Center for Complexity and Self-Management of Chronic Disease

Center for Complexity and Self-management of Chronic DiseaseThe burden of chronic disease

“Life is messy and complex, and the science doesn’t always match the complexity of life,” says Dr. Debra L. Barton, co-director of UMSN’s Center for Complexity and Self-management of Chronic Disease (CSCD). That’s why Dr. Barton, co-director Dr. Ivo D. Dinov, and a team of UMSN and interdisciplinary researchers are dedicating their work to support people with multiple or complex health conditions.

Cancer, dementia, heart disease, obesity, arthritis, neurodegeneration and diabetes are just a few of the chronic conditions that millions of Americans struggle to manage. These conditions not only reduce quality of life, they can be direct causes of unforgiving complications such as disability, blindness and death. In addition, they can cause severe financial strain for patients and their families. The burdens grow exponentially for the 1 in 4 Americans who have multiple chronic conditions.*

Finding hope through self-management

For many patients, successful self-management of their condition(s) can improve quality of life. For example, patients can learn how improved adherence to a treatment plan could lead to fewer medications, less stress, and improved cognitive function. Giving patients the resources they need to take an active role in managing their own health can help them physically, emotionally and financially.

Nursing is precisely suited to push forward the science of self-management of complex and chronic conditions because of its long-established emphasis on prevention, living well with chronic disease, health education, and patient advocacy and involvement.

What the center does

Center for Complexity and Self-management of Chronic DiseaseCSCD researchers are dedicated to tackling the questions surrounding care for these complex conditions. They’re committed to understanding how one condition may impact another, why a woman with cancer and heart disease may need different treatment than a man with the same conditions, and why some people need more support to follow a treatment plan.

With funding from the National Institutes of Nursing Research, CSCD provides an infrastructure to support interdisciplinary approaches to self-management research at U-M and beyond. In addition, one of the most vital components of the center is funding interdisciplinary pilot projects, which could lead to additional studies with a larger reach.

Current projects:

Other resources include methodological consulting, data analytics, training opportunities, and a seminar series.

Recent Progress

1. Support for Open Data-Sharing
In support of open-science, we developed a novel statistical approach that enables the harmonization, merging, and sharing of complex datasets without compromising sensitive information like person identifiable elements (NIHMSID 1012970, DOI: 10.1080/00949655.2018.1545228). The DataSifter provides on-the-fly de-identification of structured and unstructured sensitive high-dimensional data such as clinical data from electronic health records (EHR). The technique provides complete administrative control over the balance between risk of data re-identification and preservation of the data information. Our simulation results suggest that the DataSifter can provide privacy protection while maintaining data utility for different types of outcomes of interest. The application of DataSifter on a large autism dataset provides a realistic demonstration of its promise practical applications.

2. Application of Compressive Big Data Analytics (CBDA) in Biomedical and Health Studies
CSCD investigators introduced a scalable computational statistics method for addressing some of the challenges associated with handling complex, incongruent, incomplete and multi-source data and analytics challenges. The CBDA mathematical framework enables the study of the ergodic properties and the asymptotics of the specific statistical inference approaches. We implemented and validated the high-throughput CBDA method using pure R and several simulated datasets as well as a real neuroimaging-genetics of Alzheimer's disease case-study (PMCID: PMC6116997, DOI: 10.1371/journal.pone.0202674).

3. Visualization of High-dimensional Diabetes Data
CSCD developed a distributed webapp for visually interrogating complex data archives. It allows all users to address health questions like: Do patient phenotypes (e.g., race, gender, and age), clinical settings (e.g., admission type, time in hospital, medical specialty of admitting physician), and treatment regiments (e.g., number of lab test performed, HbA1c test result, diagnosis, number of medication, diabetes medications, number of outpatient, inpatient, and emergency visits in the year before the hospitalization) affect diabetes treatment outcomes?
Examples of specific driving healthcare challenges that can be addressed include:

  • Data science and predictive analytics (DSPA) data wrangling methods to preprocess the data and generate a computable data object.
  • The use of linear (PCA) and non-linear (t-SNE) dimensionality reduction methods to project the high-dimensional data into 2D or 3D space.
  • Visual and exploratory data analytics to interrogate the low-dimensional projection, identify clusters of patients, and explore the intrinsic lower-dimensional structure of the data.

This open-science project provides a low-cost solution for interactive visual analytics, hypothesis generation, and pattern identification for complex biomedical and healthcare case-studies. It does not require any special software or licensing and supports evidence-based discovery science and provides semi-automated clinical decision support for health practitioners.

4. Curricular Developments

  • A new data science course has been approved and was offered as a Summer MOOC course. It builds technical skills and provides a tool chest of resources to manage and interrogate heterogeneous datasets.
  • An electronic textbook (EBook) on Scientific Methods for Health Sciences is developed and widely shared with the entire community. This multilingual EBook is utilized by over 50,000 users worldwide.

Supporting the work

If you would like to support the center, please contact the Office of Development and Alumni Relations, (734) 763-9710 or There are opportunities to support research development, students and trainees and pilot projects.


400 N. Ingalls, Room 3245, Ann Arbor, MI 48109