Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: a practical geocoding and data linkages protocol for protected health information v1

Methods. This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. Results. Completion of this protocol generates three neighborhood risk indices (i.e., a Neighborhood Disadvantage Index, a Murder Rate Index, and a Assault Rate Index) for patients’ coded census tract locations. Intended Usage. This protocol can be used by research personnel and clinic staff who do not have prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: a practical geocoding and data linkages protocol for protected health information v1

Methods. This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. Results. Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients’ coded census tract locations. Intended Usage. This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

The Wisconsin Longitudinal Study: Overview, Data Linkages, and Future Plans

The WLS is a study of Wisconsin high school class of 1957 graduates, with follow-ups in 1964, 1975, 1993, 2004, 2011, and 2020. The data reflect the life course of the graduates (and their siblings), initially covering education, switching to family, career, and social participation in midlife, and physical and mental health, cognitive status, caregiving, and social support as respondents age. The WLS is linked to multiple administrative data sources including: parent earnings from state tax records (1957-60) and Social Security earnings and benefits for respondents; 1940 Census data; characteristics of high schools and colleges, employers, industries, and communities of residence; voting records from 2000-2018; Medicare claims; and the National Death Index. Efforts are underway to expand the racial/ethnic and educational composition of the WLS by supplementing the original sample with a new cohort of age-matched adults drawn from Wisconsin’s Black, Hispanic, Asian-American, and Native American communities.

Motives for withdrawal of participation in biobanking and participants’ willingness to allow linkages of their data

Data repositories, like research biobanks, seek to optimise the number of responding participants while simultaneously attempting to increase the amount of data donated per participant. Such efforts aim to increase the repository’s value for its uses in medical research to contribute to improve health care, especially when data linkage is permitted by participants. We investigated individuals’ motives for participating in such projects and potential reasons for their withdrawal from participation in a population-based biobank. In addition, we analysed how these motives were related to various characteristics of the participants and their willingness to permit data linkage to their personal data for research. These questions were explored using a sample of participants in the Dutch Lifelines biobank (n = 2615). Our results indicated that motives for participation and withdrawal were premised on benefits or harm to society and to the individuals themselves. Although general values and trust both played key roles in participation, potential withdrawal and willingness to permit data linkage, they were differentially associated with motives for participation and withdrawal. These findings support and nuance previous findings by highlighting the distinctiveness and complexity of decision making regarding participation in or withdrawal from data donation. We suggest some new directions for improving recruitment, retention and safeguarding strategies in biobanking. In addition, our data provide initial evidence regarding how factors may relate with the probability that individuals will agree to data linkages, when controlling for their unique effects. Future research should further investigate how perceptions of harm and benefits may influence decision making on withdrawal of participation.

Health risk behaviours among people with severe mental ill health during the COVID-19 pandemic: Analysis of linked cohort data

Background People with severe mental ill health (SMI) experience a mortality gap of 15–20 years. COVID-19 has amplified population health inequalities, and there is concern that people with SMI will be disproportionately affected. Understanding how health risk behaviours have changed during the pandemic is important when developing strategies to mitigate future increases in health inequalities. Methods We sampled from an existing cohort of people with SMI. Researchers contacted participants by phone or post to invite them to take part in a survey about how the pandemic had affected them. We asked people about their health risk behaviours and how these had changed during the pandemic. We created an index of changed behaviours, comprising dietary factors, smoking, lack of exercise, and drinking patterns. By creating data linkages, we compared their responses during pandemic restrictions to responses they gave prior to the pandemic. Outcomes 367 people provided health risk data. The mean age of the participants was 50.5 (range = 20 to 86, SD ± 15.69) with 51.0% male and 77.4% white British. 47.5% of participants reported taking less physical activity during the pandemic and of those who smoke 54.5% reported smoking more heavily. Self-reported deterioration in physical health was significantly associated with an increase in health risk behaviours (adjusted OR for physical health 1.59, 95%CI 1.22–2.07; adjusted OR for Age 0.99, 95%CI 0.98–1.00). Interpretation COVID-19 is likely to amplify health inequalities for people with SMI. Health services should target health risk behaviours for people with SMI to mitigate the immediate and long lasting impacts of the COVID-19 pandemic.

Modern international large-scale assessment in education: an integrative review and mapping of the literature

Research in international large-scale assessment (ILSA) has become an increasingly popular field of study in education. Consequently, interest and debate in the field by practitioners, researchers, policymakers, and the public has grown over the past decades. This study adopts a descriptive bibliometric approach to map modern research on ILSA in education and provide an up-to-date picture of the recent developments and structure of the field. The analysis of 2,233 journal articles indexed in the Web of Sciences database revealed that ILSA research in education is an emerging field in a stage of exponential growth that has become increasingly international with recent substantive contributions from China, Spain, and Turkey. Research in the field is currently produced by a tupid network of scholars with diverse geographical backgrounds that engage frequently in national and international research collaborations. Also, the field is relatively interdisciplinary and has developed grounded on nine differentiated historical paths. The PISA program has received the greatest attention in the field, and a wide variety of topics have been addressed in the literature in the last decades, including equity and quality education, globalization and education policy, measurement and statistics, student motivation and self-concept, and interpersonal relationships. The paper concludes by pointing to the potential of future ILSA research to make use of new more relevant instrumentation, data linkages, and trans-regional collaborations.

Migration Between Indian Reserves and Off-Reserve Areas: an Exploratory Analysis Using Census Data Linkage

New data linkages between censuses show that migration flows between Indian reserves and off-reserve areas from 2006 to 2011 and from 2011 to 2016 resulted in negative net migration for Indian reserves, meaning that—overall—more people left Indian reserves than entered them. These results differ from the portrait shown by the retrospective information from the 2011 and 2016 censuses, which indicates positive net migration for Indian reserves. A comparison of the information in the two sources revealed two types of inconsistencies that contributed to the observed differences: (1) inconsistencies in migrant status, and (2) inconsistencies in the origin location of migrants, i.e., the retrospective information about a migrant’s place of residence 5 years earlier does not match the place where the migrant was enumerated in the previous census. Results from this paper suggest that there are limitations to using retrospective information on the place of residence 5 years prior to a census to derive estimates of internal migration flows for small geographic areas, such as Indian reserves. New data linkages are a source of information that can be used to validate and improve these estimates, as well as to derive alternative estimates. However, data linkages also have limitations and require careful preparation before use, particularly when it comes to calculating weights to accurately account for unlinked records.

Health risk behaviours among people with severe mental ill health during the COVID-19 pandemic: analysis of linked cohort data

Background People with severe mental ill health (SMI) experience a mortality gap of 15-20 years. COVID-19 has amplified population health inequalities, and there is concern that people with SMI will be disproportionately affected. Understanding how health risk behaviours have changed during the pandemic is important when developing strategies to mitigate future increases in health inequalities. Methods We sampled from an existing cohort of people with SMI. Researchers contacted participants by phone or post to invite them to take part in a survey about how the pandemic had affected them. We asked people about their health risk behaviours and how these had changed during the pandemic. We created an index of changed behaviours, comprising dietary factors, smoking, lack of exercise, and drinking patterns. By creating data linkages, we compared their responses during pandemic restrictions to responses they gave prior to the pandemic. Outcomes 367 people provided health risk data. 47.5% of participants reported taking less physical activity during the pandemic and of those who smoke 54.5% reported smoking more heavily. Self-reported deterioration in physical health and younger age were significantly associated with an increase in health risk behaviours (adjusted OR for physical health 1.59, 95%CI 1.22-2.07; adjusted OR for Age 0.99, 95%CI 0.98-1.00). Interpretation COVID-19 is likely to amplify health inequalities for people with SMI. Health services should target health risk behaviours for people with SMI to mitigate the immediate and long lasting impacts of the COVID-19 pandemic.

US Federal Data Policy: An Update on The Federal Data Strategy and The Evidence Act

IntroductionThe US federal data landscape is evolving through the implementation of the Foundations for Evidence-Based Policymaking Act of 2018 and the 2020 Action Plan of the Federal Data Strategy (FDS). The Act and Plan seek better data governance; making data accessible and useful for the American public, businesses, and researchers; and improving how the government uses data to make decisions and for program oversight. Objectives and ApproachThis paper provides a brief overview of the Evidence Act, describing what has already been implemented and what is forthcoming and how it involves population data linkages. We will also describe the FDS, using the Five Safes framework to categorize its priorities for federal agencies. ResultsWe explain how the Evidence Act established new roles for Chief Data, Evaluation, and Statistical Officials. We describe efforts to set learning agendas and data inventories in agencies. We point to some successes, such as new repositories for tools and metadata, and progress on forming an advisory committee to explore how the US could build a National Secure Data Service. We tie the FDS action plan to these Evidence Act efforts, showing how agencies and communities of practice are expected to develop over time. We focus on the ten actions that involve shared solutions across government that focus on ethics, privacy, tools and standards. Conclusion / ImplicationsThis paper shares updates on US federal data policy that started with the 2016 Commission for Evidence-based Policymaking, up through the current administration’s efforts to leverage data as a strategic asset. We highlight accomplishments, opportunities, and challenges for federal policy, noting how political will and funding ultimately affect progress.