the influence of race, sex, and social disadvantage on

Dow

nloadedfrom

http://journals.lww.com

/ajpmrby

BhDMf5ePH

Kav1zEoum1tQ

fN4a+kJLhEZgbsIH

o4XMi0hC

ywCX1AW

nYQp/IlQ

rHD3i3D

0OdR

yi7TvSFl4Cf3VC

1y0abggQZXdtw

nfKZBYtws=

on05/03/2021

Downloadedfromhttp://journals.lww.com/ajpmrbyBhDMf5ePHKav1zEoum1tQfN4a+kJLhEZgbsIHo4XMi0hCywCX1AWnYQp/IlQrHD3i3D0OdRyi7TvSFl4Cf3VC1y0abggQZXdtwnfKZBYtws=on05/03/2021

1

The influence of race, sex, and social disadvantage on self-reported health in

patients presenting with chronic musculoskeletal pain

Abby L. Cheng, MD1

Ethan C. Bradley, BA2

Brian K. Brady, BA3

Ryan P. Calfee, MD4

Lisa M. Klesges, PhD, MS5

Graham A. Colditz, MD, DrPH, MPH5

Heidi Prather, DO6

1Department of Orthopaedic Surgery, Division of Physical Medicine and Rehabilitation

Washington University in St. Louis School of Medicine St. Louis, MO, USA

2The Brown School at Washington University in St. Louis St. Louis, MO, USA

3Washington University in St. Louis School of Medicine St. Louis, MO, USA

4Department of Orthopaedic Surgery, Division of Hand and Microsurgery Washington

University in St. Louis School of Medicine St. Louis, MO, USA

5Department of Surgery, Division of Public Health Sciences Washington University in St. Louis

School of Medicine St. Louis, MO, USA

6Weill Cornell Medical College New York City, NY, USA

American Journal of Physical Medicine & Rehabilitation Articles Ahead of Print DOI: 10.1097/PHM.0000000000001774

Copyright © 2021 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.

ACCEPTED

2

Corresponding author:

Abby L Cheng, MD

Campus Box 8233

660 South Euclid Avenue

St. Louis, MO 63110

Phone: 314-747-2828

Fax: 314-514-3555

[email protected]

Author disclosures:

Competing interests:

None of the authors have conflicts of interest or competing interests to disclose.

Funding for the project:

This study was supported by the Doris Duke Charitable Foundation and by grant K23AR074520

from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (Dr. Cheng).

Neither funding body had any role in the study design; collection, analysis, or interpretation of

the data; writing of the report; or decision to submit the report for publication.

Financial benefit to the authors:

None of the authors expect financial benefit from this study or manuscript.


ACCEPTED

3

Previous presentations:

Data reported in this manuscript have been partially presented in abstract/poster format at the

2021 Association of Academic Physiatrists (AAP) Annual Meeting. The dataset used in this

study has also been used for another manuscript that is currently under review at another journal.

The data analyses in the two manuscripts do not overlap.


ACCEPTED

4

ABSTRACT

Objective:

To better address sociodemographic-related health disparities, this study examined which

sociodemographic variables most strongly correlate with self-reported health in patients with

chronic musculoskeletal pain.

Design:

This single-center, cross-sectional study examined adult patients followed by a physiatrist for

chronic (≥4 years) musculoskeletal pain. Sociodemographic variables considered were race, sex,

and disparate social disadvantage (measured as residential address in the worst versus best Area

Deprivation Index national quartile). The primary comparison was the adjusted effect size of

each variable on physical and behavioral health (measured by Patient-Reported Outcomes

Measurement Information System (PROMIS)).

Results:

In 1,193 patients (age 56.3±13.0 years), disparate social disadvantage was associated with worse

health in all domains assessed (PROMIS Physical Function Β -2.4 points [95%CI -3.8–-1.0],

Pain Interference 3.3 [2.0–4.6], Anxiety 4.0 [1.8–6.2], and Depression 3.7 [1.7–5.6]). Black race

was associated with greater anxiety than white race (3.2 [1.1–5.3]), and female sex was

associated with worse physical function than male sex (-2.5 [-3.5–-1.5]).


ACCEPTED

5

Conclusion:

Compared to race and sex, social disadvantage is more consistently associated with worse

physical and behavioral health in patients with chronic musculoskeletal pain. Investment to

ameliorate disadvantage in geographically defined communities may improve health in

sociodemographically at-risk populations.

Key Words:

Socioeconomic factors; Health status disparities; Chronic pain; Musculoskeletal diseases


ACCEPTED

6

What is Known:

Sociodemographic characteristics such as Black race, female sex, and high social disadvantage

correlate with disparities in care and worse outcomes across numerous health domains.

What is New:

Compared to race and sex, in patients with chronic musculoskeletal pain, social disadvantage is

more consistently associated with worse self-reported physical and behavioral health. However,

patients of Black race and female sex are more likely to live in socially disadvantaged

neighborhoods. The results support that strategic investment into geographically defined,

disadvantaged communities may be an efficient strategy to improve health in patients with

multiple sociodemographic risk factors.


ACCEPTED

7

INTRODUCTION

Health outcomes are strongly influenced by sociodemographic factors.1 Patient characteristics

such as Black race, female sex, and social disadvantage correlate with worse symptom reporting,

access to care, and re-hospitalization rates in patients with a variety of medical conditions

including cardiopulmonary disease, mental health disorders, and acute and chronic pain.2-4

Disparate morbidity and mortality rates from the COVID-19 pandemic have especially

highlighted these issues.5 Multiple underlying phenomena likely contribute to sociodemographic-

driven disparities. Systemic racism, sexism, and epigenetics can all influence physical and

behavioral health, independent of income level.6-8

Furthermore, intersectional effects of

sociodemographic variables such as Black race and female sex can also exacerbate disparities.9,10

In order to design and appropriately prioritize effective interventions to address these disparities,

Penman-Aguilar et al. and Alegria et al. advocate for assessment of the relative effects of

sociodemographic variables.9 ,11

Relationships between sociodemographic variables and chronic

pain are particularly important to explore because 20% of Americans live with chronic pain that

interferes with employment, family responsibilities, and wellness,12

and disabling chronic pain

disproportionately affects socially at-risk populations.13 ,14

As the standard of care for pain

management evolves in response to the opioid crisis, new treatment guidelines should consider

ways to also mitigate the disproportionate burden of chronic pain in these select

sociodemographic groups.

The purpose of this study was to examine associations between sociodemographic variables (i.e.,

race, sex, and social disadvantage) and self-reported physical and behavioral health in patients


ACCEPTED

8

with chronic musculoskeletal pain. The hypothesis was that, consistent with findings in other

patient populations, Black race, female sex, and social disadvantage would each independently

correlate with worse self-reported physical and behavioral health.

METHODS

This cross-sectional study analyzed electronic medical records from a single tertiary care

academic institution in St. Louis, MO, USA. The institution acts as a safety net medical center

for the surrounding urban and multi-state rural region. Study procedures were conducted in

accordance with the Declaration of the World Medical Association. University Institutional

Review Board approval was granted prior to data collection with a waiver of informed consent.

Data analysis was performed in 2019 and 2020. Reporting for this study conforms to all

STROBE guidelines (see Supplemental Checklist, Supplemental Digital Content 1,

http://links.lww.com/PHM/B282).

Study population

The study population consisted of adult patients (18 years and older) who presented to a board-

certified sports medicine physical medicine and rehabilitation physician (physiatrist) for non-

operative management of one or more chronic painful musculoskeletal disorders. The study

physiatrists exclusively manage musculoskeletal conditions, so for this study, a chronic

musculoskeletal disorder was defined as presentation multiple times to one of the eight sports

medicine physiatrists at the study institution for musculoskeletal pain in one or more body

regions. The clinical presentations had to occur at least once between January 1, 2000 –

December 31, 2011 and once between June 22, 2015 – November 1, 2017, which means all


ACCEPTED

9

patients had pain for a minimum of four years at the time of data analysis. The first time interval

was chosen because it represents the period when the physicians accepted the transfer of care for

chronic pain management (including opioid management) for any patient. The second interval

was chosen because it captured the time when collection of Patient-Reported Outcomes

Measurement Information System (PROMIS®) measures became standard of care for all patients

presenting to the physicians. After an eligible initial presentation, eligible follow-up

presentations included visits for routine monitoring / medication refills and for pain flares. All

consecutive eligible patients were included. Pediatric patients and those with pain that resolved

to the point of not requiring follow-up were excluded from the study.

Measures

Sociodemographic variables of interest were patients’ self-reported race, biological sex, and

degree of social disadvantage, as measured by the 2018 Area Deprivation Index (ADI).4 The

ADI was developed by Singh, Kind, and colleagues to rank communities using a composite score

derived from 17 variables that quantify various domains such as residents’ income, education,

employment, and housing quality. Each 9-digit zip code is assigned an ADI national percentile

from 1 to 100, with a larger percentile representing worse social disadvantage. ADI values are

systematically missing for zip codes that do not match an ADI (e.g., if offshore) and that

represent a post office (PO) box or a business entity responsible for large volume mail delivery.

In accordance with published literature and in order to facilitate intuitive, clinically relevant data

interpretation, socially disadvantaged communities were defined as those with zip codes in the

worst ADI national quartile, and disparate social disadvantage was defined as comparison

between communities with zip codes in the most versus least disadvantaged ADI national


ACCEPTED

10

quartiles.15

Patients’ age, use of chronic opioid therapy for pain management, and health insurance status

were also recorded because they are known to be associated with patients’ self-reported health.16

For this study, chronic opioid therapy was defined as maintaining adherence to an opioid

contract and receiving repeated opioid prescriptions of a relatively consistent dose and quantity

from the study physicians at regular time intervals, which necessitated that patients present for

in-person follow-up appointments at least every three months, report they are taking the

medication as prescribed, pass random urine drug screens at least annually, and maintain a stable

functional level without an escalating opioid dose.

The primary comparison measure was the adjusted effect size of each sociodemographic variable

(race, sex, and social disadvantage) on patients’ self-reported physical and behavioral health, as

measured by PROMIS®

.17

Patients completed the PROMIS Computer Adaptive Test (CAT)

measures prior to the physician encounter as standard of care during their clinic visits. Physical

health was quantified using PROMIS Physical Function v1.2 (later switched to v2.0) and Pain

Interference v1.1, and behavioral health was quantified with the PROMIS Anxiety v1.0 and

Depression v1.0 domains. Because PROMIS Physical Function versions 1.2 and 2.0 yield

comparable scores, the versions were combined during statistical analysis.18

Of note, the

PROMIS Anxiety domain was not collected as standard of care until ten months after

implementation of the other domains, which resulted in missing data without systematic bias.

Scores for all PROMIS domains are normalized with a mean of 50 and standard deviation of 10,

and higher scores represent more of the domain. For instance, compared to the reference


ACCEPTED

11

population, a score of 60 on PROMIS Depression represents more (worse) depression symptoms

than average, but a score of 60 on PROMIS Physical Function represents more (better) physical

function than average. The general reference population used to standardize PROMIS scores

mirrored the race, sex, age, and education distribution reported from the 2000 United States

General Census.19

Minimum meaningful effect sizes on PROMIS scores were estimated based on published within-

group minimal clinically important differences (MCIDs) in patients with conservatively managed

musculoskeletal pain.20-22

Additionally, any effect size smaller than the standard error of

measurement for each PROMIS CAT domain at the study institution was considered to not be

clinically meaningful. Therefore, meaningful differences were defined as 2.2 points for Physical

Function, 2.0 for Pain Interference, 3.0 for Anxiety, and 3.2 for Depression.

Because race and biological sex are typically fixed variables throughout a person’s lifetime

whereas the level of social disadvantage related to a person’s local community can change, a

sub-analysis was performed to compare the proportion of patients in each ADI quartile who

identified as each race and sex.

Statistical analysis

Each PROMIS measure was modeled using multiple linear regression. Independent variables

included in each model were patients’ self-reported race (categorized as white, Black, or other),

self-reported biological sex (male or female), and disparate social disadvantage (i.e., comparison

between the most and least disadvantaged ADI national quartiles).15

Covariates included age


ACCEPTED

12

(categorized by decade) and current chronic opioid use status (yes or no). The covariates were

chosen a priori because of their established effects on physical and behavioral health. Health

insurance status was not included as a covariate because it captures essentially the same

construct as ADI (e.g., socioeconomic status), and it does not stratify patients as effectively as

ADI. Model fits were assessed by inspection of residuals, histograms, and 1:1 plots of observed

versus modeled dependent variables. Some outliers were observed for each of the four PROMIS

measures, as determined by Cook’s D and DFFITS, so robust variants of each regression were

run with a Huber loss function. The pattern of significance did not change, so the traditional

regression model results are reported. For the sub-analysis, Pearson’s chi-square tests were

performed to compare the race and sex demographic breakdown between each ADI national

quartile. Missing data were omitted from all analyses, and p<.05 was set as the level of

significance a priori. All data analyses were performed using SAS Base v9.4 (SAS Institute, Inc.,

Cary, NC) and R (4.0.2, Vienne, Austria).

RESULTS

In total, 1,193 patients (mean age 56.3 ± 13.0 years) met the study criteria (Figure 1). The

majority of patients self-reported white race (981, 82.2%) and female sex (843, 70.7%), and

patients were nearly evenly distributed across all four ADI national quartiles of social

disadvantage (Table 1).

Disparate social disadvantage was independently associated with clinically meaningfully worse

self-reported physical and behavioral health in all domains assessed (PROMIS Physical Function

Β -2.4 points [95% CI -3.8 to -1.0], Pain Interference 3.3 [2.0 to 4.6], Anxiety 4.0 [1.8 to 6.2],


ACCEPTED

13

and Depression 3.7 points [1.7 to 5.6]) (Table 2, Figure 2). Even when compared to the

intermediate national ADI quartiles (Q2 and Q3), pain interference was meaningfully worse in

communities scoring in the worst ADI quartile.

Black race was independently and meaningfully associated with greater anxiety than white race

(3.2 points [1.1 to 5.3]), and female sex was associated with worse physical function than male

sex (-2.5 points [-3.5 to -1.5]). Furthermore, sub-analysis revealed that, compared to less

disadvantaged communities, a relatively greater proportion of Black and female patients lived in

the most disadvantaged communities (Worst ADI national quartile: 122/312 (39.1%) Black vs

Best quartile: 10/250 (4.0%) Black, p<.001; Worst quartile: 239/312 (76.6%) female vs Best

quartile: 164/250 (65.6%) female, p=.036) (Table 3).

DISCUSSION

This study examined associations between sociodemographic variables and self-reported health

in patients who presented to a physiatric practice for chronic musculoskeletal pain. Disparate

social disadvantage was the single variable consistently associated with meaningfully worse

physical and behavioral health in all domains assessed. Contrary to the study hypothesis, Black

race was only independently associated with worse anxiety symptoms, and female sex was only

independently associated with worse physical function. However, Black race and female sex

were disproportionately represented in the most disadvantaged communities.

The study findings are consistent with established literature, but they also address gaps identified

by Penman-Aguilar et al. and Alegria et al. regarding identification of which sociodemographic


ACCEPTED

14

variables most strongly influence health and outcomes and should therefore be prioritized when

designing interventions.9 ,11 ,14

The findings build on previous work by Wright et al., which also

demonstrated that social environment is associated with both physical and behavioral health in

patients presenting for orthopedic conditions.15

In contrast to Wright’s analysis, this study

specifically focused on patients with chronic pain who were managed by non-operative

specialists. These patients are less likely than the general orthopedic population to have

spontaneous pain resolution or to be candidates for a surgical intervention, so it is possible the

psychological and long-term social burden of these patients’ musculoskeletal conditions may be

greater. Furthermore, this study incorporated race into the discussion of important

sociodemographic variables, whereas race was not a focus in Wright’s study. Because the study

institution is located in a region with an especially deep-rooted history and persistence of racial

segregation and race-related health disparities,25

associations between race and self-reported

health were particularly important to examine. Black patients are less likely to be prescribed

controlled substances, be screened for depression, or receive behavioral health care.26

The study findings can be interpreted as encouraging because, unlike a person’s race or sex,

social disadvantage can be systematically modified on a large scale. Targeted intervention to

address upstream social factors such as education, housing, employment, income support,

nutrition support, care coordination, and provider biases are effective means to improving overall

health, facilitating treatment adherence, and reducing healthcare expenditures.27 ,28

That is,

genetic and epigenetic variables may indeed play some role in health disparities,8 but based on

the study findings, strategic efforts to create opportunities and improve geographic

neighborhoods may be the most important type of intervention to achieve health equity in the


ACCEPTED

15

chronic pain population. Even the higher anxiety levels in Black patients may be addressed by

tackling societal issues such as housing discrimination and exposure to disproportionate police

violence.29

The study results also highlight the importance of interpreting PROMIS scores within the context

of the geographic or patient population of interest. That is, during development, the PROMIS

domain scores were standardized using a population with a demographic distribution that

mirrored the 2000 United States General Census, which was 12.3% Black, 75.1% white, and

included people with a wide variety of educational levels. In contrast, the metro region around

the study institution clinic sites is 29.9% Black,30

and the distribution of this study population

was 15.6% Black. Therefore, average PROMIS Anxiety scores in the community surrounding

the study institution may be worse than a T-score of 50. Until health equity is achieved across the

entire sociodemographic spectrum, healthcare providers striving to practice personalized

medicine should consider interpreting a patient’s PROMIS scores within the context of his/her

sociodemographics, rather than simply comparing patients’ T-scores to the general U.S.

population. That is, the “average” person represented in the U.S. Census likely is not very

representative of patients in sociodemographic minorities. It is important to keep in mind that

social variables may be influencing patients’ self-reported health as much, if not more than, the

condition for which they present for medical attention.

Limitations

There are several study limitations. First, the demographic distribution required that race

categories were condensed to White, Black, and Other, and biological sex was recorded as a


ACCEPTED

16

binary variable and could not take into account gender identity. Second, the patient population

was derived from a single department of a single institution. Nevertheless, the sample size was

large, and the study institution draws from a diverse, multi-state urban and rural catchment area.

These factors should be considered when interpreting the generalizability of the study. Third, the

variables examined in this study only accounted for a modest proportion of the variance observed

for each health measure (R2 = 0.06-0.11). This was to be expected since the purpose of the study

was to evaluate relative associations between sociodemographic variables and health, rather than

to create a prediction model or to capture all potential variables which could be influencing

health (such as more detailed features of patients’ musculoskeletal conditions). Finally, this

cross-sectional study cannot establish causality between sociodemographic variables and self-

reported health.

CONCLUSIONS

In summary, in patients presenting with chronic musculoskeletal pain, social disadvantage was

associated with worse physical function, pain interference, anxiety, and depression symptoms,

whereas Black race was only associated with more anxiety symptoms, and female sex was only

associated with worse physical function. However, patients of Black race and female sex were

more likely to live in socially disadvantaged neighborhoods. If these findings are confirmed with

prospective investigation, the results support that strategic investment to ameliorate disadvantage

in geographically defined communities may be an effective strategy to improving health in these

at-risk populations.


ACCEPTED

17

ACKNOWLEDGMENTS

This authors thank Lee Rhea, PhD, and Matthew Schuelke, PhD (Washington University), for

their assistance with statistical analysis for this study.


ACCEPTED

18

REFERENCES

1. Meyer PA, Penman-Aguilar A, Campbell VA, Graffunder C, O'Connor AE, Yoon PW.

Conclusion and future directions: CDC Health Disparities and Inequalities Report -

United States, 2013. MMWR Suppl 2013;62(3):184-6.

2. Mossey JM. Defining racial and ethnic disparities in pain management. Clin. Orthop. Relat.

Res. 2011;469(7):1859-70.

3. Asada Y, Whipp A, Kindig D, Billard B, Rudolph B. Inequalities in multiple health outcomes

by education, sex, and race in 93 US counties: why we should measure them all. Int J

Equity Health 2014;13:47.

4. Kind AJH, Jencks S, Brock J, et al. Neighborhood socioeconomic disadvantage and 30-day

rehospitalization: a retrospective cohort study. Ann. Intern. Med. 2014;161(11):765-74.

5. Dorn AV, Cooney RE, Sabin ML. COVID-19 exacerbating inequalities in the US. Lancet

2020;395(10232):1243-44.

6. Paradies Y, Ben J, Denson N, et al. Racism as a Determinant of Health: A Systematic Review

and Meta-Analysis. PLoS One 2015;10(9):e0138511.

7. Molix L. Sex differences in cardiovascular health: does sexism influence women's health?

Am. J. Med. Sci. 2014;348(2):153-5.

8. Horvath S, Gurven M, Levine ME, et al. An epigenetic clock analysis of race/ethnicity, sex,

and coronary heart disease. Genome Biol. 2016;17(1):171.

9. Penman-Aguilar A, Talih M, Huang D, Moonesinghe R, Bouye K, Beckles G. Measurement

of Health Disparities, Health Inequities, and Social Determinants of Health to Support the

Advancement of Health Equity. J. Public Health Manag. Pract. 2016;22 Suppl 1(Suppl

1):S33-42.


ACCEPTED

19

10. Bey GS, Jesdale B, Forrester S, Person SD, Kiefe C. Intersectional effects of racial and

gender discrimination on cardiovascular health vary among black and white women and

men in the CARDIA study. SSM Popul Health 2019;8:100446.

11. Alegría M, NeMoyer A, Falgàs Bagué I, Wang Y, Alvarez K. Social Determinants of Mental

Health: Where We Are and Where We Need to Go. Current psychiatry reports

2018;20(11):95.

12. Dahlhamer J, Lucas J, Zelaya C, et al. Prevalence of Chronic Pain and High-Impact Chronic

Pain Among Adults - United States, 2016. MMWR. Morbidity and mortality weekly

report 2018;67(36):1001-06.

13. Janevic MR, McLaughlin SJ, Heapy AA, Thacker C, Piette JD. Racial and Socioeconomic

Disparities in Disabling Chronic Pain: Findings From the Health and Retirement Study. J.

Pain 2017;18(12):1459-67.

14. Green CR, Hart-Johnson T. The association between race and neighborhood socioeconomic

status in younger Black and White adults with chronic pain. J. Pain 2012;13(2):176-86.

15. Wright MA, Adelani M, Dy C, OʼKeefe R, Calfee RP. What is the Impact of Social

Deprivation on Physical and Mental Health in Orthopaedic Patients? Clin. Orthop. Relat.

Res. 2019;477(8):1825-35.

16. Goesling J, Lin LA, Clauw DJ. Psychiatry and Pain Management: at the Intersection of

Chronic Pain and Mental Health. Current psychiatry reports 2018;20(2):12.

17. Cella D, Yount S, Rothrock N, et al. The Patient-Reported Outcomes Measurement

Information System (PROMIS): progress of an NIH Roadmap cooperative group during

its first two years. Med. Care 2007;45(5 Suppl 1):S3-S11.

18. PROMIS Score Cut Points. Secondary PROMIS Score Cut Points 2019.


ACCEPTED

20

http://www.healthmeasures.net/score-and-interpret/interpret-scores/promis/promis-score-

cut-points.

19. Liu H, Cella D, Gershon R, et al. Representativeness of the Patient-Reported Outcomes

Measurement Information System Internet panel. J. Clin. Epidemiol. 2010;63(11):1169-

78.

20. Lee AC, Driban JB, Price LL, Harvey WF, Rodday AM, Wang C. Responsiveness and

Minimally Important Differences for 4 Patient-Reported Outcomes Measurement

Information System Short Forms: Physical Function, Pain Interference, Depression, and

Anxiety in Knee Osteoarthritis. J. Pain 2017;18(9):1096-110.

21. Kroenke K, Yu Z, Wu J, Kean J, Monahan PO. Operating characteristics of PROMIS four-

item depression and anxiety scales in primary care patients with chronic pain. Pain Med.

2014;15(11):1892-901.

22. Chen CX, Kroenke K, Stump TE, et al. Estimating minimally important differences for the

PROMIS pain interference scales: results from 3 randomized clinical trials. Pain

2018;159(4):775-82.

23. Schalet BD, Cook KF, Choi SW, Cella D. Establishing a common metric for self-reported

anxiety: linking the MASQ, PANAS, and GAD-7 to PROMIS Anxiety. J. Anxiety

Disord. 2014;28(1):88-96.

24. Choi SW, Schalet B, Cook KF, Cella D. Establishing a common metric for depressive

symptoms: linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression. Psychol.

Assess. 2014;26(2):513-27.

25. Oliveri R. Setting the stage for Ferguson: Housing discrimination and segregation in St.

Louis. Mo L Rev. 2015;80:1053.


ACCEPTED

21

26. Hahm HC, Cook BL, Ault-Brutus A, Alegría M. Intersection of race-ethnicity and gender in

depression care: screening, access, and minimally adequate treatment. Psychiatr. Serv.

2015;66(3):258-64.

27. Taylor LA, Tan AX, Coyle CE, et al. Leveraging the Social Determinants of Health: What

Works? PLoS One 2016;11(8):e0160217.

28. Anastas TM, Miller MM, Hollingshead NA, Stewart JC, Rand KL, Hirsh AT. The Unique

and Interactive Effects of Patient Race, Patient Socioeconomic Status, and Provider

Attitudes on Chronic Pain Care Decisions. Ann. Behav. Med. 2020;54(10):771-82.

29. Bor J, Venkataramani AS, Williams DR, Tsai AC. Police killings and their spillover effects

on the mental health of black Americans: a population-based, quasi-experimental study.

Lancet 2018;392(10144):302-10.

30. Bureau USC. QuickFacts. Secondary QuickFacts.

https://www.census.gov/quickfacts/stlouiscitymissouricounty.


ACCEPTED

22

FIGURE LEGENDS

Figure 1. Flowsheet of included patients presenting for evaluation of chronic musculoskeletal

pain between 2015 and 2017

Figure 2. Relative associations between sociodemographics and health

Caption 2. The y-axis represents the absolute value of each regression coefficient when also

controlling for age and chronic opioid status. “Social Disadvantage” depicts the comparison of

outcome measures between communities with disparate social disadvantage, which is defined as

communities in the most versus least disadvantaged national quartiles of the Area Deprivation

Index (Q4 versus Q1). “Race” depicts the comparison of Black versus white. “Sex” depicts the

comparison of female versus male. Error bars represent the standard error.


ACCEPTED

23

Figure 1


ACCEPTED

24

Figure 2


ACCEPTED

25

Table 1. Sociodemographics and self-reported health of 1,193 patients evaluated for chronic

musculoskeletal pain between 2015-2017.

Mean (SD) or

n (%)

Missing (n)

Age (Years) 56.3 (13.0) 0

Female Sex 843 (70.7%) 0

Race 0

White 981 (82.2%)

Black 186 (15.6%)

Other 26 (2.2%)

ADI quartile 80

1 (Least disadvantaged) 250 (22.5%)

2 271 (24.3%)

3 280 (25.2%)

4 (Most disadvantaged) 312 (28.0%)

Insurance 171

Private 511 (51.0%)

Medicare 464 (46.3%)

Medicaid 26 (2.6%)

Other 1 (0.0%)

Chronic Opioid Use 352 (31.6%) 80

PROMIS Score

Physical Function 37.2 (7.6) 36

Pain Interference 63.7 (7.1) 40

Anxiety 55.1 (10.5) 256

Depression 50.4 (10.4) 53

Meets Clinically Significant Thresholda

Anxiety 237 (25.3%) 256

Depression 200 (17.5%) 53

a Predetermined thresholds for clinically significant behavioral health disorders are defined as

PROMIS Depression ≥ 59.9 and PROMIS Anxiety ≥ 62.3 based on established linkage tables

developed by Schalet et al.23 ,24

Abbreviations: ADI (Area Deprivation Index), PROMIS (Patient-Reported Outcomes

Measurement Information System).


ACCEPTED

26

Table 2. Associations between sociodemographic variables and self-reported health in 1,193

patients evaluated for chronic musculoskeletal pain.

PROMIS domain n R2 Social Variable Β (SE) 95% CI p

Physical Function 993 0.108

Race = Black

Sex = Female

ADI = Q2

ADI = Q3

ADI = Q4

-1.1 (0.7)

-2.5 (0.5)

-1.0 (0.7)

-1.0 (0.7)

-2.4 (0.7)

-2.5

-3.5

-2.4

-2.4

-3.8

to 0.2

to -1.5

to 0.3

to 0.3

to -1.0

.11

< .001

.13

.13

< .001

Pain Interference 988 0.088

Race = Black

Sex = Female

ADI = Q2

ADI = Q3

ADI = Q4

1.6 (0.7)

0.6 (0.5)

2.1 (0.6)

2.2 (0.6)

3.3 (0.7)

0.3

-0.4

0.9

0.9

2.0

to 2.8

to 1.5

to 3.3

to 3.4

to 4.6

.015

.25

.001

< .001

< .001

Anxiety 808 0.066

Race = Black

Sex = Female

ADI = Q2

ADI = Q3

ADI = Q4

3.2 (1.1)

1.5 (0.8)

1.3 (1.1)

1.9 (1.1)

4.0 (1.1)

1.1

-0.1

-0.8

-0.2

1.8

to 5.3

to 3.1

to 3.4

to 3.9

to 6.2

.003

.06

.22

.08

< .001

Depression 977 0.059

Race = Black

Sex = Female

ADI = Q2

ADI = Q3

ADI = Q4

0.8 (1.0)

1.8 (0.7)

0.8 (1.0)

1.4 (0.9)

3.7 (1.0)

-1.1

0.43

-1.0

-0.5

1.7

to 2.6

to 3.2

to 2.7

to 3.2

to 5.6

.44

.01

.39

.14

< .001

A multiple linear regression model is presented for each PROMIS domain. Reference values for

the sociodemographic variables of interest include: Race=White, Sex=Male, ADI=Q1 (quartile

with the least social disadvantage). Age and chronic opioid use are also covariates in the models.

The p-value for all four models was <.001. Boldface indicates statistical significance for the

respective independent variable (p<.05).

Abbreviations: PROMIS (Patient-Reported Outcomes Measurement Information System), ADI

(Area Deprivation Index).


ACCEPTED

27

Table 3. Relationships between fixed demographic variables and living in a socially

disadvantaged community.

ADI Quartile

Demographic

n (%)

Q1

(n=250)

Q2

(n=271)

Q3

(n=280)

Q4

(n=312) p-value

Black Race 10 (4.0%) 14 (5.2%) 32 (11.4%) 122 (39.1%) <.001

Female Sex 164 (65.6%) 189 (69.7%) 197 (70.4%) 239 (76.6%) .036

Q1 represents the least disadvantaged national quartile, and Q4 represents the most

disadvantaged national quartile. Boldface indicates statistical significance when comparing all

four quartiles (p<.05).

Abbreviation: ADI (Area Deprivation Index).


ACCEPTED

the influence of race, sex, and social disadvantage on

Documents