Related Literature
Knowledge and findings through SPHERE and related Precision Health initiatives can inform and inspire future efforts to address health disparities.
Links are included for those articles that are publicly available.
Systematic Modeling Framework
We created a systematic modeling framework to understand how the transition towards precision medicine can affect race/ethnic disparities in health across the United States. We illustrated this approach with our initial publication on race disparities in cardiovascular disease treatment:
• Basu S, Sussman JB, Hayward RA. Black-White Cardiovascular Disease Disparities After Target-Based Versus Personalized Benefit–Based Lipid and Blood Pressure Treatment. MDM Policy & Practice. 2017;2: 2381468317725741. doi:10.1177/2381468317725741.
http://journals.sagepub.com/doi/pdf/10.1177/2381468317725741 (PDF)
Randomized Trials
We designed strategies to conduct randomized trials in ways that can clarify how and why some people benefit more than others from the same therapy:
• Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, Hayward RA. Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine. 2017;14: e1002410. doi:10.1371/journal.pmed.1002410.
Variations in Nutrition Program Management
We looked at how variations in nutrition program management affected health disparities among low-income Americans:
• Berkowitz SA, Basu S, Meigs JB, Seligman HK. Food Insecurity and Health Care Expenditures in the United States, 2011-2013.Health Serv Res. 2017 Jun 13. doi: 10.1111/1475-6773.12730. [Epub ahead of print] PubMed PMID: 28608473.
• Choi SE, Seligman H, Basu S. Cost Effectiveness of Subsidizing Fruit and Vegetable Purchases Through the Supplemental Nutrition Assistance Program. Am J Prev Med. 2017 May;52(5):e147-e155. doi: 10.1016/j.amepre.2016.12.013. Epub 2017 Jan 30. PubMed PMID: 28153648; PubMed Central PMCID: PMC5401647.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5401647/pdf/nihms848136.pdf (PDF)
• Berkowitz SA, Seligman HK, Rigdon J, Meigs JB, Basu S. Supplemental Nutrition Assistance Program (SNAP) Participation and Health Care Expenditures Among Low-Income Adults. JAMA Intern Med. 2017 Nov 1;177(11):1642-1649. doi: 10.1001/jamainternmed.2017.4841. PubMed PMID: 28973507; PubMed Central PMCID: PMC5710268.
Modeling Population Health Disparities
We devised a strategy to improve modeling of population health disparities:
• Suen SC, Goldhaber-Fiebert JD, Basu S. Matching Microsimulation Risk Factor Correlations to Cross-sectional Data: The Shortest Distance Method. Med Decis Making. 2018 May;38(4):452-464. doi: 10.1177/0272989X17741635. Epub 2017 Nov 29. PubMed PMID: 29185378; PubMed Central PMCID: PMC5913001.
Risk/Benefit Calculators
We examined how using precision risk/benefit calculators rather than single biomarkers can help improve treatment outcomes:
• Leibowitz M, Cohen-Stavi C, Basu S, Balicer RD. Targeting LDL Cholesterol: Beyond Absolute Goals Toward Personalized Risk.Curr Cardiol Rep. 2017 Jun;19(6):52. doi: 10.1007/s11886-017-0858-6. Review. PubMed PMID: 28432662; PubMed Central PMCID: PMC5815375.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5815375/pdf/nihms935189.pdf (PDF)
• Basu S, Sussman JB, Hayward RA. Detecting Heterogeneous Treatment Effects to Guide Personalized Blood Pressure Treatment: A Modeling Study of Randomized Clinical Trials. Ann Intern Med. 2017 Mar 7;166(5):354-360. doi: 10.7326/M16-1756. Epub 2017 Jan 3. PubMed PMID: 28055048; PubMed Central PMCID: PMC5815372.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5815372/pdf/nihms935177.pdf (PDF)
• Baum A, Scarpa J, Bruzelius E, Tamler R, Basu S, Faghmous J. Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial. Lancet Diabetes Endocrinol. 2017 Oct;5(10):808-815. doi: 10.1016/S2213-8587(17)30176-6. Epub 2017 Jul 12. PubMed PMID: 28711469; PubMed Central PMCID: PMC5815373.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5815373/pdf/nihms935193.pdf(PDF)
• Basu S, Raghavan S, Wexler DJ, Berkowitz SA. Characteristics Associated With Decreased or Increased Mortality Risk From Glycemic Therapy Among Patients With Type 2 Diabetes and High Cardiovascular Risk: Machine Learning Analysis of the ACCORD Trial. Diabetes Care. 2018 Mar;41(3):604-612. doi: 10.2337/dc17-2252. Epub 2017 Dec 26. PubMed PMID: 29279299; PubMed Central PMCID: PMC5829969.
Treating Type 2 Diabetes
We have created a set of equations that can be used to help identify the best treatments for people with type 2 diabetes, across multiple race/ethnic groups who have different risks and benefits from treatments:
• Basu S, Sussman JB, Berkowitz SA, Hayward RA, Yudkin JS. Development and validation of Risk Equations for Complications Of type 2 Diabetes (RECODe) using individual participant data from randomised trials. The Lancet Diabetes & Endocrinology. 2017;5: 788–798. doi:10.1016/S2213-8587(17)30221-8