1. Lincoln R. Sheets, MD, PhD University of Missouri Twitter: #AMIA2017
The Effect of Neighborhood Disadvantage on Diabetes Prevalence
Informatics to Reduce Disparities in Vulnerable Health Populations
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Lincoln R. Sheets, MD, PhD
University of Missouri
Twitter: #AMIA2017

2. Disclosure
I have no relevant relationships with commercial interests to disclose.
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3. Learning Objectives
After participating in this session the learner should be better able to:
Distinguish between patient-level and neighborhood-level socioeconomic indices
Understand the predictive power of health disparities in diabetes risk
Apply a geospatial socioeconomic index to predictions of health disparities
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4. Background: Diabetes Mellitus Type II
Diabetes mellitus: excess serum glucose causing tissue damage1
1/12 of world population, 1/9 of Americans, rates are rising2
US alone: $176B in medical costs + $69B in lost productivity3
25% undiagnosed4
Socioeconomic status is more predictive than race or ethnicity5, worldwide6
Education, occupation and income independently decrease risk by 25-30%7
Mayo Foundation for Medical Education and Research; 2017.
The World Bank; 2017.
US Centers for Disease Control and Prevention; 2014.
US Centers for Disease Control and Prevention; 2017.
Karlamangla, Merkin, Crimmins, & Seeman; 2010.
Leone, Coast, Narayanan, & de Graft Aikins; 2012.
Agardh, Allebeck, Hallqvist, Moradi, & Sidorchuk; 2011.
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5. Background: Limitations to Decision Support
Primary-care clinicians must prioritize screenings by pre-test probability
Point-of-care tools needed to assess social determinants1
However, patient income and education are often not discussed in clinic
Area Deprivation Index (ADI) measures socioeconomic neighborhood disadvantage2
Walker, Strom Williams, Egede, 2016.
Kind, Jencks, Brock, Yu, Bartels, Ehlenbach, et al., 2014.
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6. Background: Objective
The objective of this study was to develop and evaluate a clinically useful tool for more practically identifying the social determinants which impact diabetes prevalence in a patient population.
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7. Methods: Population and Setting
Primary-care patients1 at the University of Missouri
Enrolled in Medicare (US federal health insurance for elderly and disabled2)
65 years or older
N = 4,770
Diagnoses, demographics, clinical features from University of Missouri EMR
Popejoy, Jaddoo, Sherman, Howk, Nguyen, & Parker; 2015.
US Centers for Medicare and Medicaid Services; 2017.
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8. Methods: Statistical Methodology
Independent variable: ADI quintile as nominal scale variable (non-linear)
Dependent variable: diabetes mellitus (ICD-9 = 250.x) present or absent
Association: logistic regression
Adjusted for patient age (continuous integer)
Adjusted for gender (categorical: “female”, “male”, “other”)
Adjusted for race/ethnicity (categorical: “white/non-Hispanic” (88%), “other”)
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9. Results: Missouri ADI Quintiles
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10. Results: Effects on Diabetes Prevalence
* p<0.05, ** p<0.01, *** p<0.001
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11. Results: Diabetes Prevalence by ADI Quintile*
* p<0.05
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12. Conclusions: Study Findings
Positive non-linear association of ADI with diabetes mellitus prevalence
Prevalence in ADI quintile 1 significantly lower than all other quintiles
Quintiles 2 through 5 not statistically different
Statistical equivalence of quintile 5 with 2-4 may be due to small size of quintile 5
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13. Conclusions: Limitations and Next Steps
LIMITATION: Population sample limited to elderly, Missouri, mostly white
LIMITATION: Small sample size in the most disadvantaged neighborhoods
PROPOSAL: Repeat with larger & more representative samples of entire US
LIMITATION : Predictive power limited to single well-known health disparity
PROPOSAL: Repeat with multiple outcomes including cardiac and oncologic
LIMITATION : Demonstrated predictive power but not clinical effectiveness
PROPOSAL: Follow up with mixed-method pilot of clinical implementation
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14. Conclusions: Translational Implications
ADI can convert patient address into a significant predictor of diabetes
Potential tool for clinical alerts of socioeconomic risk factors
Does not require clinicians to ask for patients’ income, education, etc.
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15. Question 1
The Area Deprivation Index (ADI) does NOT include which of the following measures?
That is, the ADI includes all of these choices EXCEPT which one?
Median family income in US dollars
Median home value in US dollars
Degree of urban population density
Percent of families below federal poverty level
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16. Answer 1
Median family income in US dollars
Median home value in US dollars
Degree of urban population density
Percent of families below federal poverty level
Explanation: The ADI is applicable to both urban an rural settings. It is composed of 17 measures including the median family income in US dollars, median home value in US dollars, percent of families below federal poverty level, and 14 others.
(Kind, Jencks, Brock, Yu, Bartels, Ehlenbach, et al.; 2014)
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17. Question 2
The association of diabetes prevalence with neighborhood disadvantage followed which of these patterns?
Diabetes prevalence was LOWEST in the LEAST disadvantaged neighborhoods
Diabetes prevalence was HIGHEST in the LEAST disadvantaged neighborhoods
There was no association between neighborhood disadvantage and diabetes prevalence
Diabetes prevalence could not be accurately measured at the neighborhood level
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18. Answer 2
Diabetes prevalence was LOWEST in the LEAST disadvantaged neighborhoods
Diabetes prevalence was HIGHEST in the LEAST disadvantaged neighborhoods
There was no association between neighborhood disadvantage and diabetes prevalence
Diabetes prevalence could not be accurately measured at the neighborhood level
Explanation: In this study population, the prevalence of diabetes in the least disadvantaged neighborhoods (ADI quintile 1) is significantly lower than all other neighborhood quintiles after adjusting for age, gender, and race/ethnicity.
The literature shows that disparities in diabetes prevalence are more strongly associated with socioeconomic status than with race or ethnicity.
(Karlamangla, Merkin, Crimmins, & Seeman; 2010)
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19. AMIA is the professional home for more than 5,400 informatics professionals, representing frontline clinicians, researchers, public health experts and educators who bring meaning to data, manage information and generate new knowledge across the research and healthcare enterprise.
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20. Acknowledgements
Thanks to my co-authors and funders: University of Missouri, School of Medicine Gregory F. Petroski, PhD Julie Jaddoo, MS Vaishnavi Raman, MBBS Jerry C. Parker, PhD Laura E. Henderson Kelley, MD, MPH, FAAP University of Missouri, Center for Applied Research and Engagement Systems Yan Barnett, MA Chris Barnett, MA University of Wisconsin School of Medicine and Public Health Amy J. H. Kind, MD, PhD National Institute on Minority Health and Health Disparities Centers for Medicare and Medicaid Services
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21. Email me at: SheetsLR@health.Missouri.edu
Thank you!
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