A comparative study of clinical trial and real-world data in patients with diabetic kidney disease.

A growing body of research is focusing on real-world data (RWD) to supplement or replace randomized controlled trials (RCTs). However, due to the disparities in data generation mechanisms, differences are likely and necessitate scrutiny to validate the merging of these datasets. We compared the characteristics of RCT data from 5734 diabetic kidney disease patients with corresponding RWD from electronic health records (EHRs) of 23,523 patients. Demographics, diagnoses, medications, laboratory measurements, and vital signs were analyzed using visualization, statistical comparison, and cluster analysis. RCT and RWD sets exhibited significant differences in prevalence, longitudinality, completeness, and sampling density. The cluster analysis revealed distinct patient subgroups within both RCT and RWD sets, as well as clusters containing patients from both sets. We stress the importance of validation to verify the feasibility of combining RCT and RWD, for instance, in building an external control arm. Our results highlight general differences between RCT and RWD sets, which should be considered during the planning stages of an RCT-RWD study. If they are, RWD has the potential to enrich RCT data by providing first-hand baseline data, filling in missing data or by subgrouping or matching individuals, which calls for advanced methods to mitigate the differences between datasets.

Online interviews for qualitative health research in Africa: a scoping review.

Online interviews can be powerful tools in global health research. In this article, we review the literature on the use of and challenges associated with online interviews in health research in Africa and make recommendations for future online qualitative studies. The scoping review methodology was used. We searched on Medline and Embase in March 2022 for qualitative articles that used internet-based interviews as a data collection method. Following full-text reviews, we included nine articles. We found that online interviews were typically conducted via Microsoft Teams, Zoom, Skype, WhatsApp, Facebook Messaging and E-mail chats. Online interviews were used in Africa because of the restrictions imposed by the coronavirus disease 2019 pandemic and the need to sample participants across multiple countries or communities. Recruitment for online interviews occurred online, interviews were characterised by inaudible sounds, the inability to use video options and the challenges of including people with low income and education. We recommend that researchers critically evaluate the feasibility of online interviews within a particular African locality before fully implementing this data collection approach. Researchers may also collaborate with community-based organisations to help recruit a more socioeconomically diverse sample because of the potential of excluding participants with limited internet access.

A qualitative exploration of forensic pathology service staff perceptions of the implementation barriers and facilitators of manual- and electronic injury mortality surveillance system methods in South Africa.

BACKGROUND: Injury mortality surveillance systems are critical to monitor changes in a population's injury outcomes so that relevant injury prevention responses may be adopted. This is particularly the case in South Africa, where the injury burden is nearly twice the global rate. Regular evaluations of surveillance systems are pivotal to strengthening surveillance capacity, performance, and cost effectiveness. The National Injury Mortality Surveillance System (NIMSS) is an injury mortality surveillance system that is currently focused in Mpumalanga and utilises manual and electronic web-based systems for data collection. This study explored Forensic Pathology Service (FPS) staff perceptions of the implementation barriers and facilitators of manual- and electronic injury mortality surveillance system methods. METHODS: A qualitative study was employed using purposive sampling. Forty-seven participants, aged 29 to 59 years comprising 31 males and 16 females were recruited across 21 FPS facilities that serve the province. The formative evaluation occurred over the November 2019 to November 2022 period. Twelve focus group discussions were thematically analysed to determine emerging themes and patterns related to the use of the system using the WHO surveillance system guidelines as a framework. RESULTS: The key themes concerning the barriers and facilitators were located along WHO attributes of simplicity, acceptability, timeliness, flexibility, data quality and stability. Distinctions between the manual and e-surveillance systems were drawn upon across the attributes highlighting their experience with the system, user preference, and its contextual relevance. With Mpumalanga predominantly rural, internet connectivity was a common issue, with most participants consequently showing a preference for the manual system, even though the electronic system's automated internal validation process was of benefit. The data quality however remained similar for both methods. With program stability and flexibility, the manual system proved more beneficial as the dataset was reported to be easily transferrable across computer devices. CONCLUSION: Obtaining FPS perceptions of their experiences with the system methodologies are pertinent for the enhancement of injury surveillance systems so to improve prospective engagements with the systems. This will facilitate timely and accurate injury mortality information which is vital to inform public policy, and injury control and prevention responses.

Comparative performance of national civil registration and vital statistics systems: a global assessment.

Objective: To assess the current state of the world's civil registration and vital statistics systems based on publicly available data and to propose strategic development pathways, including priority interventions, for countries at different levels of civil registration and vital statistics performance. Methods: We applied a performance assessment framework to publicly available data, using a composite indicator highly correlated with civil registration and vital statistics performance which we then adjusted for data incomparability and missing values. Findings: Globally, civil registration and vital statistics systems score on average 0.70 (0-1 scale), with substantial variations across countries and regions. Scores ranged from less than 0.50 in emerging systems to nearly 1.00 in the most developed systems. Approximately one fifth of the world's population live in the 43 countries with low system performance (< 0.477). Irrespective of system development, health sector indicators consistently scored lower than other determinants of civil registration and vital statistics performance. Conclusion: From our assessment, we provide three main recommendations for how the health sector can contribute to improving civil registration and vital statistics systems: (i) enhanced health sector engagement in birth and death notification; (ii) a more systematic approach to training cause of death diagnostics; and (iii) leadership in the implementation of verbal autopsy methods. Four different civil registration and vital statistics improvement pathways for countries at different levels of system development are proposed, that can constitute a blueprint for regional civil registration and vital statistics strengthening activities that countries can adapt and refine to suit their capabilities, resources, and particular challenges.

Cost and statistical efficiency of posture assessment by inclinometry and observation, exemplified by paper mill work.

Postures at work are paramount in ergonomics. They can be determined using observation and inclinometry in a variety of measurement scenarios that may differ both in costs associated with collecting and processing data, and in efficiency, i.e. the precision of the eventual outcome. The trade-off between cost and efficiency has rarely been addressed in research despite the obvious interest of obtaining precise data at low costs. Median trunk and upper arm inclination were determined for full shifts in 28 paper mill workers using both observation and inclinometry. Costs were estimated using comprehensive cost equations; and efficiency, i.e. the inverted standard deviation of the group mean, was assessed on basis of exposure variance components. Cost and efficiency were estimated in simulations of six sampling scenarios: two for inclinometry (sampling from one or three shifts) and four for observation (one or three observers rating one or three shifts). Each of the six scenarios was evaluated for 1 through 50 workers. Cost-efficiency relationships between the scenarios were intricate. As an example, inclinometry was always more cost-efficient than observation for trunk inclination, except for observation strategies involving only few workers; while for arm inclination, observation by three observers of one shift per worker outperformed inclinometry on three shifts up to a budget of €20000, after which inclinometry prevailed. At a budget of €10000, the best sampling scenario for arm inclination was 2.5 times more efficient than the worst. Arm inclination could be determined with better cost-efficiency than trunk inclination. Our study illustrates that the cost-efficiency of different posture measurement strategies can be assessed and compared using easily accessible diagrams. While the numeric examples in our study are specific to the investigated occupation, exposure variables, and sampling logistics, we believe that inclinometry will, in general, outperform observation. In any specific case, we recommend a thorough analysis, using the comparison procedure proposed in the present study, of feasible strategies for obtaining data, in order to arrive at an informed decision support.

The Hawthorne Effect: Quality and Outcomes in Neurosurgery.

Measure something, and it gets better-this is what is called as the Hawthorne effect (also known as the observer effect). The Hawthorne factory experiments in 1920s were remarkable industrial data collection and analysis exercises that lead to Edwards Deming's quality revolution. The Harvard Medical Practice Study (1991), Leape's "Error in Medicine" (1994), and the Bristol pediatric cardiac report (2001) are among many documents that have revealed the huge gap between best practices and actual medical practice. Alarmed by the poor standards of quality at the most respected institutions, the medical fraternity therefore began visiting facilities in different fields and observing their quality assessment processes. The next leap for neurosurgery is to realize that it is unacceptable to treat patients with no regard for the standard of clinical outcomes. The traditional neurosurgery residency training has long ignored the most important issues of self-assessment, reappraisal, relearning, and measurement of skill and surgical outcomes. However, the experience taken from disparate fields, especially cardiac surgery, may encourage research and progress in measurement and improvement of quality in neurosurgery. Like cardiac surgeons, neurosurgeons must examine and analyze the results of their interventions. The concept of quality measurement is the most important single advance we can make in neurosurgery practice. Meticulous and precise measurement of outcomes will allow future progress of our specialty.

A new acquisition protocol for conducting studies with children: The science camp research experience.

In the last 50 years, the study of brain development has brought major discoveries to education and medicine, changing the lives of millions of children and families. However, collecting behavioral and neurophysiological data from children has specific challenges, such as high rates of data loss and participant dropout. We have developed a science camp method to collect data from children using the benefits of positive peer interactions and interactive and engaging activities, to allow researchers to better collect data repeatedly and reliably from groups of children. A key advantage of this approach is that by increasing participant engagement, attention is also increased, thereby increasing data quality, reducing data loss, and lowering attrition rates. This protocol describes the step-by-step procedure for facilitation of a science camp, including behavioral, electrophysiological, and participatory engagement activities. As this method is robust but also flexible, we anticipate that it can also be applied to different demographics and research needs.

The confidante method to measure abortion: implementing a standardized comparative analysis approach across seven contexts.

BACKGROUND: Obtaining representative abortion incidence estimates is challenging in restrictive contexts. While the confidante method has been increasingly used to collect this data in such settings, there are several biases commonly associated with this method. Further, there are significant variations in how researchers have implemented the method and assessed/adjusted for potential biases, limiting the comparability and interpretation of existing estimates. This study presents a standardized approach to analyzing confidante method data, generates comparable abortion incidence estimates from previously published studies and recommends standards for reporting bias assessments and adjustments for future confidante method studies. METHODS: We used data from previous applications of the confidante method in Côte d'Ivoire, Ethiopia, Ghana, Java (Indonesia), Nigeria, Uganda, and Rajasthan (India). We estimated one-year induced abortion incidence rates for confidantes in each context, attempting to adjust for selection, reporting and transmission bias in a standardized manner. FINDINGS: In each setting, majority of the foundational confidante method assumptions were violated. Adjusting for transmission bias using self-reported abortions consistently yielded the highest incidence estimates compared with other published approaches. Differences in analytic decisions and bias assessments resulted in the incidence estimates from our standardized analysis varying widely from originally published rates. INTERPRETATION: We recommend that future studies clearly state which biases were assessed, if associated assumptions were violated, and how violations were adjusted for. This will improve the utility of confidante method estimates for national-level decision making and as inputs for global or regional model-based estimates of abortion.

Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses.

BACKGROUND: Social media is an important information source for a growing subset of the population and can likely be leveraged to provide insight into the evolving drug overdose epidemic. Twitter can provide valuable insight into trends, colloquial information available to potential users, and how networks and interactivity might influence what people are exposed to and how they engage in communication around drug use. OBJECTIVE: This exploratory study was designed to investigate the ways in which unsupervised machine learning analyses using natural language processing could identify coherent themes for tweets containing substance names. METHODS: This study involved harnessing data from Twitter, including large-scale collection of brand name (N=262,607) and street name (N=204,068) prescription drug-related tweets and use of unsupervised machine learning analyses (ie, natural language processing) of collected data with data visualization to identify pertinent tweet themes. Latent Dirichlet allocation (LDA) with coherence score calculations was performed to compare brand (eg, OxyContin) and street (eg, oxys) name tweets. RESULTS: We found people discussed drug use differently depending on whether a brand name or street name was used. Brand name categories often contained political talking points (eg, border, crime, and political handling of ongoing drug mitigation strategies). In contrast, categories containing street names occasionally referenced drug misuse, though multiple social uses for a term (eg, Sonata) muddled topic clarity. CONCLUSIONS: Content in the brand name corpus reflected discussion about the drug itself and less often reflected personal use. However, content in the street name corpus was notably more diverse and resisted simple LDA categorization. We speculate this may reflect effective use of slang terminology to clandestinely discuss drug-related activity. If so, straightforward analyses of digital drug-related communication may be more difficult than previously assumed. This work has the potential to be used for surveillance and detection of harmful drug use information. It also might be used for appropriate education and dissemination of information to persons engaged in drug use content on Twitter.

Current state of rare disease registries and databases in Australia: a scoping review.

BACKGROUND: Rare diseases (RDs) affect approximately 8% of all people or > 400 million people globally. The Australian Government's National Strategic Action Plan for Rare Diseases has identified the need for a national, coordinated, and systematic approach to the collection and use of RD data, including registries. Rare disease registries (RDRs) are established for epidemiological, quality improvement and research purposes, and they are critical infrastructure for clinical trials. The aim of this scoping review was to review literature on the current state of RDRs in Australia; to describe how they are funded; what data they collect; and their impact on patient outcomes. METHODS: We conducted a literature search on MEDLINE, EMBASE, CINAHL and PsychINFO databases, in addition to Google Scholar and grey literature. Dissertations, government reports, randomised control trials, conference proceedings, conference posters and meeting abstracts were also included. Articles were excluded if they did not discuss RDs or if they were written in a language other than English. Studies were assessed on demographic and clinical patient characteristics, procedure or treatment type and health-related quality of life captured by RDRs or databases that have been established to date. RESULTS: Seventy-four RDRs were identified; 19 were global registries in which Australians participated, 24 were Australian-only registries, 10 were Australia and New Zealand based, and five were Australian jurisdiction-based registries. Sixteen "umbrella" registries collected data on several different conditions, which included some RDs, and thirteen RDRs stored rare cancer-specific information. Most RDRs and databases captured similar types of information related to patient characteristics, comorbidities and other clinical features, procedure or treatment type and health-related quality of life measures. We found considerable heterogeneity among existing RDRs in Australia, especially with regards to data collection, scope and quality of registries, suggesting a national coordinated approach to RDRs is required. CONCLUSION: This scoping review highlights the current state of Australian RDRs, identifying several important gaps and opportunities for improvement through national coordination and increased investment.

Sampling Procedures for the Collection of Electronic Health Record Data From Federally Qualified Health Centers, 2021-2022 National Ambulatory Medical Care Survey.

As part of modernization efforts, in 2021 the National Ambulatory Medical Care Survey (NAMCS) began collecting electronic health records (EHRs) for ambulatory care visits in its Health Center (HC) Component. As a result, the National Center for Health Statistics (NCHS)needed to adjust the approaches used in the sampling design for the HC Component. This report provides details on these changes to the 2021-2022 NAMCS.

'Snapshot' cohort study design across multi-site research settings: challenges, strategies, and solutions.

Multi-site research studies redefine cohort studies by simultaneously providing a cross-sectional snapshot of patients and monitoring them over time, to evaluate outcomes. However, careful design is crucial to minimize potential biases, such as seasonal variations, that may arise during the study period. Addressing snapshot study challenges requires strategic solutions: implementing multi-stage sampling for representativeness, providing rigorous data collection training, using translation techniques and content validation for cultural and linguistic appropriateness, streamlining ethical approval processes, and applying comprehensive data management for follow-up and missing data. These strategies can optimize the efficacy and ethicality of snapshot studies.

A consensus score to combine inferences from multiple centres.

Experiments in which data are collected by multiple independent resources, including multicentre data, different laboratories within the same centre or with different operators, are challenging in design, data collection and interpretation. Indeed, inconsistent results across the resources are possible. In this paper, we propose a statistical solution for the problem of multi-resource consensus inferences when statistical results from different resources show variation in magnitude, directionality, and significance. Our proposed method allows combining the corrected p-values, effect sizes and the total number of centres into a global consensus score. We apply this method to obtain a consensus score for data collected by the International Mouse Phenotyping Consortium (IMPC) across 11 centres. We show the application of this method to detect sexual dimorphism in haematological data and discuss the suitability of the methodology.

A roadmap for using DHIS2 data to track progress in key health indicators in the Global South: experience from sub-saharan Africa.

High quality health data as collected by health management information systems (HMIS) is an important building block of national health systems. District Health Information System 2 (DHIS2) software is an innovation in data management and monitoring for strengthening HMIS that has been widely implemented in low and middle-income countries in the last decade. However, analysts and decision-makers still face significant challenges in fully utilizing the capabilities of DHIS2 data to pursue national and international health agendas. We aimed to (i) identify the most relevant health indicators captured by DHIS2 for tracking progress towards the Sustainable Development goals in sub-Saharan African countries and (ii) present a clear roadmap for improving DHIS2 data quality and consistency, with a special focus on immediately actionable solutions. We identified that key indicators in child and maternal health (e.g. vaccine coverage, maternal deaths) are currently being tracked in the DHIS2 of most countries, while other indicators (e.g. HIV/AIDS) would benefit from streamlining the number of indicators collected and standardizing case definitions. Common data issues included unreliable denominators for calculation of incidence, differences in reporting among health facilities, and programmatic differences in data quality. We proposed solutions for many common data pitfalls at the analysis level, including standardized data cleaning pipelines, k-means clustering to identify high performing health facilities in terms of data quality, and imputation methods. While we focus on immediately actionable solutions for DHIS2 analysts, improvements at the point of data collection are the most rigorous. By investing in improving data quality and monitoring, countries can leverage the current global attention on health data to strengthen HMIS and progress towards national and international health priorities.

DeepTSE: A Time-Sensitive Deep Embedding of ICU Data for Patient Modeling and Missing Data Imputation.

Missing data is a common problem in the intensive care unit as a variety of factors contribute to incomplete data collection in this clinical setting. This missing data has a significant impact on the accuracy and validity of statistical analyses and prognostic models. Several imputation methods can be used to estimate the missing values based on the available data. Although simple imputations with mean or median generate reasonable results in terms of mean absolute error, they do not account for the currentness of the data. Furthermore, heterogeneous time span of data records adds to this complexity, especially in high-frequency intensive care unit datasets. Therefore, we present DeepTSE, a deep model that is able to cope with both, missing data and heterogeneous time spans. We achieved promising results on the MIMIC-IV dataset that can compete with and even outperform established imputation methods.

Value and Implementation of the Aggregate Safety Assessment Plan.

Aggregate safety assessment involves evaluation of the totality of safety data to characterize the emerging safety profile of a product. The Drug Information Association-American Statistical Association Interdisciplinary Safety Evaluation scientific working group recently published an approach to developing an Aggregate Safety Assessment Plan (ASAP). Creation of an ASAP facilitates a consistent approach to safety data collection and analysis across studies and minimizes important missing data at the time of regulatory submission. A critical aspect of the ASAP is identification of the Safety Topics of Interest (STOI). The STOI, as defined in the ASAP, comprises adverse events (AEs), which have the potential to impact the benefit: risk profile of a product and typically require specialized data collection or analyses. While there are clear benefits to developing an ASAP for a drug development program, multiple concerns may be encountered with implementation. This article uses the examples of two STOIs to demonstrate the benefits and efficiencies gained with implementation of the ASAP in safety planning as well as in optimally characterizing the emerging safety profile of a product.

Using automated active infrared counters to estimate footfall on urban park footpaths: behavioural stability and validity testing.

BACKGROUND: Using infrared counters is a promising unobtrusive method of assessing footfall in urban parks. However, infrared counters are susceptible to reliability and validity issues, and there is limited guidance for their use. The aims of this study were to (1) determine how many weeks of automated active infrared count data would provide behaviourally stable estimates of urban park footfall for each meteorological season, and (2) determine the validity of automated active infrared count estimates of footfall in comparison to direct manual observation counts. METHODS: Three automated active infrared counters collected daily footfall counts for 365 days on three footpaths in an urban park within Northampton, England, between May 2021 - May 2022. Intraclass correlation coefficients were used to compare the behavioural stability of abbreviated data collection schedules with total median footfall within each meteorological season (Spring, Summer, Autumn, Winter). Public holidays, events, and extreme outliers were removed. Ten one-hour manual observations were conducted at the site of an infrared counter to determine the validity of the infrared counter. RESULTS: At least four-weeks (28 days) of infrared counts are required to provide 'good' to 'excellent' (Intraclass correlation > 0.75, > 0.9, respectively) estimates of median daily footfall per meteorological season in an urban park. Infrared counters had, on average, -4.65 counts per hour (95% LoA -12.4, 3.14; Mean absolute percentage error 13.7%) lower counts compared to manual observation counts during one-hour observation periods (23.2 ± 15.6, 27.9 ± 18.9 counts per hour, respectively). Infrared counts explained 98% of the variance in manual observation counts. The number of groups during an observation period explained 78% of the variance in the difference between infrared and manual counts. CONCLUSIONS: Abbreviated data collection schedules can still obtain estimates of urban park footfall. Automated active infrared counts are strongly associated with manual counts; however, they tend to underestimate footfall, often due to people in groups. Methodological and practical recommendations are provided.

Calibration Weighting Methods for the National Center for Health Statistics Research and Development Survey.

Objectives The Research and Development Survey (RANDS) is a series of web-based, commercial panel surveys that have been conducted by the National Center for Health Statistics (NCHS) since 2015. RANDS was designed for methodological research purposes,including supplementing NCHS' evaluation of surveys and questionnaires to detect measurement error, and exploring methods to integrate data from commercial survey panels with high-quality data collections to improve survey estimation. The latter goal of improving survey estimation is in response to limitations of web surveys, including coverage and nonresponse bias. To address the potential bias in estimates from RANDS,NCHS has investigated various calibration weighting methods to adjust the RANDS panel weights using one of NCHS' national household surveys, the National Health Interview Survey. This report describes calibration weighting methods and the approaches used to calibrate weights in web-based panel surveys at NCHS.