Social Determinants of

Health and Diabetes CareUNIVERSITY OF NEBRASKA MEDICAL CENTER DIABETES SYMPOSIUM

CYNTHIA HERRICK, MD, MPHS, FACP

OCTOBER 15, 2021

Financial Disclosures

Spouse consults for Merck and Eli Lilly.

Learning

Objectives

At the end of this lecture, you will be able to…

Define Social Determinants of Health (SDoH) and understand the current professional society guideline recommendations around incorporating them in clinical care

Apply the evidence on food insecurity and cost related medication underuse to the care of patients with diabetes

Create a plan to incorporate social needs assessment in your role as a healthcare provider using the National Academy of Science, Engineering and Medicine’s 5A framework.

What are the Social Determinants

of Health?

Conditions in which people are

born, grow, live, work and age

Shaped by distribution of

Money, power, resources

Global, national and local

levels

Social need → health inequities

Healthy People 2020

https://www.barhii.org/barhii-framework

Professional Society Guidelines

ACP (2018)

Recommends increased screening and collection of social determinants

of health data to aid in health impact assessments and support

evidence-driven decision making

AAFP (2019):

Recommends knowing how patients are affected by SDoH and helping

address their needs to improve their health

Professional Society Guidelines

ADA (2019-2021):

Providers should assess food insecurity, housing instability, financial

barriers, social capital/social community support and apply to

treatment decisions.

Refer to local resources and incorporate self-management support from

lay health coaches, community health workers where possible.

ACC/AHA (2019):

Clinicians should evaluate the social determinants of health that affect

individuals to inform treatment decisions

Clinical Case

43 yo woman with class 3 obesity (BMI 50), Type 2

diabetes (A1C 11), hypertension, hyperlipidemia presents

for initial evaluation.

She has been uninsured since her last pregnancy 5

years ago.

She infrequently checks blood sugars but notes some

as high as 400s and others as low as 50.

She is on metformin and Humulin 70/30 that she is able

to get for $25 at Walmart.

Calorie and

carbohydrate

restricted diet

Physical

activity

Medication

management

Eye exams

Self-monitoring

of blood

glucose

Foot care

Outpatient

visits at least

2-4 times per

year

Sufficient income Safe employment

Health literacy/numeracyEducation Food security

Adequate housing

Health insurance

Poverty

Walkable neighborhoods

Structural Racism

ChildcareSocial support

Freedom from discrimination

Lack of environmental

exposuresSafe, convenient transportation

UpstreamUNDERSTANDING THE CONTEXT

All of these are inter-related and impact the patient’s health

Point of care intervention is less feasible, but they influence how a

person is able to manage their conditions

Socioeconomic status

Income, Education, Occupation

Social context

Structural racism, discrimination

Physical environment

Neighborhood, Housing

National Center for Chronic Disease Prevention and Health Promotion

Division of Diabetes Translation

Diabetes Indicator: Diagnosed Diabetes; Year: 2018

Disclaimer: This is a user-generated report. The findings and conclusions are those of the user and do not necessarily represent the views of the CDC.www.cdc.gov/diabetes/dataSource:

Percentage

SVI Theme: Overall SVI; SVI Variable: Overall SVI; Natural Breaks; Year: 2018Overall Social Vulnerability Index; Year: 2018

National Center for Chronic Disease Prevention and Health Promotion

Division of Diabetes Translation

Diabetes Indicator: Diagnosed Diabetes; Nebraska; Natural Breaks; Year: 2018

Disclaimer: This is a user-generated report. The findings and conclusions are those of the user and do not necessarily represent the views of the CDC.

www.cdc.gov/diabetes/dataSource:

Percentage

SVI Theme: Overall SVI; SVI Variable: Overall SVI; Natural Breaks; Year: 2018

SES interventions and Diabetes

Outcomes

No clear literature examining change in income,

education, employment on diabetes outcomes

No diabetes outcomes reported from interventions

targeting wage, early childhood

education/access/quality

Difficult studies to do, but these are the upstream factors

that require system level intervention

Hill-Briggs et al, Diabetes Care Nov 2020, https://doi.org/10.2337/dci20-0053

https://dsl.richmond.edu/panorama/redlining/#loc=12/38.635/-90.393&mapview=graded

https://www.cityhealthdashboard.com/ne/omaha/

Structural Racism and Chronic Disease

2018 City health Dashboard:

Omaha Diabetes Prevalence

1937 HOLC Map

A: Best, B: Desirable, C: Declining, D: Hazardous

2018 City health Dashboard:

Omaha Population Distribution

https://ncrc.org/holc-health/

Structural

Racism

and

Chronic

Disease

Housing/Built Environment and

Diabetes Outcomes Moving to Opportunity Study (HUD demonstration project)

4498 women with children, 5 cities, high poverty census tracts

Randomized to 1 of 3 arms

10 y f/u: Reduction in prevalence of diabetes by -4.3% (95% CI -8.5, -0.7) in arm moving to low poverty census tract

Systematic Reviews and Meta-analyses demonstrate

Areas with more green space had

lower incidence of diabetes [OR diabetes high v low greenspace 0.72 (0.61-0.85)]

Objectively measured walkability was associated with

Lower rates of obesity, diabetes and hypertension

https://www.huduser.gov/Publications/pdf/MTOFullReport.pdf

Twohig-Bennet and Jones, 2018 Env Res, 166, 628-637.

Chandrabose et al., 2019 Obesity Reviews, 20, 41-54.

Community level data don’t necessarily

predict individual level need

36,577 patients screened for social risks during a clinic visit in a community

health center 2016-2018

Majority from California, Massachusetts, Oregon

48% White, 30% Black, 23% Hispanic; 58% Female

Individual risk

derived from screening questions on food, housing, financial resource strain

Community level risk

defined by census tract social deprivation index using geocoded address data

Cottrell et al, JAMA Netw Open 2020; 3(10):e2016852

Overall, 30% of population screened positive for 1 or more social risks

Using community level SDI data as a ‘diagnostic test’ to predict

individual level social risk screen

Sensitivity: 60%

Specificity: 43%

Accuracy: 48%

High SDI Low SDI Total

Social risk screen + 6516 4342 10858

Social risk screen - 14681 11038 25719

21197 15380 36577

Cottrell et al, JAMA Netw Open 2020; 3(10):e2016852

DownstreamINTERVENTION AT THE POINT OF CARE

Easily screened in a clinical setting,

Have the potential to directly influence management,

Can provide a window to additional needs and an

opportunity for more immediate intervention

Food insecurity

Cost related medication underuse

Medical Care 57:S133-S139, June 2019.

70% of eligible patients screened

82% of screened patients with ICD10 code recorded

86% of patients requesting resources received referral guide

Food Insecurity

Food Insecurity Screening Tools

USDA Food security module (most commonly used in studies)

Versions: 18 (household), 10 (adult), 6 (short form) questions

10 item scale can be stratified as follows, according to positive answers

high food security(0),

marginal food security (1-2),

low food security (3-5),

very low food security (6-10)

6 item scale is a positive screen if answered 2 or more questions positively

Questions cover worry about food running out, food not lasting, inability

to afford balanced meals, frequency of skipped meals, eating less,

hunger, weight loss, frequency of not eating for a whole day

Food secure

Food insecure

Food Insecurity Screening Tools

The Hunger Vital Sign (derived from USDA food security module and

validated)

Most commonly used in clinical screening

Affirmative answer (often true or sometimes true) on one or both

statements can constitute a positive screen

97% sensitivity, 93% specificity, 93% accuracy

Gunderson et al., Public Health Nutrition, 2017, 20(8), 1367-71

Food insecurity and Diabetes

Several studies have examined relationship between

Food insecurity and risk for diabetes

Food insecurity and diabetes control

Food insecurity and risk for diabetes Cross sectional

NHANES 1999-2002

10 question USDA food security module

4423 [3088 food secure (0 + responses), 1005 mild food insecurity (1-5

+ responses), severe food insecurity (6-10 + responses)]

Seligman et al., J Gen Intern Med, 2007 Jul;22(7):1018-23.

Food insecurity and diabetes control

Cross sectional

NHANES 1999-2008

2557 adults with diabetes all income levels, 12.2% food insecure

In adjusted logistic regression model

Individuals with food insecurity were 50% more likely to have A1C >9%

aOR 1.53 (95% CI 1.07-2.19)

Of note, not having health insurance was among the strongest predictors of poor diabetes control

aOR 2.49 (95%CI 1.41-4.40) [ref: private]

Medicare, Medicaid and other public showed no difference v. private

insurance

Berkowitz et al., Diabetes Care, 2013, 36:3093-3099

Food insecurity and diabetes control Prospective cohort 2013-2016

391 individuals – random sample from primary care network

Evaluation of relative effect of individually reported food insecurity and community

level defined food access

21% reported food insecurity, 31% resided in area with low food access

Berkowitz et al, Diabetes Care 2018,

41: 1188-1195

Food insecurity and diabetes

control in low-income populations Cross-sectional survey and chart review

711 patients in Chicago, San Francisco safety net clinics (46% with food insecurity)

➢ 28% reported > 1 severe hypoglycemic event in the last year

➢ aOR of hypoglycemia (1.95, 95% CI 1.48-5.91)

➢ Adjusted for age, race, language, education, income, tobacco use, insulin, BMI, renal disease, monitoring, medication adherence, problem alcohol use, comorbidities, hypoglycemia knowledge

Seligman et al., Diabetes Care, 2012, 35: 233-238

Seligman et al., Arch Intern Med, 2011, 171:1204-1206.

407 patients in urban safety net setting 2013 (40.5% with food insecurity)

Cross-sectional

Food insecurity was associated with poor glucose control (A1C > 8.0%)

aOR 2.23 (1.22-4.10) AND significant interaction by SNAP participation

Role of SNAP in diabetes control among

low-income patients with food insecurity

Mayer et al, Public Health Nutrition 2015, 19: 1103-1111

Interventions addressing Food

Insecurity in Diabetes

SNAP incentives (policy level)

Programs incentivizing fruit/vegetable purchasing and disincentivizing SSB

purchases change buying behavior

No health outcome data yet

Produce Prescriptions (clinic level)

Patients screened in clinic and given monthly produce voucher for local grocery

Increase fruit and vegetable consumption

May not affect A1C, BMI, SBP

Olsho et al, Am J Clin Nutr,; 2016; 104:423-35

Xie et al, Public Health Nutr, 2021 24 (12): 3945-55

Interventions addressing Food

Insecurity in Diabetes

Food boxes + Recipes (food bank level)

Improvements in diabetes distress, food insecurity, food/supply tradeoffs

Variable effect on A1C

Medically tailored meals (food bank –clinic integration)

Small pilot demonstrating improvement in HEI score, less hypoglycemia,

less food insecurity, improved mental health

Ongoing RCT to examine A1C impact

Seligman et al, Health Affairs, 2015, 34:11

Seligman et al., AJPH, 2018, 108(9)pp. 1227-1234

Berkowitz et al. J Gen Intern Med. 2019 Mar;34(3):396-404.

https://clinicaltrials.gov/ct2/show/NCT04828785

Cost Related Medication Underuse

Cost Related Medication Underuse (CRMU)

(also described as Cost Related Nonadherence (CRN)

14-20% of adults with diabetes report delaying medication due to cost (may be >25% among those on insulin)

2013-2014 BRFSS, N=44,925 with diabetes

CRN: taking less medication than prescribed because of cost

Prevalence: 16.5% among US adults with diabetes

Most dominant factors contributing

<65: Health insurance, household income, general health

>65: Depression, household income, age

Hill Briggs, Diabetes Care, 2020, https://doi.org/10.2337/dci20-0053

Kang et al, Diabetes Res Clin Pract, 2018 143:24-33

Cost Coping

Strategies

Cohen and Cha NCHS Data Brief 2019 Aug; 349: 1-8

Impact of CRN in individuals with

chronic conditions

Among NHIS participants 2000-2014, CRN was associated with

higher all cause mortality across conditions

Diabetes aHR 1.18 (95%CI 1.1-1.3)

Cardiovascular disease aHR 1.15 (95%CI 1.1-1.2)

Hypertension aHR 1.22 (95% CI 1.2-1.3)

Van Alsten and Harris, Prev Chronic Dis 2020; 17:E151

Impact of CRMU/CRN in individuals

with chronic conditions Health and Retirement Survey 1998-2006

6353 individuals with at least one CVD (diabetes, CAD, CHF, stroke)

19.5% reported cutting back on medication because of cost

between 1998-2004

Heisler et al, Med Care. 2010 February ; 48(2): 87–94.

Predicted probability of

hospitalization in the next

survey wave controlling for:

A: respondent’s age, gender,

race, education, living

arrangement, and net worth

in 1998.

B: All A variables +

cardiovascular comorbidities,

indicator of having psychiatric

problems diagnosed and self-

reported health status in 1998.

Heisler et al, Med Care. 2010 February ; 48(2): 87–94.

Measures of Financial Strain and

Cost-related Medication Underuse

General Financial Strain Measure:

During the past 12 months, how often did it happen that you did not have enough money to buy food, clothes, or other things you needed?

Options: Never, not very often, fairly often, very often

Medication Specific Financial Strain:

In the past 12 months, did you use less medication than was prescribed because of cost?

Options: Yes, No

Financial Strain and CRN in Patients

with Diabetes

Retrospective cohort, n=7773 with 11,820 new scripts

Kaiser Permanente Northern California Diabetes Registry

Survey, administrative, and electronic health record data

5% general strain only, 6 % medication specific strain, 2% both

Lyles et al. Health Serv Res 2016 Apr;51(2):610-24

Lyles et al. Health Serv Res 2016 Apr;51(2):610-24

Tradeoffs Between Medication and

Basic Needs 270 individuals in St. Louis 2016-2017

Inclusion criteria:

Age 35-80

English speaking,

at least 1 chronic condition

At least 1 prescription medication

Recruited from FQHCs, local newspaper and tertiary care center

endocrine clinic

Analysis part of a larger mixed-methods study

30% of the sample had diabetes

80% non-Hispanic Black, mean comorbidities 4.5, mean age ~55

Individuals with diabetes were more likely than those without diabetes to have

ever talked with a healthcare professional about medication cost

66.7% v. 49.2% (p=0.008)

Filed for bankruptcy

38.3% v. 21.7% (p=0.005)

Asked for/received samples

51.9% v. 29.1% (p=0.001)

Herrick et al, J Gen Intern Med 2021 Sep;36(9):2867-2869.

Individuals with diabetes were more likely than those without diabetes to

Spend less on basic needs to pay for medications

58% v. 41% (p=0.01) despite being

more likely to be privately insured or on Medicare (40.7% v. 22.8%, p=0.003)

less likely to have lowest income (monthly <$800) (33.8% v. 51.3%, p=0.008)

Of those who spent less on basic needs, ~80% spent less on food/groceries

People with diabetes were 78% more likely to report cutting back on basic

needs to pay for medication

aOR 1.78 (95% CI 1.01-3.14)

Model adjusted for age, # other comorbidities, sex, race/ethnicity, and insurance

status

Herrick et al, J Gen Intern Med, 2021 Sep;36(9):2867-2869.

What happens with adherence and outcomes

when medications are fully covered? Study in US after MI in 5855 patients

Randomized to full coverage of post-MI med v. usual coverage

Median follow-up 394 days

Significant improvement in adherence in full coverage group (4-6%)

Significant reduction in

total fatal/nonfatal vascular events or revascularization [HR 0.89 (95%CI 0.80-0.99)]

first fatal/nonfatal vascular event [HR 0.86 (95% CI 0.74-0.99)]

Study in Ontario 786 primary care patients

Randomized to free distribution of essential meds v. usual care

12 month follow-up

Significant increase in % patients taking all medications [11.6% (95%CI 4.9,18.4)]

Significant decrease in systolic blood pressure [-7.2 mmHg (95%CI -11.7, -2.8)]

A1C not significantly different [-0.38 (95%CI -0.76, 0.00)]

Choudhry et al, N Engl J Med 2011; 365:2088-2097

Persaud et al, JAMA Intern Med, 2020, 180(1): 27-34

Adjustment Assistance

Alignment Advocacy

Awareness

Activities

focused

on

Individuals

Activities

focused

on

Communities

https://www.nationalacademies.org/our-work/integrating-social-needs-care-into-the-delivery-of-health-care-to-improve-the-nations-health

Clinical Case

43 yo woman with class 3 obesity (BMI 50), Type 2 diabetes (A1C 11),

hypertension, hyperlipidemia presents for initial evaluation

She has been uninsured since her last pregnancy 5 years ago

She infrequently checks blood sugars but notes some as high as 400s

and others as low as 50

She is on metformin and Humulin 70/30 that she is able to get for $25 at

Walmart

How can we

begin

addressing

social needs

in the clinic

setting?

Awareness

Develop clinic workflows that allow screening

with standard tools

Document using Z codes (Z55-Z65) – Z59* covers

housing/food/economic circumstances

How can we begin addressing social needs in the clinic setting?

Assistance

Refer to local community-based

organizations

Resources:

Social workers,

The EveryONE Project,

United Way 211

How can we begin addressing social needs in the clinic setting?

Adjustment

Consider social need when

defining a plan using shared

decision making with the patient

How can we begin addressing social needs in the community?

Alignment

Community based participatory

research

Cross-sector data sharing and

intervention

Medical education curricula

incorporating a social

determinants framework

How can we

begin

addressing

social needs

in the

community?

Advocacy

Medicaid expansion – recent MO example

SNAP expansion

Insulin price caps

Acknowledgments

Mentors:

Graham Colditz, MD, DrPH, Aimee James, PhD, MPH, Amy McQueen, PhD, Debra Haire-Joshu, PhD

Current and former trainees:

Oluwatobe Alawode, MS3, Laura Hollar, MD, Ritika Puri, MD, Katrina Han, MD

Statistician: Sarah Humble, MS

Coordinator: Roxann Williams, MPH

Funding:

K23HD096204 (PI: Herrick)

R01MD010445 (PI: James)

T32DK007120 (PI: Semenkovich – funding student work)