Promoting data harmonization to evaluate vaccine hesitancy in LMICs: approach and applications.

BACKGROUND: Factors influencing the health of populations are subjects of interdisciplinary study. However, datasets relevant to public health often lack interdisciplinary breath. It is difficult to combine data on health outcomes with datasets on potentially important contextual factors, like political violence or development, due to incompatible levels of geographic support; differing data formats and structures; differences in sampling procedures and wording; and the stability of temporal trends. We present a computational package to combine spatially misaligned datasets, and provide an illustrative analysis of multi-dimensional factors in health outcomes. METHODS: We rely on a new software toolkit, Sub-National Geospatial Data Archive (SUNGEO), to combine data across disciplinary domains and demonstrate a use case on vaccine hesitancy in Low and Middle-Income Countries (LMICs). We use data from the World Bank's High Frequency Phone Surveys (HFPS) from Kenya, Indonesia, and Malawi. We curate and combine these surveys with data on political violence, elections, economic development, and other contextual factors, using SUNGEO. We then develop a stochastic model to analyze the integrated data and evaluate 1) the stability of vaccination preferences in all three countries over time, and 2) the association between local contextual factors and vaccination preferences. RESULTS: In all three countries, vaccine-acceptance is more persistent than vaccine-hesitancy from round to round: the long-run probability of staying vaccine-acceptant (hesitant) was 0.96 (0.65) in Indonesia, 0.89 (0.21) in Kenya, and 0.76 (0.40) in Malawi. However, vaccine acceptance was significantly less durable in areas exposed to political violence, with percentage point differences (ppd) in vaccine acceptance of -10 (Indonesia), -5 (Kenya), and -64 (Malawi). In Indonesia and Kenya, although not Malawi, vaccine acceptance was also significantly less durable in locations without competitive elections (-19 and -6 ppd, respectively) and in locations with more limited transportation infrastructure (-11 and -8 ppd). CONCLUSION: With SUNGEO, researchers can combine spatially misaligned and incompatible datasets. As an illustrative example, we find that vaccination hesitancy is correlated with political violence, electoral uncompetitiveness and limited access to public goods, consistent with past results that vaccination hesitancy is associated with government distrust.

Exchanging words: Engaging the challenges of sharing qualitative research data.

In January 2023, a new NIH policy on data sharing went into effect. The policy applies to both quantitative and qualitative research (QR) data such as data from interviews or focus groups. QR data are often sensitive and difficult to deidentify, and thus have rarely been shared in the United States. Over the past 5 y, our research team has engaged stakeholders on QR data sharing, developed software to support data deidentification, produced guidance, and collaborated with the ICPSR data repository to pilot the deposit of 30 QR datasets. In this perspective article, we share important lessons learned by addressing eight clusters of questions on issues such as where, when, and what to share; how to deidentify data and support high-quality secondary use; budgeting for data sharing; and the permissions needed to share data. We also offer a brief assessment of the state of preparedness of data repositories, QR journals, and QR textbooks to support data sharing. While QR data sharing could yield important benefits to the research community, we quickly need to develop enforceable standards, expertise, and resources to support responsible QR data sharing. Absent these resources, we risk violating participant confidentiality and wasting a significant amount of time and funding on data that are not useful for either secondary use or data transparency and verification.

BEYOND LEGAL FRAMEWORKS AND SECURITY CONTROLS FOR ACCESSING CONFIDENTIAL SURVEY DATA IN THE UNITED STATES: ENGAGING DATA USERS IN DATA PROTECTION.

With growing demand for data reuse and open data within the scientific ecosystem, protecting the confidentialy of survey data and privacy of data subjects is increasingly important. Doing so requires more than legal procedures and technological controls; it requires social and behavioral intervention. In this research note, we delineate the disclosure risks of various types of survey data (e.g., longitudinal data, social network data, sensitive information, biomarkers, and geographic data), the current motivation for data reuse, and challenges to data protection. Despite rigorous efforts to protect data, there are still threats to protection of confidentiality in microdata. Unintentional data breaches, protocol violations, and data misuse are observed even in well-established restricted data access systems, indicating that the systems may all rely heavily on trust. Creating and maintaining that trust is critical to secure data access. We suggest four ways of building trust; User-Centered Design Practices; Promoting Trust for Protecting Confidential Data; General Training in Research Ethics; and Specific Training in Data Security Protocols, with an example of a new project 'Researcher Passport' by the Inter-university Consortium for Political and Social Research. Continuous user-focused improvements in restricted data access systems are necessary so that we promote a culture of trust among the research and data user community, train both in the general topic of responsible research and in the specific requirements of these systems, and offer systematic and holistic solutions.

RESTRICTED DATA MANAGEMENT: THE CURRENT PRACTICE AND THE FUTURE.

Many organizations across the world that manage restricted data have adapted the Five Safes framework (safe data, safe projects, safe people, safe setting, safe output) for their management of restricted and confidential data. While the Five Safes have been well integrated throughout the data life cycle, organizations encounter several challenges with regard to safe data management. In this paper, we review current practices for restricted data management, and discuss challenges and future directions. We focus on data use agreements, disclosure risk review, and training. In the future, organizations managing restricted data may need proactively to take into consideration reducing inequalities in access to scientific data, preventing unethical use of data, and managing various types of data.

NATIONAL ADDICTION AND HIV DATA ARCHIVE PROGRAM: DEVELOPING AN APPROACH FOR REUSE OF SENSITIVE AND CONFIDENTIAL DATA.

Sharing data produced through health research projects has been increasingly recognized as a way to advance science more rapidly by facilitating discovery and increasing rigor and reproducibility. Much of the data collected from human subjects includes sufficient sociodemographic detail and/or covers sensitive topics, and thus requires restricted data management and sharing practices. Over the last two decades, scientific organizations, presidential memoranda, and other sources have all called for increasing opportunities to share data. Recognizing the value of shared data, the National Institutes of Health issued a new Data Management and Sharing Policy, effective January 25, 2023. Prior to this updated policy, in 2009, the National Institute on Drug Abuse recognized the value of sharing data and established an archive, the National Addiction and HIV Data Archive Program. This program focused on sharing data, often highly sensitive, generated from social and behavioral addiction research, including quantitative and qualitative assessments as well as biomarker and imaging data. NAHDAP has developed practices and curation standards to ensure datasets are improved and usable, and provides technical assistance for both data depositors and users. We share three key lessons learned working to disseminate sensitive data over the last 13 years, including (1) protecting the confidentiality of human subjects; (2) ensuring careful consideration of costs for archiving data requiring protection ; and (3) providing support to facilitate the discovery and use of the data.

How do properties of data, their curation, and their funding relate to reuse?

Despite large public investments in facilitating the secondary use of data, there is little information about the specific factors that predict data's reuse. Using data download logs from the Inter-university Consortium for Political and Social Research (ICPSR), this study examines how data properties, curation decisions, and repository funding models relate to data reuse. We find that datasets deposited by institutions, subject to many curatorial tasks, and whose access and preservation is funded externally, are used more often. Our findings confirm that investments in data collection, curation, and preservation are associated with more data reuse.

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

Radical collaboration during a global health emergency: development of the RDA COVID-19 data sharing recommendations and guidelines.

Background: The coronavirus disease 2019 (COVID-19) global pandemic required a rapid and effective response. This included ethical and legally appropriate sharing of data. The European Commission (EC) called upon the Research Data Alliance (RDA) to recruit experts worldwide to quickly develop recommendations and guidelines for COVID-related data sharing. Purpose: The purpose of the present work was to explore how the RDA succeeded in engaging the participation of its community of scientists in a rapid response to the EC request. Methods: A survey questionnaire was developed and distributed among RDA COVID-19 work group members. A mixed-methods approach was used for analysis of the survey data. Results: The three constructs of radical collaboration (inclusiveness, distributed digital practices, productive and sustainable collaboration) were found to be well supported in both the quantitative and qualitative analyses of the survey data. Other social factors, such as motivation and group identity were also found to be important to the success of this extreme collaborative effort. Conclusions: Recommendations and suggestions for future work were formulated for consideration by the RDA to strengthen effective expert collaboration and interdisciplinary efforts.

Fostering global data sharing: highlighting the recommendations of the Research Data Alliance COVID-19 working group.

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for clinicians, researchers, policy- and decision-makers, funders, publishers, public health experts, disaster preparedness and response experts, infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations), and other potential users. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.

Qualitative Data Sharing: Data Repositories and Academic Libraries as Key Partners in Addressing Challenges.

Data sharing is increasingly perceived to be beneficial to knowledge production, and is therefore increasingly required by federal funding agencies, private funders, and journals. As qualitative researchers are faced with new expectations to share their data, data repositories and academic libraries are working to address the specific challenges of qualitative research data. This paper describes how data repositories and academic libraries can partner with researchers to support three challenges associated with qualitative data sharing: (1) obtaining informed consent from participants for data sharing and scholarly reuse; (2) ensuring that qualitative data are legally and ethically shared; and (3) sharing data that cannot be deidentified. This paper also describes three continuing challenges of qualitative data sharing that data repositories and academic libraries cannot specifically address-research using qualitative big data, copyright concerns, and risk of decontextualization. While data repositories and academic libraries can't provide easy solutions to these three continuing challenges, they can partner with researchers and connect them with other relevant specialists to examine these challenges. Ultimately, this paper suggests that data repositories and academic libraries can help researchers address some of the challenges associated with ethical and lawful qualitative data sharing.

A large, open source dataset of stroke anatomical brain images and manual lesion segmentations.

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

Retirement in the 1950s: Rebuilding a Longitudinal Research Database.

In 2010, ICPSR began a long process of recovering data from Gordon Streib's Cornell Study of Occupational Retirement (CSOR). Because these unique data fill a gap in our understanding of US retirement history, we determined that an extensive data recovery project was warranted. This paper describes the scope of the data collection and the steps in ICPSR's recovery process. Though the data recovery was ultimately successful, this paper documents the amount of time invested and costs associated with this kind of recovery work. It also highlights the value of these data for future research in understanding gender and retirement in a historic context. In addition to the resulting publicly available data arising from this project, extensive paper medical records are housed at ICPSR for on-site analysis or for a future digitization project. These data would provide unique health information on older women and men traced over a period of time in the 1950s and represents future work for ICPSR to undertake.

A Data-Driven Approach to Appraisal and Selection at a Domain Data Repository.

Social scientists are producing an ever-expanding volume of data, leading to questions about appraisal and selection of content given finite resources to process data for reuse. We analyze users' search activity in an established social science data repository to better understand demand for data and more effectively guide collection development. By applying a data-driven approach, we aim to ensure curation resources are applied to make the most valuable data findable, understandable, accessible, and usable. We analyze data from a domain repository for the social sciences that includes over 500,000 annual searches in 2014 and 2015 to better understand trends in user search behavior. Using a newly created search-to-study ratio technique, we identified gaps in the domain data repository's holdings and leveraged this analysis to inform our collection and curation practices and policies. The evaluative technique we propose in this paper will serve as a baseline for future studies looking at trends in user demand over time at the domain data repository being studied with broader implications for other data repositories.

Social network and health outcomes among African American cardiac rehabilitation patients.

OBJECTIVE: We tested the hypotheses that the number of close social network members and the health-related support provided by social network members are predictive of coping efficacy and health behaviors. METHODS: Cross-sectional data were collected from 115 African Americans enrolled in cardiac rehabilitation. Measures included the social convoy model, SF-36, the Social Interaction Questionnaire, the Patient Self-Efficacy Questionnaire, and an investigator-developed assessment of health behaviors. RESULTS: Bivariate relationships were found between the number of inner network members and coping efficacy (r = .19, P < .05) and health behaviors (r = .18, P < .06), and between health-related support and coping efficacy (r = .22, P < .05) and health behaviors (r = .28, P < .001). Regression analyses support the hypothesis that close network members predicted better coping efficacy (ß = .18, P < .05) and health behaviors (ß = .19, P < .05). Health-related support also predicted coping efficacy (ß = .23, P < .05) and health behaviors (ß = .30, P < .01). CONCLUSION: African Americans with larger inner networks have more health support, better health behaviors, and higher coping efficacy. The number of close social network members and related health-support promote health through health behaviors and coping efficacy.

Correlates of depression at baseline among African Americans enrolled in cardiac rehabilitation.

PURPOSE: To compare baseline psychosocial characteristics of African Americans entering phase 2 cardiac rehabilitation who have depression symptoms at or above threshold (Center for Epidemiological Studies Depression Scale [CES-D] score >or=16) with those who do not (CES-D score <16). METHODS: A nonrandom sample of 112 men and women (n = 78 without depression, n = 34 with depression) was recruited through local phase 2 cardiac rehabilitation programs. Data were obtained by a structured interview and brief physical examination using several reliable and valid instruments. Chi-square tests, Kruskal-Wallis 2-sample tests, Spearman rank correlation coefficients, and logistic regression models were used for analyses. RESULTS: We found that 30% of the participants were above the depression symptom threshold. Demographic characteristics were not significantly different between individuals at or above threshold and those below threshold. However, depressed individuals above threshold were more likely to be dissatisfied with their neighborhoods (P = .01) and had lower optimism scores (P < .0001), higher stress scores (P < .0001), lower adaptive coping scores (P = .05), and higher problematic coping scores (P < .01) than their counterparts who were below threshold. In the logistic regression model, the odds of being above the depression symptom threshold increased with stress (P < .001) and decreased with optimism (P = .03); none of the other psychosocial characteristics had an independent effect on depression symptoms. CONCLUSIONS: At baseline, African Americans starting phase 2 cardiac rehabilitation with depression symptoms at or above threshold had more stress and fewer stress resilience factors. Assessing depression and stress resilience factors is important and may lead to more active participation in cardiac rehabilitation once enrolled, as well as optimal cardiovascular health outcomes.

Women of the 1950s and the "normative" life course: the implications of childlessness, fertility timing, and marital status for psychological well-being in late midlife.

We explore women's psychological well-being in late midlife in relation to childlessness and timing of entry into motherhood. Using two U.S. surveys, Health and Retirement Study (HRS) (1992) and National Survey of Families and Households (NSFH) (Sweet, Bumpass, & Call, 1988), we assess the well-being of childless women in their 50s compared to mothers with early, delayed, or normatively timed first births. We focus on the cohorts born between 1928 and 1941, who experienced strong normative pressures during the baby boom with regard to marriage and child-bearing. We find few differences among childless women but lower well-being among early mothers, related to singlehood and poorer socioeconomic status. Unmarried mothers are significantly disadvantaged regardless of maternal timing, controlling for socioeconomic status. Current maternal demands are independently related to well-being and help to explain observed differences in family satisfaction. Overall, childlessness and off-time child-bearing are related to midlife well-being through their link with more proximate factors, particularly current marital status, health, and socioeconomic status.

It takes two: marriage and smoking cessation in the middle years.

OBJECTIVES: In this prospective study of smoking cessation among married individuals in midlife we examine correspondence in the change of each partner's smoking status with that of the other, independent of established psychosocial correlates of smoking cessation. METHODS: Using longitudinal data from the first two waves of the Health and Retirement Study, 1992-1994, hierarchical logistic regression models were estimated for married male and female smokers separately. RESULTS: Findings support our hypothesis of correspondence in the smoking cessation of married male and female smokers net of other sociodemographic, health, and health behavior characteristics. DISCUSSION: These findings suggest that initiation and maintenance of this positive lifestyle change may be more easily achieved when both marital partners are given information and support to quit smoking at the same time.

Who expects to continue working after age 62? The retirement plans of couples.

OBJECTIVES: This study examines the individual, spousal, and household characteristics associated with the retirement expectations of husbands and wives. METHODS: Using data from the 1992 Health and Retirement Study, subjective probabilities of working full-time after reaching age 62 and age 65 are used to measure retirement expectations. The retirement expectations of husbands and wives are modeled simultaneously using a joint-generalized least-squares approach. RESULTS: Within a marriage, retirement expectations are shaped by individual, spousal, and household characteristics. We observe some gender differences in cross-spousal influence with wives' retirement expectations being more influenced by husbands' resources and constraints than vice versa. Nonetheless, individual and household factors associated with retirement expectations are widely shared by husbands and wives. DISCUSSION: Husbands and wives both respond to individual and joint constraints and opportunities when planning for retirement. Findings support that there is considerable overlap in retirement planning of husbands and wives during early parts of the retirement decision-making process. However, inequity in cross-spousal influences is a defining characteristic of retirement decision making. Implications for both policy makers and practitioners are briefly discussed.