findings we articulate collaborative proposals for industry and the HAS to improve acceptance of HRQoL evidence and create a positive feedback loop between HAS and industry along four dimensions (1) patient perception, (2) testing hierarchy, (3) trial design and (4) data collection.

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.

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.

Ethical Data Collection for Medical Image Analysis: a Structured Approach

Due to advancements in technology such as data science and artificial intelligence, healthcare research has gained momentum and is generating new findings and predictions on abnormalities leading to the diagnosis of diseases or disorders in human beings. On one hand, the extensive application of data science to healthcare research is progressing faster, while on the other hand, the ethical concerns and adjoining risks and legal hurdles those data scientists may face in the future slow down the progression of healthcare research. Simply put, the application of data science to ethically guided healthcare research appears to be a dream come true. Hence, in this paper, we discuss the current practices, challenges, and limitations of the data collection process during medical image analysis (MIA) conducted as part of healthcare research and propose an ethical data collection framework to guide data scientists to address the possible ethical concerns before commencing data analytics over a medical dataset.

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.

Online Health Communities: an alternative feasible data registry tool for developing countries

Given the many challenges facing healthcare access in many developing countries and the added limitations observed in emergencies like COVID-19 pandemic, the authors here discuss an alternative and feasible approach to overcome all these limitations.

A systems science approach to identifying data gaps in national data sources on adolescent suicidal ideation and suicide attempt in the United States

BACKGROUND

Suicide is currently the second leading cause of death among adolescents ages 10-14, and third leading cause of death among adolescents ages 15-19 in the United States (U.S). Although we have numerous U.S. based surveillance systems and survey data sources, the coverage offered by these data with regard to the complexity of youth suicide had yet to be examined. The recent release of a comprehensive systems map for adolescent suicide provides an opportunity to contrast the content of surveillance systems and surveys with the mechanisms listed in the map.

OBJECTIVE

To inform existing data collection efforts and advance future research on the risk and protective factors relevant to adolescent suicide.

METHODS

We examined data from U.S. based surveillance systems and nationally-representative surveys that included (1) observations for an adolescent population and (2) questions or indicators in the data that identified suicidal ideation or suicide attempt. Using thematic analysis, we evaluated the codebooks and data dictionaries for each source to match questions or indicators to suicide-related risk and protective factors identified through a recently published suicide systems map. We used descriptive analysis to summarize where data were available or missing and categorized data gaps by social-ecological level.

RESULTS

Approximately 1-of-5 of the suicide-related risk and protective factors identified in the systems map had no supporting data, in any of the considered data sources. All sources cover less than half the factors, except the Adolescent Brain Cognitive Development Study (ABCD), which covers nearly 70% of factors.

CONCLUSIONS

Examining gaps in suicide research can help focus future data collection efforts in suicide prevention. Our analysis precisely identified where data is missing and also revealed that missing data affects some aspects of suicide research (e.g., distal factors at the community and societal level) more than others (e.g., proximal factors about individual characteristics). In sum, our analysis highlights limitations in current suicide-related data availability and provides new opportunities to identify and expand current data collection efforts.

Qualitative Research and Cancer Nursing: A Guide for Novice Researchers

OBJECTIVE

To introduce the cancer nurse to qualitative research.

DATA SOURCES

A search of published literature including articles and books was conducted to inform the article using University libraries (University of Galway and University of Glasgow) and CINAHL, Medline, and Google Scholar databases using broad terms, including qualitative research, qualitative methods, paradigm, qualitative, and cancer nursing.

CONCLUSION

It is important for cancer nurses wishing to read, critically appraise, or undertake qualitative research to understand the origins and different methods employed in qualitative research.

IMPLICATIONS FOR NURSING PRACTICE

The article is of relevance for cancer nurses globally who wish to read, critique, or undertake qualitative research.