Opioids and Disability Among Women of Reproductive Age

Abstract

Background:

Despite nearly one in five U.S. women of reproductive age reporting a disability, limited research exists on opioid behaviors in this vulnerable population. This study examined associations between disability and past-year prescription opioid use and misuse, and described types of opioids, sources, and motives for opioid misuse among nonpregnant women of reproductive age. In addition, the effects of social, medical, and behavioral determinants of health on opioid use and misuse were assessed in this population of women with disabilities.

Materials and Methods:

Data were used from the 2015–2019 National Survey on Drug Use and Health (n = 93,679). Descriptive statistics and logistic regression models were used in the analyses.

Results:

Overall, 48.0% of women with a disability reported past-year prescription of any opioid use compared to 32.3% of women without disabilities, and 10.4% of women with disabilities reported opioid misuse relative to 4.2% of women without disabilities. Hydrocodone was the most used (29.3%) and misused (5.87%) opioid. Women with disabilities had higher adjusted odds of opioid use (adjusted odds ratio [AOR] 1.59; 95% confidence interval [CI], 1.50–1.67) and misuse (AOR 2.01; 95% CI, 1.82–2.21) than those without disabilities. Tobacco, alcohol use, and poor to fair health were all associated with higher odds of opioid misuse. For their last opioid misuse, 5.2% attained the opioids from a dealer or stranger, and 22.1% used opioids to get high.

Conclusion:

Women with disabilities are at an amplified risk for prescription opioid use and misuse. Improved medical provider education, training and capacity, and reinforcing related community-based support programs for this population are imperative.

Introduction

Increasing morbidity and mortality associated with opioid misuse and opioid dependence among the U.S. population led the U.S. Department of Health and Human Services (HHS) to declare opioid misuse a national public health emergency.¹ The HHS designated women a priority population of interest regarding substance use,² as prescription opioid-related deaths have increased by 415% since 1999 for U.S. women.³ Overall, U.S. women are more likely to be dispensed an opioid than men, and those aged 25–34 years have twice the odds of men to fill an opioid prescription.⁴

Adverse effects of opioid misuse have been well documented in the general population of women of reproductive age.³ However, certain populations of women of reproductive age, such as those with disabilities, are particularly susceptible to opioid misuse.⁵ For example, some women with a disability have significantly greater odds of opioid abuse/dependency than comparable women without a disability.⁶ Furthermore, individuals with disabilities are distinctly experiencing increases in opioid-related deaths.⁷ For instance, evidence indicates that those with disabilities can account for up to 80% of prescription opioid overdose fatalities within a population.⁸ In addition, evidence indicates that while only representing 3% of the U.S. population, a subset of individuals with disabilities accounted for 25% of U.S. prescription opioid overdose deaths.⁷

Sociodemographic characteristics can influence prescription opioid and usage in individuals with disabilities; therefore, examination of opioid behaviors and associated characteristics (e.g., social determinants of health) is warranted.¹ Social determinants of health (SDOH) are associated with opioid misuse, directly affecting risk behavior and exacerbating the medical conditions of opioid users and those with disabilities.^9,10 SDOH that have been associated with higher odds of opioid utilization and opioid-related deaths include age, race/ethnicity, residential area, and socioeconomic factors.^9,11,12 For example, 2017 opioid overdose death rates in the United States were higher for non-Hispanic whites (19.4/100,000), compared to rates of 12.9 for non-Hispanic blacks and 6.8 for Hispanics.¹² As for disability, prevalence among the young and middle-aged U.S. population was highest in American Indians/Alaska Natives and lowest among Asians.¹³

In addition, medical and behavioral determinants of health, including tobacco and alcohol use, are associated with opioid habits and disability among the U.S. population.^8,14 Reproductive-aged women experiencing certain health challenges have a higher risk of misusing prescription opioids and having an opioid use disorder than similar women without such health-related conditions.⁵ Individuals with disabilities facing diminished health also experience an increase in opioid overdose deaths.⁸ Opioids, tobacco, and alcohol use are associated with disability as well, as use of these substances increase with severity of disability.⁶

Higher rates of opioid use and misuse among U.S. adults with disabilities may be partly due to concurrent pain syndromes,⁷ but this diverse population's multifaceted health needs prevent ascribing a single overarching cause for these higher rates.¹ Despite ∼18% of U.S. women of reproductive age having some type of disability,¹³ limited research exists related to opioids in this vulnerable population of women.^1,6 More specifically, comprehensive examination of prescription opioid practices that include social, medical, and behavioral determinants of health among nonpregnant women of reproductive age with disabilities has been lacking.^1,15 In addition, an increasing number of women with a disability are seeking to become pregnant^16

–19 and both opioid use and misuse have multiple adverse effects for both mother and infant, such as comorbid psychiatric conditions²⁰ and shorter breastfeeding duration.¹⁹

Nearly half of pregnancies in the United States are unintended²¹ and the adverse consequences of perinatal opioid use for mothers, infants, and communities further contribute to identifying opioid use in this subpopulation as a notable public health matter.⁶

Research is needed to better understand opioid behaviors and the associated effects of social, medical, and behavioral determinants of health among nonpregnant women of reproductive age with disabilities. This vulnerable population can benefit from focused interventions based on relevant research evidence to reduce misuse of prescription opioids and associated adverse health outcomes. Therefore, the purpose of this study was to examine associations between disability and past-year prescription opioid use and misuse, as well as describe opioid types used and misused, and sources and motives for past-year opioid misuse among nonpregnant women of reproductive age. In addition, the effects of social, medical, and behavioral determinants of health on opioid use and misuse were assessed in this vulnerable population of women with disabilities.

Material and Methods

Data description

Data from the National Survey on Drug Use and Health (NSDUH) for the years 2015–2019 were used in this study. The NSDUH collects highly private and confidential information, such as use of illicit drugs, among the noninstitutional U.S. population aged 12 years or older using audio computer-assisted self-interviewing (ACASI). The ACASI is designed to reduce bias by increasing honest responses related to sensitive behaviors such as reporting substance abuse or misuse.²² Self-reported substance use and mental health measures are collected in the survey, which also assesses mental and/or substance use disorders and treatment.²² The NSDUH data for this study included 93,679 nonpregnant women aged 15–44 years.²³ NSDUH assessed pregnancy status based on responses to the question, “Are you currently pregnant?”²⁴

Measures

Past-year prescription opioid use

Any prescription opioid use, in any form, within the past year was assessed based on NSDUH questions that identify past-year use of opioids. Respondents who provided a positive response to opioid use within “the past 12 months,” were asked further questions to determine classification of use and misuse.²⁴

Past-year prescription opioid prescribed use

Defined as past-year use of prescription opioids as prescribed or directed by a medical prescriber without misuse or opioid use disorder.

Past-year prescription opioid misuse

Prescription opioid misuse was defined as “use in any way that a doctor did not direct you to use them within the past year.”²⁴ Prescription opioid use disorder, defined as “dependence or abuse within the past year,” was included in prescription opioid misuse data for this study.²⁴

Disability

The Centers for Disease Control and Prevention (CDC) characterizes disability as any physical or mental condition hindering a person's ability to perform routine activities and customarily participate within the provincial environment.²⁵ NSDUH standardized disability questions, recommended by the HHS’ Substance Abuse and Mental Health Services Administration,²⁶ were used in this study. Three main disability measures were created; specifically, sensory, cognitive, and disabilities related to activities of daily living.^26,27 A dichotomous disability status variable was also created to identify those women with self-reported disabilities as indicated by limitations related to sensory, cognitive, and activities of daily living functioning and those without disabilities.

Respondents were classified as having a sensory disability based on positive responses to the following: (1) “Are you deaf or do you have serious difficulty hearing?” (2) “Are you blind or do you have serious difficulty seeing, even when wearing glasses?”²⁴ Respondents were classified as having a cognitive disability based on positive responses to, “Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?”²⁴ Respondents were classified as having an activities of daily living disability based on a positive response to one of the following questions: (1) “Do you have serious difficulty walking or climbing stairs?” (2) “Do you have difficulty dressing or bathing?” (3) “Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone such as visiting a doctors' office, or shopping?”²⁴

Types of past-year prescription opioid use and last misuse

Respondents were asked to identify the types of prescription opioids used and misused within the past year. The types of opioids include the following: hydrocodone, oxycodone, tramadol, morphine, fentanyl, buprenorphine, oxymorphone, demerol, hydromorphone, methadone, and other pain relievers.

Sources of past-year prescription opioid for last misuse

Respondents were asked to select as many responses as possible from the following sources of prescription opioids for last misuse: (1) one or more than one doctor; (2) stole from doctor's office, clinic, hospital, or pharmacy; (3) got for free, bought, or took without asking from friend or relative; (4) drug dealer or other stranger; (5) and other source.²⁴ Based on prior research,²⁸ the sources were combined into four groups: (1) obtained from a medical system source (combined groups 1, 2 from above), (2) obtained from friends or relatives (group 3 above), (3) obtained from a dealer or stranger (group 4 above), and (4) obtained from other sources (group 5 from above).

Motives for last past-year prescription opioid misuse

Respondents were asked to identify the main reason they last misused a pain reliever (opioid). The reasons for misuse included “unknown, relieving physical pain, relaxing or relieving tension, experimenting to see what it's like, feeling good or getting high, helping with sleep, helping with feelings or emotions, increasing or decreasing effects of other drugs, hooked or having to have drug, and some other reason.”²⁴ These motives were also categorized into two groups, pain and nonpain relief motives, based on prior research studies.^29,30

Social determinants of health

Available measures were separated into two groups consistent with recognized categories of social determinants of health^31,32: (1) Economic Stability included family income (<$50000, ≥$50000) and working cell phone (yes/no); and (2) Social and Community Context included age (15–17, 18–25, and 26–44 years), race/ethnicity (Hispanic, black non-Hispanic, white non-Hispanic, or other non-Hispanic), household size (1–2, 3–4, ≥5), and county size—metropolitan (yes/no).

Medical determinants of health

Past-year perceived overall health status (excellent, very good, good, fair, poor) was included.

Behavioral determinants of health

Tobacco, alcohol, and illicit drug use in the past year (yes/no) were assessed.

Statistical analysis

Descriptive statistics and bivariate analyses using the chi-squared test examined disability in nonpregnant women of reproductive age and past-year opioid use and misuse. In addition, associations between sample characteristics and past-year opioid use and misuse among nonpregnant women of reproductive age with and without disabilities were examined. Finally, bivariate analyses examined the type of opioid use and misuse, and the motives and source for last opioid misuse among nonpregnant women of reproductive age by disability status.

Unadjusted logistic regression models estimated the direction and size of the association between disability (overall disability status and by disability type) and any opioid use, disability and opioid prescribed use, and disability and opioid misuse (including opioid use disorder) among nonpregnant women of reproductive age. These models were then adjusted for the measures noted above to estimate adjusted odds ratios (AOR) with corresponding 95% confidence intervals (CI).^6,7,12,33 In addition, binary logistic regression was used to assess associations between social, medical, and behavioral determinants of health and any opioid use (vs. no opioid use), opioid prescribed use (vs. no opioid prescribed use), and opioid misuse (vs. no opioid misuse) among nonpregnant women of reproductive age with disabilities. The data were weighted for all analyses to account for the complex NSDUH study design, and analyzed using Stata/MP 16.1 (StataCorp, College Station, TX, USA).

Results

Past-year opioid use and misuse by disability status

In this sample, 16.5% of nonpregnant women of reproductive age reported a disability; specifically, 10.4% reported a cognitive disability, 6.8% reported a disability related to activities of daily living, and 4.9% reported a sensory disability (Table 1). Proportions of past-year prescription opioid use and misuse among women with a disability were significantly (p ≤ 0.05) higher than those among women without a disability. Overall, 48.0% of women with a disability reported any form of past-year prescription opioid use compared to 32.3% of women without a disability, and 10.4% of women with a disability reported past-year prescription opioid misuse compared to 4.2% of women without a disability.

Table 1.

Disability and Past-Year Prescription Opioid Use and Misuse Among Nonpregnant Women of Reproductive Age (n = 93,679)

	Entire sample^a	No opioid use	Any opioid use	Opioid prescribed use^b	Opioid misuse^c
	Weighted % (95% CI)^d	Weighted % (95% CI)^d	Weighted % (95% CI)^d	Weighted % (95% CI)^d	Weighted % (95% CI)^d
Disability
No	83.5 (83.1–83.9)	67.7 (67.3–68.2)	32.3 (31.8–32.7)	28.1 (27.6–28.5)	4.19 (4.00–4.39)
Yes	16.5 (16.1–16.9)	52.0 (51.0–53.0)	48.0 (47.0–49.0)	37.6 (36.6–38.6)	10.4 (9.71–11.1)
Sensory disability
No	95.1 (94.9–95.3)	65.6 (65.2–66.0)	34.4 (34.0–34.8)	29.3 (28.9–29.7)	5.03 (4.84–5.23)
Yes	4.88 (4.69–5.07)	55.0 (52.9–57.1)	45.0 (42.9–47.1)	36.3 (34.4–38.2)	8.70 (7.64–9.89)
Difficulty hearing
No	98.5 (98.4–98.6)	65.3 (64.9–65.7)	34.7 (34.3–35.1)	29.5 (29.1–30.0)	5.17 (4.96–5.37)
Yes	1.53 (1.43–1.63)	54.8 (51.1–58.4)	45.2 (41.6–48.9)	36.8 (33.7–40.1)	8.38 (6.60–10.6)
Difficulty seeing
No	96.3 (96.1–96.4)	65.5 (65.1–65.9)	34.5 (34.1–34.9)	29.4 (29.0–29.8)	5.08 (4.88–5.29)
Yes	3.73 (3.56–3.89)	54.7 (52.5–56.7)	45.3 (43.3–47.5)	36.7 (34.6–38.8)	8.69 (7.52–10.0)
Cognitive disability (difficulty thinking)
No	89.6 (89.3–89.8)	66.6 (66.2–67.1)	33.4 (32.9–33.8)	28.9 (28.5–29.4)	4.43 (4.24–4.64)
Yes	10.4 (10.2–10.7)	51.9 (50.7–53.0)	48.1 (47.0–49.3)	36.1 (34.9–37.4)	12.0 (11.1–13.0)
Daily living activities disability
No	93.2 (92.9–93.4)	66.6 (66.2–67.0)	33.4 (33.0–33.8)	28.6 (28.2–29.1)	4.75 (4.55–4.96)
Yes	6.84 (6.60–7.08)	44.8 (43.6–46.1)	55.2 (53.9–56.4)	43.6 (42.2–45.1)	11.5 (10.4–12.8)
Difficulty dressing
No	99.0 (98.9–99.1)	65.5 (65.1–65.9)	34.5 (34.1–34.9)	29.4 (29.0–29.8)	5.12 (4.91–5.32)
Yes	1.03 (0.94–1.14)	31.6 (28.4–35.0)	68.4 (65.0–71.6)	54.0 (49.6–58.3)	14.4 (11.8–17.4)
Difficulty with errands
No	94.8 (94.6–95.0)	66.1 (65.6–66.5)	33.9 (33.5–34.4)	29.1 (28.6–29.5)	4.85 (4.65–5.05)
Yes	5.18 (4.98–5.39)	47.2 (45.7–48.6)	52.8 (51.4–54.3)	40.8 (39.3–42.3)	12.0 (10.8–13.4)
Difficulty walking
No	97.2 (97.1–97.4)	66.1 (65.6–66.5)	33.9 (33.5–34.4)	28.9 (28.5–29.3)	5.06 (4.86–5.26)
Yes	2.77 (2.63–2.92)	32.6 (30.3–35.0)	67.4 (65.0–69.7)	56.7 (53.9–59.4)	10.7 (8.93–12.9)

All entries, except Difficulty with errands (No/Yes)—Entire Sample, are significant at a p ≤ 0.05.

Chi-squared test assessed associations between disability and the study sample, opioid use (No vs. Any), opioid prescribed use, and opioid misuse.

Entire Sample results presented in column format—remaining results presented in row format.

Opioid prescribed use (no misuse or use disorder); dissection of any opioid use results.

Opioid misuse includes use disorder; dissection of any opioid use results.

Values are weighted percentages with 95% confidence intervals (CI).

Women with daily living-related disabilities had a significantly higher proportion (55.2%) of any past-year prescription opioid use compared to those women with no such disability (33.4%), whereas women with cognitive disabilities, or difficulty thinking, conveyed an elevated proportion (12.0%) of past-year prescription opioid misuse compared to those women with no such disability (4.4%). Among women with an activities of daily living disability, 56.7% of those with difficulty walking reported past-year opioid prescribed use; whereas, 14.4% of those with difficulty dressing reported past-year prescription opioid misuse. Of women with a sensory disability, 36.3% reported past-year opioid prescribed use compared to 29.3% of women with no such disability, and 8.7% reported past-year prescription opioid misuse compared to 5.0% of women with no such disability.

Past-year opioid prescribed use and opioid misuse were similar among women reporting difficulty hearing (use = 36.8%, misuse = 8.4%) and seeing (use = 36.7%, misuse = 8.7%) in comparison to women with no such difficulties (hearing: use = 29.5%, misuse = 5.2%; seeing: use = 29.4%, misuse = 5.1%).

White non-Hispanic race/ethnicity, older age (26–44), family income <$50,000, metropolitan dwelling, good health, and alcohol use within the past year were significantly associated (p ≤ 0.05) with a higher prevalence of any prescription opioid use among nonpregnant women of reproductive age with a disability (Table 2). These same factors, in addition to household size of three to four and tobacco use, were also significantly associated with a higher prevalence of prescription opioid misuse among women with disabilities.

Table 2.

Sample Characteristics by Past-Year Prescription Opioid Use and Misuse Among Nonpregnant Women of Reproductive Age with Disabilities (n = 17,551)

	No opioid use	Any opioid use	Opioid prescribed use^a	Opioid misuse^b
	Weighted % (95% CI)^c	Weighted % (95% CI)^c	Weighted % (95% CI)^c	Weighted % (95% CI)^c
Social determinants of health
Economic
Income—Family
<$50 K	58.1 (56.6–59.6)	63.6 (62.0–65.2)	63.4 (61.8–65.0)	64.3 (60.6–67.8)
≥$50 K	41.9 (40.4–43.4)	36.4 (34.8–38.0)	36.6 (35.0–38.2)	35.7 (32.2–39.4)
Cell phone works
No	1.24 (0.97–1.58)	2.10 (1.63–2.69)	1.77 (1.33–2.37)	3.26 (1.87–5.61)
Yes	98.8 (98.4–99.0)	97.9 (97.3–98.4)	98.2 (97.6–98.7)	96.7 (94.4–98.1)
Social and community context
Age, years
15–17	21.3 (20.3–22.3)	10.2 (9.49–11.0)	9.61 (8.86–10.4)	12.4 (10.8–14.2)
18–25	35.5 (34.3–36.7)	27.3 (26.1–28.5)	25.8 (24.6–27.0)	32.6 (30.0–35.3)
26–44	43.3 (41.7–44.8)	62.5 (61.0–64.0)	64.6 (63.1–66.1)	55.0 (51.6–58.3)
Race/ethnicity
White, non-Hispanic	56.1 (54.7–57.5)	62.2 (60.6–63.8)	61.3 (59.5–63.0)	65.7 (62.7–68.5)
Black, non-Hispanic	13.0 (12.1–14.1)	12.7 (11.7–13.7)	14.0 (12.8–15.2)	8.14 (6.57–10.1)
Other, non-Hispanic	8.97 (8.23–9.78)	6.93 (6.28–7.65)	6.63 (5.85–7.50)	8.04 (6.24–10.3)
Hispanic	21.9 (20.9–23.0)	18.1 (16.9–19.5)	18.1 (16.8–19.6)	18.1 (15.5–21.1)
Household size
1–2	23.1 (21.6–24.6)	24.5 (23.3–25.8)	23.6 (22.3–24.9)	27.8 (25.4–30.5)
3–4	47.8 (46.3–49.2)	47.6 (46.2–48.9)	47.7 (46.2–49.3)	47.0 (43.5–50.5)
≥5	29.2 (27.8–30.6)	27.9 (26.6–29.2)	28.7 (27.2–30.2)	25.2 (22.4–28.2)
Metropolitan
No	13.8 (12.8–14.7)	16.6 (15.5–17.6)	17.1 (15.9–18.3)	14.7 (12.9–16.7)
Yes	86.2 (85.3–87.2)	83.4 (82.4–84.5)	82.9 (81.7–84.1)	85.3 (83.3–87.1)
Medical determinants of health
Health status
Fair/poor	15.5 (14.7–16.3)	31.3 (29.8–32.7)	31.4 (30.0–32.8)	30.8 (28.0–33.8)
Good	31.9 (30.7–33.2)	34.9 (33.5–36.3)	34.7 (33.3–36.1)	35.7 (32.9–38.6)
>Good/excellent	52.6 (51.3–53.8)	33.9 (32.6–35.2)	33.9 (32.6–35.3)	33.5 (30.3–36.9)
Behavioral determinants of health
Tobacco use
No	70.6 (69.3–71.8)	52.8 (51.2–54.3)	56.7 (54.9–58.5)	38.5 (35.7–41.4)
Yes	29.4 (28.2–30.7)	47.2 (45.7–48.8)	43.3 (41.5–45.1)	61.5 (58.6–64.3)
Alcohol use
No	37.3 (35.8–38.7)	26.3 (24.8–27.9)	29.1 (27.5–30.8)	16.3 (13.9–18.9)
Yes	62.7 (61.3–64.2)	73.7 (72.1–75.2)	70.9 (69.2–72.5)	83.7 (81.1–86.1)
Illicit drug use
No	70.1 (68.8–71.4)	53.2 (51.8–54.6)	67.9 (66.2–69.7)	0.00 (0.00–0.00)
Yes	29.9 (28.6–31.2)	46.8 (45.4–48.2)	32.1 (30.3–33.8)	100.0 (100–100)

All entries, except Household size—Any opioid use, are significant at a p ≤ 0.05.

Chi-squared test assessed associations between study sample with disabilities and opioid use (No vs. Any), opioid prescribed use, and opioid misuse.

Opioid prescribed use = No misuse or use disorder; dissection of Any opioid use results.

Opioid misuse includes use disorder; dissection of Any opioid use results.

Values are weighted percentages with 95% CI.

Nonpregnant women of reproductive age with a disability had greater adjusted odds of any past-year prescription opioid use (AOR 1.59; 95% CI, 1.50–1.67) and misuse (AOR 2.01; 95% CI, 1.82–2.21) than similar women without a disability (Table 3). Women reporting activities of daily living disability had a higher likelihood (AOR 1.83; 95% CI, 1.73–1.94) of any past-year prescription opioid use than those women without such a disability. In addition, women with a cognitive disability had a higher likelihood (AOR 2.13; 95% CI, 1.92–2.37) of past-year prescription opioid misuse than those women without a cognitive disability.

Table 3.

Odds Ratios (95% Confidence Intervals) for Past-Year Prescription Opioid Use and Misuse based on Disability Among Nonpregnant Women of Reproductive Age (n = 93,679)

Nonpregnant women of reproductive age
	Any opioid use		Opioid prescribed use^a		Opioid misuse^b
	Unadjusted odds ratios (95% CI)^c	Adjusted ^c odds ratios (95% CI)^c	Unadjusted odds ratios (95% CI)^c	Adjusted ^c odds ratios (95% CI)^c	Unadjusted odds ratios (95% CI)^c	Adjusted ^c odds ratios (95% CI)^c
Disability
Yes	1.94 (1.85–2.03)	1.59 (1.50–1.67)	1.74 (1.66–1.83)	1.54 (1.46–1.63)	2.65 (2.44–2.89)	2.01 (1.82–2.21)
No	Reference	Reference	Reference	Reference	Reference	Reference
Sensory disabilities
Yes	1.56 (1.44–1.70)	1.34 (1.23–1.47)	1.48 (1.36–1.61)	1.32 (1.21–1.44)	1.80 (1.57–2.06)	1.43 (1.23–1.66)
No	Reference	Reference	Reference	Reference	Reference	Reference
Cognitive disabilities
Yes	1.85 (1.76–1.95)	1.48 (1.39–1.56)	1.60 (1.51–1.70)	1.41 (1.33–1.50)	2.94 (2.66–3.24)	2.13 (1.92–2.37)
No	Reference	Reference	Reference	Reference	Reference	Reference
Daily living activities disabilities
Yes	2.45 (2.33–2.59)	1.83 (1.73–1.94)	2.26 (2.14–2.39)	1.81 (1.71–1.93)	2.61 (2.32–2.95)	1.80 (1.59–2.03)
No	Reference	Reference	Reference	Reference	Reference	Reference

All entries are significant at a p ≤ 0.05.

Opioid prescribed use = No misuse or use disorder; dissection of Any opioid use results.

Opioid misuse includes use disorder; dissection of Any opioid use results.

Logistic regression models (odds ratios with 95% CI for each measure: Any opioid use, Opioid prescribed use, Opioid misuse) adjusted for family income, functioning cell phone, age, race/ethnicity, household size, metropolitan dwelling, health status, tobacco use, alcohol use, illicit drug use (removed for misuse).

Differences in determinants of health associated with past-year prescription opioid use and misuse among nonpregnant women of reproductive age with a disability are illustrated in Table 4. Overall, women of younger age that included 15–17 years (OR 0.43; 95% CI, 0.39–0.49) and 18–25 years (OR 0.54; 95% CI, 0.49–0.60), other non-Hispanic race/ethnicity (OR 0.76; 95% CI, 0.66–0.89), Hispanic (OR 0.82; 95% CI, 0.71–0.95), small household sizes of one to two (OR 0.83; 95% CI, 0.72–0.96), and metropolitan dwelling (OR 0.87; 95% CI, 0.79–0.97) were associated with significantly (p-value ≤0.05) lower odds of any past-year prescription opioid use compared to their counterparts of older-aged women, white non-Hispanics, larger households, and rural living, respectively.

Table 4.

Odds Ratios (95% Confidence Intervals) for Correlates of Past-Year Prescription Opioid Use and Misuse Among Nonpregnant Women of Reproductive Age with Disabilities (n = 17,551)

Nonpregnant women of reproductive age with disabilities
	Any opioid use	Opioid prescribed use^a	Opioid misuse^b
	Odds ratios (95% CI)^c	Odds ratios (95% CI)^c	Odds ratios (95% CI)^c
Social determinants of health
Economic
Income—Family
<$50 K	1.07 (0.98–1.16)	1.06 (0.96–1.17)	1.06 (0.90–1.24)
≥$50 K	Reference	Reference	Reference
Cell phone works
No	Reference	Reference	Reference
Yes	0.80 (0.54–1.19)	0.93 (0.61–1.43)	0.46 (0.24–0.86)
Social and community context
Age, years
15–17	0.43 (0.39–0.49)	0.42 (0.37–0.47)	1.32 (1.08–1.61)
18–25	0.54 (0.49–0.60)	0.55 (0.50–0.61)	1.06 (0.93–1.22)
26–44	Reference	Reference	Reference
Race/ethnicity
White, non-Hispanic	Reference	Reference	Reference
Black, non-Hispanic	0.95 (0.84–1.08)	1.01 (0.88–1.15)	0.58 (0.45–0.74)
Other, non-Hispanic	0.76 (0.66–0.89)	0.73 (0.61–0.86)	1.01 (0.74–1.37)
Hispanic	0.82 (0.71–0.95)	0.81 (0.70–0.94)	0.90 (0.74–1.10)
Household size
1–2	0.83 (0.72–0.96)	0.82 (0.71–0.95)	1.20 (0.99–1.45)
3–4	0.95 (0.86–1.06)	0.96 (0.86–1.06)	1.08 (0.89–1.30)
≥5	Reference	Reference	Reference
Metropolitan
No	Reference	Reference	Reference
Yes	0.87 (0.79–0.97)	0.87 (0.78–0.96)	1.20 (1.00–1.43)
Medical determinants of health
Health status
Fair/poor	2.42 (2.18–2.69)	2.47 (2.24–2.73)	1.69 (1.41–2.04)
Good	1.50 (1.36–1.64)	1.50 (1.37–1.64)	1.34 (1.13–1.60)
>Good/excellent	Reference	Reference	Reference
Behavioral determinants of health
Tobacco use
No	Reference	Reference	Reference
Yes	1.41 (1.27–1.57)	1.43 (1.28–1.60)	2.37 (2.09–2.68)
Alcohol use
No	Reference	Reference	Reference
Yes	1.20 (1.08–1.34)	1.26 (1.12–1.42)	2.18 (1.83–2.60)
Illicit drug use
No	Reference	Reference	NA
Yes	1.86 (1.69–2.05)	0.96 (0.86–1.07)	NA

Entries in bold are significant at a p ≤ 0.05.

Opioid prescribed use = No misuse or use disorder; dissection of Any opioid use results.

Opioid misuse includes use disorder; dissection of Any opioid use results.

Values are binary logistic regression odds ratios with 95% CI for each measure (Any opioid use, Opioid prescribed use, Opioid misuse).

Compared to very good to excellent health, poor to fair health (OR 2.42; 95% CI, 2.18–2.69), and good health (OR 1.50; 95% CI, 1.36–1.64) were associated with increased likelihood of any past-year prescription opioid use. Illicit drug use (OR 1.86; 95% CI, 1.69–2.05), tobacco use (OR 1.41; 95% CI, 1.27–1.57), and alcohol use (OR 1.20; 95% CI, 1.08–1.34) were also associated with significantly higher odds of any past-year prescription opioid use.

Possession of a functioning cell phone (OR 0.46; 95% CI, 0.24–0.86) and black non-Hispanic race/ethnicity (OR 0.58; 95% CI, 0.45–0.74) were significantly associated with lower odds of past-year prescription opioid misuse, compared to those with no functioning cell phone and white non-Hispanic race/ethnicity. Significantly higher odds of past-year prescription opioid misuse, including use disorder, were observed among women reporting tobacco use (OR 2.37; 95% CI, 2.09–2.68), alcohol use (OR 2.18; 95% CI, 1.83–2.60), poor to fair health (OR 1.69; 95% CI, 1.41–2.04), good health (OR 1.34; 95% CI, 1.13–1.60), to be 15–17 years of age (OR 1.32; 95% CI, 1.08–1.61), metropolitan dwelling (OR 1.20; 95% CI, 1.00–1.43), and dwelling in a household size of one to two (OR 1.20; 95% CI, 0.99–1.45), compared to their corresponding references (see Table 4).

Types of any past-year opioid use and misuse by disability status

Nonpregnant women of reproductive age with a disability reported greater any past-year use and misuse of various types of prescription opioids than women without a disability (Table 5). The following specific prescription opioids had elevated proportions of any past-year use and misuse by women with a disability versus women without a disability: hydrocodone (any use = 29.3% vs. 17.4%) (misuse = 5.87% vs. 2.21%), oxycodone (any use = 17.3% vs. 8.87%) (misuse = 3.95% vs. 1.33%), and tramadol (any use = 13.3% vs. 6.14%) (misuse = 1.93% vs. 0.68%). Women with a disability were significantly associated (p ≤ 0.05) with greater any past-year use (2.03%) and misuse (0.33%) of fentanyl than those without disabilities. Prevalence of both any opioid use and misuse among women with activities of daily living disability was greater than women with sensory or cognitive disabilities.

Table 5.

Types of Past-Year Opioid Use and Misuse Among Nonpregnant Women of Reproductive Age (n = 93,679) by Disability Status

Nonpregnant women of reproductive age
	Without disabilities	With disabilities	Sensory disabilities	Cognitive disabilities	Daily living activities disabilities
	Weighted % ^a (95% CI)^b	Weighted % ^a (95% CI)^b	Weighted % ^a (95% CI)^b	Weighted % ^a (95% CI)^b	Weighted % ^a (95% CI)^b
Fentanyl
Any use	0.67 (0.59–0.76)	2.03 (1.71–2.41)	1.88 (1.37–2.59)	2.17 (1.77–2.64)	3.24 (2.62–4.01)
Misuse	0.09 (0.06–0.13)	0.33 (0.23–0.48)	0.29 (0.14–0.58)	0.47 (0.32–0.68)	0.46 (0.28–0.76)
Buprenorphine
Any use	0.79 (0.72–0.88)	2.48 (2.20–2.79)	1.91 (1.49–2.44)	2.91 (2.52–3.36)	3.42 (2.83–4.13)
Misuse	0.24 (0.20–0.28)	0.83 (0.69–1.01)	0.46 (0.30–0.71)	1.07 (0.85–1.35)	0.98 (0.75–1.27)
Oxymorphone
Any use	0.24 (0.19–0.29)	1.04 (0.86–1.26)	0.95 (0.64–1.40)	1.21 (0.97–1.51)	1.42 (1.13–1.78)
Misuse	0.11 (0.08–0.15)	0.43 (0.32–0.59)	0.39 (0.23–0.66)	0.57 (0.40–0.82)	0.47 (0.31–0.70)
Hydromorphone
Any use	0.74 (0.66–0.82)	2.31 (2.03–2.63)	1.36 (1.02–1.80)	2.44 (2.04–2.93)	3.81 (3.29–4.42)
Misuse	0.07 (0.05–0.10)	0.35 (0.23–0.54)	0.35 (0.15–0.80)	0.47 (0.29–0.75)	0.47 (0.27–0.81)
Methadone
Any use	0.27 (0.23–0.32)	1.28 (1.07–1.53)	0.91 (0.62–1.33)	1.52 (1.25–1.84)	1.99 (1.62–2.46)
Misuse	0.07 (0.05–0.09)	0.40 (0.30–0.53)	0.35 (0.18–0.67)	0.50 (0.38–0.71)	0.52 (0.34–0.77)
Oxycodone
Any use	8.87 (8.60–9.15)	17.3 (16.5–18.0)	15.4 (14.1–16.9)	17.8 (17.0–18.7)	21.9 (20.5–23.4)
Misuse	1.33 (1.22–1.44)	3.95 (3.56–4.38)	3.13 (2.50–3.92)	4.75 (4.20–5.36)	4.9 (4.19–5.71)
Hydrocodone
Any use	17.4 (17.1–17.8)	29.3 (28.4–30.2)	26.1 (24.5–27.8)	29.4 (28.1–30.6)	35.5 (34.2–36.8)
Misuse	2.21 (2.07–2.36)	5.87 (5.36–6.43)	4.74 (3.95–5.68)	6.84 (6.13–7.62)	6.64 (5.77–7.64)
Morphine
Any use	2.14 (1.99–2.30)	6.03 (5.55–6.55)	5.05 (4.26–5.98)	6.14 (5.52–6.82)	8.67 (7.76–9.69)
Misuse	0.17 (0.13–0.21)	0.69 (0.55–0.87)	0.48 (0.30–0.77)	0.92 (0.72–1.18)	0.95 (0.74–1.23)
Tramadol
Any use	6.14 (5.93–6.35)	13.3 (12.5–14.1)	11.2 (10.1–12.3)	13.8 (12.8–14.9)	17.9 (16.7–19.2)
Misuse	0.68 (0.59–0.77)	1.93 (1.65–2.27)	1.57 (1.21–2.05)	2.31 (1.96–2.74)	2.45 (1.96–3.07)
Demerol
Any use	0.32 (0.26–0.38)	0.80 (0.61–1.05)	0.85 (0.51–1.41)	0.92 (0.70–1.23)	1.04 (0.71–1.54)
Misuse	0.03 (0.02–0.05)	0.04 (0.02–0.08)	0.06 (0.02–0.14)	0.05 (0.02–0.11)	0.05 (0.02–0.14)
Other
Any use	9.26 (8.93–9.60)	14.1 (13.3–14.8)	13.9 (12.6–15.2)	14.0 (13.0–15.1)	16.2 (15.1–17.2)
Misuse	0.32 (0.25–0.41)	0.96 (0.73–1.25)	0.54 (0.30–0.96)	1.24 (0.91–1.68)	1.22 (0.86–1.74)

No opioid use results excluded from the table.

All entries, except Demerol misuse entries, are significant at a p ≤ 0.05.

Chi-squared test assessed associations between disability (overall and by type) and various types of opioid use and misuse.

Values are weighted percentages from bivariate comparisons based on Any use versus Nonuse, and Misuse versus Nonmisuse.

Values are weighted percentages with 95% CI.

Sources for past-year opioid misuse by disability status

Among nonpregnant women of reproductive age with a disability who reported past-year opioid misuse, 55.0% obtained prescription opioids from friends or relatives for their last misuse (Table 6). Approximately, 5.2% of these women with a disability utilized a dealer or stranger to acquire prescription opioids for their last misuse compared to 3.9% of their peers without a disability; women with a cognitive disability had a prominent proportion (5.2%) of those acquiring prescription opioids from a dealer or stranger.

Table 6.

Source of Opioid for Last Misuse Among Nonpregnant Women of Reproductive Age who Reported Past-Year Opioid Misuse (n = 5,061) by Disability Status

Nonpregnant women of reproductive age
	Without disabilities	With disabilities	Sensory disabilities	Cognitive disabilities	Daily living activities disabilities
	Weighted % (95% CI)^a	Weighted % (95% CI)^a	Weighted % (95% CI)^a	Weighted % (95% CI)^a	Weighted % (95% CI)^a
Medical system	34.8 (32.7–36.9)	36.4 (33.6–39.3)	38.7 (32.1–45.7)	34.1 (31.0–37.3)	35.5 (31.8–39.3)
Friends or relatives	56.9 (54.8–59.0)	55.0 (51.8–58.1)	52.5 (45.0–60.0)	57.2 (53.6–60.8)	58.0 (53.8–62.0)
Dealer or stranger	3.91 (3.16–4.83)	5.15 (3.95–6.69)	4.35 (2.23–8.31)	5.22 (3.87–7.02)	4.52 (3.19–6.36)
Other	4.37 (3.41–5.58)	3.47 (2.52–4.77)	4.44 (1.89–10.1)	3.48 (2.37–5.06)	2.03 (1.09–3.76)

The results were not statistically significant at a p ≤ 0.05.

Values are weighted percentages with 95% CI.

Motives for past-year opioid misuse by disability status

A greater proportion (60.9%) of nonpregnant women of reproductive age with a disability who reported past-year opioid misuse acknowledged their last misuse of a prescription opioid was for nonpain relief, compared to 52.2% for women without a disability (Table 7). The top three significantly associated (p ≤ 0.05) nonpain relief motives for these women with a disability versus their counterparts without a disability were to get high (misuse = 22.1% vs. 19.0%), for emotions (misuse = 21.2% vs. 11.8%), and for sleep (misuse = 20.8% vs. 16.4%).

Table 7.

Motives for Last Opioid Misuse Among Nonpregnant Women of Reproductive Age who Reported Past-Year Opioid Misuse (n = 5,061) by Disability Status

Nonpregnant women of reproductive age
	Without disabilities	With disabilities	Sensory disabilities	Cognitive disabilities	Daily living activities disabilities
	Weighted % (95% CI)^a	Weighted % (95% CI)^a	Weighted % (95% CI)^a	Weighted % (95% CI)^a	Weighted % (95% CI)^a
To relieve pain
No	27.7 (26.0–29.5)	28.8 (26.4–31.4)	30.2 (23.9–37.4)	29.5 (27.0–32.1)	28.1 (24.1–32.3)
Yes	72.3 (70.5–74.0)	71.2 (68.6–73.6)	69.8 (62.6–76.1)	70.5 (67.9–73.0)	71.9 (67.7–75.9)
For nonpain motives^b
No	47.8 (45.3–50.3)	39.1 (35.8–42.5)	37.9 (30.6–45.8)	37.3 (33.9–40.8)	38.7 (34.4–43.3)
Yes	52.2 (49.7–54.7)	60.9 (57.5–64.2)	62.1 (54.2–69.4)	62.7 (59.2–66.1)	61.3 (56.7–65.6)
To relax
No	72.1 (69.7–74.4)	68.8 (66.1–72.6)	68.8 (62.8–74.3)	67.4 (64.4–70.2)	67.6 (63.4–71.6)
Yes	27.9 (25.6–30.3)	31.2 (28.6–33.9)	31.2 (25.7–37.2)	32.6 (29.8–35.6)	32.4 (28.4–36.6)
To experiment
No	93.6 (92.1–94.8)	92.9 (91.2–94.3)	94.9 (92.0–96.8)	91.7 (89.5–93.4)	94.4 (92.2–96.0)
Yes	6.41 (5.22–7.86)	7.12 (5.72–8.82)	5.09 (3.18–8.05)	8.34 (6.62–10.5)	5.64 (4.03–7.83)
To get high
No	81.0 (79.2–82.7)	77.9 (74.9–80.5)	81.3 (75.9–85.8)	76.3 (72.9–79.4)	74.6 (70.2–78.5)
Yes	19.0 (17.3–20.8)	22.1 (19.5–25.1)	18.7 (14.2–24.1)	23.7 (20.6–27.1)	25.4 (21.5–29.8)
For sleep
No	83.6 (82.0–85.1)	79.2 (76.5–81.5)	80.1 (73.5–85.3)	78.2 (75.2–80.9)	80.6 (76.5–84.1)
Yes	16.4 (14.9–18.0)	20.8 (18.5–23.5)	19.9 (14.7–26.5)	21.8 (19.1–24.8)	19.4 (15.9–23.5)
For emotions
No	88.2 (86.7–89.6)	78.8 (76.2–81.1)	82.6 (76.4–87.4)	76.1 (73.2–78.8)	78.1 (74.7–81.1)
Yes	11.8 (10.4–13.3)	21.2 (18.9–23.8)	17.4 (12.6–23.6)	23.9 (21.2–26.8)	21.9 (18.9–25.3)
For other drug effect
No	98.3 (97.7–98.8)	95.8 (94.1–97.1)	96.7 (93.8–98.2)	94.9 (92.6–96.5)	95.0 (92.6–96.6)
Yes	1.66 (1.20–2.29)	4.17 (2.93–5.91)	3.33 (1.77–6.20)	5.12 (3.52–7.38)	5.01 (3.39–7.36)
Because hooked
No	96.6 (95.7–97.3)	96.1 (94.9–97.0)	96.2 (92.9–98.0)	95.4 (94.0–96.6)	94.6 (92.2–96.3)
Yes	3.44 (2.74–4.30)	3.91 (2.98–5.11)	3.77 (1.97–7.10)	4.56 (3.43–6.04)	5.39 (3.71–7.77)
For other reason
No	96.1 (95.0–96.9)	93.9 (92.1–95.3)	93.9 (89.6–96.5)	94.6 (92.5–96.1)	93.1 (90.3–95.1)
Yes	3.94 (3.11–4.97)	6.10 (4.69–7.89)	6.06 (3.46–10.4)	5.42 (3.89–7.51)	6.90 (4.85–9.73)

Entries in bold are significant at a p ≤ 0.05.

Chi-squared testing assessed associations between disability (overall and by type) and various motives for last opioid misuse.

Values are weighted percentages with 95% CI.

Grouped motives: to relax, experiment, get high; for sleep, emotions, other drug effect, other reasons; and because hooked.

Discussion

The use of opioids among adults with disabilities in the United States is a mounting public health concern as this vulnerable population is at increased risk of frequent prescription opioid exposure due to chronic health conditions, and such prolonged exposure notably amplifies the risk for opioid-related disorders, including opioid misuse and opioid overdose.⁷ In this study, the prevalence of past-year prescription opioid use (48.0%) and misuse (10.4%) among nonpregnant women of reproductive age with disabilities was substantially higher than that among women without disabilities. Our findings also show that women with disabilities are twice as likely to misuse prescription opioids within the past year than women without disabilities even after adjusting for multiple social, medical, and behavioral health determinants.

Specifically, women with activities of daily living disability were more likely to use prescription opioids and those with a cognitive disability were more likely to misuse prescription opioids within the past year than women without such disabilities. Intermittent versus sustained opioid use may explain the different finding of opioid misuse by disability type; therefore, studying health practitioners' opioid prescribing practices for women with disabilities is an important research undertaking. Acknowledging the enduring opioid crisis obligates federal and state intervention efforts to enhance screening methods of vulnerable populations, like women of reproductive age with disabilities, at risk for opioid-related disorders, and increase access to appropriate health care by leveraging electronic health records and telehealth capabilities.

Advanced health technology allows for expanded screening platforms throughout health systems and community programs to amplify collection of relevant data; such as mental health, social determinants, and so on. Furthermore, it is imperative to improve medical provider education, training and capacity, and reinforce related community-based support programs.³⁴

Social determinants of health include multiple factors accountable for over 80% of health outcomes, particularly behavioral health outcomes related to opioid habits.³⁵ The ongoing pandemic brought about public health actions, such as social isolation policies, that influenced health determinants and have been correlated with at least 40 states reporting an increase in drug overdose deaths. For example, Virginia reported a 67% increase in drug overdose deaths following its implementation of a stay-at-home order.³⁴ In the present study, three social determinants of health—age (15–17 years), household size (1–2), and metropolitan dwelling (yes)—were found to be significantly associated with a lower likelihood of using but a higher likelihood of misusing prescription opioids within the past year among women of reproductive age with disabilities.

Medical determinants of health such as perceiving a poor to fair overall health status and behavioral determinants of health such as tobacco and alcohol use were significantly associated with higher likelihood of both using and misusing prescription opioids within the past year. Health determinants are intrinsically linked to the ongoing and worsening opioid crisis; therefore, effective health policy targeting these observed health determinants, medical and behavioral, can lead to reduced opioid misuse and opioid-related overdose fatalities among this group of women with disabilities.³⁵

Hydrocodone and oxycodone, commonly associated with opioid overdose fatalities,¹⁷ had the greatest prevalence of use and misuse among nonpregnant women of reproductive age with disabilities, which was markedly greater than the prevalence among women without disabilities. Women with activities of daily living disability reported higher rates of use and misuse of nearly all opioids compared to those with other disabilities. Approximately two-thirds of the >90,000 overdose deaths from 2019 to 2020 included an opioid³⁶ and >42% of opioid-related deaths among U.S. women from 2017 to 2018 included a prescription opioid³⁷; therefore, these findings warrant further investigation and action to prevent opioid misuse and opioid-related fatalities, considering disability type, among this vulnerable population of women.

The findings of this study indicate that at least 60% of nonpregnant women of reproductive age with a disability who reported past-year opioid misuse reported a nonpain relief motive for their last misuse; such as to get high, for emotions, or for sleep. While opioids can be appropriately prescribed for pain management, this finding suggests that there may have been more applicable nonopioid treatment options for nonpain motives, which may have been related to determinants of health measures. However, over half of this group of women attained opioids for their last misuse from a nonmedical source, with >5% obtaining the opioids from a dealer or stranger.

The finding of nonmedical sources supplying opioids to these women is concerning, as at least 36,000 overdose deaths in 2019 were linked to illegally fabricated synthetic opioids,³⁸ and during the recent coronavirus pandemic, overdose deaths from illicit-black market synthetic opioids has precipitously proliferated.³⁴ In addition, this population of women with disabilities had a strikingly higher rate of use and misuse of fentanyl, which is primarily responsible for an increasing rate of overdose deaths from synthetic opioids³¹; therefore, research not only into health practitioners' prescribing practices for opioids but also into community and public health strategies to curtail the illicit distribution of opioids is necessary.

Limitations

NSDUH data are restricted to the civilian noninstitutional population, representing ∼79% of the U.S. population.³⁹ Studies indicate a higher prevalence of substance use and abuse among the institutionalized U.S. population¹⁵; therefore, the results of the present study could be underestimated and not generalizable to institutionalized U.S. populations. The NSDUH does not collect data on prescription opioid use for withdrawal symptoms or opioid use disorder, which is a reasonable and sometimes necessary purpose for the use of buprenorphine or methadone. Furthermore, prescribed opioid dosage and duration are missing from data, which could be important in validating appropriate opioid prescribing practices. Severity of disabilities, level of pain, and related comorbidities were not captured by NSDUH questions; therefore, respondents with milder disabilities may not have been identified.

As policymakers target health determinants in pursuit of health, it is paramount for the NSDUH to enhance its data collection methods for health determinant factors to facilitate effective research studies that can guide effective health policy. Social desirability bias is inherently embedded within survey data and the cross-sectional nature of the NSDUH data does not allow for determining temporal or causal inferences.

Conclusion

Nonpregnant women of reproductive age with a disability are at an amplified risk for opioid use and misuse. Recognizing the persistent opioid crisis that has worsened due to the ongoing pandemic, health officials at all levels of government are obliged to enhance current health policies and/or create new health policies to reduce opioid exposure, curb opioid misuse and opioid disorders, and prevent opioid-related fatalities; particularly for vulnerable, at-risk populations like nonpregnant women of reproductive age with disabilities. The physical health and mental health of reproductive-aged women are critical for the wellbeing of future generations. Therefore, effectively mitigating the effects of opioids before reproduction will prevent associated adverse consequences for prospective children and families.

The findings of this study warrant expanded screenings for associated risk factors, enhanced relevant data collection, and further research specific to women of reproductive age with disabilities. To effectively abate the adverse effects of the opioid crisis in this vulnerable population of women with disabilities, public health officials must enact informed policies that also consider associated determinants of health.

Footnotes

Authors' Contributions

E.J.B.: Principal author responsible for all aspects of the research project, from conceptualization, methodology, statistical analysis, and writing of the original draft.

P.K.: As the general overseer, reviewed and provided guidance on all aspects of the research. P.K. assisted in conceptualizing the research objectives; maintained the original research data set; reviewed, guided, and approved statistical analysis; assisted with developing the methodology and methods design; verified all research activities; and approved the final draft after the review and editing process.

H.M. and A.Z.P.: Reviewed and provided feedback on all aspects of the research—Conceptualization to Statistical Analysis—and was instrumental in the “Writing—review and editing” role.

Ethical Approval

This study was exempt from review by the Institutional Review Board at the researchers' institution given that it used existing data (secondary data analysis) that are publicly available.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

References

Lauer

, Henly

, Brucker

. Prescription opioid behaviors among adults with and without disabilities: United States, 2015–2016. Disabil Health J, 2019; 12(3):519–522; doi: 10.1016/j.dhjo.2018.12.001

Ali

, McClellan

, West

, et al. Medical Marijuana laws, Marijuana use, and opioid-related outcomes among women in the United States. Womens Health Issues, 2021; 31(1):24–30; doi: 10.1016/j.whi.2020.09.003

Sanmartin

, Ali

, Novak

, et al. Sources and main motivations for prescription opioid misuse among reproductive-aged parenting women in the United States. Subst Use Misuse, 2019; 54(8):1332–1336; doi: 10.1080/10826084.2019.1580294

Schieber

, Guy

, Seth

, et al. Variation in adult outpatient opioid prescription dispensing by age and sex: United States, 2008–2018. MMWR Morb Mortal Wkly Rep, 2020; 69(11):298–302; doi: 10.15585/mmwr.mm6911a5

Sanmartin

, Ali

, Chen

, et al. Prescription opioid misuse, sources of opioids and reasons for opioid misuse among reproductive aged parenting women with major depressive episode. Addict Behav, 2019; 98:106057; doi: 10.1016/j.addbeh.2019.106057

Kitsantas

, Adams

, Cheema

, et al. Opioid use in pregnant women with mental health-related disabilities. Arch Gynecol Obstet, 2021; 303(6):1531–1537; doi: 10.1007/s00404-020-05953-0

Liaw

, Kuo

, Raji

, et al. Opioid prescribing among adults with disabilities in the United States after the 2014 federal hydrocodone rescheduling regulation. Public Health Rep, 2020; 135(1):114–123; doi: 10.1177/0033354919892638

Kuo

, Raji

, Goodwin

. Association of disability with mortality from opioid overdose among US medicare adults. JAMA Netw Open, 2019; 2(11):e1915638; doi: 10.1001/jamanetworkopen.2019.15638

Hager

, Blue

, Zhang

, et al. OPIOIDS: Cultivating interprofessional collaboration to find solutions to public health problems. J Interprof Care, 2019; 33(1):120–124; doi: 10.1080/13561820.2018.1516634

10.

Berghs

, Atkin

, Hatton

, et al. Rights to social determinants of flourishing? A paradigm for disability and public health research and policy. BMC Public Health, 2019; 19(1):997; doi: 10.1186/s12889-019-7334-8

11.

Barocas

, Wang

, Marshall

BDL

, et al. Sociodemographic factors and social determinants associated with toxicology confirmed polysubstance opioid-related deaths. Drug Alcohol Depend, 2019; 200:59–63; doi: 10.1016/j.drugalcdep.2019.03.014

12.

Moriya

, Xu

. The complex relationships among race/ethnicity, social determinants, and opioid utilization. Health Serv Res, 2021; 56:310–322; doi: 10.1111/1475-6773.13619

13.

Okoro

, Hollis

, Cyrus

, et al. Prevalence of disabilities and health care access by disability status and type among adults—United States, 2016. MMWR Morb Mortal Wkly Rep, 2018; 67:882–887; doi: 10.15585/mmwr.mm6732a3

14.

Montiel Ishino

, McNab

, Gilreath

, et al. A comprehensive multivariate model of biopsychosocial factors associated with opioid misuse and use disorder in a 2017–2018 United States national survey. BMC Public Health, 2020; 20(1):1740; doi: 10.1186/s12889-020-09856-2

15.

Han

, Compton

, Blanco

, et al. Prescription opioid use, misuse, and use disorders in U.S. adults: 2015 National Survey on drug use and health. Ann Intern Med, 2017; 167:293–301; doi: 10.7326/M17-0865

16.

Brown

, Lunsky

, Wilton

, et al. Pregnancy in women with intellectual and developmental disabilities. J Obstet Gynaecol Can, 2016; 38(1):9–16; doi: 10.1016/j.jogc.2015.10.004

17.

Centers for Disease Control and Prevention (CDC). Opioid painkiller prescribing: Where you live makes a difference. National Center for Injury Prevention and Control. Atlanta, GA; 2017. Available from: https://www.cdc.gov/vitalsigns/opioid-prescribing [Last accessed: May 1, 2022].

18.

Hayatbakhsh

, Flenady

, Gibbons

, et al. Birth outcomes associated with cannabis use before and during pregnancy. Pediatr Res, 2012; 71(2):215–219; doi: 10.1038/pr.2011.25

19.

Metz

, Brown

, Martins

, et al. Characteristics of drug use among pregnant women in the United States: Opioid and non-opioid illegal drug use. Drug Alcohol Depend, 2018; 183:261–266; doi: 10.1016/j.drugalcdep.2017.11.010

20.

Arnaudo

, Andraka-Christou

, Allgood

. Psychiatric co-morbidities in pregnant women with opioid use disorders: Prevalence, impact, and implications for treatment. Curr Addict Rep, 2017; 4(1):1–13; doi: 10.1007/s40429-017-0132-4

21.

Finer

, Zolna

. Declines in unintended pregnancy in the United States, 2008–2011. N Engl J Med, 2016; 374:843–852; doi: 10.1056/NEJMsa1506575

22.

Substance Abuse and Mental Health Services Administration (SAMHSA). National Survey on Drug Use and Health (NSDUH). Rockville, MD; 2020. Available from: https://www.samhsa.gov/data/data-we-collect/nsduh-national-survey-drug-use-and-health [Last accessed: May 1, 2022].

23.

Ellington

, Strid

, Tong

, et al. Characteristics of women of reproductive age with laboratory-confirmed SARS-CoV-2 infection by pregnancy status: United States, January 22–June 7, 2020. MMWR Morb Mortal Wkly Rep, 2020; 69:769–775; doi: 10.15585/mmwr.mm6925a1

24.

Center for Behavioral Health Statistics and Quality (CBHSQ). 2016 National Survey on Drug Use and Health Public Use File Codebook. Substance Abuse and Mental Health Services Administration. Rockville, MD; 2018. Available from: https://www.datafiles.samhsa.gov/sites/default/files/field-uploads-protected/studies/NSDUH-2016/NSDUH-2016-datasets/NSDUH-2016-DS0001/NSDUH-2016-DS0001-info/NSDUH-2016-DS0001-info-codebook.pdf [Last accessed: May 1, 2022].

25.

Centers for Disease Control and Prevention (CDC). Disability and Health Overview. National Center on Birth Defects and Developmental Disabilities. Atlanta, GA; 2020. Available from: https://www.cdc.gov/ncbddd/disabilityandhealth/disability.html [Last accessed: May 1, 2022].

26.

Altman

, Bernstein

Disability and health in the United States, 2001–2005. National Center for Health Statistics. Centers for Disease Control and Prevention. Atlanta, GA; 2008. Available from: https://www.cdc.gov/nchs/data/misc/disability2001-disability2005.pdf [Last accessed: May 1, 2022].

27.

Krahn

, Walker

, Correa-De-Araujo

. Persons with disabilities as an unrecognized health disparity population. Am J Public Health, 2015; 105(S2):S198–S206; doi: 10.2105/AJPH.2014.302182

28.

Hudgins

, Porter

, Monuteaux

, et al. Prescription opioid use and misuse among adolescents and young adults in the United States: A national survey study. PLoS Med, 2019; 16(11):1–15; doi: 10.1371/journal.pmed.1002922

29.

McCabe

, Boyd

, Cranford

, et al. Motives for nonmedical use of prescription opioids among high school seniors in the United States: Self-treatment and beyond. Arch Pediatr Adolesc Med, 2009; 63(8):739–744; doi: 10.1001/archpediatrics.2009.120

30.

Schepis

, Wastila

, Ammerman

, et al. Prescription opioid misuse motives in US older adults. Pain Med, 2020; 21(10):2237–2243; doi: 10.1093/pm/pnz304

31.

Centers for Disease Control and Prevention (CDC). About Social Determinants of Health (SDOH). Atlanta, GA; 2021. Available from: https://www.cdc.gov/socialdeterminants/about.html [Last accessed: May 1, 2022].

32.

US Department of Health and Human Services (HHS). Healthy People 2030. Social Determinants of Health. Office of Disease Prevention and Health Promotion. Rockville, MD; 2021. Available from: https://health.gov/healthypeople/objectives-and-data/social-determinants-health [Last accessed: May 1, 2022].

33.

Griesler

, Hu

, Wall

, et al. Assessment of prescription opioid medical use and misuse among parents and their adolescent offspring in the US. JAMA Netw Open, 2021; 4(1):e2031073; doi: 10.1001/jamanetworkopen.2020.31073

34.

Narayan

, Balkrishnan

. A health crisis within a health crisis: Opioid access in the COVID-19 pandemic. Subst Abuse, 2021; 42(2):148–152; doi: 10.1080/08897077.2021.1900981

35.

Bohler

, Thomas

, Clark

, et al. Addressing the Opioid Crisis through Social Determinants of Health: What Are Communities Doing? Opioid Policy Research Collaborative at Brandeis University, FL; 2021. Available from: https://opioid-resource-connector.org/sites/default/files/2021-02/Issue%20Brief%20-%20Final.pdf [Last accessed: May 1, 2022].

36.

Ahmad

, Rossen

, Sutton

Provisional drug overdose death counts. National Center for Health Statistics. Centers for Disease Control and Prevention. Atlanta, GA; 2021. Available from: https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm#citation [Last accessed: May 1, 2022].

37.

Wilson

, Kariisa

, Seth

, et al. (2020). Drug and opioid-involved overdose deaths—United States, 2017–2018. MMWR Morb Mortal Wkly Rep, 2020; 69(11):290–297; doi: 10.15585/mmwr.mm6911a4

38.

Centers for Disease Control and Prevention (CDC). Fentanyl. Opioid Basics. Opioid Overdose. National Center for Injury Prevention and Control. Atlanta, GA; 2021. Available from: https://www.cdc.gov/drugoverdose/opioids/index.html [Last accessed: May 1, 2022].

39.

US Bureau of Labor Statistics (BLS). Population Level [CNP16OV]. FRED. St. Louis, MO; 2020. Available from: https://fred.stlouisfed.org/series/CNP16OV [Last accessed: May 1, 2022].