Trajectories and correlates of opioid prescription receipt among patients experiencing interpersonal violence.

Interpersonal violence increases vulnerability to the deleterious effects of opioid use. Increased opioid prescription receipt is a major contributor to the opioid crisis; however, our understanding of prescription patterns and risk factors among those with a history of interpersonal violence remains elusive. This study sought to identify 5-year longitudinal patterns of opioid prescription receipt among patients experiencing interpersonal violence within a large healthcare system and sociodemographic and clinical characteristics associated with prescription patterns. This secondary analysis examined electronic health record data from January 2004-August 2019 for a cohort of patients (N = 1,587) referred for interpersonal violence services. Latent class growth analysis was used to estimate trajectories of opioid prescription receipt over a 5-year period. Standardized differences were calculated to assess variation in sociodemographic and clinical characteristics between classes. Our cohort had a high prevalence of prescription opioid receipt (73.3%) and underlying co-morbidities, including chronic pain (54.6%), substance use disorders (39.0%), and mental health diagnoses (76.9%). Six prescription opioid receipt classes emerged, characterized by probability of any prescription opioid receipt at the start and end of the study period (high, medium, low, never) and change in probability over time (increasing, decreasing, stable). Classes with the highest probability of prescription opioids also had the highest proportions of males, chronic pain diagnoses, substance use disorders, and mental health diagnoses. Black, non-Hispanic and Hispanic patients were more likely to be in low or no prescription opioid receipt classes. These findings highlight the importance of monitoring for synergistic co-morbidities when providing pain management and offering treatment that is trauma-informed, destigmatizing, and integrated into routine care.

Introduction

Prescription and non-prescription opioid use disorder and overdose is a national public health crisis: an estimated 115 people die every day due to opioid overdose [1]. Despite efforts to reduce opioid prescriptions, the number of individuals who receive prescriptions each year remains high [2]. Opioid prescription receipt greatly increases a person’s risk for adverse outcomes, particularly when prescribed long-term; approximately 21–29% of individuals prescribed long-term opioid therapy for chronic pain use them non-medically [3] and 8–12% develop an opioid use disorder (OUD) [4, 5]. A better understanding of factors associated with long-term opioid prescription receipt, particularly among high-risk populations, can help facilitate efforts to prevent OUD.

Consistent with other substance use disorder research, evidence shows that rather than a single linear trajectory, opioid use follows multiple trajectories characterized by significant heterogeneity in behaviors [6-9]. Prior research has examined opioid use trajectories among diverse groups of patients including veterans, patients receiving care for HIV, patients enrolled in a Medicaid lock-in program, and individuals receiving treatment for OUD [9-Pharmacoepidemiol Drug Saf. 2019 ">12]. Opioid use trajectories have been shown to vary by amount of use (e.g., low, medium, high) and use patterns over time (e.g., increasing use, decreasing use). Trajectory patterns are associated with numerous factors including demographics, comorbid conditions, substance use history, and social/family conditions. These relationships, however, vary based on the population studied.

One population particularly vulnerable to deleterious effects of opioid use is individuals with a history of interpersonal violence. Experiences of interpersonal violence (e.g., intimate partner violence, sexual assault), have been linked to an increased likelihood of prescription opioid use, [13-15] non-medical prescription opioid use, [13, 14] OUD, [16-18] and heroin use [19]. Opioid prescription receipt has also been found to increase odds of OUD in this population. A recent study found that among individuals with a history of interpersonal violence, people who had received an opioid prescription in the past year had a four times greater odds of reporting opioid misuse behaviors compared to those without an opioid prescription [20].

Evidence also suggests that interpersonal violence may influence opioid use trajectories: two studies showed that physical, sexual, and emotional abuse history influences recovery trajectories for individuals seeking treatment for substance use disorders (including OUD), with higher abuse levels predicting more severe psychiatric and social problems and slower recovery times [9, 21]. Although providing some evidence about the role of interpersonal violence in opioid use trajectories, these studies were conducted after treatment for a diagnosed substance use disorder and did not differentiate by type of substance. Thus, our understanding of prescription opioid use patterns among individuals with a history of interpersonal violence remains elusive. Identifying distinct patterns of prescription opioid use among survivors of interpersonal violence and associated characteristics will help us understand who is at risk for escalating opioid use and OUD and provide potential targets for future interventions.

The purpose of this study was to examine longitudinal trajectories of prescription opioid use among patients experiencing interpersonal violence. Specifically, we aimed to:

Aim 1. Classify 5-year longitudinal patterns of opioid prescription receipt among patients experiencing interpersonal violence.

Aim 2. Identify sociodemographic and clinical characteristics associated with opioid prescription receipt patterns among survivors of interpersonal violence.

Methods

Design

We conducted a secondary analysis of electronic health record (EHR) data available from January 2004–August 2019 for a cohort of patients referred for interpersonal violence services between December 2009–June 2019 at the University of North Carolina (UNC) Health System (N = 1,587). We used latent class growth analysis (LCGA) to estimate trajectories of opioid prescription receipt over a 5-year period. We then examined associations between trajectory patterns and sociodemographic and clinical characteristics. This study was approved by the University of North Carolina Institutional Review Board (#20–0988).

Study population

The study population consisted of all adult patients (≥ 18 years of age at baseline) referred to the UNC Hospitals Beacon Program for interpersonal violence services between December 2009–June 2019 with at least one year of data in the EHR during the study period. The Beacon Program provides comprehensive, coordinated care to UNC Health System patients, families, and employees experiencing a interpersonal violence, including intimate partner violence, human trafficking, sexual assault, and elder/vulnerable adult abuse. Individuals were excluded if they had evidence of an OUD at baseline (diagnosis or buprenorphine, methadone, or naltrexone prescription), had a cancer diagnosis, or were receiving end-of-life care during the study period as opioid prescribing practices differ for these conditions.

Variables

All data analyzed in this study were electronically abstracted from the UNC Health System EHR databases. This included data related to opioid prescription receipt and sociodemographic and clinical characteristics. Given that experiences of interpersonal violence are often chronic rather than an isolated event, we did not limit data extraction based on date of referral to the Beacon Program, but rather extracted relevant variables across the study period (January 2004–August 2019).

Opioid prescription receipt

We examined prescription drug records for opioid medications prescribed during the study period. Then, we recorded all opioid prescriptions during the 60 months following baseline. We focused on the following opioids commonly prescribed for pain: butorphanol, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, levorphanol, meperidine, morphine, opium, oxycodone, oxymorphone, pentazocine, propoxyphene, tapentadol, and tramadol. Dosage and duration information was not readily available from the EHR data, especially prior to April 2014; thus, we could not calculate morphine milligram equivalents for prescriptions. Therefore, outcome variable was defined as a binary indicator of any prescription opioid receipt for each month of the study period.

Socio-demographic and clinical characteristics

Demographic characteristics were assessed at the beginning of the study period and included age, race, ethnicity, and sex. We examined insurance status across the study period, categorizing patients as insured, not insured, or varying status. We used International Classification of Diseases 9th and 10th edition (ICD-9 and ICD-10) codes to identify medical diagnoses during the study period. Diagnoses included chronic pain conditions, substance use disorders, and mental health conditions. We calculated the Charlson comorbidity index, a method of categorizing comorbidities based on ICD codes, for each patient [22, 23]. Lastly, we collected information on social history, namely, smoking and illicit drug use history. Social history is routinely screened for by providers within the UNC Health System and documented within each patient’s EHR. Specific information on diagnostic codes used to define characteristics is presented in S1 Table.

Statistical analyses

LCGA was used to examine opioid prescription receipt over a 5-year time period and identify groups with similar trajectories (Aim 1). Baseline was defined as date of first opioid prescription during the study period (January 2004–August 2019). Those who did not have an opioid prescription during the study period were placed in a “Never” class. We fit models with 2 to 7 groups to determine the optimal number of trajectories. Model selection was based on a combination of model fit, adequacy, and interpretability. We assessed fit using Akaike’s information criterion (AIC), [24] Bayesian information criterion (BIC), [25] bootstrap likelihood ratio test (BLRT), and entropy. The LCGA was fitted using MPlus, version 8.4.

To estimate the association between sociodemographic and clinical characteristics with trajectory groups, we estimated the prevalence of patient characteristics within each latent class. The analysis was weighted using patient’s posterior probabilities for belonging to a given class. We estimated and graphed standardized differences to assess the degree of similarity and dissimilarity between classes within a given covariate (“Never” class used as reference group). The analysis was conducted in Stata SE, version 6.1.

Results

Baseline population characteristics

Between December 2009 and June 2019, 4,296 individuals were referred to the Beacon Program for interpersonal violence services. We excluded 2,709 people resulting in a final sample size of 1,587. The primary reason for exclusion was < 1 year of data in the EHR. Cohort characteristics are presented in Table 1. Our cohort was largely female (87.5%), White, non-Hispanic (55.2%) or Black, non-Hispanic (29.3%), insured (77.3%), and had a mean age of 41.4 years (SD = 16.3). Over half (58.3%) had a current smoking history and almost half (44.8%) had a lifetime history of illicit drug use. Our cohort exhibited a high prevalence of chronic pain (54.6%), substance use disorders (39.0%), and mental health diagnoses (76.9%) during the study period. The most common chronic pain diagnoses were chronic low back pain (33.5%) and chronic pain, not elsewhere classified (30.0%). Alcohol (18.0%) and cannabis (16.1%) related disorders were the most prevalent substance use diagnoses; however, rates were similar across all drug related diagnoses. Notably, 13.0% received an OUD diagnosis after baseline (participants were excluded if OUD diagnosis was present at baseline). The most common mental health related diagnoses included depression (65.5%) and anxiety related disorders (53.9%). The mean Charlson comorbidity index was 1.4 (SD = 2.1), which corresponds to an estimated 10-year survival of 94% [23, 26].

Table 1

Characteristics of study participants, and by latent class of prescription opioid trajectories: Dec 2009- Jun 2019.

	Total cohort who received Beacon program assistance (n = 1,587)a	Never prescribed opioids (n = 423.0, 26.7%)a	Opioid prescription receipt trajectories developed using LCGA analysis
			Class 1	Class 2	Class 3	Class 4	Class 5	Class 6
			Start medium, end medium, moderate decrease (n = 75.4, 4.8%)b	Start high, end high, slight increase (n = 32.4, 2.0%)b	Start medium, end low, sharp decrease (n = 206.2, 13.0%)b	Start low, end high, sharp increase (n = 51.1, 3.2%)b	Start low, end medium, moderate increase (n = 216.6, 13.6%)b	Start low, end low, stable (n = 582.3, 36.7%)b
Age (mean, SD)	41.4 (16.3)	39.4 (17.4)	43.4 (14.9)	47.3 (15.5)	42.5 (14.9)	41.9 (15.5)	40.7 (15.6)	42.0 (16.4)
Sex
Male	198 (12.5)	51 (12.1)	15.2 (20.1)	4.4 (13.7)	20.3 (9.8)	7.1 (14.0)	27.8 (12.9)	72.2 (12.4)
Female	1,389 (87.5)	372 (87.9)	60.2 (79.9)	27.9 (86.3)	185.9 (90.2)	44.0 (86.1)	188.7 (87.2)	510.2 (87.6)
Race/ Ethnicity
White, non-Hispanic	847 (55.2)	210 (52.6)	48.1 (64.0)	23.4 (72.2)	119.4 (59.2)	29.2 (60.0)	116.7 (55.2)	300.2 (53.0)
Black, non-Hispanic	449 (29.3)	101 (25.3)	20.6 (27.4)	7.9 (24.5)	62.3 (30.9)	13.5 (27.8)	69.8 (33.0)	173.9 (30.7)
Hispanic	163 (10.6)	61 (15.3)	5.9 (7.9)	1.0 (3.1)	12.4 (6.2)	3.4 (7.0)	15.0 (7.1)	64.3 (11.4)
Other, non-Hispanic	76 (5.0)	27 (6.8)	0.5 (0.7)	0.6 (0.2)	7.6 (3.8)	2.6 (5.3)	10.1 (4.8)	28.2 (5.0)
Insurance
Insured	1,150 (77.3)	301 (78.6)	58.9 (80.7)	25.7 (80.3)	152.0 (77.8)	37.0 (76.3)	156.7 (76.4)	418.6 (76.1)
Not insured	166 (11.2)	39 (10.2)	5.6 (7.7)	1.1 (3.5)	21.5 (11.0)	4.1 (8.5)	22.7 (11.1)	72.0 (13.1)
Varying	171 (11.5)	43 (11.2)	8.5 (11.6)	5.2 (16.2)	22.0 (11.3)	7.3 (15.1)	25.7 (12.5)	59.3 (10.8)
Smoking History
Never	347 (21.9)	129 (30.6)	8.4 (11.2)	6.3 (19.4)	35.6 (17.3)	6.5 (12.8)	37.1 (17.1)	124.1 (21.3)
Now	923 (58.3)	212 (50.4)	49.6 (66.0)	19.8 (61.2)	134.0 (65.2)	29.2 (57.2)	136.6 (63.1)	341.8 (58.7)
Past	314 (19.8)	80 (19.0)	17.1 (22.8)	6.3 (19.4)	36.0 (17.5)	15.4 (30.1)	42.8 (19.8)	116.4 (20.0)
Illicit Drug—Ever	707 (44.8)	186 (44.3)	36.0 (48.6)	15.1 (46.7)	83.4 (40.6)	23.6 (46.3)	109.0 (50.4)	253.9 (43.7)
Chronic pain
Chronic Pain, not elsewhere classified	476 (30.0)	37 (8.8)	59.2 (78.5)	28.5 (88.1)	95.5 (46.3)	32.9 (64.4)	87.9 (40.6)	135.0 (23.2)
Chronic pain syndrome	138 (8.7)	5 (1.2)	26.6 (35.3)	13.3 (41.1)	24.8 (12.0)	19.7 (38.6)	24.1 (11.1)	24.4 (4.2)
Fibromyalgia	202 (12.7)	15 (3.6)	21.2 (28.1)	10.8 (33.5)	35.4 (17.2)	10.2 (20.0)	41.4 (19.1)	68.1 (11.7)
Irritable Bowel Syndrome	58 (3.7)	4 (1.0)	7.2 (9.5)	2.0 (6.2)	13.5 (6.5)	3.4 (6.6)	9.6 (4.4)	18.4 (3.2)
Interstitial cystitis/Bladder pain syndrome	15 (1.0)	0 (0.0)	1.1 (1.4)	1.0 (3.1)	2.4 (1.2)	1.2 (2.0)	5.4 (2.5)	4.1 (0.7)
Migraine	249 (15.7)	33 (7.8)	24.3 (32.3)	8.1 (25.1)	40.8 (19.8)	10.6 (20.8)	47.5 (21.9)	84.6 (14.5)
Chronic low back pain	532 (33.5)	63 (14.9)	45.3 (60.1)	22.0 (68.0)	90.5 (43.9)	27.6 (53.9)	103.5 (47.8)	180.1 (30.9)
Chronic Fatigue Syndrome	28 (1.8)	2 (0.5)	2.1 (2.8)	0.0 (0.0)	3.2 (1.6)	3.8 (7.5)	4.9 (2.3)	12.0 (2.1)
Endometriosis	45 (2.8)	1 (0.2)	3.1 (4.1)	2.0 (6.2)	9.0 (4.4)	5.6 (11.0)	12.6 (5.8)	11.7 (2.0)
Substance use disorders
Alcohol related disorders	286 (18.0)	67 (15.8)	20.6 (27.3)	4.3 (13.3)	41.7 (20.2)	10.2 (19.9)	43.3 (20.0)	99.0 (17.0)
Opioid related disorders	206 (13.0)	29 (6.9)	17.6 (23.3)	9.1 (28.2)	39.8 (19.3)	16.3 (31.9)	34.9 (16.1)	59.3 (10.2)
Cannabis related disorders	256 (16.1)	56 (13.2)	16.5 (21.8)	3.1 (9.5)	38.0 (18.4)	10.9 (21.4)	45.5 (21.0)	86.1 (14.8)
Sedative, hypnotic, or anxiolytic related disorders	113 (7.1)	17 (4.0)	11.5 (15.2)	2.0 (6.2)	22.7 (11.0)	9.2 (18.1)	16.5 (7.6)	34.1 (5.9)
Cocaine related disorders	229(14.4)	35 (8.3)	16.3 (21.7)	7.4 (22.9)	39.4 (19.1)	8.1 (15.9)	42.1 (19.4)	80.6 (13.8)
Other psychoactive substance related disorders	209 (13.2)	47 (11.1)	15.0 (19.9)	7.2 (22.2)	35.2 (17.1)	11.2 (21.9)	28.5 (13.2)	64.9 (11.2)
Unspecified/other drug dependence	178 (11.2)	18 (4.3)	17.2 (22.8)	5.6 (17.4)	32.6 (15.8)	9.3 (18.3)	35.8 (16.6)	59.4 (10.2)
Mental health
Depression	1,040 (65.5)	257 (60.8)	60.1 (79.7)	28.0 (86.6)	142.5 (69.1)	40.0 (78.2)	153.9 (71.1)	358.5 (61.6)
Schizophrenia, schizotypal, delusional, and other non-mood psychotic disorders	212 (13.4)	70 (16.6)	12.8 (17.0)	3.0 (9.4)	29.0 (14.0)	8.8 (17.2)	24.0 (11.1)	64.4 (11.1)
Anxiety, dissociative, stress related, somatoform and other non-psychotic mental disorders	856 (53.9)	195 (46.1)	53.8 (71.3)	22.9 (70.6)	130.1 (63.1)	39.1 (76.6)	129.2 (59.7)	285.9 (49.1)
Disorders of adult personality and behavior	206 (13.0)	50 (11.8)	17.6 (23.3)	6.0 (18.6)	30.4 (14.8)	9.9 (19.3)	31.6 (14.6)	60.5 (10.4)
Other mood disorders	417 (26.3)	101 (23.9)	32.6 (43.3)	16.6 (51.2)	63.1 (30.6)	15.6 (30.6)	60.5 (27.9)	127.6 (21.9)
Charlson comorbidity index (mean, SD)	1.4 (2.1)	0.8 (1.6)	2.4 (2.5)	2.8 (2.7)	1.7 (2.1)	2.4 (2.7)	1.7 (2.2)	1.3 (2.0)

aCounts, percentages, and means are unweighted.

bCounts, percentages, and means are weighted by patient’s posterior probabilities for belonging to a given class.

SD = standard deviation.

Longitudinal patterns of opioid prescription receipt among patients experiencing interpersonal violence

Table 2 shows the model fitting results for the LGCA. Four-hundred-twenty-three patients (26.7%) did not receive an opioid prescription during the study period and, thus, were not included in the LGCA. Fit indices favored solutions with an increasing number of classes. The BLRT behaved similarly, yielding significant test results comparing a K-class model relative to a K—1-class model with an increasing number of classes before becoming unstable at a 7-class solution. We selected a 6-class model for further analyses based on fit indices, interpretability, class size, and stability.

Table 2

Model fit statistics (n = 1,164).

Number of Classes	AIC	BIC	Adjusted BIC	BLRT		Entropy
				Test Statistic	p value
1 Class	31860.484	31870.603	31864.251	--	--	--
2 Class	26787.922	26813.220	26797.339	5078.562	0.0000	0.931
3 Class	25646.498	25686.975	25661.565	1147.424	0.0000	0.889
4 Class	25176.558	25232.213	25197.274	475.941	0.0000	0.830
5 Class	24885.560	24956.395	24911.926	296.997	0.0000	0.758
6 Class	24689.233	24775.247	24721.249	202.327	0.0000	0.738
7 Class	24585.536	24686.728	24623.201	109.698	0.0000	0.757

Note. AIC = Akaike information criteria; BIC = Bayesian information criteria; BLRT = Bootstrap Likelihood Ratio Test.

Fig 1 shows trajectories of opioid prescription receipt based on estimated probability over time. We characterized the trajectory classes based on beginning probability, ending probability, and degree of change as follows:

Fig 1

Estimated probability of opioid prescription receipt by latent class trajectories.

Never: no opioid prescription receipt (26.7%)

Class 1: Start medium, end medium, moderate decrease (4.8%)

Class 2: Start high, end high, slight increase (2.0%)

Class 3: Start medium, end low, sharp decrease (13.0%)

Class 4: Start low, end high, sharp increase (3.2%)

Class 5: Start low, end medium, moderate increase (13.6%)

Class 6: Start low, end low, stable (36.7%)

Most participants clustered around patterns indicative of no or minimal opioid prescription probability (63.4%, Never and Class 6). The remaining classes, which characterize higher probabilities of opioid prescription receipt, demonstrated considerable variability over time. The two classes that began with a medium probability (Class 1 and Class 3), both showed decreases over time. The only class that began with a high probability (Class 2), remained high over time and slightly increased. Two classes that started low (Class 4 and 5), demonstrated increases over time.

Sociodemographic and clinical characteristics associated with opioid prescription receipt trajectories

A number of sociodemographic and clinical characteristics were associated with opioid prescription receipt trajectories (Table 1, Fig 2). A summary of the key characteristics for each trajectory group is provided in S2 Table. Overall, prescription opioid classes (Classes 1–6) demonstrated higher prevalence for smoking, chronic pain, substance use disorders, mental health disorders, and Charlson comorbidity index compared with the Never class. Never class were also the youngest among all classes. Class 6 (start low, end low) was most like the Never class, demonstrating the smallest standardized mean differences. The largest proportion of White, non-Hispanic patients was seen in the classes with the highest probability of prescription opioids (Class 1, 2, and 4). Males were also more likely to be represented in these classes whereas the highest proportion of females were in Class 3 (start medium, end low, sharp decrease). Black, non-Hispanic patients were more likely to be in low prescription opioid classes (Class 3, 5, and 6) and Hispanic and Other, non-Hispanic patients in the Never class. The largest proportion of insured patients were seen in higher prescription opioid classes (Class 1 and 2). Those with varying insurance status were also more likely to be represented in high prescription opioid classes (Class 2 and 4). Classes 1, 2, and 4, which demonstrated the highest probabilities of opioid use, had the highest proportions of chronic pain diagnoses, substance use disorders, including OUD, and mental health diagnoses.

Fig 2

Standardized mean differences in patient characteristics by latent class.

Comparison of Class 4 and Class 5

Individuals who clustered around Class 4 and Class 5 both started with a low probability of opioid prescription; however, Class 4 demonstrated a sharp increase in the probability of opioid prescription over time whereas Class 5 demonstrated a more moderate increase. We compared these two classes to identify factors that may contribute to the increased opioid prescription trajectory for Class 4. Both classes had similar demographics except Class 5 had a higher proportion of Black, non-Hispanics. Class 4 had a higher prevalence of chronic pain diagnoses (except for interstitial cystitis/bladder pain syndrome and migraine) and mental health disorders. Class 4 also had a higher prevalence of substance use disorders.

Comparison of Class 1 and Class 3

Class 1 and Class 3 both started with a medium probability of prescription opioid use; Class 3 demonstrated a sharp decline over time and Class 1 demonstrated a more moderate decrease. We compared these two classes to identify factors that may contribute to the sharper decline in opioid prescription trajectory for Class 3. Both classes had similar demographics except Class 3 had a higher proportion of females. Class 3 had a lower prevalence of most pain diagnoses (except endometriosis and interstitial cystitis/bladder pain syndrome), substance use disorders, and mental health diagnoses compared to Class 1.

Discussion

This study is the first to examine longitudinal prescription opioid trajectories among a cohort of patients with a history of interpersonal violence. Almost three-quarters (73.3%) received an opioid prescription during the study period (January 2004–August 2019). We identified six distinct patterns of opioid prescriptions over time, varying by starting and ending probability (high, medium, low, never) and degree of change over time (increase, decrease, stable). These findings indicate significant heterogeneity in opioid prescriptions over time among those impacted by interpersonal violence. This heterogeneity echoes findings from prior research that have modelled opioid use trajectories among other high-risk populations and provides further evidence of the presence of clinically significant subgroups of people who use prescription opioids [9-Pharmacoepidemiol Drug Saf. 2019 ">12].

Sociodemographic and clinical characteristics varied by prescription opioid trajectory class. Males were more likely to be represented in classes with the highest probability of being prescribed opioids (Class 1, 2, & 4) and the highest proportion of females was seen in Class 3 (start medium, end low, sharp decrease). These findings are consistent with prior research showing that women are more likely to be prescribed and use lower doses of opioids compared to men; [27] however, contrast with other studies indicating that women, in general, are more likely to be prescribed opioids [28-31]. The higher proportion of males in the higher prescription opioid use classes may be problematic given that prior research indicates that men are at higher risk of misusing opioids [31-33]. The potential role of interpersonal violence on these differences is unclear. Only one study has examined sex differences in prescription opioid use among individuals with a history of interpersonal violence and did not find significant differences between males and females [34]. Additional work is needed to understand better sex differences in the relationship between interpersonal violence and prescription opioid use.

Race was also associated with class membership in our study. Specifically, Black, non-Hispanic patients were more likely to be in low prescription opioid classes (Class 3, 5, and 6) and Hispanic and Other, non-Hispanic patients in the Never class. This is consistent with the abundance of research highlighting racial/ethnic disparities in opioid prescribing practices through the U.S. [35-38]. Specifically, individuals of racial/ethnic minority statues are less likely than Whites to be prescribed opioids for pain and more likely to have pain symptoms underestimated by providers [35-38]. These disparities in pain management can be attributed to mistrust and biases by providers; [39, 40] negative experiences that often influence a patient’s engagement in healthcare services, quality of patient-provider communication, and health outcomes [41-44]. These consequences may be exacerbated in patients with a history of interpersonal violence, which has also been shown to lead to discomfort, perceived discrimination, stigma, and mistrust in the healthcare environment [45-47]. Additional research is needed to understand better the intersection of interpersonal violence and racial/ethnic bias on the pain experience and to develop evidence-based approaches to mitigate their effects on pain management.

There was high prevalence of underlying co-morbidities within our cohort including chronic pain (54.6%), substance use disorders (39.0%), and mental health diagnoses (76.9%). The classes with the highest probability of prescription opioids (Classes 1, 2, and 4) also had the highest proportions of chronic pain diagnoses, substance use disorders, and mental health diagnoses. These co-morbidities also appeared to contribute to escalating risk of opioid use over time when comparing Class 4 to Class 5 and Class 1 to Class 3. Our findings also echo prior literature indicating that long-term prescription opioid use increases risk for OUD, both in the general population and among those with a history of IPV [3, 20]. In our cohort, 13.0% received an OUD diagnosis after baseline and this proportion was markedly higher among classes with higher probability of prescription opioid receipt over time (e.g., Class 4 = 31.9%; Class 2 = 28.2%; Class 1 = 23.3%). Prior research estimates that, in the general population, about 8–12% of people who are prescribed opioids will develop an OUD [3-5]. While we are unable to determine if the increased rates of OUD in our study are a result of new OUD cases or increased monitoring/identification, our findings provide some preliminary evidence that IPV may increase risk for the development of OUD among those prescribed opioids. Additional research is needed to better understand this potential risk.

The high rates of co-morbidities found in this study, especially among classes with higher probability of prescription opioid receipt over time, is consistent with the abundance of research demonstrating that chronic pain, substance use, and mental health conditions are common consequences of interpersonal violence [48-51]. More recently, researchers have begun to establish that these consequences are not independent of each other, but rather interact synergistically (often referred to as syndemic health conditions [52]), resulting in worsening health [20, 50, 53, 54]. Given this, it may be particularly important to monitor for synergistic co-morbidities when providing pain management and offer treatment that is trauma-informed, destigmatizing, and integrated into routine care.

A major limitation, however, in providing effective pain management for patients impacted by interpersonal violence is a lack of trauma-informed pain management strategies. Trauma-informed care provides a model to assist health providers understand, recognize, and respond to the effects of trauma on patients’ health [55]. Interventions based on a trauma-informed approach have shown promise for improving patient outcomes in a variety of settings; [56-62] however, limited evidence exists on the impact of trauma-informed care on pain-related outcomes. Additional research is needed to examine how current evidence-based pain management practices can be tailored to better address the specific challenges (e.g., comorbid conditions) survivors of interpersonal violence face during their path to recovery.

Limitations

There are several limitations, which should be acknowledged. 1) Our sample was limited to UNC Health Care System patients identified as experiencing interpersonal violence and referred for services. This misses those who may be experiencing interpersonal violence but were not identified or referred, thus reducing our generalizability. In addition, our study was conducted in one health care system in North Carolina, which also impacts generalizability. 2) Given the secondary nature of our data, we were not be able to determine the frequency, duration, or severity of interpersonal violence, which has been shown in prior work to impact opioid use [34]. 3) We were only able to examine longitudinal trends in opioid prescription receipt, and unable to examine trends in prescription dosage among people affected by interpersonal violence. This is important because prior studies show that higher doses and longer durations of opioid receipt are associated with OUD and overdose. Our study findings suggest that people with exposure to interpersonal violence are at high risk for both opioid use and OUD and may, therefore, be an important factor to consider when prescribing opioids. However, without information on morphine milligrams equivalent or days’ supply, it is difficult to identify those who may specifically benefit from an opioid prescribing-related intervention. 4) Lastly, we examine the probabilities of receiving an opioid prescription for pain management, which may overestimate the people who actually fill and consume those prescription opioids. However, given the high prevalence of pain, substance use disorders, mental health disorders, and Charlson morbidity index among those receiving opioid prescriptions in this study, it is feasible to expect that vast majority of these prescriptions may have been filled and consumed.

Conclusions

Results from this exploratory study suggest that people with exposure to interpersonal violence are at high risk for prescription opioid use (>70%) and OUD (13%). The different trajectories may help target interventions to people with interpersonal violence exposure who are at the highest risk for OUD (trajectories 1, 2, and 4). The high rates of co-morbid conditions among trajectories with higher probability of prescription opioid receipt over time highlight the importance of monitoring for co-morbidities among patients receiving care for chronic pain and the need for improved trauma-informed pain management strategies.

International classification of diseases 9th and 10th edition (ICD-9, ICD-10) codes used to define model covariates.

(DOCX)

Click here for additional data file.

Summary of key characteristics for each opioid prescription trajectory class.

(DOCX)

Click here for additional data file.

6 Jun 2022

Trajectories and correlates of opioid prescription receipt among patients experiencing interpersonal violence

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Reviewer #1: This study examined trajectories of prescription opioid receipt and associated baseline characteristics among patients who were referred to interpersonal violence services in an academic healthcare system. This study is potentially of interest to researchers and clinicians, however the manuscript has some weaknesses. The analysis is limited by not being able to distinguish high-risk opioid prescribing from any prescription opioid receipt, and this limitation should be more robustly addressed. Additionally, some aspects of the methods need clarification. Finally, it is not clear from the discussion what implications these findings have for clinical practice.

MAJOR COMMENTS

• Not distinguishing some indication of high-risk opioid prescribing (e.g., long-term or high dose) from any prescription opioid receipt seems like an important limitation, given that the introduction frames potential adverse outcomes (nonmedical use, OUD) in terms of prevalence among patients receiving long-term opioid therapy. The authors note that dosage and duration information is not readily available prior to April 2014 – might it be possible to analyze a subset of the data for which these measures are available to gain some insight into patterns of high-risk prescriptions (perhaps just looking at characteristics associated with high-risk prescriptions, if there is not enough longitudinal data to examine trajectories over time)?

• Related to the above point, the introduction could make a better case for why it is useful to examine trajectories of any prescription opioid receipt in this population (e.g., are there data demonstrating increased risk for adverse outcomes resulting from any opioid exposure in this population)? The section of the introduction stating “one population particularly vulnerable to deleterious effects of opioid use is individuals with a history of interpersonal violence” could be expanded in this regard. Additionally, the need for research examining high-risk opioid prescriptions in this population deserves more attention in the limitations section.

• Please explain in the methods why baseline was defined differently for patients who had ≥1 opioid prescription (date of first prescription) and patients who had none (date of first EHR entry), rather than defining consistently as the date of first EHR entry during the study period for all patients.

• It is unclear to me in the methods whether referral for interpersonal violence services preceded the measured trajectory of opioid receipt. Did the referral always precede the baseline date (first opioid prescription or EHR entry during the study period), or could patients have entered the cohort prior to being referred?

• The variables section of the methods should more clearly state that the outcome was defined as binary indicator of any prescription opioid receipt in each month.

• Please provide more information about how smoking and illicit drug use history were measured – are these routinely screened for and documented in this health care setting?

• The implications highlighted in the discussion do not clearly follow from these findings. For example, the authors argue that history of interpersonal violence should be considered when assessing for opioid risk and in pain management care – this is presumably because people with a history of interpersonal violence are at higher risk for adverse opioid-related outcomes than those without such a history, however that was not examined in this study. What are the implications of findings from the present study—which examined trajectories of any prescription opioid receipt and associated baseline characteristics among people with history of interpersonal violence—for clinical care?

MINOR COMMENTS:

• Abstract, line 15: I suggest clarifying here that classes are characterized by “probability of any prescription opioid receipt at the start and end of the study period” and “change in probability over time”

• Introduction, line 23: I think this is meant to say “opioid use disorder” instead of “opioid disorder”

• Introduction, lines 41-43: Please clarify whether interpersonal violence is associated with increased likelihood of these outcomes.

Reviewer #2: The authors estimated trajectories of prescription opioid receipt over a period of 5 years among a large cohort of patients with histories of interpersonal violence. Using latent class growth analysis, they found 6 patterns of prescription opioid receipt; patterns with higher probability of opioid receipt had higher proportions of men, chronic pain diagnoses, mental health and SUD diagnoses. The authors conclude that their findings highlight the importance of accounting for these factors in opioid risk assessments and pain management plans. This exploratory analysis adds to the limited literature on prescription opioid use among people with IPV, and the paper is well-written. The conclusions and clinical importance of this work is less clear, especially because no dosage information was available and the design did not include any outcome assessment. I believe the paper could be strengthened by more clearly connecting the findings to specific clinical implications.

Major comments

Well-written paper addressing gaps in knowledge about prescription opioid use among people with histories of IPV.

Were models looking at frequency or number of opioid prescriptions, or at something else? At what time intervals? It is not clear to me exactly what “high, medium, low, etc.” means here, which limits the ability to interpret the clinical relevance of the findings.

I believe the paper would be stronger if the authors could more directly explain how their findings add new understanding that will inform clinical practice. It is notable that such a high proportion of this cohort with histories of IPV had received an opioid prescription, that mental health and substance -related diagnoses were so prevalent, and that these comorbidities were (not surprisingly) more prevalent among classes with higher probability of receiving opioid prescriptions. What do we take away from this? I appreciate the discussion about synergism and the need for better screening tools/practices. Perhaps one implication is that when treating people with chronic pain and IPV, it may be particularly important to monitor closely and to offer treatment that is trauma-informed, destigmatizing and integrated, so that synergistic comorbidities are addressed.

Minor comments

It seems all data was electronically abstracted from the EHR, but I do not see that this is made explicit. Please clarify.

Would be helpful to characterize the cohort by type of IPV–in particular, how many were older /vulnerable adults vs people who had experienced intimate partner violence/sexual trauma.

In Table 1, under the class analysis, the percentages are based on the total cohort of 1587. I wonder if these should be calculated based on the number of participants who received an opioid rx and were included in the class analysis.

Line 160: the text states that 70.8% of participants were in the never + class 6 groups; however, the percentages in those groups do not sum to 70%. This may need correction or clarification.

The lack of dose/MED is a significant limitation. While noted in the limitations section, I wonder if the authors could discuss this further. What specifically is the clinical relevance of the classes that were identified by their model, and how does the lack of MED data affect the clinical implications?

Does the calculation of standardized differences allow for calculations of significance and for adjustment for confounders? I am not sure what to make of differences in prevalence alone. Especially regarding gender differences, as the numbers were small.

Line 217: prescription opioid users could be rephrased to more patient-centered language (“people who use prescription opioids”)

How do the authors interpret the prevalence of opioid related diagnoses in the different classes, given that people with OUD at baseline were excluded: does this suggest that these patients developed OUD or simply reflect better capture of diagnoses, or both?

Line 258: Given that the data does not include any development of OUD or other outcomes, I’m not sure the findings justify this statement.

Line 259: Please change "abuse" to "use"

I believe the conclusions (final paragraph) are overstated and recommend that they be reworded. Without dose information, I’m not sure this can be considered “comprehensive.” And I’m not convinced that the authors have described how “understanding profiles of risk informs clinical screening protocols and precision of clinical treatment plans…” I think the key questions are how these risk profiles translate into improving appropriate / safety of opioid prescribing and chronic pain management in general for this population.

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Reviewer #2: No

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20 Jul 2022

See attached Response to Reviewers Letter

Submitted filename: IPRC Opioid Trajectory Response to Reviewers.docx

Click here for additional data file.

17 Aug 2022

Trajectories and correlates of opioid prescription receipt among patients experiencing interpersonal violence

PONE-D-22-04197R1

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30 Aug 2022

PONE-D-22-04197R1

Trajectories and correlates of opioid prescription receipt among patients experiencing interpersonal violence

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