Abstract
Background
Sickle cell disease (SCD) is a heritable chronic health condition characterized by pain symptoms throughout the life course that are routinely treated with opioids.
Objective
This study examined differences in substance use disorders in Black American adults with SCD compared to those with other chronic conditions or with no chronic conditions.
Design
Data from a population-representative sample of Black Americans with SCD, other chronic conditions, and no chronic conditions were obtained from the National Survey of American Life (NSAL) database. Diagnosis of substance use disorder was determined by structured clinical interview. Hierarchical models controlling for covariates (demographics, socioeconomic status, self-rated health, and mood disorders) compared odds of diagnosis between the three groups.
Participants
The sample included 4238 African-American and Black Caribbean participants from the NSAL study who were 18 years of age or older.
Main Measures
Measures included age, sex, income, education, marital status, employment, possession of health insurance, health conditions, and substance use disorders diagnosed by structured clinical interview.
Key Results
Controlling for age, sex, and socioeconomic status, there were no differences in odds of a drug use disorder when comparing individuals with SCD to Black adults with other chronic conditions (OR = 1.12; p = 0.804) or no chronic condition (OR = 2.09; p = 0.102). SCD was, however, associated with greater odds of alcohol use disorders when compared to the groups with other chronic conditions (OR = 2.15; p = 0.01) and no chronic conditions (OR = 5.11; p < 0.001). This effect was not better accounted for by socioeconomic status, marital status, self-rated physical health, or the presence of a mood disorder.
Conclusions
SCD was not a risk factor for drug use disorders. Further data will be needed to understand the factors contributing to increased risk of alcohol use disorders in SCD and the role uncontrolled pain symptoms may have in driving substance use.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11606-022-07786-w.
KEY WORDS: sickle cell, chronic disease, substance use disorder, drug use disorder, alcohol use disorder
INTRODUCTION
Sickle cell disease (SCD) is a genetic blood disorder that primarily affects people of African descent, with one in every 365 Black Americans in the U.S.A. living with SCD.1 Vaso-occlusive pain episodes are the hallmark of sickle cell disease and the predominant reason for medical visits in this patient population.2 In addition to having these unpredictable, acute pain episodes, adults with SCD are more likely to also experience chronic SCD-related pain, which presents a more complex and long-term challenge for medical management.
Currently, opioids are the recommended pharmacological treatment for both acute and chronic SCD-related pain.3 Individuals with SCD who experience pain throughout their life course have often been exposed to opioids for pain management from early childhood, and in adulthood can present high levels of tolerance and, to achieve adequate analgesia, may require much larger opioid doses compared to opioid-naïve patients.4,5 In the context of chronic pain, patients may require daily use of high-dose opioids to maintain ideal levels of functioning. Concerns about the dangers of frequent, high-dose opioids and the potential for substance dependence create a challenging dilemma for prescribers who are attempting to balance safety with appropriate pain management. This tension is exacerbated by racial bias and other cultural factors that may lead to misinterpretation of behavioral symptoms and poor patient-provider communication.6–8 Medical providers often perceive adults with SCD as being at increased risk of substance abuse and dependence and, thus, are hesitant to prescribe or administer opioids as recommended for pain treatment.6,9–13 As a result, pain management is inconsistent and often inadequate for patients with SCD pain.
Understanding patterns of substance use in SCD and other chronic health conditions may help decrease hesitancy to prescribe opioids (or other potentially addictive pharmacological therapies) when indicated. However, there is a lack of population-based studies on substance use disorders in SCD. The limited data that do exist suggest that patients with SCD are not at an increased risk for substance use disorders or opioid-related deaths compared to their non-SCD counterparts,14 and a majority of perceived drug use disorder cases in SCD can be better explained by pain-related symptoms and coping behaviors that mimic addiction symptoms rather than a true substance use disorder.15 Paradoxically, patients with SCD who do not receive adequate pain treatment may be at the highest risk of self-medicating with illicit drugs or alcohol and are more likely to develop a substance use disorder.16–18 These data, however, are mostly based on convenience samples, retrospective medical record reviews, or case reports. More population-level data are needed to determine whether factors associated with SCD or SCD-related treatment are associated with increased risk for substance use disorders.
The current study addresses differences in substance use disorders, measured by standard clinical interview, in a population-representative sample, among Black SCD patients compared to those with other chronic conditions or no conditions. A secondary goal of this study was to evaluate whether demographics, socioeconomic status (SES), or self-rated physical health, an index of disease severity, better accounted for group differences in substance use disorders.
METHODS
This study examines a national household probability sample of Black adults in the U.S.A., 18 years of age or older, who participated in the National Survey of American Life (NSAL) study, which was undertaken starting in 2004 to examine the racial, ethnic, and cultural influences on mental disorders and mental health.19 The NSAL sample was restricted to individuals who could conduct a structured interview in English. Institutionalized individuals were not enrolled in the study. Individuals in prisons, jails, nursing homes, and long-term medical or dependent care settings were excluded.
The NSAL at the University of Michigan Research Center for Group Dynamics conducted interviews with 6800 adults sampled from households located in the coterminous 48 states. More than 300 trained professionals conducted interviews and utilized a modified version of the Collaborative Psychiatric Epidemiology Surveys.19 The study attempted to match racial backgrounds of interviewers and participants, when possible, to control interview quality by reducing the effects of social and cultural differences.
Out of 5008 African-American and Caribbean Black samples (collectively referred to as Black herein) in the NSAL data, N = 4846 answered questions regarding their health conditions. Only Black individuals with at least one chronic condition or no conditions were included in the analysis (N = 4238).
Measures
NSAL data on age, sex, income, education, marital status, employment, possession of health insurance, substance use, and chronic health disorder were obtained for analysis.
Sociodemographic Factors
The sociodemographic factors measured in the NSAL questionnaire were age, household income, education level, marital status, employment status, number of children, and poverty index. Age was measured in years, as was education level. Household income was measured in U.S. dollars and adjusted for household size by dividing the household income by the square root of the household size.20 Marital status was categorized as married or cohabitating, previously married, or never married. Employment status was categorized as employed, unemployed, or not in the labor force. The poverty index variable (income-to-needs ratio) ranges from 0 to 17. Higher means less poverty. For the current regression analyses, the poverty index was dichotomized such that 0–1 means “in poverty” and > 1 means “not in poverty.”21
Substance Use Disorder
The NSAL diagnostic questionnaire utilized the Composite International Diagnostic Interview (CIDI),22 developed by the World Health Organization World Mental Health Survey (WHO WMH) Initiative using the Diagnostic and Statistical Manual IV-TR (DSM-IV-TR) and the International Classification of Diseases version 10 (ICD-10) diagnosis criteria. The WHO WMH-CIDI is a structured, lay-administered diagnostic interview containing 16 screening items for DSM-IV diagnoses with up to 789 additional questions to solidify diagnoses, including substance abuse and dependence. For this study, we use the DSM5 criteria and have combined the DSM-IV substance abuse and dependence disorder diagnoses into single substance use disorder diagnoses. The current analyses include dependent variables alcohol use disorders, drug use disorders, and a combined substance use disorders. Nicotine dependence was excluded.
Prescription medication misuse was evaluated based on one self-report item, “Have you ever used tranquilizers, stimulants, pain killers, or other prescription drugs either without the recommendation of a health professional, or for any reason other than a health professional said you should use them?”
Chronic Health Conditions
Chronic conditions were determined by self-report. Within the larger NSAL interview, participants were provided a list of 14 medical conditions and asked to identify which, if any, conditions they have been informed of by their doctor. For the current study, only SCD and six categories of non-heritable chronic conditions (i.e., asthma, arthritis, cancer, diabetes, chronic obstructive pulmonary disease (COPD), and cardiovascular disease) were included. Self-rated physical health was assessed by asking participants, “How would you rate your overall physical health at the present time? Would you say it is excellent, very good, good, fair, or poor?” Responses were collected on a scale from 1 for “excellent” to 5 for “poor” and reverse coded for the current analysis (1 for “poor” and 5 for “excellent”).
Analysis
Survey participants were categorized into the following mutually exclusive groups based on health condition: (1) sickle cell disease, (2) all other chronic conditions, and (3) no chronic conditions. Within each group, descriptive statistics, such as sample means and standard deviations, medians and interquartile ranges, or sample proportions, were calculated. Analysis of variance (ANOVA) and chi-square tests were used for between-group comparisons, using non-parametric equivalents when appropriate. The probability of a substance use disorder and prescription drug misuse were modeled using logistic regression as a function of participant group (i.e., participants with sickle cell, any other condition, or no condition) and covariates. Covariates were identified and included in hierarchical models that considered four groups of covariates: age and gender (model 1); model 1 + education, poverty status, employment, insurance type, marital status (model 2); model 2 + physical health rating (model 3); and model 3 + mood disorder diagnosis (model 4). Odds ratios and 95% confidence interval were estimated for the comparison between participants with sickle cell and those with non-heritable chronic conditions, as well participants with no condition.
RESULTS
This study included 4238 participants, 2545 (60.1%) female, with a mean age of 42.9 years (± 16.44 years). They were categorized into one of the following mutually exclusive groups: “SCD” (n = 85), chronic disease other than SCD (“other condition”; n = 2416), or those without a health condition (“no condition”; n = 1737; Table 1).
Table 1.
Demographics and Substance Use Disorder Diagnoses for Adults with Sickle Cell Disease Compared to Adult Black Americans With and Without a Chronic Health Condition
| Measure | SCD (n = 85) | Other condition (not SCD) (n = 2416) | No condition (n = 1737) | Total (n = 4238) | Overall p value |
SCD vs other condition p value |
SCD vs no condition p value |
|---|---|---|---|---|---|---|---|
| Age, mean (SD) | 39.98 ± 13.91 | 49.1 ± 16.49 | 34.47 ± 12.12 | 42.92 ± 16.44 | < 0.001 | < 0.001 | < 0.001 |
| Sex, female | 68 (80%) | 1630 (67.5%) | 847 (48.8%) | 2545 (60.1%) | < 0.001 | 0.015 | < 0.001 |
| Household income, median (IQR) | 17,000 (10,000–37,000) | 23,000 (11,165–42,000) | 30,000 (17,500–48,176) | 26,000 (13,404.8–45,000) | < 0.001 | 0.151 | < 0.001 |
| Education | < 0.001 | 0.977 | 0.017 | ||||
| No high school | 25 (29.4%) | 727 (30.1%) | 301 (17.3%) | 1053 (24.8%) | |||
| High school | 29 (34.1%) | 798 (33%) | 671 (38.6%) | 1498 (35.3%) | |||
| Any post-secondary | 31 (36.5%) | 891 (36.9%) | 765 (44%) | 1687 (39.8%) | |||
| Marital status | < 0.001 | 0.004 | 0.043 | ||||
| Never married | 34 (40%) | 584 (24.2%) | 761 (43.8%) | 1379 (32.5%) | |||
| Divorced/separated/widowed | 25 (29.4%) | 947 (39.2%) | 323 (18.6%) | 1295 (30.6%) | |||
| Married/cohabitating | 26 (30.6%) | 885 (36.6%) | 653 (37.6%) | 1564 (36.9%) | |||
| Work status | < 0.001 | 0.522 | < 0.001 | ||||
| Not in labor force | 24 (28.2%) | 805 (33.3%) | 169 (9.7%) | 998 (23.5%) | |||
| Employed | 51 (60%) | 1388 (57.5%) | 1364 (78.5%) | 2803 (66.1%) | |||
| Unemployed | 10 (11.8%) | 223 (9.2%) | 204 (11.7%) | 437 (10.3%) | |||
| Children in household, yes | 37 (43.5%) | 627 (26%) | 644 (37.1%) | 1308 (30.9%) | < 0.001 | < 0.001 | 0.23 |
| Poverty index, mean (SD) | 2.36 ± 2.8 | 2.52 ± 2.35 | 2.86 ± 2.49 | 2.66 ± 2.42 | < 0.001 | 0.55 | 0.075 |
| Poverty (poverty index ≤ 1) | 44 (51.8%) | 987 (40.9%) | 533 (30.7%) | 1564 (36.9%) | < 0.001 | 0.045 | < 0.001 |
| Health insurance type | < 0.001 | 0.13 | < 0.001 | ||||
| None | 19 (22.4%) | 386 (16%) | 448 (25.8%) | 853 (20.1%) | |||
| Private | 40 (47.1%) | 1386 (57.4%) | 1083 (62.3%) | 2509 (59.2%) | |||
| Public | 26 (30.6%) | 643 (26.6%) | 206 (11.9%) | 875 (20.6%) | |||
| Physical health rating, median (IQR) | 3 (2–4) | 3 (2–4) | 4 (3–5) | 4 (3–4) | < 0.001 | 0.998 | < 0.001 |
| Mood disorder | 15 (17.6%) | 334 (13.8%) | 139 (8%) | 488 (11.5%) | < 0.001 | 0.32 | 0.002 |
| Substance abuse (alcohol or drug) | 15 (17.6%) | 262 (10.8%) | 132 (7.6%) | 409 (9.7%) | < 0.001 | 0.051 | 0.001 |
| Alcohol abuse | 14 (16.5%) | 232 (9.6%) | 104 (6%) | 350 (8.3%) | < 0.001 | 0.038 | < 0.001 |
| Drug abuse | 6 (7.1%) | 133 (5.5%) | 87 (5%) | 226 (5.3%) | 0.617 | 0.543 | 0.409 |
| Prescription abuse | 7 (8.2%) | 144 (6%) | 76 (4.4%) | 227 (5.4%) | 0.042 | 0.392 | 0.099 |
Prevalence of Substance Use Disorders by Group
In pairwise univariate models, individuals living with SCD presented with a marginally higher prevalence of substance use disorders (17.6%; n = 15) compared to the other-condition group (10.8%; p = 0.051) and higher prevalence than the no-condition group (7.6%; p = 0.001). When examining alcohol and drug use disorders separately, the SCD group had a significantly higher prevalence of alcohol use disorders (16.5%; n = 14) compared to both the other-condition (9.6%, p = 0.038) and the no-condition group (6.0%, p < 0.001). However, the prevalence of drug use disorders did not differ between the SCD (7.1%, n = 6), other-condition (5.5%, p = 0.543), and no-condition (5%, p = 0.409) groups. Self-reported prescription drug misuse for the SCD group (8.2%, n = 7) was not significantly higher compared to that of the other-condition group (6%; p = 0.392) and was only marginally higher than that of the no-condition group (4.4%; p = 0.099).
Multivariable Group Comparisons on Alcohol and Drug Use Disorders
Table 2 presents the odds of alcohol use disorder for people with SCD compared to the no-condition and the other-chronic condition group when controlling for covariates. In model 1 (controlling for age and sex), SCD had 5.11 times (95% CI: 2.72, 9.60; p < 0.001) greater odds of alcohol use compared to the no-condition group and 2.15 times (95% CI: 1.17, 3.95; p = 0.014) greater odds compared to the other-condition group. The effect remained when controlling for socioeconomic status variables and marital status (model 2). Although both poorer self-rated health (OR = 0.82, 95% CI: 0.73, 0.93, p = 0.001) and the presence of a mood disorder were associated with increased odds of having an alcohol use disorder (OR = 3.47, 95% CI: 2.62, 4.6, p < 0.001), these potential mediators were unable to fully account for group differences in alcohol use disorders (Table 2, model 4).
Table 2.
Hierarchical Logistic Regression Models Examining Differences in Alcohol Use Disorders Between Black American Adults with Sickle Cell Disease Versus Those with Other Chronic Health Conditions or No Conditions
| Alcohol use | Model 1 | Model 2 | Model 3 | Model 4 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | Level | Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value |
| SCD vs no condition | 5.11 | (2.72, 9.6) | < 0.001 | 4.53 | (2.38, 8.63) | < 0.001 | 3.83 | (2, 7.36) | < 0.001 | 3.66 | (1.87, 7.17) | < 0.001 | |
| SCD vs other condition (not SCD) | 2.15 | (1.17, 3.95) | 0.014 | 2.00 | (1.08, 3.73) | 0.028 | 1.97 | (1.05, 3.68) | 0.034 | 1.99 | (1.05, 3.8) | 0.036 | |
| Age | 0.99 | (0.99, 1) | 0.046 | 0.98 | (0.97, 0.99) | 0.001 | 0.98 | (0.97, 0.99) | < 0.001 | 0.99 | (0.98, 1) | 0.009 | |
| Sex | Male | 3.51 | (2.78, 4.44) | < 0.001 | 4.22 | (3.3, 5.41) | < 0.001 | 4.29 | (3.34, 5.5) | < 0.001 | 4.74 | (3.66, 6.13) | < 0.001 |
| Education | No high school (ref) | 0.001 | 0.001 | 0.001 | |||||||||
| High school | 0.59 | (0.44, 0.78) | < 0.001 | 0.59 | (0.44, 0.79) | < 0.001 | 0.58 | (0.43, 0.78) | < 0.001 | ||||
| Any post-secondary | 0.65 | (0.48, 0.88) | 0.005 | 0.65 | (0.48, 0.89) | 0.006 | 0.64 | (0.47, 0.87) | 0.004 | ||||
| Poverty | Yes (poverty index ≤ 1) | 1.32 | (1, 1.73) | 0.05 | 1.26 | (0.96, 1.67) | 0.096 | 1.29 | (0.97, 1.7) | 0.078 | |||
| Work status | Not in labor force (ref) | 0.775 | 0.593 | 0.679 | |||||||||
| Employed | 1.02 | (0.73, 1.42) | 0.898 | 1.15 | (0.82, 1.62) | 0.406 | 1.16 | (0.82, 1.64) | 0.389 | ||||
| Unemployed | 1.15 | (0.75, 1.77) | 0.509 | 1.23 | (0.8, 1.89) | 0.34 | 1.15 | (0.74, 1.78) | 0.543 | ||||
| Insurance type | None (ref) | < 0.001 | 0.001 | 0.001 | |||||||||
| Private | 0.58 | (0.43, 0.77) | < 0.001 | 0.59 | (0.44, 0.79) | < 0.001 | 0.59 | (0.44, 0.79) | 0.001 | ||||
| Public | 0.99 | (0.7, 1.39) | 0.941 | 0.95 | (0.67, 1.34) | 0.763 | 0.92 | (0.64, 1.31) | 0.626 | ||||
| Marital status | Never married (ref) | 0.013 | 0.019 | 0.045 | |||||||||
| Divorced/separated/widowed | 1.64 | (1.17, 2.31) | 0.004 | 1.6 | (1.14, 2.25) | 0.007 | 1.55 | (1.09, 2.18) | 0.013 | ||||
| Married/cohabitating | 1.17 | (0.86, 1.58) | 0.326 | 1.15 | (0.85, 1.56) | 0.366 | 1.21 | (0.89, 1.66) | 0.221 | ||||
| Physical health rating | 0.78 | (0.69, 0.87) | < 0.001 | 0.82 | (0.73, 0.93) | 0.001 | |||||||
| Mood disorder | Yes | 3.47 | (2.62, 4.6) | < 0.001 | |||||||||
| Model N | 4223 | 4222 | 4221 | 4221 | |||||||||
| R squared | 0.059 | 0.090 | 0.098 | 0.126 | |||||||||
Table 3 presents the same multivariable models for odds of drug use disorder. In model 1 (controlling for age and sex), the SCD group did not have a significantly greater odds of drug use disorder compared to the no-condition group (p = 0.053) or the other-chronic-condition group (p = 0.652). The marginal difference between the SCD group and the no-condition group was accounted for by the addition of sociodemographic covariates in the model.
Table 3.
Hierarchical Logistic Regression Models Examining Differences in Drug Use Disorders Between Black American Adults with Sickle Cell Disease Versus Those with Other Chronic Health Conditions or No Conditions
| Drug use | Model 1 | Model 2 | Model 3 | Model 4 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | Level | Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value |
| SCD vs no condition | 2.37 | (0.99, 5.7) | 0.053 | 2.09 | (0.86, 5.07) | 0.102 | 1.73 | (0.71, 4.23) | 0.229 | 1.57 | (0.63, 3.94) | 0.334 | |
| SCD vs other condition (not SCD) | 1.22 | (0.51, 2.89) | 0.652 | 1.12 | (0.47, 2.67) | 0.804 | 1.09 | (0.46, 2.62) | 0.843 | 1.08 | (0.44, 2.65) | 0.866 | |
| Age | 0.97 | (0.96, 0.98) | < 0.001 | 0.97 | (0.96, 0.98) | < 0.001 | 0.97 | (0.96, 0.98) | < 0.001 | 0.97 | (0.96, 0.99) | < 0.001 | |
| Sex | Male | 3.01 | (2.27, 4) | < 0.001 | 3.52 | (2.61, 4.73) | < 0.001 | 3.59 | (2.66, 4.83) | < 0.001 | 3.99 | (2.93, 5.44) | < 0.001 |
| Education | No high school (ref) | 0.068 | 0.073 | 0.067 | |||||||||
| High school | 0.71 | (0.5, 1) | 0.05 | 0.71 | (0.5, 1) | 0.05 | 0.71 | (0.5, 1.01) | 0.057 | ||||
| Any post-secondary | 0.66 | (0.46, 0.96) | 0.03 | 0.67 | (0.46, 0.97) | 0.034 | 0.65 | (0.44, 0.95) | 0.027 | ||||
| Poverty | Yes (poverty index ≤ 1) | 1.38 | (0.99, 1.92) | 0.054 | 1.32 | (0.95, 1.83) | 0.102 | 1.36 | (0.97, 1.91) | 0.074 | |||
| Work status | Not in labor force (ref) | 0.363 | 0.141 | 0.150 | |||||||||
| Employed | 1.32 | (0.86, 2.02) | 0.209 | 1.52 | (0.98, 2.35) | 0.062 | 1.55 | (0.99, 2.42) | 0.056 | ||||
| Unemployed | 1.41 | (0.84, 2.37) | 0.199 | 1.53 | (0.91, 2.58) | 0.112 | 1.41 | (0.82, 2.41) | 0.214 | ||||
| Insurance type | None (ref) | 0.005 | 0.011 | 0.016 | |||||||||
| Private | 0.58 | (0.41, 0.82) | 0.002 | 0.6 | (0.43, 0.84) | 0.003 | 0.6 | (0.42, 0.85) | 0.004 | ||||
| Public | 0.89 | (0.59, 1.35) | 0.592 | 0.85 | (0.56, 1.3) | 0.461 | 0.82 | (0.53, 1.26) | 0.368 | ||||
| Marital status | Never married (ref) | 0.285 | 0.313 | 0.655 | |||||||||
| Divorced/separated/widowed | 1.3 | (0.86, 1.97) | 0.208 | 1.28 | (0.85, 1.94) | 0.24 | 1.2 | (0.79, 1.82) | 0.403 | ||||
| Married/cohabitating | 0.97 | (0.68, 1.38) | 0.858 | 0.96 | (0.67, 1.37) | 0.818 | 1.02 | (0.71, 1.45) | 0.928 | ||||
| Physical health rating | 0.74 | (0.64, 0.85) | < 0.001 | 0.8 | (0.69, 0.92) | 0.002 | |||||||
| Mood disorder | Yes | 4.07 | (2.95, 5.6) | < 0.001 | |||||||||
| Model N | 4223 | 4222 | 4221 | 4221 | |||||||||
| R squared | 0.05 | 0.073 | 0.083 | 0.12 | |||||||||
Multivariable Group Comparisons on Self-Report Prescription Misuse
For prescription misuse, when controlling for age and sex, the SCD group had a 2.54 (95% CI: 1.12, 5.76; p = 0.025) increased odds of prescription misuse compared to the no-condition group. There were no differences between SCD and other chronic conditions (p = 0.467). Group differences between the SCD and the no-condition groups remained when controlling for socioeconomic status and marital status (model 2). However, after controlling for self-rated physical health, the difference between the SCD and the no-condition groups on prescription misuse was no longer significant (OR = 2.07, 95% CI: 0.9, 4.76, p = 0.086; Table 4, model 3). Better self-rated health was associated with decreased odds of prescription use disorder (OR = 0.76, 95% CI: 0.66, 0.88, p < 0.001; Table 4).
Table 4.
Hierarchical Logistic Regression Models Examining Differences in Self-Report Prescription Medication Misuse Between Black American Adults with Sickle Cell Disease Versus Those with Other Chronic Health Conditions or No Conditions
| Prescription use | Model 1 | Model 2 | Model 3 | Model 4 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | Level | Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value |
| SCD vs no condition | 2.54 | (1.12, 5.76) | 0.025 | 2.58 | (1.14, 5.87) | 0.023 | 2.07 | (0.9, 4.76) | 0.086 | 1.9 | (0.82, 4.43) | 0.137 | |
| SCD vs other condition (not SCD) | 1.34 | (0.6, 2.99) | 0.467 | 1.35 | (0.61, 3.01) | 0.464 | 1.31 | (0.58, 2.93) | 0.516 | 1.29 | (0.57, 2.92) | 0.542 | |
| Age | 0.99 | (0.98, 0.99) | 0.002 | 0.99 | (0.98, 1) | 0.056 | 0.99 | (0.97, 1) | 0.027 | 0.99 | (0.98, 1) | 0.124 | |
| Sex | Male | 1.77 | (1.35, 2.33) | < 0.001 | 1.83 | (1.38, 2.43) | < 0.001 | 1.87 | (1.41, 2.48) | < 0.001 | 1.96 | (1.47, 2.62) | < 0.001 |
| Education | No high school (ref) | 0.251 | 0.209 | 0.266 | |||||||||
| High school | 0.9 | (0.62, 1.31) | 0.58 | 0.91 | (0.62, 1.33) | 0.625 | 0.92 | (0.63, 1.35) | 0.675 | ||||
| Any post-secondary | 1.18 | (0.81, 1.72) | 0.392 | 1.21 | (0.83, 1.78) | 0.32 | 1.2 | (0.82, 1.77) | 0.347 | ||||
| Poverty | Yes (poverty index ≤ 1) | 1.14 | (0.82, 1.6) | 0.433 | 1.09 | (0.78, 1.53) | 0.618 | 1.11 | (0.79, 1.56) | 0.547 | |||
| Work status | Not in labor force (ref) | 0.042 | 0.009 | 0.012 | |||||||||
| Employed | 1.62 | (1.04, 2.5) | 0.031 | 1.87 | (1.2, 2.91) | 0.006 | 1.88 | (1.2, 2.95) | 0.006 | ||||
| Unemployed | 1.88 | (1.09, 3.24) | 0.023 | 2.06 | (1.19, 3.55) | 0.01 | 1.94 | (1.12, 3.38) | 0.018 | ||||
| Insurance type | None (ref) | 0.581 | 0.755 | 0.812 | |||||||||
| Private | 0.85 | (0.59, 1.21) | 0.361 | 0.87 | (0.61, 1.25) | 0.463 | 0.89 | (0.62, 1.27) | 0.517 | ||||
| Public | 1 | (0.64, 1.56) | 1 | 0.95 | (0.61, 1.49) | 0.831 | 0.93 | (0.59, 1.46) | 0.748 | ||||
| Marital status | Never married (ref) | 0.697 | 0.737 | 0.666 | |||||||||
| Divorced/separated/widowed | 1.17 | (0.78, 1.77) | 0.441 | 1.15 | (0.77, 1.74) | 0.495 | 1.11 | (0.73, 1.67) | 0.633 | ||||
| Married/cohabitating | 1.14 | (0.8, 1.62) | 0.465 | 1.13 | (0.8, 1.61) | 0.487 | 1.18 | (0.83, 1.67) | 0.368 | ||||
| Physical health rating | 0.72 | (0.63, 0.83) | < 0.001 | 0.76 | (0.66, 0.88) | < 0.001 | |||||||
| Mood disorder | Yes | 2.85 | (2.06, 3.94) | < 0.001 | |||||||||
| Model N | 4220 | 4219 | 4218 | 4218 | |||||||||
| R squared | 0.018 | 0.024 | 0.036 | 0.056 | |||||||||
DISCUSSION
Sickle cell is a heritable disease that presents in infancy and is associated with severe pain episodes throughout the life course. Opioid prescription is extremely common for pain management in this population.5,23,24 Due to the early onset of pain episodes, compared to other chronic conditions, SCD patients typically have greater long-term exposure to opioids and other pain-relieving substances.14,24 Long-term prescription drug use increases the likelihood of a substance use disorder.25 Thus, it would be expected that adults with SCD would have a higher risk of drug dependence and opioid use disorders compared to other Black adults with a chronic condition, particularly when those chronic conditions are not associated with pain and long-term opioid treatment.
In the current large population-based sample, adults with SCD were no more likely to have a drug use disorder diagnosis than Black Americans with other chronic conditions or no conditions. Thus, despite evidence suggesting that SCD is associated with a greater likelihood of having an opioid prescription and long-term opioid use,14,26 current data showed that Black adults with SCD were not at greater risk for developing a drug use disorder compared to other Black Americans. These data should address some of the misconceptions and data surrounding opioid prescribing for patients with SCD.
Increased Prevalence of Alcohol Use Disorders in SCD
Although drug use disorders did not differ between groups, there was an almost two times greater odds of alcohol-related disorders among individuals living with SCD compared to Black adults with other chronic conditions, and almost four times greater odds compared to those with no health conditions. To our knowledge, this is the first study to report on alcohol use disorders in a population-based sample of adults with SCD; however, these findings are consistent with data suggesting that in SCD, alcohol may be used as an alternative to seeking medical care for pain.27,28 The use of alcohol as a coping mechanism is common among patients with chronic painful medical disorders due to its pain-reducing qualities28 and anxiolytic effects.29 Further, for some patients, the greater access and availability of alcohol compared to opioids, and its relatively low cost, may make alcohol a more desirable alternative to visiting the emergency room for treatment. Indeed, alcohol-abusing SCD patients have fewer emergency room visits, unscheduled clinic visits, and hospital days, and lower overall health care use.30 Thus, alcohol may be an alternative pain relief method that adults with SCD use, and sometimes abuse, to cope with pain and avoid medical visits.
Role of Socioeconomic Factors
The secondary goal of this study was to determine whether risk factors such as socioeconomic status (SES) affect the rate of substance use in individuals with SCD. Household income was only marginally lower for the SCD participants compared to that of other Black Americans. Variation in household income could be explained by the tendency for those with SCD to be single, with or without children,31,32 resulting in fewer working members within the household. Increased exposure to financial and relationship-related stress may increase the likelihood of adults with SCD turning to maladaptive coping mechanisms, such as alcohol use.33
Many studies have pointed to sociodemographic characteristics as a primary predictor of substance use.34,35 Our results also show an association between SES and substance use; however, SES alone was not a primary predictor of substance use in SCD, and only the poverty index and education level had any significant impact on an individual’s odds for reporting substance use. Regardless of disease condition, individuals in poverty had a 1.3 times greater odds of reporting an alcohol use disorder. However, when controlling for the poverty index, differences in odds of alcohol use disorders between SCD and the other groups were not significantly reduced, which suggests that poverty is not accounting for these differences in alcohol use disorders.
Link Between Chronic Disease and Substance Use
Consistent with prior studies, all disease conditions, not SCD alone, were associated with greater odds of substance use disorders.36–38 There are reported disparities in SES, pain, and mental health that are likely to contribute to substance use.37,39,40 However, the link between living with a chronic condition in adulthood and substance use behaviors is likely very complex. Unlike other non-heritable conditions, those living with SCD often have symptoms early in childhood and are burdened with managing a chronic disease throughout their life course,41 yet even chronic conditions that may be present later in adulthood were associated with increased substance use disorders. Regardless, those suffering from chronic conditions likely share common characteristics and stressors that differ from those of their healthy counterparts. These factors presumably contribute to an increase in substance use, particularly the use of alcohol. Factors such as disease onset, disease severity, quality of treatment, and prognosis also undoubtedly contribute to patterns of substance use. Patients with substance use disorders are more likely to have a chronic disease and chronic pain, and have higher disease burden42,43 and symptom severity44; substance use disorders are associated with the development and exacerbation of many chronic conditions, and likewise, high symptom burden associated with chronic conditions may lead to increased substance use.
The Mediating Role of Psychological Distress and Mood Disorders
Chronic disease and mental health disorders are often co-morbid43,45; underidentification and undertreatment of mental illness among those living with SCD or other chronic illnesses may lead these patients to cope using substances as an alternative to mental health treatments.46 Several studies have shown that psychological distress associated with a chronic disease can increase risk of substance use disorders.47–49 In the current data, presence of a mood disorder was associated with almost four times greater odds of having a substance use disorder. Despite the link between psychological distress and substance use, accounting for mood disorders did not explain the association between SCD and alcohol use. This may have been a limitation of the available data (e.g., no measures of pain and opioid treatment) and sample size. More data understanding characteristics of psychosocial distress and mental health among Black Americans living with chronic conditions will help inform substance use intervention development and implementation.
Study Strengths and Limitations
Our results are strengthened by a structured clinical interview and a population-representative sample of Black Americans. Although this study has several strengths, some limitations should be noted. First, despite the large sample size, there was a limited number of adults with SCD available (85 out of 4846) and some comparisons are based on fewer than 10 participants. However, this is potentially the largest sample of SCD adults with structured interview data, and the lack of oversampling and targeted recruitment of adults with SCD decreased risk of selection bias inherent in most disease-specific studies.
The second notable limitation is the diagnosis of medical conditions based on self-report. It is possible that some participants who reported having SCD disease may only carry sickle cell trait or have no hemoglobin abnormality at all.50 It is estimated that approximately 1 out of every 365 Black or African-American births is diagnosed with SCD51 while 1 out of 57 presented with SCD in the current sample and we do not have data on the validity of the self-report SCD diagnosis. It should be noted, however, that if the SCD group was composed of mostly asymptomatic sickle cell trait carriers, we would expect their substance use patterns to be more similar to the no-condition control group. Further, our analyses were also able to include a measure of perceived physical health, which was strongly correlated with substance use disorders across all conditions. Thus, even if there are no true differences between SCD and other chronic conditions, it is clear from these data that among Black Americans, living with any chronic condition is associated with an increased risk of developing a substance use disorder.
Finally, there was no measure of pain or pain history, and the prescription misuse measure does not specify opioids. Thus, these data are not able to determine whether differences observed are associated with pain or pain treatment.
CONCLUSIONS
Personal biases, preconceptions of hospital staff, and racial discrimination often lead to the improper labeling of SCD patients as “drug-seekers.” These misconceptions result in improper pain treatment and create a strained relationship between patients and staff.52 Poor treatment and symptom management at the hospital likely discourage patients from returning for treatment when future pain crises arise. Instead, patients may attempt to manage their own pain with alcohol or other substances.53 Future studies should aim to replicate the current findings and understand the factors that increase alcohol use disorders in chronic illness. Ultimately, we may find that improving medical care and adequately addressing pain and other symptoms among minority patients will lead to decreased substance use disorders among Black Americans with chronic conditions.
Supplementary Information
(DOCX 29 kb)
Declarations
Conflict of Interest
The authors have no conflicts of interest to report.
Footnotes
Publisher’s Note
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