Robust Data Collection Research Articles

Identification of Disability for Inclusive Development: Lessons from Disability Census of Kerala, India

The identification and representation of Persons with Disabilities (PwDs) has been a contentious issue due to the lack of methodological standardization. This study scrutinizes the methodological innovation made by the Indian state of Kerala for identifying PwDs in its special census on disability in 2015. The study used disability data from the 15th Census of India and the Disability Census (DC) of Kerala and used descriptive statistics to compare the two on demographic, health, social, and economic dimensions. The prevalence of disability in Kerala was found to be around 2.2% in both the censuses. However, between the two data sources, the share of different disabilities varied from 1% in the case of mental retardation to as much as 10% in the case of locomotor disability. Robust data collection in two stages by trained health professionals, incorporation of detailed categories of disabling conditions, and inclusion of a large number of dimensions in DC made it an effective survey. It is important to improve the identification of PwDs to properly address their unique needs and promote an inclusive development. Defining “mental retardation” to capture disability can be derogatory to many; however, disability in mental health has been defined as “mental retardation” and “mental illness” in the 15th round of the Census of India. As a result, these are the terms that will be used throughout the article.

Growth Disturbances Following Paediatric Anterior Cruciate Ligament Reconstruction: A Systematic Review.

Growth disturbances after transphyseal paediatric anterior cruciate ligament (ACL) reconstruction have led to the development of physeal-sparing techniques. The aim of this study is to investigate growth disturbances following paediatric ACL reconstruction and identify associated risk factors. A systematic search on PubMed, Scopus and Web of Science databases was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify case series reporting paediatric ACL reconstructions. Of 518 articles, 78 met the inclusion criteria, and data related to growth disturbances and graft failures were extracted. A total of 2,693 paediatric ACL reconstructions resulted in 70 growth disturbances (2.6%): 17 were varus, 26 were valgus, 13 were shortening, 14 were lengthening and five patients had reduced tibial slope. Some patients showed deformities in more than one plane. Coronal plane deformities were seen more frequently with eccentric physeal arrest and lengthening with intraepiphyseal tunnelling. Shortening and reduced tibial slope were related to large central physeal arrest and anterior tibial physeal arrest, respectively. Sixty-two studies documented 166 graft failures in 2,120 reconstructions (7.8%). The extraphyseal technique was least likely to result in growth disturbances and graft failure. Paediatric ACL reconstruction is a safe and effective treatment of rupture. Growth disturbances are least likely following extraphyseal tunnelling, and those resulting from transphyseal techniques can be minimised by reducing drill size, drilling steep and avoiding the physeal periphery. The insertion of hardware, synthetic material, or a bone plug through the drilled physis should be avoided. There is a greater need for robust long-term data collection, such as national ligament registries, to standardise practice and evaluate the risk of growth disturbance and re-ruptures in this treatment.

Mix design development for geopolymer treated expansive subgrades using artificial neural network

Expansive soils have been inevitable during road constructions due to its widespread availability. Conventional stabilizers; cement and lime have not been quite reliable in terms of economic, environmental, and durability perspectives. Thus, geopolymer has come into limelight as a sustainable alternative. However, the complexity of geopolymer-soil behaviour makes it challenging to predict their strength development patterns and it requires a large number of trial and error tests in laboratory scale due to the soil specific nature of such mixes. To date, no research has been attempted to develop design charts for geopolymer-expansive soil stabilization field applications. This study proposes design charts using Artificial neural networks (ANN) to predict the 28-day unconfined compressive strength (UCS) of Class F fly ash (FA) based geopolymer stabilized expansive soil (PI = 32% – 46%) to support field implementation of this novel geopolymer based soil stabilization. The model was developed based on a robust collection of literature data; FA/Soil, Activator/FA, Na2SiO3/NaOH, alkaline molarity, and their respective 28-day UCS values. Three contour plots for mix design: FA/soil vs. NaOH molarity, activator/FA vs. Na2SiO3/NaOH, and Na2SiO3/NaOH vs. NaOH molarity were proposed based on the model predictions. The proposed mix design charts were validated through the in-house tests which showed reasonable match in comparison with the predicted 28-day UCS using the contour plots. The proposed mix design charts can be employed for in-situ geopolymer-expansive subgrade stabilization to achieve strengths between 0.6 and 2.1 MPa.

HpMCA: a Python-based graphical program for energy-dispersive X-ray diffraction data collection and analysis

ABSTRACT Energy-dispersive X-ray diffraction (EDXD) at synchrotron beamlines is commonly used for the study of material properties under high pressure and/or high temperature. Experimenters typically rely on the availability of robust data collection and analysis at a beamline, but this has become increasingly difficult, especially with the introduction of multi-element detectors that generate complex, multi-dimensional data sets. These data sets have energy resolution, and they can also be resolved in relation to sample position, diffraction angle, or different external stimuli. We report a new Python-based graphical program, hpMCA, for EDXD data collection and analysis that streamlines the experimental process for the beamline users. The program features a user-friendly interface, capability for online viewing and analyzing data from multi-element energy-dispersive detectors, and includes features useful for working with samples under high pressure and/or high temperature, such as crystal phase identification, real-time unit cell lattice refinement, and pressure determination based on an equation of state.

An Argument for State-Driven Quality Collaboratives.

A major challenge in quality improvement (QI) within the medical field is adequate sample size. To effect real change, not only do we need to amass large quantities of data for analysis, but also interventions require larger groups of patients and providers to recognize the effect of said intervention. With that, single-institution projects not only may fail to demonstrate change but also may have limited applicability outside of the individual practice. Opportunities for more robust data collection, analysis, and intervention—in terms of both numbers and resources—are necessary for continued advancement in surgical quality initiatives.

The Pathways to Prevention program: nutrition as prevention for improved cancer outcomes.

Adequate nutrition is central to well-being and health and can enhance recovery during illness. Although it is well known that malnutrition, both undernutrition and overnutrition, poses an added challenge for patients with cancer diagnoses, it remains unclear when and how to intervene and if such nutritional interventions improve clinical outcomes. In July 2022, the National Institutes of Health convened a workshop to examine key questions, identify related knowledge gaps, and provide recommendations to advance understanding about the effects of nutritional interventions. Evidence presented at the workshop found substantial heterogeneity among published randomized clinical trials, with a majority rated as low quality and yielding mostly inconsistent results. Other research cited trials in limited populations that showed potential for nutritional interventions to reduce the adverse effects associated with malnutrition in people with cancer. After review of the relevant literature and expert presentations, an independent expert panel recommends baseline screening for malnutrition risk using a validated instrument following cancer diagnosis and repeated screening during and after treatment to monitor nutritional well-being. Those at risk of malnutrition should be referred to registered dietitians for more in-depth nutritional assessment and intervention. The panel emphasizes the need for further rigorous, well-defined nutritional intervention studies to evaluate the effects on symptoms and cancer-specific outcomes as well as effects of intentional weight loss before or during treatment in people with overweight or obesity. Finally, although data on intervention effectiveness are needed first, robust data collection during trials is recommended to assess cost-effectiveness and inform coverage and implementation decisions.

Surveillance of Mpox Cases Attending Sexual Health Services in England (SOMASS): design, implementation and initial findings from the SOMASS data collection tool, 2022

ObjectivesWe aimed to design and implement a data collection tool to support the 2022 mpox (monkeypox) outbreak, and to describe clinical and epidemiological data from individuals with mpox attending sexual...

Optimal weighted GAN and U-Net based segmentation for phenotypic trait estimation of crops using Taylor Coot algorithm

Accurate and robust collection of plant phenotypic data offers theoretical as well as technical support to support the growth of crop science and to ensure ecological security, agricultural growth, and food security. Identifying phenotypic traits of crops refers to the detection of difference exists in plant features caused due to interaction of the environment and plant genetics. It is an important research discussed in plant breeding as it permits breeders to find a variety of crops with physical features, like stress resistance, and high yield. Manual measurement of phenotypic traits in the area is labor intensive and causes inaccurate results and these issues are resolved by developing a method based on the Taylor Coot algorithm for segmenting plant regions and biomass area to detect emergence counting and to estimate the biomass of crops. The process of counting emergence and estimating the biomass is performed in a parallel way using a Deep Residual Network (DRN) that is trained by developed optimization. The segmentation framework is done using Generative Adversarial Network (GAN) and U-Net to segment the plant regions and biomass area. For instance, the extraction of vegetation indices makes the process of biomass estimation to generate more optimal features using a deep learning model. Moreover, the proposed model obtains minimal Mean Absolute Difference (MAD), Standard Absolute Difference (SDAD), %Difference (%D) as 0.073, 0.074, and 16.45 for emergence counting. Moreover, the DRN shows higher performance by attaining minimum MAD, SDAD, and %D as 0.069, 0.096, and 14.85 for biomass estimation.

Quality and Impact of Manuscripts Using The Society of Thoracic Surgeons National Databases

BackgroundThe Society of Thoracic Surgeons (STS) National Databases contain the largest and most comprehensive cardiac, thoracic, and congenital surgery data available. In this analysis characteristics of manuscripts that used the STS National Databases were examined to evaluate the quality and impact of these publications. MethodsManuscripts published from 2010 to 2020 that used the STS National Databases (Adult Cardiac, General Thoracic, and Congenital Heart Surgery) were examined. The number of times cited per published manuscript, journal of publication, and journal CiteScore were assessed. Author characteristics, including institutional association and h-index, were evaluated for first and last authors. ResultsOf 539 published manuscripts, 179 (33.2%) used the Adult Cardiac Surgery, 128 (23.7%) the Congenital Heart Surgery, 85 (15.8%) the General Thoracic Surgery, and 136 (25.2%) used unspecified STS National Databases. Collectively the analyzed manuscripts were cited 18,943 times, with a mean of 35.1 (range, 0-528) citations per manuscript. Manuscripts were published in 97 journals with a mean year-adjusted CiteScore of 5.6 (range, 0.1-60.2). Authors associated with 361 institutions contributed to published manuscripts. The mean h-index of first authors was 24.6 (range, 0-100) and of last authors was 44.1 (range, 0-164). From 2010 to 2020 first authors wrote a mean of 1.6 (range, 1-31) and last authors wrote a mean of 1.9 (range, 1-29) STS National Databases publications. ConclusionsThe STS National Databases are some of the most robust data collection sources available to cardiothoracic surgeons. These data have enabled valuable research in respected journals from authors of varying experience levels.

A Systematic Review of Inflammatory Bowel Disease Epidemiology and Health Outcomes in Sexual and Gender Minority Individuals

A Systematic Review of Inflammatory Bowel Disease Epidemiology and Health Outcomes in Sexual and Gender Minority Individuals

FAMILY-CENTERED COLLABORATIVE CARE FOR PATIENTS WITH CHRONIC MENTAL ILLNESS: A NARRATIVE LITERATURE REVIEW

IntroductionChronic mental illnesses are long-lasting and recurring, require continuous care as well as an integrated and collaborative approach to organize the care. This study sought to examine whether family center collaborative care is an acceptable treatment option for individuals with chronic mental illness.ObjectivesIs the family-centered collaborative care suitable for patients with chronic mental illness?MethodsFrom the years 2000 to 2021, ten electronic databases relating to family-centered collaborative care for mental illness were searched adopting PRISMA’s checklistResultsAfter systematic search, 27 articles and a thesis were found. According to moderate to high quality qualitative research, family-centered collaborative care was considered acceptable intervention, though a few studies supporting it.ConclusionsThis study examines theoretical, methodological, and practical considerations as a basis for more robust data collection based on individual experiences and evidence-based practice.Reference(s)1.Wittchen, H.-U., et al., The size and burden of mental disorders and other disorders of the brain in Europe 2010. European neuropsychopharmacology, 2011. 21(9): p. 655-679.2.CUTLER, J.L., Kaplan and Sadock’s synopsis of psychiatry. 2016, LWW.3.Thornicroft, G., et al., The personal impact of schizophrenia in Europe. Schizophrenia research, 2004. 69(2-3): p. 125-132.4.Vigo, D., G. Thornicroft, and R. Atun, Estimating the true global burden of mental illness. The Lancet Psychiatry, 2016. 3(2): p. 171-178.Disclosure of InterestNone Declared

Configurações explicativas do desenvolvimento da resiliência nas redes de suprimentos da administração pública

Abstract Social capital is an important theoretical lens for explaining interorganizational relationships and building resilience in supply networks. This study corroborates this perspective, aiming to identify which attributes of social capital contribute to explaining the development of resilience in public administration supply networks and how they combine in this explanation. A qualitative and descriptive research was carried out, using interviews with the repertoire grid technique for data collection. Data were analyzed using Honey’s content analysis and coincidence analysis (CNA). The research results indicate that social capital proved to be fundamental to developing resilience in supply networks in public administration, through the attributes of sharing technical information, precision in communication, anticipation in communicating relevant information, reciprocity, trust, transparency, and commitment. The originality of the study lies in the use of social capital theory in studies on resilience in public administration and in the adoption of a robust data collection and analysis method that has not yet been explored in research in Brazilian public administration. The main contributions of the study were: (1) to highlight social capital as a multilevel construct that influences the development of resilience, (2) to expand studies on resilience in public administration, (3) to provide information that public managers can use to avoid or minimize the risks that jeopardize the provision of public services, and (4) to adopt an unprecedented research method in the Brazilian public administration.

Holyoke Early Access to Recovery and Treatment (HEART): A case study of a court-based intervention to reduce opioid overdose

The District Court in Holyoke, Massachusetts, is among the first courts nationwide to provide access to medications for opioid use disorder (MOUD) and other treatment. The program uses an innovative multisectoral approach to serve a primarily Latinx population living in communities of concentrated poverty with high opioid overdose rates. We document the origins, adaptations, and current status of program operations, including the use of on-site peer recovery specialists and robust data collection efforts. From August 16, 2021, to February 28, 2022, of the 1040 individuals who entered the court for an arraignment, 47.9% (n = 498) were eligible for program participation. Of those 498 individuals, 54.2% (n = 270) spoke with a recovery specialist. Many self-identified as Latinx (53.0%) and male (69.3%). Over one-fourth (27.0%) were connected to a long-term peer recovery specialist and 11.5% were directly connected to a MOUD provider. Semi-structured interviews with key implementers and participants revealed a shared appreciation for the life-saving efforts of the program. We conclude with practical and theoretical considerations required to offer linkage to MOUD in court-based contexts. Future efforts will assess participant outcomes to determine whether the program is an effective and feasible intervention that can be adopted by other court-based settings.

Data collection and outcome measurement in student counseling services in Irish higher education institutions: Counselors’ views on challenges and facilitators.

With increasing concerns around student mental health, student counseling services (SCSs) in higher education are being challenged to show evidence of their contribution to the personal and academic development of students through evidence-based approaches including systematic data collection and standardized outcome measurement. This study aims to document data collection and measurement processes in SCSs in higher education institutions (HEIs) in Ireland and to explore the opinions of staff in SCSs on the feasibility and functionality of a standardized national database for SCSs to enhance robust data collection and measurement across the sector. Twenty-three interviews were conducted and thematically analyzed with staff in SCSs in 22 HEIs (85% of publicly funded HEIs). Findings revealed that the process of data collection in SCSs is mainly focused on individual counseling with variations among SCSs in collecting clients' demography, presenting issues, and clinical and educational outcomes. Lack of human and financial resources, no access to an electronic data management system, negative opinions about standardized outcome measures, and concerns over data protection and misuse of data were impeding factors for systematic data collection in SCSs. There is strong support among SCSs for the establishment of a national database which is facilitated by secured funding from the Higher Education Authority, a strong sense of community and trust among student counselors, and a history of data collection through the Psychological Counsellors in Higher Education Ireland association. Findings offer insights into challenges and facilitators for robust data collection and measurement at a national level. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Abstract 2019: Quantum Electrochemical Spectroscopy (QES), a broad bioanalytical platform for robust and scalable data collection from biological systems

Abstract 2019: Quantum Electrochemical Spectroscopy (QES), a broad bioanalytical platform for robust and scalable data collection from biological systems

Demonstrating proof of concept for value-based agreements in Europe: two real-world cases.

Value-based agreements (VBAs) link access, reimbursement, or price to the real-world usage and impact of a medicine, thereby enabling patient access while reducing clinical or financial uncertainty for the payer. VBAs have the potential to support improved patient outcomes, given the value-oriented approach to care, and lead to overall savings, while enabling payers to share risk and reduce uncertainty. This commentary outlines the key challenges, enablers, and a framework for successful implementation by comparing the experience of two VBAs for AstraZeneca medicines, aiming to increase confidence in their future use. Engagement by payers, manufacturers, physicians, and provider institutions, and robust data collection systems that are accessible, simple to use, and add little burden to physicians were key to successfully negotiating a VBA that worked for all stakeholders. In both country systems, a legal/policy framework enabled innovative contracting. These examples demonstrate proof of concept for VBA implementation in different settings, and may inform future VBAs.

Should we rethink neurodegeneration?

The therapy of many neurological disorders has advanced markedly during recent decades. Not so for neurodegenerative disorders. Early detection, deep individual genotyping and phenotyping, and personalized therapies have been suggested as the way forward. However, we still do not know enough about the aetiology and molecular basics of these diseases. In fact, the term neurodegenerative disorder may be a misleading categorization that constitutes a major cognitive barrier against better characterization and understanding of these disorders. Therefore, we need to go back to the basics and employ novel, open-minded observational study protocols that combine very extensive and robust clinical, molecular and epidemiological data collection methods. Moreover, we need to reconsider our basic orientation towards these diseases to increase our chances of finding out what we are actually trying to care for and cure.

Digitalising cities: A methodology to map evaluation requirements into robust and feasible data collection approaches

The sustainable development of cities relies on the implementation of multi-sectoral actions towards carbon neutrality, reducing the air pollutants emissions. The actions' decision-making process for cities transformation should be supported by lessons learnt from previous interventions and KPIs (Key Performance Indicators). To do so, gathering real data becomes pivotal, complementing simulation tools (currently used), solving the inherent uncertainties due to assumptions. Data collection methodologies are then necessary, being the main driver for digital cities and providing better mechanisms for informed decision-making. Most of the cities still operate in silos and do not always implement the strategic plans supported with a digitalization of the municipal processes. Within this perspective, this paper presents a methodology to support cities in the preparation of monitoring programmes to collect real data in a robust and feasible manner. Taking the KPIs and the Smart Cities urban strategies into account, this paper concludes with some lessons learnt within cities to deploy monitoring approaches. From the city challenges to the review of the plans, all the process is driven by real data and KPIs. The methodology has been applied in the mySMARTLife project (Grant Agreement #731297) and deployed into the cities of Nantes (France), Hamburg (Germany) and Helsinki (Finland).

An IoT-Based Wristband for Automatic People Tracking, Contact Tracing and Geofencing for COVID-19.

The coronavirus disease (COVID-19) pandemic has triggered a huge transformation in the use of existing technologies. Many innovations have been made in the field of contact tracing and tracking. However, studies have shown that there is no holistic system that integrates the overall process from data collection to the proper analysis of the data and actions corresponding to the results. It is critical to identify any contact with infected people and to ensure that they do not interact with others. In this research, we propose an IoT-based system that provides automatic tracking and contact tracing of people using radio frequency identification (RFID) and a global positioning system (GPS)-enabled wristband. Additionally, the proposed system defines virtual boundaries for individuals using geofencing technology to effectively monitor and keep track of infected people. Furthermore, the developed system offers robust and modular data collection, authentication through a fingerprint scanner, and real-time database management, and it communicates the health status of the individuals to appropriate authorities. The validation results prove that the proposed system identifies infected people and curbs the spread of the virus inside organizations and workplaces.

Single-dose intravenous ketamine or intramuscular naltrexone for high-utilization inpatients with alcohol use disorder: pilot trial feasibility and readmission rates

BackgroundAlcohol use disorder (AUD) accounts for millions of acute care encounters annually in the United States. Hospitalization represents a vital opportunity to intervene pharmacologically, but low medication adherence is a significant barrier. Two single-dose, adherence-independent interventions are well suited for pre-discharge administration: intravenous (IV) ketamine and intramuscular (IM) naltrexone. Their feasibility and readmission-reducing efficacy in hospital settings are not well-established.MethodsA 3-arm, open-label randomized trial was conducted at our safety-net medical hospital among high-utilization inpatients with severe AUD. Consented adults (age 18–65) were randomized to (1) IV ketamine (KET) 0.5 mg/kg over 40 min, (2) IM naltrexone (NTX) 380 mg once, or (3) linkage alone (LA). The primary clinical outcome was 30-day all-cause hospital readmission rate. All were provided enhanced linkage to outpatient addiction clinic.ResultsWe consented and randomized 44 participants (n = 13, 14, 17 for KET, NTX, LA, respectively), with a mean of 3.2 past-year hospitalizations. Compared to the LA arm, both the KET arm (RR 0.37, p = 0.17) and NTX arm (RR 0.52, p = 0.27) had a lower 30-day readmission rate, though the differences were nonsignificant. Immediate acceptability ratings of KET and NTX were 9.50 and 9.17 out of 10, respectively. No serious adverse events or illicit ketamine use was reported.ConclusionsBoth interventions are feasible and showed promise in reducing readmissions for high-utilization AUD inpatients. Despite randomization, baseline characteristics may have differed in ways that biased against the control arm. Additional pragmatic studies—with larger sample size, blinding, and robust follow-up data collection—are needed to verify findings and better understand mediating factors.ClinicalTrials.gov Identifier NCT04562779. Registered 24 September 2020. https://clinicaltrials.gov/ct2/show/NCT04562779