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Call for papers: Conference on computational social science, April 2017, U-M

Sioban Harlow honored with 2017 Sarah Goddard Power Award for commitment to women's health

Post-doc fellowship in computational social science for summer or fall 2017, U-Penn

ICPSR Summer Program scholarships to support training in statistics, quantitative methods, research design, and data analysis

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Next Brown Bag

Mon, Feb 13, 2017, noon:
Daniel Almirall, "Getting SMART about adaptive interventions"

Understanding variation in treatment intensity of poor prognosis cancers

a PSC Research Project

Investigators:   Sandra Lynn Wong, Edward Norton, Jennifer J. Griggs, Mousumi Banerjee, John Z. Ayanian

The overall goal of this application is to support my continued training and development as an independent investigator in cancer-focused, policy-relevant health services research. My career development plan is based on coursework, guidance from mentors, and practical application of skills through research. I have three principal training goals for the period supported by this award mechanism: (1) acquire expertise in advanced quantitative methods, (2) acquire expertise in health care systems and health care policy, and (3) advance my career development skills including dissemination of results, and translation of research findings into policy and practice. Ultimately, I hope to apply my research toward improving the value and efficiency of healthcare.

Some of my past and current work characterizing the association between variation in practice patterns and patient outcomes furthered my interests in more broadly studying the organization of healthcare systems. To that end, my main research goals are to better understand variation in the care of patients with cancer. The major themes of my research interests are reflected in the specific aims of this proposal. My scientific goals include the following: (1) to characterize variation in the delivery of health care at the health system level, (2) to identify determinants of variation, and (3) to understand the relationship between variations and outcomes.

In this project, we will examine treatment intensity in order to gain a better understanding of variation in cancer care and its implications. Poor prognosis cancers of the lung, esophagus, pancreas, and liver account for 40% of all cancer deaths and a sizable share of overall cancer care costs. Despite little improvement in survival rates over time, how intensively patients are treated has increased dramatically. Treatment intensity, as reflected by average expenditures, varies widely across health systems. The root causes of such variation and its implications for patients have not been assessed in a systematic fashion.

We will use national Medicare data to identify which types of services account for variation in overall expenditures, including the use of diagnostic studies, surgical procedures, systemic chemotherapy, radiation therapy, and end-of-life care. We will then identify the main determinants of variation, focusing on both patient factors (such as race and socioeconomic status) and systems-level factors. Finally, we will assess the relationship between treatment intensity and patient outcomes. This project will lay a foundation for identifying areas of both overtreatment and undertreatment of patients with poor prognosis cancers and facilitate efforts to improve both efficiency and quality of care.

Funding Period: 07/01/2012 to 10/16/2015

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