Real-world data are not always big data: the case for primary data collection on medication use in pregnancy in the context of birth defects research.

Many examples of the use of real-world data in the area of pharmacoepidemiology include "big data," such as insurance claims, medical records, or hospital discharge databases. However, "big" is not always better, particularly when studying outcomes with narrow windows of etiologic relevance. Birth defects are such an outcome, for which specificity of exposure timing is critical. Studies with primary data collection can be designed to query details about the timing of medication use, as well as type, dose, frequency, duration, and indication, that can better characterize the "real world." Because birth defects are rare, etiologic studies are typically case­control in design, like the National Birth Defects Prevention Study, Birth Defects Study to Evaluate Pregnancy Exposures, and Slone Birth Defects Study. Recall bias can be a concern, but the ability to collect detailed information about both prescription and over-the-counter medication use and other exposures such as diet, family history, and sociodemographic factors is a distinct advantage over claims and medical record data sources. Case­control studies with primary data collection are essential to advancing the pharmacoepidemiology of birth defects. This article is part of a Special Collection on Pharmacoepidemiology.

A practical guide to cross-cultural and multi-sited data collection in the biological and behavioural sciences.

Researchers in the biological and behavioural sciences are increasingly conducting collaborative, multi-sited projects to address how phenomena vary across ecologies. These types of projects, however, pose additional workflow challenges beyond those typically encountered in single-sited projects. Through specific attention to cross-cultural research projects, we highlight four key aspects of multi-sited projects that must be considered during the design phase to ensure success: (1) project and team management; (2) protocol and instrument development; (3) data management and documentation; and (4) equitable and collaborative practices. Our recommendations are supported by examples from our experiences collaborating on the Evolutionary Demography of Religion project, a mixed-methods project collecting data across five countries in collaboration with research partners in each host country. To existing discourse, we contribute new recommendations around team and project management, introduce practical recommendations for exploring the validity of instruments through qualitative techniques during piloting, highlight the importance of good documentation at all steps of the project, and demonstrate how data management workflows can be strengthened through open science practices. While this project was rooted in cross-cultural human behavioural ecology and evolutionary anthropology, lessons learned from this project are applicable to multi-sited research across the biological and behavioural sciences.

Mixed Mode Substantially Increases Hospital Consumer Assessment of Healthcare Providers and Systems Response Rates Relative to Single-Mode Protocols.

BACKGROUND: Low response rates (RRs) can affect hospitals' data collection costs for patient experience surveys and value-based purchasing eligibility. Most hospitals use single-mode approaches, even though sequential mixed mode (MM) yields higher RRs and perhaps better patient representativeness. Some hospitals may be reluctant to incur MM's potential additional cost and complexity without knowing how much RRs would increase. OBJECTIVE: The aim of this study was to estimate the differences in RR and patient representation between MM and single-mode approaches and to identify hospital characteristics associated with the largest RR differences from MM of single-mode protocols (mail-only, phone-only). RESEARCH DESIGN: Patients were randomized within hospitals to one of 3 modes (mail-only, phone-only, MM). SUBJECTS: A total of 17,415 patients from the 51 nationally representative US hospitals participating in a randomized HCAHPS mode experiment. RESULTS: Mail-only RRs were lowest for ages 18-24 (7%) and highest for ages 65+ (31%-35%). Phone-only RRs were 24% for ages 18-24, increasing to 37%-40% by ages 55+. MM RRs were 28% for ages 18-24, increasing to 50%-60% by ages 65-84. Lower hospital-level mail-only RRs strongly predicted greater gains from MM. For example, a hospital with a 15% mail-only RR has a predicted MM RR >40% (with >25% occurring in telephone follow-up). CONCLUSION: MM increased representation of hard-to-reach (especially young adult) patients and hospital RRs in all mode experiment hospitals, especially in hospitals with low mail-only RRs.

Trends in Sexual Orientation and Gender Identity Data Collection.

OBJECTIVE: The aim of this study was to determine response patterns to sexual orientation and gender identity (SOGI) questions in the Behavioral Risk Factor Surveillance System (BRFSS) over time and to assess nonresponse and indeterminate responses by demographic characteristics. METHODS: This is a secondary data analysis of the SOGI module of the BRFSS. We used data from 46 states and Guam that implemented SOGI questions between 2014 and 2022. We used weighted analyses that accounted for the sampling design, determined SOGI response patterns by year, and assessed nonresponse and indeterminate responses by demographic characteristics. RESULTS: Over time, increasing numbers self-reported as sexual and gender minority respondents, while heterosexual identity declined. Sexual orientation nonresponse and indeterminate responses increased with time, while respondents' reports of not knowing gender identity declined. Hispanic, older, respondents, those with lower education, and those who completed the questionnaire in Spanish had higher SOGI nonresponse and indeterminate responses. CONCLUSIONS: The low amount of SOGI nonresponse and indeterminate responses in the BRFSS can be instructive for the implementation of SOGI questions in medical settings. SOGI data collection in all settings requires improving procedures for the groups that have been shown to have elevated nonresponse and indeterminate response.

Approaches to protocol standardization and data harmonization in the ECHO-wide cohort study.

The United States (U.S.) National Institutes of Health-funded Environmental influences on Child Health Outcomes (ECHO)-wide Cohort was established to conduct high impact, transdisciplinary science to improve child health and development. The cohort is a collaborative research design in which both extant and new data are contributed by over 57,000 children across 69 cohorts. In this review article, we focus on two key challenging issues in the ECHO-wide Cohort: data collection standardization and data harmonization. Data standardization using a Common Data Model and derived analytical variables based on a team science approach should facilitate timely analyses and reduce errors due to data misuse. However, given the complexity of collaborative research designs, such as the ECHO-wide Cohort, dedicated time is needed for harmonization and derivation of analytic variables. These activities need to be done methodically and with transparency to enhance research reproducibility. IMPACT: Many collaborative research studies require data harmonization either prior to analyses or in the analyses of compiled data. The Environmental influences on Child Health Outcomes (ECHO) Cohort pools extant data with new data collection from over 57,000 children in 69 cohorts to conduct high-impact, transdisciplinary science to improve child health and development, and to provide a national database and biorepository for use by the scientific community at-large. We describe the tools, systems, and approaches we employed to facilitate harmonized data for impactful analyses of child health outcomes.

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/ .

Recommendations for data collection in cohort studies of preterm born individuals - The RECAP Preterm Core Dataset.

BACKGROUND: Preterm birth (before 37 completed weeks of gestation) is associated with an increased risk of adverse health and developmental outcomes relative to birth at term. Existing guidelines for data collection in cohort studies of individuals born preterm are either limited in scope, have not been developed using formal consensus methodology, or did not involve a range of stakeholders in their development. Recommendations meeting these criteria would facilitate data pooling and harmonisation across studies. OBJECTIVES: To develop a Core Dataset for use in longitudinal cohort studies of individuals born preterm. METHODS: This work was carried out as part of the RECAP Preterm project. A systematic review of variables included in existing core outcome sets was combined with a scoping exercise conducted with experts on preterm birth. The results were used to generate a draft core dataset. A modified Delphi process was implemented using two stages with three rounds each. Three stakeholder groups participated: RECAP Preterm project partners; external experts in the field; people with lived experience of preterm birth. The Delphi used a 9-point Likert scale. Higher values indicated greater importance for inclusion. Participants also suggested additional variables they considered important for inclusion which were voted on in later rounds. RESULTS: An initial list of 140 data items was generated. Ninety-six participants across 22 countries participated in the Delphi, of which 29% were individuals with lived experience of preterm birth. Consensus was reached on 160 data items covering Antenatal and Birth Information, Neonatal Care, Mortality, Administrative Information, Organisational Level Information, Socio-economic and Demographic information, Physical Health, Education and Learning, Neurodevelopmental Outcomes, Social, Lifestyle and Leisure, Healthcare Utilisation and Quality of Life. CONCLUSIONS: This core dataset includes 160 data items covering antenatal care through outcomes in adulthood. Its use will guide data collection in new studies and facilitate pooling and harmonisation of existing data internationally.

Indigenous governance, ethics and data collection in Australian clinical registries.

OBJECTIVES: To examine Indigenous Governance of Data processes in Australian clinical registries. DESIGN, SETTING, PARTICIPANTS: Audit (via desktop review and interviews) of registries in the Australian Register of Clinical Registries from 17 January 2022 to 30 April 2023. MAIN OUTCOME MEASURES: The number of clinical registries collecting ethnicity data, reporting Aboriginal and/or Torres Strait Islander representation on registry governance or steering committees, and reporting human research ethics committee approval. RESULTS: A total of 107 clinical registries were reviewed. Of these registries, 65 (61%) collected ethnicity data; when these were grouped by geographical coverage, those most likely to collect ethnicity data were binational (24/40 [60%]), national (19/26 [73%]) or state based (19/26 [73%]). Of the registries that collected ethnicity data, 29 (45%) classified their ethnicity item as Aboriginal and/or Torres Strait Islander. Only eight clinical registries (7%) reported Aboriginal and/or Torres Strait Islander representation on their governance or steering committees. Human research ethics approval was reported in 94 registries (88%), with only 11 (12%) having Aboriginal human research ethics committee approval. CONCLUSION: Significant variability is evident in clinical registry recording of Indigenous governance of data, meaning that Aboriginal and Torres Strait Islander communities remain invisible in data which is used to inform policy, clinical models of care, health services and initiatives. Radical change is required to facilitate meaningful change in quality indicators for clinical registries nationally.

Describing diversity of real world data sources in pharmacoepidemiologic studies: The DIVERSE scoping review.

PURPOSE: Real-world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real-world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence. METHODS: In a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes. RESULTS: Across the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty-six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization. CONCLUSIONS: The dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE.

Contemporary Practice and Considerations for Real-World Data Source Identification and Feasibility Assessment.

PURPOSE: There has been rapid growth in the variety and number of real-world data (RWD) sources, as well as the number of regulatory documents that provide guidance for assessing the suitability of RWD sources for pharmacoepidemiology studies. This study aims to assess differences in RWD guidance and variability in current practice for identifying and assessing RWD for studies with regulatory purpose. METHODS: Key criteria for feasibility assessment were mapped against relevant regulatory guidance documents across US, EU, and Asia-Pacific regions. An online survey was designed and deployed to International Society for Pharmacoepidemiology members to understand current practice. Findings were summarized and used to inform key considerations and recommendations. RESULTS: Eleven RWD guidance documents were identified and mapped against 14 RWD assessment criteria. Variability was seen across these documents in guidance for these criteria. Between December 2022 and January 2023, 37 survey respondents reported having used RWD for post-marketing commitments (34, 92%) and/or background epidemiology (28, 76%). RWD were mostly identified through literature (33, 89%) and data landscaping (26, 70%); guidance documents referenced included: Food and Drug Administration (20, 54%), European Network for Centres for Pharmacoepidemiology and Pharmacovigilance (17, 46%), European Medical Agency (16, 43%), and Structured Process to Identify Fit-For-Purpose Data (11, 30%). Challenges for conducting feasibility assessments included RWD accessibility, ability to complete validation, and RWD provider responsiveness. CONCLUSIONS: Existing guidelines are used extensively by researchers, but key criteria for RWD identification and feasibility assessment are not reflected consistently and challenges remain. Recommendations have been made reflecting study findings.

Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Development and Pilot of a Metadata List and Catalogue in Europe.

PURPOSE: Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency-funded project (EUPAS39322), defined a set of metadata to describe real-world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct. METHODS: A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe. RESULTS: A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype. CONCLUSIONS: A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe.

The use of linked administrative data in Australian randomised controlled trials: A scoping review.

BACKGROUND/AIMS: The demand for simplified data collection within trials to increase efficiency and reduce costs has led to broader interest in repurposing routinely collected administrative data for use in clinical trials research. The aim of this scoping review is to describe how and why administrative data have been used in Australian randomised controlled trial conduct and analyses, specifically the advantages and limitations of their use as well as barriers and enablers to accessing administrative data for use alongside randomised controlled trials. METHODS: Databases were searched to November 2022. Randomised controlled trials were included if they accessed one or more Australian administrative data sets, where some or all trial participants were enrolled in Australia, and where the article was published between January 2000 and November 2022. Titles and abstracts were independently screened by two reviewers, and the full texts of selected studies were assessed against the eligibility criteria by two independent reviewers. Data were extracted from included articles by two reviewers using a data extraction tool. RESULTS: Forty-one articles from 36 randomised controlled trials were included. Trial characteristics, including the sample size, disease area, population, and intervention, were varied; however, randomised controlled trials most commonly linked to government reimbursed claims data sets, hospital admissions data sets and birth/death registries, and the most common reason for linkage was to ascertain disease outcomes or survival status, and to track health service use. The majority of randomised controlled trials were able to achieve linkage in over 90% of trial participants; however, consent and participant withdrawals were common limitations to participant linkage. Reported advantages were the reliability and accuracy of the data, the ease of long term follow-up, and the use of established data linkage units. Common reported limitations were locating participants who had moved outside the jurisdictional area, missing data where consent was not provided, and unavailability of certain healthcare data. CONCLUSIONS: As linked administrative data are not intended for research purposes, detailed knowledge of the data sets is required by researchers, and the time delay in receiving the data is viewed as a barrier to its use. The lack of access to primary care data sets is viewed as a barrier to administrative data use; however, work to expand the number of healthcare data sets that can be linked has made it easier for researchers to access and use these data, which may have implications on how randomised controlled trials will be run in future.

How flash mobs can be used for data collection in healthcare? A scoping review protocol.

BACKGROUND: Traditional research methods often involve a lengthy process, but the emergence of flash mobs as an innovative data collection method offers the potential to gather substantial data within a short time frame. Flash mobs draw inspiration from the concept of large groups organizing through the internet or mobile devices to perform a prearranged action in public. In healthcare research, flash mobs serve as research organizing method to investigate clinically relevant questions on a large scale within a limited period. AIMS: This study aims to present a study protocol for a scoping review that comprehensively maps the existing literature on the use of flash mobs as a data collection method in healthcare research. METHODS: The review will follow established guidelines and include steps such as identifying the research question, identifying relevant studies, selecting studies, charting the data, and collating and summarizing the results. The review will utilize databases, manual screening of additional sources, and covidence for study selection and data charting. The findings will be summarized using descriptive statistics and a descriptive synthesis of qualitative data. The review protocol has been registered with the Open Science Framework. RESULTS: The results of this scoping review will provide insights into different flash mob designs, motivations, and the data collection process, contributing to the development of high-quality flash mob data collections in healthcare research.

Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: the Western Australian Cancer Registry experience.

BACKGROUND: Current processes collecting cancer stage data in population-based cancer registries (PBCRs) lack standardisation, resulting in difficulty utilising diverse data sources and incomplete, low-quality data. Implementing a cancer staging tiered framework aims to improve stage collection and facilitate inter-PBCR benchmarking. OBJECTIVE: Demonstrate the application of a cancer staging tiered framework in the Western Australian Cancer Staging Project to establish a standardised method for collecting cancer stage at diagnosis data in PBCRs. METHODS: The tiered framework, developed in collaboration with a Project Advisory Group and applied to breast, colorectal, and melanoma cancers, provides business rules - procedures for stage collection. Tier 1 represents the highest staging level, involving complete American Joint Committee on Cancer (AJCC) tumour-node-metastasis (TNM) data collection and other critical staging information. Tier 2 (registry-derived stage) relies on supplementary data, including hospital admission data, to make assumptions based on data availability. Tier 3 (pathology stage) solely uses pathology reports. FINDINGS: The tiered framework promotes flexible utilisation of staging data, recognising various levels of data completeness. Tier 1 is suitable for all purposes, including clinical and epidemiological applications. Tiers 2 and 3 are recommended for epidemiological analysis alone. Lower tiers provide valuable insights into disease patterns, risk factors, and overall disease burden for public health planning and policy decisions. Capture of staging at each tier depends on data availability, with potential shifts to higher tiers as new data sources are acquired. CONCLUSIONS: The tiered framework offers a dynamic approach for PBCRs to record stage at diagnosis, promoting consistency in population-level staging data and enabling practical use for benchmarking across jurisdictions, public health planning, policy development, epidemiological analyses, and assessing cancer outcomes. Evolution with staging classifications and data variable changes will futureproof the tiered framework. Its adaptability fosters continuous refinement of data collection processes and encourages improvements in data quality.

Determining an Appropriate Sample Size for Qualitative Interviews to Achieve True and Near Code Saturation: Secondary Analysis of Data.

BACKGROUND: In-depth interviews are a common method of qualitative data collection, providing rich data on individuals' perceptions and behaviors that would be challenging to collect with quantitative methods. Researchers typically need to decide on sample size a priori. Although studies have assessed when saturation has been achieved, there is no agreement on the minimum number of interviews needed to achieve saturation. To date, most research on saturation has been based on in-person data collection. During the COVID-19 pandemic, web-based data collection became increasingly common, as traditional in-person data collection was possible. Researchers continue to use web-based data collection methods post the COVID-19 emergency, making it important to assess whether findings around saturation differ for in-person versus web-based interviews. OBJECTIVE: We aimed to identify the number of web-based interviews needed to achieve true code saturation or near code saturation. METHODS: The analyses for this study were based on data from 5 Food and Drug Administration-funded studies conducted through web-based platforms with patients with underlying medical conditions or with health care providers who provide primary or specialty care to patients. We extracted code- and interview-specific data and examined the data summaries to determine when true saturation or near saturation was reached. RESULTS: The sample size used in the 5 studies ranged from 30 to 70 interviews. True saturation was reached after 91% to 100% (n=30-67) of planned interviews, whereas near saturation was reached after 33% to 60% (n=15-23) of planned interviews. Studies that relied heavily on deductive coding and studies that had a more structured interview guide reached both true saturation and near saturation sooner. We also examined the types of codes applied after near saturation had been reached. In 4 of the 5 studies, most of these codes represented previously established core concepts or themes. Codes representing newly identified concepts, other or miscellaneous responses (eg, "in general"), uncertainty or confusion (eg, "don't know"), or categorization for analysis (eg, correct as compared with incorrect) were less commonly applied after near saturation had been reached. CONCLUSIONS: This study provides support that near saturation may be a sufficient measure to target and that conducting additional interviews after that point may result in diminishing returns. Factors to consider in determining how many interviews to conduct include the structure and type of questions included in the interview guide, the coding structure, and the population under study. Studies with less structured interview guides, studies that rely heavily on inductive coding and analytic techniques, and studies that include populations that may be less knowledgeable about the topics discussed may require a larger sample size to reach an acceptable level of saturation. Our findings also build on previous studies looking at saturation for in-person data collection conducted at a small number of sites.

Automating Patient-reported Data Collection: Does it Work?

OBJECTIVE: There are several electronic patient-reported outcomes (ePROs) vendors that are being used at institutions to automate data collection. However, there is little known about their success in collecting patient-reported outcomes (PROs) and it is unknown which patients are more likely to complete these surveys. In this study, we assessed rates of PRO completion, as well as determined factors that contributed to the completion of baseline and follow-up surveys. METHODS: We queried our ePRO platform to assess rates of completion for baseline and follow-up surveys for patients from October 2019 to June 2022. All baseline surveys were administered before pediatric orthopaedic procedures, and follow-up surveys were sent at 3 months, 6 months, 1 year, and 2 years after surgery to patients with baseline data. Descriptive statistics were used to summarize the data. Univariate and multivariate analyses were performed to assess differences in patients who did and did not complete surveys. RESULTS: This study included 1313 patients during the study period. Baseline surveys were completed by 66% of the cohort (n = 873 patients). There was a significant difference in race/ethnicity and language spoken in the patients who did and did not complete baseline surveys ( P < 0.01) with lower rates of completion in African American, Hispanic, and Spanish-speaking patients. At least one follow-up was obtained for 68% of patients with baseline surveys (n = 597 patients). There were significant differences in completion rates based on race/ethnicity ( P = 0.03) and language spoken ( P = 0.01). There were lower rates of baseline completion for patients with government insurance in our multivariate analysis (odds ratio: 0.6, P < 0.01). CONCLUSION: Baseline and follow-up PRO data can be obtained from the majority of patients using automated ePRO platforms. However, additional focus is needed on collecting data from traditionally underrepresented patient groups to better understand outcomes in these patient populations. LEVEL OF EVIDENCE: Level III-retrospective cohort study.

Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial.

Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection.

Deriving life-course residential histories in brain bank cohorts: A feasibility study.

INTRODUCTION: The exposome is theorized to interact with biological mechanisms to influence risk for Alzheimer's disease but is not well-integrated into existing Alzheimer's Disease Research Center (ADRC) brain bank data collection. METHODS: We apply public data tracing, an iterative, dual abstraction and validation process rooted in rigorous historic archival methods, to develop life-course residential histories for 1254 ADRC decedents. RESULTS: The median percentage of the life course with an address is 78.1% (IQR 24.9); 56.5% of the sample has an address for at least 75% of their life course. Archivists had 89.7% agreement at the address level. This method matched current residential survey methodology 97.4% on average. DISCUSSION: This novel method demonstrates feasibility, reproducibility, and rigor for historic data collection. To our knowledge, this is the first study to show that public data tracing methods for brain bank decedent residential history development can be used to better integrate the social exposome with biobank specimens. HIGHLIGHTS: Public data tracing compares favorably to survey-based residential history. Public data tracing is feasible and reproducible between archivists. Archivists achieved 89.7% agreement at the address level. This method identifies residences for nearly 80% of life-years, on average. This novel method enables brain banks to add social characterizations.

[Analysis Of Data Quality/Completeness In Covid-19 Cases: Why A Digital Integrated Data Collection Is Also Necessary For Pandemic Control]. / Analyse zur Datenqualität/-vollständigkeit bei COVID-19-Fällen: Warum eine digitale integrierte Datenerfassung auch zur Pandemiebekämpfung notwendig ist.

BACKGROUND: Epidemiological data on the corona pandemic collected in the public health sector in Germany have been less useful in estimating vaccine effectiveness and clinical outcomes compared to other countries. METHODS: In this retrospective observational study, we examined the completeness of selected own data collected during the pandemic. Information on the important parameters of hospitalization, vaccination status and risk factors for severe course and death over different periods were considered and evaluated descriptively. The data are discussed in the extended context of required digital strategies in Germany. RESULTS: From January 1, 2022 to June 30, 2022, we found 126,920 administrative procedures related to COVID-19. With regard to the data on hospitalization, in 19,749 cases, it was stated "No", in 1,990 cases "Yes" and in 105,181 cases (83+%) "Not collected" or "Not ascertainable". Concerning vaccinations, only a small proportion of procedures contained information on the type of vaccine (11.1+%), number of vaccinations (4.4+%) and date of the last vaccination (2.1+%). The completeness of data on chronic conditions/risk factors in COVID-19-related deaths decreased over four consecutive periods between 2020 and 2022 as case numbers increased. CONCLUSION: Future strategies taking into account meaningfulness and completeness of data must comprise modern technical solutions with digital data collection on infections without putting the principle of data protection at risk.