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Pediatrics Jun 2024
Topics: Humans; Gender Identity; Adolescent; Female; Male; Data Collection; Child; Transgender Persons
PubMed: 38752290
DOI: 10.1542/peds.2024-065932 -
Therapie Dec 2019The discovery and quantification of adverse drug reactions has long relied on the careful analysis of spontaneously reported cases. Causality assessment (imputation) was... (Review)
Review
The discovery and quantification of adverse drug reactions has long relied on the careful analysis of spontaneously reported cases. Causality assessment (imputation) was a fundamental feature of individual case report analysis. This was complemented by analysis of aggregated cases, and of disproportionality analyses in spontaneous reports databases. In the absence of more specific information sources, these have resulted in the discovery of many new adverse reactions, altering drug information. It has led to the withdrawal from the market of many drugs, but its use for risk quantification remains fraught with uncertainty. The recent access to population-wide claims or electronic health records databases have confirmed for spontaneous reporting a predominant role in hypothesis generation for serious adverse drug reactions, notably those that result in hospital admission or death. In these cases, the events are identifiable at the population level, and can be quantified precisely using the tools of modern pharmacoepidemiology, to generate specific benefit-risk analyses. Spontaneous reporting remains irreplaceable in signal and alert generation in drug safety, despite its inherent limitations. For signal strengthening and assessment, more systematic and quantitative methods should be sought, such as claims databases for reactions resulting in hospital admissions.
Topics: Adverse Drug Reaction Reporting Systems; Data Collection; Databases, Factual; Drug-Related Side Effects and Adverse Reactions; Electronic Health Records; Humans; Patient Safety; Pharmacoepidemiology; Pharmacovigilance
PubMed: 31623850
DOI: 10.1016/j.therap.2019.09.004 -
BMC Oral Health Oct 2022This scoping review reports on studies that collect survey data using quantitative research to measure self-reported oral health status outcome measures. The objective... (Review)
Review
BACKGROUND
This scoping review reports on studies that collect survey data using quantitative research to measure self-reported oral health status outcome measures. The objective of this review is to categorize measures used to evaluate self-reported oral health status and oral health quality of life used in surveys of general populations.
METHODS
The review is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) with the search on four online bibliographic databases. The criteria include (1) peer-reviewed articles, (2) papers published between 2011 and 2021, (3) only studies using quantitative methods, and (4) containing outcome measures of self-assessed oral health status, and/or oral health-related quality of life. All survey data collection methods are assessed and papers whose methods employ newer technological approaches are also identified.
RESULTS
Of the 2981 unduplicated papers, 239 meet the eligibility criteria. Half of the papers use impact scores such as the OHIP-14; 10% use functional measures, such as the GOHAI, and 26% use two or more measures while 8% use rating scales of oral health status. The review identifies four data collection methods: in-person, mail-in, Internet-based, and telephone surveys. Most (86%) employ in-person surveys, and 39% are conducted in Asia-Pacific and Middle East countries with 8% in North America. Sixty-six percent of the studies recruit participants directly from clinics and schools, where the surveys were carried out. The top three sampling methods are convenience sampling (52%), simple random sampling (12%), and stratified sampling (12%). Among the four data collection methods, in-person surveys have the highest response rate (91%), while the lowest response rate occurs in Internet-based surveys (37%). Telephone surveys are used to cover a wider population compared to other data collection methods. There are two noteworthy approaches: 1) sample selection where researchers employ different platforms to access subjects, and 2) mode of interaction with subjects, with the use of computers to collect self-reported data.
CONCLUSION
The study provides an assessment of oral health outcome measures, including subject-reported oral health status and notes newly emerging computer technological approaches recently used in surveys conducted on general populations. These newer applications, though rarely used, hold promise for both researchers and the various populations that use or need oral health care.
Topics: Humans; Oral Health; Quality of Life; Schools; Self Report; Surveys and Questionnaires
PubMed: 36192721
DOI: 10.1186/s12903-022-02399-5 -
Social Science Research Jan 2023This article reviews recent methodological research that bears on the collection of egocentric network data. It begins with background on setting egocentric network... (Review)
Review
This article reviews recent methodological research that bears on the collection of egocentric network data. It begins with background on setting egocentric network boundaries and principal types of instruments that obtain information about such networks. It then discusses innovations in data collection and studies of data quality. The bulk of these address questions about "name generator" instruments that obtain information about the alters and relationships in a subject's network. Among topics receiving substantial attention in recent research are mitigation of respondent burden, interviewer effects, survey mode, and the performance of name generators in longitudinal studies. Potentially fruitful innovations supplement conventional question-and-answer surveys with visual elements that promise to better engage respondents and reduce the demands that name generator-based data collection poses. We close by highlighting both accomplishments of this body of research and some open issues.
Topics: Humans; Surveys and Questionnaires; Data Accuracy; Longitudinal Studies
PubMed: 36470633
DOI: 10.1016/j.ssresearch.2022.102816 -
European Spine Journal : Official... Mar 2023Comorbidities are significant patient factors that contribute to outcomes after surgery. There is highly variable collection of this information across the literature.... (Review)
Review
INTRODUCTION
Comorbidities are significant patient factors that contribute to outcomes after surgery. There is highly variable collection of this information across the literature. To help guide the systematic collection of best practice data, the Australian Spine Registry conducted an evidence map to investigate (i) what comorbidities are collected by spine registries, (ii) how they are collected and (iii) the compliance and completeness in collecting comorbidity data.
METHOD
A literature search was performed to identify published studies of adult spine registry data reporting comorbidities. In addition, targeted questionnaires were sent to existing global spine registries to identify the maximum number of relevant results to build the evidence map.
RESULTS
Thirty-six full-text studies met the inclusion criteria. There was substantial variation in the reporting of comorbidity data; 55% of studies reported comorbidity collection, but only 25% reported the data collection method and 20% reported use of a comorbidity index. The variation in the literature was confirmed with responses from 50% of the invited registries (7/14). Of seven, three use a recognised comorbidity index and the extent and methods of comorbidity collection varied by registry.
CONCLUSION
This evidence map identified variations in the methodology, data points and reporting of comorbidity collection in studies using spine registry data, with no consistent approach. A standardised set of comorbidities and data collection methods would encourage collaboration and data comparisons between patient cohorts and could facilitate improved patient outcomes following spine surgery by allowing data comparisons and predictive modelling of risk factors.
Topics: Adult; Humans; Australia; Spine; Registries; Surveys and Questionnaires; Comorbidity
PubMed: 36658363
DOI: 10.1007/s00586-023-07529-3 -
Clinical and Translational Science Jan 2021Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated... (Review)
Review
Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well-established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample-level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.
Topics: Big Data; Biomedical Technology; Biometry; Data Collection; Humans; Monitoring, Physiologic; Remote Sensing Technology; Research Design
PubMed: 32770726
DOI: 10.1111/cts.12865 -
American Journal of Public Health Aug 2021
Topics: COVID-19; Cause of Death; Data Collection; Disease Outbreaks; Epidemiological Monitoring; Humans; Statistics as Topic
PubMed: 34464190
DOI: 10.2105/AJPH.2021.306403 -
Journal of the American College of... Sep 2019Data collection and analysis are central to all quality improvement initiatives. However, there is often a lack of understanding about the different ways data can be... (Review)
Review
Data collection and analysis are central to all quality improvement initiatives. However, there is often a lack of understanding about the different ways data can be collected and why one mode of data collection should be selected over another. In this article, a framework is provided to help quality improvement teams identify and define the data to be collected. This is followed by a discussion of the different modes of data collection. Examples are provided to help illustrate each mode of data collection. Finally, the advantages and disadvantages of each mode are described.
Topics: Data Collection; Forecasting; Humans; Quality Improvement; Radiology; United States
PubMed: 31492402
DOI: 10.1016/j.jacr.2019.05.032 -
Addiction (Abingdon, England) Oct 2020Amazon Mechanical Turk (MTurk) provides a crowdsourcing platform for the engagement of potential research participants with data collection instruments. This review (1)...
AIMS
Amazon Mechanical Turk (MTurk) provides a crowdsourcing platform for the engagement of potential research participants with data collection instruments. This review (1) provides an introduction to the mechanics and validity of MTurk research; (2) gives examples of MTurk research; and (3) discusses current limitations and best practices in MTurk research.
METHODS
We review four use cases of MTurk for research relevant to addictions: (1) the development of novel measures, (2) testing interventions, (3) the collection of longitudinal use data to determine the feasibility of longer-term studies of substance use and (4) the completion of large batteries of assessments to characterize the relationships between measured constructs. We review concerns with the platform, ways of mitigating these and important information to include when presenting findings.
RESULTS
MTurk has proved to be a useful source of data for behavioral science more broadly, with specific applications to addiction science. However, it is still not appropriate for all use cases, such as population-level inference. To live up to the potential of highly transparent, reproducible science from MTurk, researchers should clearly report inclusion/exclusion criteria, data quality checks and reasons for excluding collected data, how and when data were collected and both targeted and actual participant compensation.
CONCLUSIONS
Although on-line survey research is not a substitute for random sampling or clinical recruitment, the Mechanical Turk community of both participants and researchers has developed multiple tools to promote data quality, fairness and rigor. Overall, Mechanical Turk has provided a useful source of convenience samples despite its limitations and has demonstrated utility in the engagement of relevant groups for addiction science.
Topics: Behavior, Addictive; Behavioral Research; Crowdsourcing; Data Accuracy; Data Collection; Humans; Patient Selection
PubMed: 32135574
DOI: 10.1111/add.15032 -
Journal of Racial and Ethnic Health... Oct 2020To describe how pediatric hospitals across the USA and Canada collect race/ethnicity and language preference (REaL) data and how they stratify quality and safety metrics...
OBJECTIVE
To describe how pediatric hospitals across the USA and Canada collect race/ethnicity and language preference (REaL) data and how they stratify quality and safety metrics using such data.
METHODS
Pediatric hospitals from the Solutions for Patient Safety network (125 US, 6 Canadian) were surveyed between January and March 2018 on collection and use of patient/family race/ethnicity data and patient/family language preference data. The study team created the survey using a formal process including pre-testing. Responses were analyzed using descriptive statistics.
RESULTS
Ninety-three of 131 (71%) hospitals completed the survey (87/125 [70%] US, 6/6 [100%] Canadian). Patient race/ethnicity was collected by 95%, parent/guardian race/ethnicity was collected by 31%, and 5/6 Canadian hospitals collected neither. Minimum government race/ethnicity categories were used without modification/addition by 68% of US hospitals. Eleven hospitals (13%) offered a multiracial/multiethnic option. Most hospitals reported collecting language preferences of parent/guardian (81%) and/or patient (87%). A majority provided formal training on data collection for race/ethnicity (70%) and language preferences (70%); fewer had a written policy (41%, 51%). Few hospitals stratified hospital quality and safety measures by race/ethnicity (20% readmissions, 20% patient/family experience, 16% other) or language preference (21% readmissions, 21% patient/family experience, 8% other).
CONCLUSIONS
The variability of REaL data collection practices among pediatric hospitals highlights the importance of examining the validity and reliability of such data, especially when combined from multiple hospitals. Nevertheless, while improvements in data accuracy and standardization are sought, efforts to identify and eliminate disparities should be developed concurrently using existing data.
Topics: Canada; Child; Data Collection; Ethnicity; Hospitals, Pediatric; Humans; Language; Racial Groups; United States
PubMed: 32056162
DOI: 10.1007/s40615-020-00716-8