Race in Clinical Algorithms - lecturepanda.app

Indiana Pharmacists Association

Race in Clinical Algorithms:The Need For a Nuanced Understanding

Andrew N. Schmelz, PharmD, BCACP

Assistant Professor of Pharmacy Practice

Clinical Pharmacy Specialist, Ambulatory Care

Butler University & Eskenazi Health

2021 IPA FALL CONFERENCE

Conflict of Interest

I have no actual of potential conflict of interest in relation to this presentation.


Learning Objectives

1. Summarize the relationship between race, genetics, and social determinants of health.

2. Describe historic events, practices, and beliefs that have contributed to systemic racism in healthcare and health disparities observed in clinical practice.

3. Evaluate the appropriateness of race in a clinical algorithm and explain its impact on the health of marginalized patient populations.

4. Develop a personalized plan for incorporating emerging knowledge of race into practice.


Defining Race

▪No consistent, agreed-upon definition

▪A fluid concept used to group people according to various factors:▪Ancestral background

▪ Social identity

▪Visual characteristics (skin color, facial features, etc.)

▪A social construct, rather than an inherent biologic trait▪ Society defines what it is and assigns people to groups

▪ Individuals may choose to adopt this racial identity or not


NIH. Race. Available at: https://www.genome.gov/genetics-glossary/Race

Ethnicity

▪Recognizes differences based mostly culture▪ Language

▪Nationality

▪Religion

▪Generally understood as acquired or self-ascribed

▪Distinct from race▪Hispanic ethnicity may include both Black and White people


LiveScience. Published February 8, 2020. Available at: https://www.livescience.com/difference-between-race-ethnicity.html

Linking Race to Genetics

▪Genetics cannot tell us someone’s race

▪Genetics is not used to define racial categories▪There is not a clear, meaningful way that it could


Soc Stud Sci. 2011;41(1):5-30.

Factors Used to Construct Race Genetic Evidence

Ancestral background /

Social identity

Visual characteristics

“Race”-Linked Genetic Diseases

▪Sickle Cell Disease (SCD)▪Globally, affects people of all races

▪ In the US, nearly all patients with SCD are Black

▪Most (not all) patients have a shared African ancestry

▪Tay-Sachs Disease▪Primarily occurs among people with Ashkenazi Jewish or French-Canadian

ancestry

▪Historic preference for marrying within same faith -> contained trait

▪Relatively small community increases chances for Founder Effect


N Engl J Med 2020; 383:1902-1903.Center for Genetics Education. Fact Sheet 42. Updated November 23, 2015. Available at: https://www.genetics.edu.au/publications-and-resources.

Social Determinants of Health


Kaiser Family Foundation. Published June 9, 2021. Available at: https://www.kff.org/coronavirus-covid-19/issue-brief/implications-of-covid-19-for-social-determinants-of-health/

Historical Context for Racial Biology

▪Racially divided healthcare during slavery

▪White physicians assumed that:▪Black bodies were fit for slavery

▪Thrived under White control

▪Introduced (bad) research supporting notions that Black bodies have:▪ Increased tolerance to pain and heat

▪ Lower lung capacity

▪ Larger muscle mass


“Report on the Diseases and Physical Peculiarities of the Negro Race.” New Orleans Medical and Surgical Journal. May 1851.

Historical Events and Policies Shaping SDOH

• Slavery

• Reconstruction

• Segregation

• Redlining

• Tuskegee

• Civil Rights Movement

Black

• Colonialism

• Assimilation

• Establishment of Indian Health Service (IHS)

Indigenous


SDOH = Social Determinants of HealthCommunities in Action: Pathways to Health Equity. Washington (DC): National Academies Press (US); Jan 11, 2017.

Clinical Algorithms

▪Written guide in graphic format that represents stepwise procedures for clinical decision making about the evaluation and management of a clinical problem.

▪Can also refer to:▪Medical calculations

▪Medical scoring


JAMA. 249(5): 627-632.

https://tools.acc.org/ldl/ascvd_risk_estimator/index.html#!/calulate/estimator/

Why Use Algorithms?

Improve and standardize care

Reduce potential errors

Allow for computerized

decision support

Estimate difficult-to-gather clinical

data

Simplify risk estimation


Journal of the American Medical Informatics Association, 9(6): S56–S57

Problems Abound

Opacity

• The algorithm is not transparent to the patients

• Likely includes ALLclinical algorithms

Damage

• The algorithm does not work in the interest of patients

• Should include NOclinical algorithms

Scale

• The algorithm has impacts on other areas of patients’ lives

• Likely includes ALLclinical algorithms


O’Neil C. Weapons of Math Destruction. Penguin Books; 2017.

Algorithm Example 1: MDRD

▪Modification of Diet in Renal Disease (MDRD) Study

▪Equation:▪GFR (mL/min/1.73 m2) =

▪186 X SCr (mg/dL)-1.154 X age-0.203 X 0.742 (if female)

▪X 1.21 for Black-American

▪X 0.753 for Japanese

▪X 1.233 for Chinese

▪Used for diagnosis and staging of kidney disease


GFR = glomerular filtration rateSCr = serum creatinine

N Engl J Med 2020; 383:874-882.

Algorithm Example 1: MDRD

▪The equation calculates GFR to be HIGHER in Black patients

▪Hypothesized this is due to “greater muscle mass” causing increased SCr levels

▪This results in:▪Underdiagnosis of Black patients with CKD

▪Delays in treatment

▪Disqualification for kidney transplant and dialysis


N Engl J Med 2020; 383:874-882.Ann Intern Med. 1999 Mar 16;130(6):461-70.

Algorithm Example 2: FRAX Tool

▪Fracture Risk Assessment Tool (FRAX)

▪Calculates a 10-year probability of fracture

▪ Variables:


N Engl J Med 2020; 383:874-882.Osteoporos Int. 2000;11(2):120-7.

Age Sex Weight HeightPrevious fracture

Parent fractured hip

Current smoking

GlucocorticoidsRheumatoid

arthiritsSecondary

osteoporosisAlcohol 3 or

more units/dayBone mineral density (BMD)

Algorithm Example 2: FRAX Tool

▪The tool estimates fracture risk to be LOWER in Black patients by a little less than half

▪Hypothesized this is due to “larger, stronger bones which develop early in life”

▪This results in:▪Underdiagnosis of Black patients with osteoporosis

▪Delays in treatment


N Engl J Med 2020; 383:874-882.Osteoporos Int. 2000;11(2):120-7.

Algorithm Example 3: PFTs

▪Pulmonary Function Tests (PFTs) or spirometry

▪Measure lung function, including:▪ Forced vital capacity (FVC)

▪ Forced expiratory volume (FEV1)

▪The tool estimates FVC and FEV1 to be 20% lower in Black patients▪Overdiagnoses Black patients with lung disease

▪Overtreatment with drug therapy and procedures


N Engl J Med 2020; 383:874-882.Can J Respir Ther. 2015;51(4):99-101.

Summary of Algorithms


Tool Input Variables Use of Race Equity Concern

Estimated glomerular filtration rate (eGFR) MDRD

Serum creatinineAge and sexRace: Black v. White or other

MDRD reports higher eGFR (by a factor of 1.21) if the patient is identified as Black

May delay referral to specialist care or listing for kidney transplant

Fracture Risk Assessment Tool (FRAX)

Age and sexWeight and heightPrevious fractureCurrent smoking…others

Returns a lower fracture risk if patient is:- Black (0.43)- Asian (0.50)- Hispanic (0.53)

Lower risk is reported for non-White women and may delay diagnosis and treatment

Pulmonary Function Tests (PFTs)

Age and sexHeightRace/ethnicity

Correction factors for persons labeled- Black (10-15%)

Misclassification of disease severity and impairment

N Engl J Med 2020; 383:874-882.

Evaluating Algorithms For Racial Equity

▪Why was race used in the first place?▪Misconception of genetics

▪ Rough substitute for SDOH

▪What are the risks and benefit of using it?▪ Undertreatment (more bad outcomes)

▪Overtreatment (unnecessary treatments/procedures)

▪Would other variables be more specific?

▪How should ambiguity be handled?


NIH. Race. Available at: https://www.genome.gov/genetics-glossary/Race

Planning For Future Practice

Assess

the patient and the need

for change

Construct

a clinical question

derived from the patient's

problem

Acquire

the best evidence through a thorough

search

Appraise

the evidence for validity

and usefulness

Integrate

evidence with clinical

expertise and patient

preferences

Apply

what you have learned

Evaluate

what you have learned


Urol Nurs. 2005;25(5):365-370.

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Race in Clinical Algorithms - lecturepanda.app