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Z Codes Can Provide Important Data on Social Determinants of Health and Improve Care: How Can We Improve Utilization?

Background

Z codes for social determinants of health (SDOH) offer a readily available tool to improve data collection of SDOH but no characterization of their utilization exists for the growing Medicare Advantage (MA) population. 

Objective

Examine the utilization of SDOH Z codes across patient characteristics, which Z codes are used most often, and which providers are documenting them.

Methods

Retrospective cohort study using 2017-2020 data from a large nationally representative sample of MA beneficiaries.

Results

Of 3,029,906 MA beneficiaries in 2017, 1.4% had Z codes documented. Utilization increased by 42% from 1.9% in 2019 to 2.7% in 2020 and has increased faster than in traditional Medicare Fee-for-Service. Issues related to housing, income, and living alone were the most often documented SDOH. Beneficiaries with disability/ESRD, were younger, dual-eligible, female, Black or Hispanic, and residing in urban areas were overrepresented as a percentage of Z codes but had a small proportion with Z codes relative to their share of the population. 

Conclusions

Social risk factors contribute to 80 percent of patients’ health-related outcomes, yet documentation remains a barrier. The findings underscore the importance of identifying SDOH which are most prevalent among disabled, minority, and low-income Medicare beneficiaries, and may be at the heart of existing healthcare disparities.

Introduction

Patient social determinants of health (SDOH) are defined as “the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.” Over the past decade, there has been increasing recognition of the important role of SDOH on health and health-related outcomes such as hospital admissions and readmissions, length of stay, emergency room visits, and use of post-acute care services.,, Addressing SDOH has been associated with better access to and engagement in health care, as well as improved health outcomes and lower costs. 

Given the strong evidence of the impact of SDOH on health outcomes and spending, there is an increasing imperative for clinicians to document and address these risk factors. In 2014, the Institute of Medicine (IOM) recommended 10 social and behavioral domains be documented in electronic health records. Providers, payers, and manufacturers are continuously seeking data to better understand the non-medical conditions that impact patients outside the healthcare setting yet lack of access to data on SDOH remains a barrier. Understanding these data will also support further research on health inequities associated with various SDOH. 

One readily available way providers can “grow their own” SDOH data is the use of Z codes, a set of psychosocial risk and economic determinant-related codes within the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) Z55.0-Z65.0. In February 2018, ICD-10-CM Official Guidelines for Coding and Reporting approved that all clinicians, not just the physicians, involved in the care of a patient can document SDOH using these Z codes.

Z codes provide a standardized approach to document and track information to identify a range of issues related to SDOH such as education and literacy, employment, housing, and ability to obtain adequate amounts of food or safe drinking water. There are nine broad categories of Z codes, each with several sub-codes:

  1. Z55 Problems related to education and literacy
  2. Z56 Problems related to employment and unemployment
  3. Z57 Occupational exposure to risk factors
  4. Z59 Problems related to housing and economic circumstances
  5. Z60 Problems related to social environment
  6. Z62 Problems related to upbringing
  7. Z63 Other problems related to primary support group, including family circumstances
  8. Z64 Problems related to certain psychosocial circumstances
  9. Z65 Problems related to other psychosocial circumstances

While there is extensive evidence that capturing this information is critical to delivering high quality care, plans and providers have been slow to adopt the use of Z codes in practice. Inovalon conducted an on-line poll of 648 participants during a webinar sponsored by American’s Health Insurance Plans (AHIP) in 2020. The responses to the survey question “Do you use Z codes to capture data on social determinants of health?” found the majority of health care practitioners/stakeholders were unaware of Z codes (70%), were aware but did not use them (8%), or were aware but did not consistently use them (18%). Only 4% reported their health plan required providers to use a select set of Z codes. 

In 2021, the Centers for Medicare and Medicaid Services (CMS) released a report titled “Utilization of Z Codes for Social Determinants of Health among Medicare Fee-For-Service Beneficiaries, 2019.” The report used complete Medicare Fee-for-Service (FFS) claims data for 2016-2019 to identify beneficiaries with Z codes ranging from Z55-65. The report notes that while Medicare FFS enrollment has decreased over time, Medicare Advantage (MA) enrollment has increased to nearly 40 percent of all Medicare beneficiaries, but similar data on MA was not available.

This report provides a similar analysis of use of Z codes within the MA population from 2017-2020 to provide a more complete picture of utilization of ICD-10 codes to document SDOH within the overall Medicare population. Where possible, we have included results from the 2021 FFS report for comparison.9

Methods

We utilized data from Inovalon’s Medical Outcomes Research for Effectiveness and Economics Registry (MORE2 Registry®). The MORE2 Registry® empowers informed insight into national and regional healthcare trends, such as hospital utilization, medication adherence, chronic disease prevalence, and treatment effectiveness. This registry of healthcare data contains information derived from more than 53 billion medical events generated by more than 338 million unique and de-identified insured individuals nationwide and represents 25 of 25 top US health plans. MORE2 includes data from Medicare Advantage plans (25% of MA lives), as well as commercial (42% of all commercial lives) and managed Medicaid (69% of enrollees) insurers.

The MA sample used for this analysis is nationally representative of overall MA beneficiary characteristics based on Medicare enrollment files provided by CMS to Inovalon through a research-focused data use agreement, including age, gender, race/ethnicity, reason for entitlement to Medicare (age or disability/ESRD), and dual eligible status. The FFS data used for comparison purposes were all extracted from the 2019 CMS report. 

MA plan enrollees were linked through a secure process to a granular source of data on SDOH to capture median household income and education level from Acxiom’s Info Base Geo© files. The SDOH data are aggregated at the 9-digit ZIP level from multiple, comprehensive individual and household databases (e.g., public records such as phone directories, government information from real estate property records, county courthouses, birth notifications, death records, U.S. Census, self-reported data, buying activity from online registrations/surveys, travel purchases, and retailers).  The data covers approximately 30 million discrete geographic areas, with an average of 5 households per neighborhood, providing a near neighborhood assignment of social risk factor characteristics. Previous research has demonstrated that sociodemographic and community-resource characteristics at the near-neighborhood level can serve as close proxies for these characteristics at the member level and are highly predictive of health behaviors and outcomes.

Results

Rates of Z Code Utilization

The percent of MA beneficiaries with one or more Z code claims increased steadily from 1.4% in 2017 to 2.7% in 2020 (Table 1, Figure 1). MA and FFS had a similar percentage of beneficiaries with Z codes in 2017, but the proportion with Z codes has increased faster in MA, with 1.9% having Z codes in 2019 compared to 1.6% in FFS (16% lower rate). The use of Z codes in MA increased by 42% in 2020 to 2.7% (note that 2020 data for FFS was not available in the most recent CMS report). This increase could be related to the COVID-19 pandemic, which served to underscore existing disparities across the continuum of healthcare. 

Most Frequently Used Z Codes

The top five Z codes representing the largest shares of all Z code claims varied in MA and FFS in 2019. The top Z code used in MA was Z59.9 Problems related to housing and economic circumstances, unspecified (63% of all documented Z codes). This code was not among the top five in FFS; however, the top Z code in FFS was Z59.0 Homelessness which is also under the main category Z59 and was the 3rd most documented code in MA (6% of all Z codes). Z59.3 Problems related to living in a residential institution—which ranked 4th among FFS Z codes—and Z59.6 Low income—which ranked 4th among MA Z codes—are under the same broad category. The 2nd most used Z code in MA was Z60.2 Problems related to living alone (8%); which ranked 3rd in FFS (12%). 

Overall, MA appears to use a more consolidated list of codes, with the top 5 representing 83% of all Z codes, while the top 5 in FFS represented 56% of all Z codes. 

Who Is Documenting Z Codes?

The largest percentage of Z codes in MA in 2019 were documented by family practice or internal medicine practitioners (22.8%) which is lower than in FFS (29.0%) (Figure 2). Acute care hospital settings represented 13.0% of documented Z codes (not shown in FFS), psychiatry/neurology 11.3% (versus 13.0% in FFS), nurse practitioners 9.6% (versus 14.0% in FFS), clinic settings 7.0% (not shown in FFS), home health 5.8% (not shown in FFS), social worker or case manager 5.8% (versus 12.0% in FFS), and emergency medicine 3.4% (not shown in FFS). The remaining 21.3% of Z codes in MA represented less than 2% each of the total. 

Z Codes by Reason for Medicare Entitlement

A lower proportion of MA beneficiaries enrolled in Medicare at age 65 (70.0%) compared to those in FFS (77.1%), and MA enrollees age 65+ represented a lower percentage of all Z codes documented compared to those age 65+ in FFS (46.9% versus 54.8%) (Table 2). MA had a larger proportion of members who enrolled due to disability and/or end stage renal disease (ESRD) (30.0%) compared to FFS (22.9%), and the MA population with disability/ESRD also represented a significantly larger proportion of Z codes documented (53.2% in MA compared to 45.2% in FFS). 

Our analysis also evaluated the percent of MA members with Z codes by reason for entitlement to Medicare (Table 2) (note these metrics were not available in the FFS which only presented percent of all Z codes across patient characteristics groups). Medicare beneficiaries with disability were 2.5 times more likely to have Z codes documented than those who enrolled at age 65 (3.3% versus 1.3%); those with ESRD or disability and ESRD were also roughly 2 times more likely to have Z codes (2.4% and 2.6% respectively). 

Z Codes by Dual Eligibility for Medicare and Medicaid Status

MA had a significantly higher proportion of beneficiaries who were dual eligible for Medicare and Medicaid compared to Medicare FFS (34.9% versus 18.9%) but a disproportionate number of Z codes were in dual eligible enrollees in both groups (58.1% in MA and 45.0% in FFS) (Table 2). 

Dual eligible members of MA plans were 2.7 times more likely to have Z codes documented compared to non-dual eligible members (3.2% versus 1.2% respectively) (Table 2). 

Z Codes by Age Group

MA had a higher percentage of beneficiaries under age 65 (18.2% versus 14.2% in FFS) and a smaller percentage age 85 and older (10.0% versus 12.5% in FFS) (Table 2). MA similarly had a larger percentage of Z codes documented in the <65 group compared to FFS (38.6% versus 33.1% respectively) and a smaller percentage in members age 85+ (9.6% in MA vs 14.5% in FFS). The middle age groups had a relatively similar distribution of beneficiaries and proportion of Z codes in MA and FFS.  

The under age 65 disabled group in MA had by far the largest percentage of beneficiaries with Z codes documented with 8.4% of members having one or more Z code, compared to 2.8% of those age 65-84 and only 1.8% of those age 85+ (Table 2). 

Z Codes by Gender

MA had a slightly larger percentage of female beneficiaries than FFS (56.8% in MA versus 54.6% in FFS) (Table 2). However, a higher proportion of all Z codes were recorded in female enrollees indicating a higher rate of SDOH relative to male Medicare beneficiaries in both MA and FFS (i.e., 61.1% of Z codes in MA and 59.7% of Z codes in FFS were in females, higher than their representation in the overall populations). 

Z Codes by Race/Ethnicity

MA had a smaller proportion of Whites than FFS (68.2% versus 79.5% in FFS), and Whites represented a lower-than-expected percentage of Z codes (63.7% in MA and 75.2% in FFS, both lower than their proportion of the overall populations) (Table 2). In contrast, MA had a larger proportion of Black and Hispanic members compared to FFS, and those groups represented a higher percentage of all Z codes relative to their representation in the overall population. Blacks comprised 16.7% of the MA population, but 23.3% of Z codes; Hispanics comprised 8.1% of the MA population but 8.7% of Z codes. 

There was a similar pattern observed in FFS, with Blacks representing 8.8% of the population but 13.2% of all Z codes documented and Hispanics comprising 5.9% of the population but 6.9% of Z codes. 

The opposite was observed among Asian/Pacific Islanders who comprised a much lower percentage of all Z codes relative to their representation in the overall population (3.8% of MA but only 1.8% of Z codes; 2.7% of FFS but only 1.1% of Z codes).  

In MA, Blacks were 1.5 times more likely to have Z codes documented compared to Whites (2.7% of beneficiaries versus 1.8% respectively), while Asians were only half as likely to have Z codes (0.9%) (Table 2).  

Z Codes by Urban/Rural Geographic Location

MA beneficiaries were more likely to reside in an urban area compared to those enrolled in traditional Medicare FFS (urban location 87.9% in MA versus 78.3% in FFS); however, beneficiaries residing in an urban area were more likely to have Z codes documented in both MA and FFS relative to their representation in the overall populations (90.5% in MA and 80.1% in FFS) (Table 2). 

MA has a much smaller percentage of members living in rural areas (12.1% compared to 21.7% of FFS enrollees) and an even smaller proportion of Z codes were documented in rural members relative to their share of the population (9.5% in MA and 19.9% in FFS). FFS had an 8.3% lower proportion of all Z codes documented in rural residing beneficiaries (19.9%) relative to their representation in the overall population (21.7%) (Table 2). 

Among MA enrollees in urban areas, 2.0% had Z codes documented compared to 1.5% of those in rural areas. 

Z Codes by SDOH Characteristics

Median Household Income

The percent of MA beneficiaries with median household income less than $15,000 annually with Z codes documented was 3.7%, which is more than 5 times higher than their proportion of the overall population of 0.7% (Table 2). Indeed, the percentage of members with Z codes decreases steadily as income increases. MA members earning between $15,000 and $40,000 per year also have a high percentage with Z codes (3.4% and 2.6% respectively). 

Members earning less than $50,000 annually represented a larger share of all Z codes documented relative to their representation in the overall population, while those earning $50,000 or more represented a lower share of Z codes relative to their percent of the overall population. 

Percent of Neighborhood with High School Diploma or Less

MA beneficiaries who lived in a neighborhood where fewer than 50% of the population has a high school diploma or less (higher education) represented 64.5% of the overall population but 61.2% of all Z codes, while those who lived in a neighborhood where 50% or more of residents have a high school diploma or less (lower education) represented 35.5% of the population but 38.9% of all Z codes (Table 2). 

MA members in low education areas were also more likely to have Z codes documented (2.1%-2.5%).

Discussion

This study fills a gap in information on utilization of Z codes among the Medicare Advantage (MA) population. CMS published an updated report in 2020 providing information on use of Z codes among Medicare beneficiaries enrolled in traditional Medicare Fee-for-Service (FFS) 2017 to 2019. 6 The report noted that MA is growing as a proportion of Medicare, comprising nearly 40 percent of Medicare beneficiaries, but similar data on MA was not available.

Studies now show a clear association between SDOH and health outcomes. Several studies have found that as much as 80% of health outcomes are related to patients’ SDOH while only 20% are attributable to the clinical care or treatment received., Increased use of Z codes to identify patient SDOH factors could provide value to providers, payers, and manufacturers. Understanding the most prevalent social risk factors within a specific population or disease area and the impact of those factors on outcomes such as medication adherence, receipt of follow-up care, inpatient and emergency room use, and other quality issues would help better target specific interventions for specific social needs for the right patients. 

A barrier to use of SDOH to improve patient care continues to be lack of data on patient’s social risk factors. A readily available way to fill that gap is use of standardized Z codes to capture information on SDOH, but utilization of the codes continues to be extremely low relative to the prevalence of social risk factors among Medicare beneficiaries. 

The CMS report showed a modest upward trend in the use of Z codes in FFS from 2017-2019 from 1.4% to 1.6% of beneficiaries with Z codes. Our analysis found that the percentage of Medicare beneficiaries enrolled in MA had a similar rate with Z codes (1.4%) in 2017, but utilization has been increasing at a faster pace, with 1.9% of MA enrollees having one or more Z codes in 2019, and an even larger proportion with Z codes in 2020 (2.7%), representing a 42% increase from 2019. This increase may be related to the COVID-19 pandemic, which served to underscore existing disparities across the continuum of health care related to SDOH factors. Even with the jump in use, the percent of Medicare beneficiaries with Z codes still represents a potentially significant undercount of those with social, economic, or other social risk factors that affect their health, as demonstrated with the income and education data we analyzed in this study. For example, our study found that 14.8% of the MA population had median household income below $40,000 annually yet only 3.3% of those beneficiaries had Z codes documented.

An earlier study using data from the 2013 Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample found fewer than 2% of overall discharges were assigned a Z code and found significant differences in the rate of utilization by age, race/ethnicity, sex, and payer type. An analysis of use of Z codes among hospitalized patients in the United States 2016-2017 found similar low rates of use of SDOH Z codes in hospital admission claims of 1.9%, largely among younger, male, Medicaid recipients or uninsured patients. A study of electronic health record data between 2015-2018 in the One Florida Clinical Research Consortium, a research network funded by the Patient-Centered Outcomes Research Institute (PCORI), also found a low rate of utilization of Z codes (2.03% of patients).

Overall, issues related to housing, income, and living alone were the most often documented social risk factors. The most frequently documented Z code in MA was Z59.9 Problems related to housing and economic circumstances unspecified, followed by Z60.2 Problems related to living alone (which ranked 3rd in FFS). Social isolation has been shown to result in higher healthcare utilization, such as hospitalizations, emergency department visits, and institutionalization and to even result in onset of disease. ., Social isolation and loneliness were exacerbated by the COVID 19 pandemic, especially among the vulnerable elderly population, so it is important to screen patients to identify and address risks related to these living circumstances.

The top Z code in FFS was Z59.0 Homelessness, which is the same main Z59 category, and was the 3rd most often used Z code in MA. The 4th most documented Z code in MA was Z59.6 Low income which falls under the same broad category, but low income did not rank among the top 5 in FFS. Z codes were most often documented by family practice or internal medicine practitioners, representing 29% of Z codes in both MA and FFS.

Medicare beneficiaries who qualified for Medicare based on disability and/or ESRD, younger (<65 years of age), dual eligible for Medicaid, female, Black/African American or Hispanic race/ethnicity, and residing in urban areas, relative to their shares of the overall MA population, were overrepresented among those with Z codes. Similar patterns were observed in FFS beneficiaries. However, those groups still had a very small proportion with Z codes relative to their share of the overall MA population. These findings underscore the importance of identifying and addressing these SDOH which are most prevalent among disabled, minority, and low-income Medicare beneficiaries, and may be at the heart of existing healthcare disparities.

Steps to Improving Utilization of Z Codes

While there is some evidence the use of Z codes to document SDOH in increasing slowly, these results indicate a need for additional and broader training efforts to assist providers in understanding the importance of collecting this information to overall quality and cost of care. Clinicians also need training on coding guidelines to assure consistent and accurate collection of SDOH data using Z codes, as well as on how to respectfully ask patients questions about sensitive social needs and how to respond to patients concerns about sharing this type of information.

A barrier to use Z codes has been lack of financial incentive as the codes are not used for reimbursement. While there is some discussion of including SDOH in payment models, the emerging value-based care models have “built-in” incentives to addressing SDOH. If providers can better understand the impact of SDOH on their performance on value-based metrics such as healthcare costs and quality outcomes, they can better appreciate the importance of collecting the information. Health plans may also provide incentives for documenting SDOH to promote their use. 

Another barrier to documenting SDOH is that clinicians may not have access to the right resources to address the identified needs, such as referral sources of services available to help address SDOH factors. There are a growing number of resources and websites that support connecting patients with community resources to address specific social needs.,, 

Limitations 

The findings reported here have several limitations. First, analyses were limited to the 25% convenience sample of Medicare Advantage beneficiaries in the MORE2 database who were continuously enrolled for at least 11 months during each year. While the sample has the same distribution as overall Medicare Advantage beneficiaries in terms of demographic characteristics, conclusions may not be generalizable to the overall population or to those who had less than 11 months of enrollment during each year evaluated. Second, the analyses were descriptive in nature and no statistical testing was completed; a more rigorous risk adjusted analysis would provide more definitive conclusions about the findings. Finally, ascertainment bias in screening for SDOH is possible based on the providers collecting the information, which could skew the results.

Conclusions

Social, economic, and environmental determinants contribute to 80% of patients’ health

and health-related outcomes yet lack of complete and consistent documentation of SDOH remains a barrier to identifying non-medical factors affecting health. Z codes remain a readily available and accessible tool to begin to capture more consistent information on SDOH across Medicare and other payers, yet they are rarely used by clinicians. Collecting this information is critical to address social risk factors and support the evolution of a value-based health care system that is equitable for all individuals.  

List of Exhibits

Table 1: Medicare Advantage (MA), Total Number of Z Code Claims, 2017 to 2020

Table 2: MA and FFS Population Distribution by Patient Characteristics 2019

Figure 1: Change in Proportion of Medicare Beneficiaries with Z Codes: MA 2017-2019 versus FFS 2017-2019

Figure 2: Percent of Z Codes by Provider/Specialty on Claims, MA 2019

Table 1: Medicare Advantage (MA), Total Number of Z Code Claims, 2017 to 2020

Year

Z Code Claim Count

Number of MA Beneficiaries with Z Codes

MA Population* 

Percent of MA Beneficiaries with Z Codes 

2017

302,179

42,325

3,029,906

1.4%

2018

432,605

51,223

3,045,701

1.7%

2019

645,128

52,318

2,729,254

1.9%

2020

663,439

57,727

2,103,333

2.7%

*Patients were required to have at least 11 months of medical coverage during each calendar year reported

Table 2: MA and FFS Population Distribution by Patient Characteristics 2019

Table 2: MA and FFS Population Distribution by Patient Characteristics 2019

MA Population % Overall

MA
% of All Z Codes

MA % with Z Codes*

FFS Population % Overall

FFS
% of All Z Codes

Original Reason for Medicare Entitlement
Age 65

70.0%

46.9%

1.3%

77.1%

54.8%

Disability

29.8%

52.8%

3.3%

22.1%

44.1%

End Stage Renal Disease (ESRD)

0.1%

0.2%

2.4%

0.5%

0.7%

Disability & ESRD

0.1%

0.2%

2.6%

0.3%

0.4%

Dual Eligible Status**
Non-dual eligible

65.1%

41.9%

1.2%

81.1%

55.0%

Dual Eligible

34.9%

58.1%

3.2%

18.9%

45.0%

Age Group
Under 65

18.2%

38.6%

8.4%

14.2%

33.1%

65-74

44.4%

31.7%

2.8%

46.6%

31.2%

75-84

27.4%

20.1%

2.8%

26.7%

21.3%

85+

10.0%

9.6%

1.8%

12.5%

14.5%

Gender
Female

56.8%

61.1%

2.1%

54.6%

59.7%

Male

43.2%

38.9%

1.7%

45.4%

40.3%

Racial/Ethnic Group
White

68.2%

63.7%

1.8%

79.5%

75.2%

Black/African American

16.7%

23.3%

2.7%

8.8%

13.2%

Hispanic/Latino

8.1%

8.7%

2.1%

5.9%

6.9%

Asian/Pacific Islander

3.8%

1.8%

0.9%

2.7%

1.1%

Other 

3.2%

2.5%

1.5%

3.1%

1.8%

Rurality
Urban

87.9%

90.5%

2.0%

78.3%

80.1%

Rural

12.1%

9.5%

1.5%

21.7%

19.9%

Social Determinants of Health (SDOH) Characteristics (MA only)
Median Household Income
<$15,000

0.7%

1.3%

3.7%

$15,000-$19,999

3.1%

5.5%

3.4%

$20,000-$29,999

11.0%

14.8%

2.6%

$30,000-$39,999

16.7%

18.7%

2.1%

$40,000-$49,999

19.4%

19.9%

2.0%

$50,000-$74,999

32.5%

28.4%

1.7%

$75,000-$99,999

12.5%

8.9%

1.4%

≥$100,000

4.1%

2.6%

1.2%

Percent with High School Diploma or Less
<10%

0.6%

0.7%

2.1%

10%-29%

18.5%

16.5%

1.8%

30%-49%

45.4%

44.0%

1.9%

50%-74%

34.6%

37.6%

2.1%

≥75%

1.0%

1.3%

2.5%

*Percent with Z Codes by reason for entitlement to Medicare not available in FFS report.

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18 Christiansen J, Lund R, Qualter P, Andersen CM, Pedersen SS, Lasgaard M. Loneliness, Social Isolation, and Chronic Disease Outcomes. Ann Behav Med. 2021 Mar 20;55(3):203-215. doi: 10.1093/abm/kaaa044. 
19 Garg A, Boynton-Jarrett R, Dworkin PH. Avoiding the Unintended Consequences of Screening for Social Determinants of Health. JAMA 2016;316(8):813–4.
20 Tools for Putting Social Determinants of Health into Action. Centers for Disease Control and Prevention.  https://www.cdc.gov/socialdeterminants/tools/index.htm 
21 Racial and Ethnic Approaches to Community Health (REACH), Centers for Disease Control and Prevention, Division of Nutrition, Physical Activity, and Obesity. https://www.cdc.gov/nccdphp/dnpao/state-local-programs/reach/current_programs/index.html 
22 UnityPoint Health, Together We Care, Find Community Resources Near You. https://www.unitypoint.org/together-we-care.aspx#:~:text=Local%20community%20resources%20can%20include%3A%20Food%20assistance,Financial%20assistance%20and%20education%20Transportation%20for%20health%20care