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Objective: Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, effective approaches for enterprise data warehouses for research (EDW4R) development, maintenance, and sustainability remain unclear. The goal of this qualitative study was to understand CTSA EDW4R operations within the broader contexts of academic medical centers and technology.Materials and Methods: We performed a directed content analysis of transcripts generated from semistructured interviews with informatics leaders from 20 CTSA hubs.Results: Respondents referred to services provided by health system, university, and medical school information technology (IT) organizations as "enterprise information technology (IT)." Seventy-five percent of respondents stated that the team providing EDW4R service at their hub was separate from enterprise IT; strong relationships between EDW4R teams and enterprise IT were critical for success. Managing challenges of EDW4R staffing was made easier by executive leadership support. Data governance appeared to be a work in progress, as most hubs reported complex and incomplete processes, especially for commercial data sharing. Although nearly all hubs (n = 16) described use of cloud computing for specific projects, only 2 hubs reported using a cloud-based EDW4R. Respondents described EDW4R cloud migration facilitators, barriers, and opportunities.Discussion: Descriptions of approaches to how EDW4R teams at CTSA hubs work with enterprise IT organizations, manage workforces, make decisions about data, and approach cloud computing provide insights for institutions seeking to leverage patient data for research.Conclusion: Identification of EDW4R best practices is challenging, and this study helps identify a breadth of viable options for CTSA hubs to consider when implementing EDW4R services. [ABSTRACT FROM AUTHOR]

Background: Obesity and multimorbidity are more prevalent among U.S. racial/ethnic minority groups. Evaluating racial/ethnic disparities in disease accumulation according to body mass index (BMI) may guide interventions to reduce multimorbidity burden in vulnerable racial/ethnic groups.Method: We used data from the 1998-2016 Health and Retirement Study on 8 106 participants aged 51-55 at baseline. Disease burden and multimorbidity (≥2 co-occurring diseases) were assessed using 7 chronic diseases: arthritis, cancer, heart disease, diabetes, hypertension, lung disease, and stroke. Four BMI categories were defined per convention: normal, overweight, obese class 1, and obese class 2/3. Generalized estimating equations models with inverse probability weights estimated the accumulation of chronic diseases.Results: Overweight and obesity were more prevalent in non-Hispanic Black (82.3%) and Hispanic (78.9%) than non-Hispanic White (70.9 %) participants at baseline. The baseline burden of disease was similar across BMI categories, but disease accumulation was faster in the obese class 2/3 and marginally in the obese class 1 categories compared with normal BMI. Black participants across BMI categories had a higher initial burden and faster accumulation of disease over time, while Hispanics had a lower initial burden and similar rate of accumulation, compared with Whites. Black participants, including those with normal BMI, reach the multimorbidity threshold 5-6 years earlier compared with White participants.Conclusions: Controlling weight and reducing obesity early in the lifecourse may slow the progression of multimorbidity in later life. Further investigations are needed to identify the factors responsible for the early and progressing nature of multimorbidity in Blacks of nonobese weight. [ABSTRACT FROM AUTHOR]

The article examines barriers to identifying residents with dementia for embedded pragmatic trials. Topics discussed include contemporary efforts to create more equitable and ethical algorithms, possible interventions for people living with Alzheimer's disease or other dementias (ADRD), and methods used for conducting the research at hand.

Development of evidence‐based practice requires practice‐based evidence, which can be acquired through analysis of real‐world data from electronic health records (EHRs). The EHR contains volumes of information about patients—physical measurements, diagnoses, exposures, and markers of health behavior—that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real‐world data into reliable real‐world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real‐world data into high‐quality, fit‐for‐purpose analytical data sets used to generate real‐world evidence. [ABSTRACT FROM AUTHOR]

Multimorbidity is widely recognized as having adverse effects on health and wellbeing and may threaten the ability of older adults to live independently. Much of what is known about multimorbidity rests on research that has largely focused on one point in time, or from a static perspective. Given that there remains a lack of agreement in the field on how to standardize multimorbidity definitions and measurement, it is not surprising that analyzing and predicting multimorbidity development, progression over time, and its impact are still largely unaddressed. As a result, there are important gaps and challenges to measuring and studying multimorbidity in a longitudinal context. This Research Practice perspective summarizes pressing challenges and offers practical steps to move the field forward. [ABSTRACT FROM AUTHOR]