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New linked data on research investments: scientific workforce, productivity, and public value.

ABSTRACT: Longitudinal micro-data derived from transaction level information about wage and vendor payments made by federal grants on multiple U.S. campuses are being developed in a partnership involving researchers, university administrators, representatives of federal agencies, and others. This paper describes the UMETRICS data initiative that has been implemented under the auspices of the Committee on Institutional Cooperation. The resulting data set reflects an emerging conceptual framework for analyzing the process, products, and impact of research. It grows from and engages the work of a diverse and vibrant community. This paper situates the UMETRICS effort in the context of research evaluation and ongoing data infrastructure efforts in order to highlight its novel and valuable features. Refocusing data construction in this field around individuals, networks, and teams offers dramatic possibilities for data linkage, the evaluation of research investments, and the development of rigorous conceptual and empirical models. Two preliminary analyses of the scientific workforce and network approaches to characterizing scientific teams ground a discussion of future directions and a call for increased community engagement.

SUBMITTER: Lane J

PROVIDER: S-EPMC4553239 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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New linked data on research investments: scientific workforce, productivity, and public value.

Lane Julia J Owen-Smith Jason J Rosen Rebecca R Weinberg Bruce B

Research policy 20150216 9

Longitudinal micro-data derived from transaction level information about wage and vendor payments made by federal grants on multiple U.S. campuses are being developed in a partnership involving researchers, university administrators, representatives of federal agencies, and others. This paper describes the UMETRICS data initiative that has been implemented under the auspices of the Committee on Institutional Cooperation. The resulting data set reflects an emerging conceptual framework for analyz ...[more]

PMID: 26335785

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Project description:BackgroundThe rise of Big Data-driven health research challenges the assumed contribution of medical research to the public good, raising questions about whether the status of such research as a common good should be taken for granted, and how public trust can be preserved. Scandals arising out of sharing data during medical research have pointed out that going beyond the requirements of law may be necessary for sustaining trust in data-intensive health research. We propose building upon the use of a social licence for achieving such ethical governance.Main textWe performed a narrative review of the social licence as presented in the biomedical literature. We used a systematic search and selection process, followed by a critical conceptual analysis. The systematic search resulted in nine publications. Our conceptual analysis aims to clarify how societal permission can be granted to health research projects which rely upon the reuse and/or linkage of health data. These activities may be morally demanding. For these types of activities, a moral legitimation, beyond the limits of law, may need to be sought in order to preserve trust. Our analysis indicates that a social licence encourages us to recognise a broad range of stakeholder interests and perspectives in data-intensive health research. This is especially true for patients contributing data. Incorporating such a practice paves the way towards an ethical governance, based upon trust. Public engagement that involves patients from the start is called for to strengthen this social licence.ConclusionsThere are several merits to using the concept of social licence as a guideline for ethical governance. Firstly, it fits the novel scale of data-related risks; secondly, it focuses attention on trustworthiness; and finally, it offers co-creation as a way forward. Greater trust can be achieved in the governance of data-intensive health research by highlighting strategic dialogue with both patients contributing the data, and the public in general. This should ultimately contribute to a more ethical practice of governance.

Project description:BackgroundReducing disease can maintain personal individual income and improve societal economic productivity. However, estimates of income loss for multiple diseases simultaneously with thorough adjustment for confounding are lacking, to our knowledge. We estimate individual-level income loss for 40 conditions simultaneously by phase of diagnosis, and the total income loss at the population level (a function of how common the disease is and the individual-level income loss if one has the disease).Methods and findingsWe used linked health tax data for New Zealand as a high-income country case study, from 2006 to 2007 to 2015 to 2016 for 25- to 64-year-olds (22.5 million person-years). Fixed effects regression was used to estimate within-individual income loss by disease, and cause-deletion methods to estimate economic productivity loss at the population level. Income loss in the year of diagnosis was highest for dementia for both men (US$8,882; 95% CI $6,709 to $11,056) and women ($7,103; $5,499 to $8,707). Mental illness also had high income losses in the year of diagnosis (average of about $5,300 per year for males and $4,100 per year for females, for 4 subcategories of: depression and anxiety; alcohol related; schizophrenia; and other). Similar patterns were evident for prevalent years of diagnosis. For the last year of life, cancers tended to have the highest income losses, (e.g., colorectal cancer males: $17,786, 95% CI $15,555 to $20,018; females: $14,192, $12,357 to $16,026). The combined annual income loss from all diseases among 25- to 64-year-olds was US$2.72 billion or 4.3% of total income. Diseases contributing more than 4% of total disease-related income loss were mental illness (30.0%), cardiovascular disease (15.6%), musculoskeletal (13.7%), endocrine (8.9%), gastrointestinal (7.4%), neurological (6.5%), and cancer (4.5%). The limitations of this study include residual biases that may overestimate the effect of disease on income loss, such as unmeasured time-varying confounding (e.g., divorce leading to both depression and income loss) and reverse causation (e.g., income loss leading to depression). Conversely, there may also be offsetting underestimation biases, such as income loss in the prodromal phase before diagnosis that is misclassified to "healthy" person time.ConclusionsIn this longitudinal study, we found that income loss varies considerably by disease. Nevertheless, mental illness, cardiovascular, and musculoskeletal diseases stand out as likely major causes of economic productivity loss, suggesting that they should be prioritised in prevention programmes.

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New linked data on research investments: scientific workforce, productivity, and public value.

Publications

New linked data on research investments: scientific workforce, productivity, and public value.

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