Prescription Opioid Medication Survey: A Tool to Collect Deep Phenotypic Data on the Multifactorial Pathways to Opioid Use Disorder in Clinical and Population-Based Cohorts

Abstract

Introduction

We are in the midst of an opioid epidemic. In the USA, more than a third of the country knows someone who has died from an opioid overdose. Prescription opioids (e.g., oxycodone, hydrocodone, and fentanyl) are commonly used and misused, and it has been estimated that approximately 8–12% of individuals who misuse opioids will subsequently develop an opioid use disorder (OUD). While emphasis has been placed on understanding OUD and the associated adverse effects, there remains a critical gap in systematically characterizing the multifactorial pathways (e.g., behavioral, clinical, genetic, and socio-demographic characteristics) that contribute to the transition from initial use to misuse to OUD.

Methods

To address this gap, we introduce the Prescription Opioid Medication Survey (POMS), an online 120-item assessment that compiles multiple validated and standardized instruments. POMS is intended for individuals with any lifetime prescription opioid use. POMS captures various aspects of prescription opioid use including data on opioid use patterns, subjective effects (e.g., euphoria, nausea), problematic use, withdrawal, OUD, overdose, treatment history, and remission. It also addresses comorbid risk factors such as surgical history, chronic pain, other substance use disorders (SUD; e.g., nicotine, alcohol, cannabis, stimulants), other addictive behaviors (i.e., gambling, sexual behaviors, and gaming), and family history of SUD and other addictive behaviors. Mental health assessments, including screening for depression and anxiety, self-reports of eight psychiatric disorders (anxiety, depression, bipolar, schizophrenia, attention-deficit/hyperactivity disorder, post-traumatic stress disorder, obsessive-compulsive disorder, eating disorders), and related mental health conditions (e.g., loneliness, suicide, trauma) are included, along with data on personality traits (e.g., risk-taking, delay discounting, wisdom) and socio-demographic factors. POMS is intended to be administered in clinical settings and large population-based cohorts, facilitating data collection that can enable discoveries to inform better prevention and intervention strategies for OUD.

Conclusion

POMS offers a comprehensive tool for systematically capturing the multifactorial risk factors associated with opioid misuse and OUD, providing insights that can inform prevention and intervention strategies.

Introduction

The current opioid crisis, which began in the late 1990s, is a major public health concern afflicting millions of people [1]. In the USA alone, over 1.1 million people have died due to opioid-related overdoses between 1999 and 2023 [2, 3]. While the number of opioid-related overdoses has declined in most states in recent years [4], globally, nearly 16 million people, including over 2.7 million in the USA, are estimated to have an opioid use disorder (OUD) [5, 6]. Although OUD affects a broad spectrum of individuals [7, 8], certain populations- including individuals from lower socioeconomic backgrounds, racial and ethnic minorities, women, rural communities, and those with co-occurring mental health conditions- are disproportionately affected due to structural inequities, socioeconomic challenges, and disparities in healthcare access [9–12].

In the early 1960s, 80% of individuals who reported opioid misuse began with heroin, while by the 2000s, 75% began with prescription opioid pain medication (e.g., hydrocodone, oxycodone, fentanyl) [13]. In 2023, 8.6 million people reported misusing prescription opioids [2–5]. Prescription opioid misuse is defined as using prescription opioids not as prescribed, or using prescription opioids without a prescription [14–16]. This includes taking prescription opioids in higher doses, more frequently, or for longer periods than prescribed, as well as using them for other reasons, such as seeking relief from chronic pain beyond what was prescribed, relief from anxiety or depression, or recreational use to induce pleasurable effects [17–22].

The transition from initial use to misuse and eventually OUD is often gradual. This process may start with either medically prescribed opioid use for accepted indications (e.g., surgery, pain), misuse of opioid medications (e.g., taking opioids without medical guidance), or recreational use. An individual’s initial subjective experience taking prescription opioids, whether positive (e.g., euphoria) or negative (e.g., nausea), can influence their continued use [23, 24]. Positive experiences, such as euphoria or analgesia (pain relief), can increase the likelihood of prescription opioid misuse. Similar observations have been made for substances such as alcohol [25–33], nicotine [34–36], cannabis [37–40], stimulants [41–45], and hallucinogens [46]. Prolonged use may lead to tolerance and pain sensitization, and may alter emotionality or executive function, among others [47–49], which may result in continued or increased use [28, 50, 51] and potentially OUD.

OUD is a chronic, relapsing condition driven by biological and environmental factors [17]. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) defines OUD as a problematic pattern of opioid use that leads to significant impairment or distress, characterized by at least two of eleven specific criteria within a 12-month period [52]. These criteria include taking larger amounts of opioids than intended, unsuccessful attempts to cut down, spending excessive time obtaining or using opioids, cravings, failure to fulfill major obligations, continued use despite problems, giving up important activities, use in hazardous situations, and experiencing tolerance or withdrawal [52]. Withdrawal symptoms can emerge when opioid use is reduced or discontinued. These symptoms encompass a range of severe physical and psychological discomforts, including nausea, muscle aches, sweating, agitation, anxiety, insomnia, diarrhea, tremors, increased heart rate, and intense cravings [53, 54]. The intensity and duration of these withdrawal symptoms vary depending on the level of dependency, the specific opioid used, and a plethora of individual factors (e.g., age, sex, mental health conditions, genetic predisposition, and polysubstance use) [55]. For example, potent opioids such as fentanyl may cause more intense and prolonged withdrawal compared to less potent opioids such as oxycodone [56, 57].

The transition from initial opioid use to OUD is multifactorial, involving a complex interplay of socio-demographic, behavioral, clinical, and genetic factors (Fig. 1) [58–60]. Individuals with other SUD, mental health conditions, or a history of trauma are particularly vulnerable [8, 61–63]. Contributing factors for OUD development also include prolonged use, high doses, and certain prescribing practices, such as prescribing opioids for conditions where the benefit with opioids is less clear (e.g., minor injuries, headaches), which increases the risk of dependency and subsequent OUD development [8, 64]. Additionally, opioid-induced hyperalgesia can paradoxically heighten sensitivity to pain, potentially leading to increased opioid use and greater risk of dependency [65, 66].

Fig. 1.

Interplay of genetic, socio-demographic, behavioral, and clinical factors in shaping opioid use and the development of an OUD.

While more emphasis has been placed on the most severe outcomes of opioid use, such as opioid overdose deaths and the development of OUD [67–70], relatively less is understood about the earlier stages of the OUD continuum, including the transition from initial opioid use to misuse and ultimately to OUD [60]. Most studies use surveys that are focused on OUD and thus miss the early stages and transition – meaning they are less informative when administered in the general population [71]. The stigma surrounding opioid misuse further complicates accurate identification of OUD risk factors (because people may be reluctant to disclose opioid misuse), emphasizing the need for methods that focus on early detection of risk for misuse and progression to an OUD [72, 73]. The directors of NIDA and the National Institute of Alcohol Abuse and Alcoholism (NIAAA) have emphasized the importance of identifying the early signs of SUD (“pre-addiction”) for effective prevention and treatment strategies [74].

To overcome these limitations and better characterize the multifactorial pathways to OUD, we introduce the Prescription Opioid Medication Survey (POMS), which is a comprehensive tool that integrates multiple key factors derived from multiple well-established questionnaires into a single survey. This approach aims to address the limitations of previous measures and provides a more holistic assessment of opioid use and its progression to OUD. POMS is designed for individuals who have used prescription opioids at least once in their lifetime. It comprises 120 questions derived from validated and standardized instruments (Table 1) and takes about 15 min (median time is 12 min) to complete online. Gating logic is employed, ensuring that not all questions are administered to all participants, thereby decreasing the overall testing time and reducing participant burden. Table 1 presents the 25 categories of POMS questions and their sources.

Table 1.

POMS comprises 120 questions that are derived from various validated assessments

Category	Numbers	Source
Opioid use history and experience
Lifetime prescription opioid use	Q1–2	Gilson et al. [75], 2009
First time use of prescription opioid	Q3–8	McCabe et al. [76], 2007; [77], 2020
Patterns of prescription opioid use	Q9–10	Elliott and Jones [78], 2019
Subjective effects upon the first and longest period of prescription opioid use	Q11–33	Opioid Checklist (Preston et al. [79], 1989), ARCI (Haertzen et al. [80], 1963), HOME (Bruehl et al. [51], 2019)
New persistent opioid use after surgery	Q34–37	Soneji et al. [81], 2016; Brummett et al. [82], 2017
History of chronic pain	Q38–40	Barth et al. [83], 2013; Nazarian et al. [84], 2021; Cleeland and Ryan [85], 1994; Melzack [86], 1987
Problematic opioid use	Q41–51	ORT (Webster and Webster [87], 2005), Opioid Checklist (Preston et al. [79] 1989), COMM (Butler et al. [88], 2007), POMAQ (Coyne et al. [89], 2020), the POMI (Knisely et al. [90], 2008), SDS (Gossop et al. [91], 1995) ASI (McLellan et al. [92], 1992), AUDIT (Saunders et al. [93], 1993; Babor et al. [94] 2001; Higgins-Biddle et al. [95], 2018)
OUD diagnosis	Q52	DAST (Skinner [96], 1982)
Illicit opioid use	Q53	ASSIST (WHO [97], 2002)
Overdose history	Q54–55	OOKS (Williams et al. [98], 2013)
Treatment for OUD	Q56–57	ASI (McLellan et al. [92], 1992)
Remission for OUD	Q58	DSM-5 (American Psychiatric Association [52], 2013)
Other problematic substance use and addictive behaviors
Other problematic substance use (e.g., alcohol, tobacco, cannabis), SUDs, and addictive behaviors (gambling, sexual behaviors, gaming)	Q59–65	Zhang [99], 2023; Vekaria [100], 2021; Levis [99], 2021
Family history of SUDs and other addictive behaviors	Q66	Deak et al. [101], 2021
AUD	Q67–76	AUDIT (Saunders et al. [93], 1993; Babor et al. [94], 2001; Higgins-Biddle et al. 95], 2018)
Comorbid psychiatric or mental health conditions
Anxiety and depression	Q77–80	GAD-2 (Kroenke et al. [102], 2007; Sapra et al. [103], 2020); PHQ-2 (Kronenke et al. [104], 2003; Gillbody et al. [105], 2007; Levey et al. [101], 2021)
Loneliness	Q81	McDonagh et al. [106], 2020; Abdellaoui et al. [107], 2019; Cacioppo et al. [108], 2012; Russell et al. [109], 1978
Trauma/PTSD	Q82–83	Adapted from the PCL-5 [110]
Self-reported psychiatric diagnoses (e.g., anxiety, depression, bipolar, schizophrenia, ADHD, PTSD, OCD, eating disorders)	Q84–91	Sullivan et al. [111], 2006
Suicide-related behaviors	Q92–93	Posner et al. [112], 2011; Gili et al. [113], 2019
Personality traits
Risk-taking	Q94	Aklin et al. [114], 2012; Sutin et al. [115], 2019
Delay discounting	Q95–104	Towe et al. [116], 2015; Gray et al. [117], 2016; Karakula et al. [118], 2016; Mishra and Lalumière [116], 2017; Evren and Bozkurt [119], 2017
Wisdom	Q105–111	Sokol et al. [120], 2018; Garland et al. [121], 2019; Wang et al. [122], 2021; Thomas et al. [123], 2022
Socio-demographics
Basic socio-demographics	Q112–120	Spiller et al. [124], 2009; Cochran et al. [125], 2017; Dydyk et al. [6], 2024; Cruden and Karmali [126], 2022; Siddiqui et al. [8], 2022

ARCI, Addiction Research Center; HOME, History of Opioid Medical Exposure; ORT, Opioid Risk Tool; COMM, Current Opioid Misuse Measure; POMAQ, Prescription Opioid Misuse and Abuse Questionnaire; POMI, Prescription Opioid Misuse Index; SDS, Severity Dependence Scale; ASI, Addiction Severity Index; AUD, Alcohol Use Disorder; AUDIT, Alcohol Use Disorder Identification Test; DAST, Drug Abuse Screening Test; ASSIST, Alcohol, Smoking and Substance Involvement Screening Test; OOKS, Opioid Overdose Knowledge Scale; DSM-5, The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; GAD-2, Generalized Anxiety Disorder 2-item; PHQ-2, Patient Health Questionnaire-2; PCL-5, PTSD Checklist for DSM-5.

POMS captures various aspects of prescription opioid use, from initial experiences to OUD self-reported diagnosis, including data on opioid use patterns, subjective effects (e.g., euphoria, nausea), misuse, withdrawal, OUD, overdose, treatment history, and remission (Fig. 2). It also addresses comorbid risk factors such as surgical history, chronic pain, other SUD (i.e., nicotine, alcohol, cannabis, stimulants), and addictive behaviors (i.e., gambling, sexual behaviors, gaming), and family history of SUD and other addictive behaviors. Mental health assessments, including screening for depression and anxiety, self-reports of eight psychiatric disorders (anxiety, depression, bipolar, schizophrenia, attention-deficit/hyperactivity disorder, post-traumatic stress disorder [PTSD], obsessive-compulsive disorder, and eating disorders), and related mental health conditions (e.g., loneliness, suicide, trauma) are included, along with data on personality traits (e.g., risk-taking, delay discounting, wisdom) and socio-demographic factors (e.g., age, sex/gender, race/ethnicity, education, income). The full list of questions, response options, and scoring is provided in online supplementary materials 1, 2 (for all online suppl. material, see https://doi.org/10.1159/000546389), formatted for REDCap and easily replicable in similar online survey platforms.

Fig. 2.

The POMS assesses the progression initial use (Q1–8) to the longest use (Q9–10), subjective effects (Q11–33), problematic opioid use (Q41–51, 53), and OUD (Q52). Questions related to withdrawal (Q46–47), overdose history (Q54–55), treatment (Q56–57), and remission (Q58) are included to account for fluctuations in problematic use and the emergence of dependence symptoms. The order in which these experiences occur can vary. POMS also includes questions related to comorbid risk factors such as new persistent opioid use after surgery (Q34–37), chronic pain (Q38–40), other problematic substance use (e.g., nicotine, alcohol [Q59–62, 67–76] as well as compulsive gambling, sexual behavior, and gaming [Q63–65], family history of substance use [Q66], mental health [e.g., anxiety, depression, loneliness; 77–93]), personality characteristics (i.e., risky behavior [Q94], delay discounting [Q95–104], wisdom [Q105–111], and socio-demographics [Q112–120]).

Methods

POMS Instrument and Procedures

Lifetime Prescription Opioid Use (Q1–2)

Q1 asks “Have you ever taken any opioid pain medications or narcotics regardless of whether they were prescribed to you or not? For example, codeine, hydrocodone, oxycodone, or morphine?” [75]. Response options include “Yes”; “No”; “I am not sure.” This question serves as gating logic to identify those with lifetime exposure. Only participants who report having taken at least one prescription opioid in their lifetime are invited to complete the full survey, ensuring the data collected are focused on individuals with direct experience.

More potent prescription opioids may precipitate the OUD development more rapidly. If participants select “Yes” or “I am not sure” in Q1, Q2 asks “Which types of opioid pain medications have you ever taken? Please select all that apply.” Response options include an extensive and customizable list of 12 prescription opioids, identified by both their generic and trade names (e.g., oxycodone, morphine, fentanyl, methadone, and buprenorphine; online suppl. materials 1, 2) [127]. There is also an open text option to add “Other” prescription opioids that may not be listed. Participants must select at least one prescription opioid to proceed to subsequent questions. We recommend excluding respondents who select “I don’t know” when deploying POMS, as their inability to name an opioid suggests uncertainty about their exposure.

First Time Use of a Prescription Opioid

To understand the circumstances surrounding the first use of prescription opioids, Q3–8 capture information on the type of first prescription opioid used, illicit use before prescription opioid use, the age at first use, reasons for use, the source of first opioid used (e.g., whether they were prescribed or obtained without a prescription), and the method of administration at the time of initial use.

Type of First Prescription Opioid (Q3)

Q2 asks to list all the types of prescription opioids they have used in their lifetime, regardless of whether they were prescribed or obtained through other means. The specific type of prescription opioid used first (e.g., oxycodone, fentanyl) is important, as it can significantly influence an individual’s expectations, initial experience, including the intensity of effects, side effects, and the potential risk for developing dependence or an OUD trajectory [128, 129]. To account for these nuanced factors, Q3 asks “What type of opioid pain medication did you take for the first time?” Response options include the same extensive and customizable list of 12 prescription opioids, identified by both their generic and trade names (e.g., oxycodone, morphine, fentanyl, methadone, and buprenorphine; online suppl. materials 1, 2) [127]. To streamline responses and ensure consistency with previous selections, the list of prescription opioids identified in Q2 can be presented for participants to choose from. Additionally, there is an open text option to add “other” prescription opioids that may not be listed. An “I don’t know” option is included to account for potential recall difficulties. Including this option helps reduce the likelihood of respondents guessing, thereby improving data accuracy.

Heroin or Illicit Fentanyl Use before Prescription Use (Q4)

The type of opioid first used is associated with varying risks of dependency and long-term adverse outcomes, with heroin and illicit fentanyl often linked to more rapid transitions to dependence compared to prescription opioids [130]. Understanding whether individuals used heroin or illicit fentanyl before prescription opioids provides insights into these dynamics. Q4 asks “Before using a prescription opioid for the first time, had you ever used heroin or illicit fentanyl (e.g., nonprescription or street fentanyl)?” Response options include “Yes”; “No”; “I’d prefer not to say.”

Age at First Use (Q5)

Younger individuals are more prone to opioid use, including nonmedical use, due to social influences and risk-taking behaviors. Initiating opioid use at younger ages may alter brain development and increase the risk of dependence and OUD [76, 131, 132]. Conversely, opioid use at older ages – whether through prescriptions for chronic pain or other means – may increase the risk of dependency due to long-term use, complications from polypharmacy, and slower drug metabolism [133]. Q5 asks about age at first use (i.e., “How old were you when you first took opioid pain medication? Your best guess is fine.”) to better characterize the sample and understand the impact of age at first use on other endpoints (e.g., likelihood of developing OUD later in life).

Primary Reason for Initial Prescription Opioid Use (Q6)

Reasons for initial prescription opioid use are associated with varying risks for subsequent prolonged use, misuse, and dependency [134]. For example, individuals prescribed opioids for chronic pain may face a higher risk of prolonged use compared to those prescribed opioids for acute surgical pain. Q6 asks “What was the primary reason you first used an opioid pain medication?” Response options include “Cancer treatment”; “Chronic pain management (e.g., arthritis, fibromyalgia)”; “Surgical pain management”; “Non-surgical, acute injury-related pain (e.g., fractured or broken bone)”; “Dental pain (e.g., tooth extraction or root canal)”; “Other pain from a specific medical condition (e.g., migraines)”; “Cough suppressant”; “To experience a sense of intense happiness, well-being, or a “high” (e.g., euphoria)”; “Other, please specify.”

Source of First Prescription Opioid (Q7)

Sources of first prescription opioids, such as healthcare providers versus illicit markets, may influence patterns of continued use and dependence [19]. To further examine the context of initial use, Q7 asks “What was the source of the opioid pain medication the first time you used them?” Response options include “I got the prescription from a healthcare provider”; “I stole from a doctor’s office, clinic, hospital, or pharmacy”; “I got it from a friend or relative for free”; “I bought it from a friend or relative”; “I bought it from a drug dealer or other stranger”; “Unintentional – I didn’t know it was an opioid”; “Other, please specify.”

Administration of First Prescription Opioid (Q8)

The route of administration can influence the onset, intensity, and reinforcing effects of opioids, which in turn can significantly impact an individual’s response and their risk of developing OUD [135, 136]. For instance, intravenous administration is associated with a substantially increased risk of OUD development, likely because it delivers the drug to the brain more rapidly [137]. Intravenous use is also associated with an elevated risk of other serious health complications [138]. To account for route of administration at first time use, Q8 asks “When you first used a prescription opioid pain medication, what method did you use to take it?” Response options include “Oral (swallowing, popping pills)”; “Intranasal or buccal (snorting, bumping, railing, cheeking, buffing)”; “Intravenous (shooting up, injecting into a vein)”; “Inhalation (smoking, chasing the dragon)”; “Injection (into muscle or under the skin; muscle shot, skin popping)”; “Transdermal (wearing a patch, sticking it on your skin)”; “Sublingual (under the tongue, dissolving)”; “Rectal (plugging, boofing)”; “Other (please specify).”

Patterns of Prescription Opioid Use (Q9–10)

The duration and recurrence of prescription opioid use are significant risk factors for developing OUD [78]. To capture patterns of prescription opioid use, POMS includes two questions related to prescription opioid use frequency. Q9 asks “What was the longest period of time that you used opioid pain medications on a daily or almost daily basis?” Response options include “1 week or less”; “More than 1 week but less than 1 month”; “1 to 3 months”; “More than 3 months but less than a year”; “A year or more.” Q10 asks “During how many separate periods in your life have you used any of these opioid pain medications on a daily or almost daily basis?” Response options include “1 or 2”; “3 or 4”; “5 or 6”; “More than 6 periods.” Q9 also serves as gating logic for the subjective effect questions after prolonged use (Q22–33), such that only individuals who report 1 month or more of daily or almost daily use are asked questions about subjective effects during prolonged use.

Subjective Effects upon the First and Longest Period of Prescription Opioid Use (Q11–33)

Schuckit’s [25] early work demonstrated that individuals who experience fewer negative effects from alcohol consumption tend to drink more over time, elevating their risk for an alcohol use disorder (AUD). In contrast, King et al. ’s [30–32] more recent longitudinal studies demonstrate that heightened sensitivity to alcohol’s rewarding effects predicts the progression to AUD, challenging the traditional understanding of tolerance. The influence of both positive and negative experiences on future substance use has also been noted for other substances, including nicotine [34–36], cannabis [37–40], stimulants [41–45], and hallucinogens [139].

Positive and negative subjective effects of prescription opioids may similarly influence individual responses to these medications, which in turn affect the likelihood of misuse, problematic use, and the development of OUD [140–143]. Prior work has shown that negative effects such as nausea and dizziness may prompt discontinuation of opioid use, while positive effects such as euphoria may reinforce continued opioid use [144, 145] and the development of OUD [145, 146]. Even when retrospectively reporting their first opioid use, individuals with OUD are more likely to describe experiencing both positive emotions, such as relaxation and euphoria, and negative symptoms, such as itching, compared to those without OUD [145, 147, 148].

To capture subjective effects upon first and longest opioid use, POMS includes 23 questions to capture 6 positive effects (i.e., “less pain”; “euphoric”; “energized”; “normal”; “relaxed or calm”; and “like the way you feel overall”) and 5 negative effects (i.e., “nauseated”; “dizzy”; “tired”; “constipated”; and “itchy”). Q11–21 assess subjective effects at the time of first use (e.g., “When you first took a prescription opioid pain medication, to what extent did you feel euphoric, (e.g., extremely happy, good, or high)?”). Response options include “Not at all”; “Mildly”; “Moderately”; “Very much”; “Extremely.” Q22 asks “At the end of the longest time that you used opioid pain medication, how well did the opioid pain medication work for you compared to when you first took them?” with response options that include “Better”; “Same”; “Worse.” Q23–33 assess subjective effects during the period of their longest opioid use (e.g., “At the end of the longest time when you used prescription opioid pain medications, did you feel euphoric (e.g., extremely happy, good, or high)?”). These 11 questions are only posed to those who indicate taking prescription opioids daily or almost daily for “1 to 3 months” or longer (Q9). Response options include “Not at all”; “Mildly; Moderately”; “Very much”; “Extremely.”

These questions and responses are adapted from well-validated questionnaires (Table 1), including the Opioid Checklist [79, 149], the Addiction Research Center Inventory (ARCI) [80], and the History of Opioid Medical Exposure (HOME) [51]. Each question can be measured individually or can be condensed using dimensionality reduction analysis, which allows for the identification of underlying factors or constructs that capture the core aspects of the subjective effects reported by the participants [51, 150, 151].

New Persistent Opioid Use after Surgery (Q34–37)

Approximately 6% of opioid-naive patients who undergo surgery will continue to use prescription opioids more than 3 months after their surgery [82]; this phenomenon is termed “new persistent opioid use” [82]. The reasons that patients continue to use opioids after surgery are complex and are not simply due to surgical pain [82]. Q34 asks “Have you ever had surgery?” with response options that include “Yes” or “No.” For those who select “Yes,” new persistent opioid use is defined by patients who responded “No” to using opioids 3 months prior to surgery (i.e., “Did you use opioid pain medication during the 3 months prior to any surgery?”), “Yes” to using prescription opioids 1 week before or after surgery (i.e., “Did you use opioid pain medication the week before or the week after any surgery?”), and “Yes” to continued use after surgery (i.e., “Did you continue to use opioid pain medications 3 to 12 months after any surgery?”) [82]. Responses to these questions can provide insights into the risk factors, patterns, and prevalence of persistent opioid use among surgical patients [82].

History of Chronic Pain (Q38–40)

Chronic pain, which is broadly defined as the experience of pain for longer than 3 months, increases opioid misuse vulnerability. POMS includes three questions designed to capture the history of chronic pain. Q38 asks “Have you ever experienced chronic pain (pain that lasts for more than 3 months)?” Response options include “Yes” or “No.” If the response is “Yes,” Q39 asks “Was your chronic pain cancer and/or non-cancer related? Please select all that apply” with response options that include “Cancer related pain” or “Non-cancer related pain.” For those who indicate they have experienced non-cancer-related chronic pain, Q40 asks “Have you ever experienced chronic non-cancer related pain in any of the following body areas? Please select all that apply.” Response options include “Head or face pain”; “Neck or back pain”; “Joint, including hip, knee, or shoulder pain”; “Stomach or abdominal pain”; “Pain all over the body”; “None of the above.” These questions help understand the role pain plays in the vulnerability to opioid misuse and OUD development, while valuable, longer questionnaires that exist, such as the Brief Pain Inventory [85] or the McGill Pain Questionnaire-Short Form [86], are not included due to the length of time they take to complete.

Problematic Opioid Use (Q41–51)

Many individuals who meet the criteria for OUD may never receive a formal diagnosis. To address this, POMS includes 10 questions that capture key aspects of prescription opioid misuse. Several opioid-specific screening tools are available for monitoring opioid use and misuse, which include the Opioid Risk Tool (ORT) [87], Current Opioid Misuse Measure (COMM) [88], Prescription Opioid Misuse and Abuse Questionnaire (POMAQ) [89], Prescription Opioid Misuse Index (POMI) [90], and Addiction Severity Index (ASI) [152]. Q41–51, which have been adapted from these questionnaires, capture prescription opioid misuse. Q41 asks “How often have you taken more of your opioid pain medication, meaning a higher dose or more frequently than prescribed for you?” Response options include “Never”; “Seldom”; “Often”; or “Very often.”

While pain comorbidities are prevalent among individuals who misuse opioids, pain is not necessarily the strongest risk factor leading to opioid misuse [153]. Many studies reported the continuous nonmedical use of opioids among persons who misuse opioids as an attempt to get high, improve sleep, decrease anxiety, feel psychologically “normal,” and other motives [19, 154–156]. These findings suggest that while pain is an important factor, the motivations for misuse and possible subsequent progression to OUD are complex and multifaceted. If individuals respond to taking more than prescribed in Q41, participants are then asked to specify their reasons from the following list of six options in Q42: “Feel euphoric,” “Feel relaxed, calmed, or to relieve stress or anxiety,” “Feel normal or more able to function in daily life,” “Relieve pain,” “Sleep,” “Other.”

Behaviors such as seeking early refills or using opioids without a prescription are opioid misuse behaviors that are key risk factors for the development of OUD, though they are not part of the diagnostic criteria for OUD. These actions often reflect motives beyond pain relief, such as seeking euphoria, reducing anxiety, or achieving a sense of normalcy [19]. Q43–51 explore behaviors indicating opioid misuse, withdrawal, and potential physiological dependence, which are derived from the Opioid Checklist [149] and the Severity Dependence Scale (SDS) [91]. Q43–47 ask about needing early refills (i.e., “How often have you ever needed refills before the planned end of your opioid pain prescription, or asked many different doctors, including emergency room doctors, for additional prescriptions?”), using an opioid pain medication that was not prescribed (i.e., “How often have you ever used an opioid pain medication that was not prescribed to you (for example, someone else’s)?”), consuming alcohol while taking opioids (i.e., “How often did you ever drink alcohol while taking your opioid pain medication?”), and experiencing unpleasant psychological or physical symptoms upon cessation (i.e., “How often have you ever experienced any unpleasant psychological feelings when you’ve stopped using opioid pain medications? This may include strong craving for more opioid pain medication, difficulty concentrating, anxiety, depression, sleep problems or restlessness.”). The response options for Q43–47 include “Never”; “Seldom”; “Often”; “Very often.”

Q48–51 ask about difficulty stopping opioid use (e.g., “Has it ever been hard to reduce or stop taking an opioid pain medication”), functional problems related to opioid use (i.e., “Have you ever failed to do what was expected of you (including missing work or school, or other family obligations), had legal problems or other serious social problems due to your use of opioid pain medications?”), feeling guilty or remorseful (i.e., “Have you ever felt guilty, worried, or remorseful about your use of opioid pain medications”), and concerns from others about their opioid use (i.e., “Has a relative, a friend, a doctor or another health worker been concerned about your use of opioid pain medications?”). Response options include “Yes”; “No”; “I’d prefer not to say.” Q41, Q43–51 items are scored 0–3 (online suppl. material). All of these measures can be assessed individually, or a measure of problematic opioid use can be created by summing the scores from Q41 and Q43–51 (total score of 30). To assess the validity of these problematic opioid use questions, we deployed POMS in a cohort of 1,049 patients from the University of California San Diego Health System (UCSD Health; online suppl. Table 1). Our findings indicate that each one-unit increase in the problematic opioid use score is associated with a 41% increase in the odds of self-reported problematic opioid use or OUD (Q52; OR = 1.41, 95% CI: 1.34–1.50, p < 0.0001). The measure demonstrates strong discriminative ability, with a c-statistic of 0.953 and 94.6% concordance, supporting its utility in capturing self-reported problematic opioid use. To ensure that participants’ responses reflect withdrawal symptoms (Q46–47; e.g., craving for more opioid pain medications, difficulty concentrating, fatigue, nausea/vomiting, chills) rather than acute post-use effects, we examined their short-term prescription opioid use as well as their problematic opioid use scores. Among the 1,049 UCSD Health patients, 369 (35%) endorsed withdrawal symptoms, which were significantly more common among those with long-term opioid exposure (376; 36%) compared to those with short-term exposure (673; 6%). This difference suggests that our questions effectively capture withdrawal rather than transient post-use effects. Furthermore, those who did not report withdrawal had a mean problematic opioid use score of 1.34 (±1.89), whereas those who reported withdrawal had a higher mean score of 8.09 (±7.00) – a nearly sixfold increase. This indicates that withdrawal endorsement is closely linked to problematic opioid use and suggests our withdrawal measures effectively distinguish true opioid withdrawal from short-term post-use effects. To further refine accuracy, we suggest considering excluding participants with short-term opioid exposure from withdrawal assessments, as withdrawal symptoms are less likely in these individuals.

OUD Diagnosis (Q52)

Q52 assesses participants’ self-reported history of OUD (“Have you ever felt that you might have a problem with, or have you ever been diagnosed with or treated for, addiction to opioids?”), which is adapted from the Drug Abuse Screening Test (DAST) [96]. Response options include “Yes, I felt I might have a problem but have never been diagnosed or treated”; “Yes, I have been diagnosed or treated”; “No.” Participants are only able to select one of these options. Because many cases of OUD may not be formally diagnosed, some individuals who felt they might have a problem could easily have met criteria for OUD had they been clinically assessed. Participants who answer “Yes” to Q52 are likely to also endorse several prior questions related to problematic opioid use (Q41–51). If a participant answers “Yes” to Q52 but scores low on Q41–51, this may indicate a misunderstanding, response oversight, or remission. To address such discrepancies, analyses such as sensitivity analyses or item response analysis should be conducted to identify and account for inconsistencies, ensuring accurate interpretation of responses within the broader data context.

Illicit Opioid Use (Q53)

Illicit opioid use is often closely tied to prescription opioid use, as many individuals who misuse prescription opioids may eventually obtain prescription opioids illegally or obtain illicit drugs such as heroin or non-pharmaceutical fentanyl when prescription medications become unavailable, too costly, or no longer provide the desired effects [130, 157]. This transition is driven by the potent addictive properties of opioids, which can lead to increased tolerance and dependence [17]. As a result, the misuse of prescription opioids can serve as an initial step to more dangerous illicit opioid use, exacerbating the risks of overdose and the development of OUD [158]. To capture these patterns, Q53 asks “Have you ever taken illicit opioid drugs like opium or heroin or pain pills obtained from an illegal source (including fentanyl)?”, with response options that include “Yes”; “No”; “I’d prefer not to say.” This question and response is adapted from the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) [97].

Overdose History (Q54–55)

Overdose is a significant risk factor associated with prescription opioid misuse and often indicates that medications may be taken at higher doses than prescribed or in ways that constitute misuse [3]. Q54 asks “Have you ever overdosed on any opioid? An opioid overdose can include convulsions/seizures, difficulty breathing, loss of consciousness/collapse, unable to be woken up, heart attack, blue skin color while using opioids.”, with response options that include “Yes”; “No”; “I’d prefer not to say.” For those who endorse ever experiencing an overdose, Q55 asks “Were you ever given naloxone or Narcan during an opioid overdose?” These questions and responses were adapted from the Opioid Overdose Knowledge Scale [98].

Treatment for OUD (Q56–57)

Treatment history may influence how individuals report current and past opioid use, and recovery progress [17]. For those who endorse having a problem with opioid use or an OUD in Q52, Q56 assesses their OUD treatment history by asking “How many times in your life have you been treated for problematic opioid use or opioid use disorder (e.g., inpatient, residential in a program, outpatient services, groups, medications)?”, with response options that include “a blank field to indicate the number of times,” “I have not been treated for opioid use,” and “I’d prefer not to say.” For those who endorse ever receiving treatment, Q57 asks “What type of treatment did you receive to treat problematic opioid use or opioid use disorder? (check all that apply),” with multiple selectable options, including “Inpatient (e.g., hospitalization),” “Residential treatment program,” “Individual therapy (e.g., psychotherapy, counseling),” “Group therapy or support groups (e.g., Narcotics Anonymous),” and “Medication based treatments (e.g., methadone, buprenorphine).” These questions and responses were adapted from the Addiction Severity Index (ASI) [92].

Remission for OUD (Q58)

An individual’s remission status for OUD can influence their responses to POMS questions regarding opioid use and related behaviors. Those in remission may report differently on their past and current opioid use, recovery progress, and associated experiences compared to individuals not in remission [17]. For those who endorse having a problem with or being diagnosed with OUD, Q58 asks “Are you currently in remission for an opioid use disorder (e.g., such as abstaining from or significantly reducing opioid use and experiencing improved function).” Response options include “Yes, in remission for 3–12 months”; “Yes, in remission for more than 12 months”; “No, not in remission”; “I’d prefer not to say.” This question and response is adapted from the DSM-5 remission criteria [52].

Other Problematic Substance Use and Addictive Behaviors

Problematic Substance Use, SUD, and Addictive Behaviors (Q59–65)

Comorbid addictive behaviors such as problematic substance use, SUD, gambling, sexual addiction, and gaming may increase the risk of developing OUD and may reflect a shared genetic risk [99, 100, 159]. Q59–65 ask “Have you ever felt that you might have a problem with, or have you ever been diagnosed with or treated for, addiction to?” for the following substances and behaviors: nicotine, alcohol, and cannabis, stimulants, gambling, sexual behaviors, and gaming. Response options include Yes, I felt I might have a problem but have never been diagnosed or treated”; “Yes, I have been diagnosed or treated”; “No”; “I'd prefer not to say.” Such single-item self-reported measures can have surprisingly high correlations with clinical diagnoses, as shown for other psychiatric disorders [160]. For those who endorse any of these measures (Q59–65), including those related to opioid use captured in the section above (Q52–54), information for services that provide referrals to local treatment facilities, support groups, and community-based organizations should be provided. For example, POMS provides resources to contact the SAMHSA National Helpline for those who endorse any of the problematic substance use questions in the USA.

Family History of SUDs and Other Addictive Behaviors (Q66)

Family history of substance use and other addictive behaviors such as gambling significantly influences the likelihood of these behaviors manifesting [161]. Q66 asks “Have any of your first degree biological relatives ( parents, siblings or children) suffered from any of these forms of addiction? Please select all that apply.” Response options include “Nicotine”; “Alcohol”; “Cannabis or marijuana”; “Other drugs; Gambling”; “None of the above”; “I’d prefer not to say.” Genetic factors can account for approximately 40–60% of the variance in risk for SUD [162]. Environmental factors due to family history, such as exposure to substances at home or modeling of addictive behaviors by family members, also play a crucial role [162].

Alcohol Use Disorder (Q67–76)

Alcohol and opioid use frequently co-occur, with problematic alcohol use significantly increasing the risk of complications related to opioid misuse and the development of OUD [163, 164]. The Alcohol Use Disorder Identification Test (AUDIT) is a 10-item screener for measuring hazardous drinking over the past year [93–95]. Q67–74 measure patterns of alcohol consumption (e.g., “How often do you have six or more drinks on one occasion?”; “How often during the last year have you been unable to remember what happened the night before because you had been drinking?”). Response options include “Never”; “Monthly or less”; “2 to 4 times a month”; “2 or 3 times a week”; “4 or more times a week,” for Q67 and “Never”; “Less than monthly”; “Monthly”; “Weekly”; “Daily or almost daily” for Q69-74 and these items are scored as 0–4. Q68 specifically asks “How many drinks containing alcohol do you have on a typical day when you are drinking?” with response options that include “1 or 2”; “3 or 4”; “5 or 6”; “7, 8, or 9”; “10 or more” and this item is also scored as 0–4. Q75–76 ask “Have you or someone else been injured because of your drinking?” and “Has a relative or a friend or a doctor or another health worker been concerned about your drinking or suggested you cut down?” Response options include “No”; “Yes, but not in the last year”; “Yes, during the last year,” and these items are scored as 0, 2, or 4, respectively. Gating logic is employed such that participants who select “Never” on Q67 or “1 or 2” on Q68 and “Never” on Q69 skip to Q75–76. AUDIT total scores range from 0 to 40, with higher scores indicating high-risk drinking and possible alcohol dependence [93–95].

Comorbid Psychiatric or Mental Health Conditions

Anxiety and Depression (Q77–80)

The presence of comorbid psychiatric conditions, such as depression or anxiety, can influence the trajectory from prescription opioid use to misuse and OUD. Individuals with these conditions may use prescription opioids not just for pain relief but also to try to modulate psychological symptoms, thereby increasing the risk of misuse [165]. In some cases, individuals may misuse prescription opioids to attempt to manage underlying conditions such as depression or anxiety [166–168]. Adapted versions of the Generalized Anxiety Disorder 2-item (GAD-2; [102, 103]) and the Patient Health Questionnaire-2 (PHQ-2; [104, 105]) are incorporated to screen for core symptoms of anxiety and depression, respectively. Whereas these items typically specify a timeframe of the last 2 weeks, POMS used the past year, in an effort to capture something more akin to trait versus state anxiety or mood. For example, Q77 asks “Over the past year, how often have you been bothered by feeling nervous, anxious, or on edge?”, and Q80 asks “Over the past year, how often have you been bothered by feeling down, depressed, or hopeless?”). Response options include “Not at all”; “Several days”; “More than half the days”; “Nearly every day.” The scores for each item (0–3) are summed, resulting in a total score ranging from 0 to 6 for each measure. A cut-off score of three or greater typically indicates potential anxiety or depression, warranting further evaluation [102, 169, 105].

Loneliness (Q81)

Loneliness, or the subjective experience of feeling lonely, is a complex biological trait linked to numerous health outcomes, including the misuse of opioids and poor OUD medication treatment outcomes [108]. Q81 asks “Do you often feel lonely?” This question is adapted from the University of California Los Angeles (UCLA) Loneliness Scale, which has excellent psychometric properties [109]. Prior research has demonstrated that this scale’s key item, with response options of “Yes” or “No,” exhibits a strong correlation with the complete UCLA Scale [107] and reduces response burden. This item is also available in other large-scale cohorts, such as UK Biobank.

Trauma/PTSD (Q82–83)

Environmental risk factors, such as exposure to trauma or stressors, can influence development of OUD [170, 171]. To capture trauma and PTSD, Q82 asks “In your lifetime, have you ever been exposed to a very stressful experience such as threatened death, actual or threatened serious injury, or actual or threatened sexual violence?” with response options that include “Yes” or “No.” Q83 asks “In your lifetime, how much have you ever been bothered by repeated, disturbing, and unwanted memories of one or more of these stressful experiences for more than a month?” The response options for this question include “Not at all,” “Mildly,” “Moderately,” “Very much,” and “Extremely,” with scores ranging from 0 to 4. Higher scores indicate greater severity of trauma-related symptoms. These two questions are adapted from the PTSD Checklist for DSM-5 (PCL-5), which have been found to be a strong indicator of PTSD [110].

Self-Reported Psychiatric Diagnoses (Q84–91)

Self-reported psychiatric diagnoses have been used in numerous studies and can be used in instruments such as POMS when a clinical diagnosis may not be available [111, 172, 173]. Q84–91 asks “Have you ever been diagnosed with or treated for any of the following conditions?”, and the following options are presented: “Anxiety (panic disorder, agoraphobia, generalized anxiety disorder, social phobia and specific phobia),” “Depression,” “Bipolar disorder or manic depressive disorder,” “Schizophrenia,” “Attention-deficit/hyperactivity disorder (ADHD or ADD),” “Post-traumatic stress disorder (PTSD),” “Obsessive-compulsive disorder (OCD),” and “Eating disorders (such as anorexia or bullimia).” Response options for each diagnosis include “Yes”; “No”; “I’d prefer not to say.” These questions have been developed based on literature showing these conditions are common in those who misuse opioids or have OUD [159].

Suicide-Related Behaviors (Q92–93)

Individuals with a history of prescription opioid misuse are at higher risk of experiencing suicidal ideation and attempts compared to those who do not misuse opioids [174–176]. Q92 asks “Have you ever had serious thoughts about killing yourself?”, and Q93 asks “Have you ever tried to kill yourself?” Response options include “Yes”; “No”; “I’d prefer not to say.” These questions have been validated in various studies, demonstrating their reliability and predictive validity for identifying those at higher risk of SUDs, including OUD, and other psychiatric conditions [112, 113, 177]. For participants endorsing any measures related to suicide, information on services providing referrals to local support and community-based organizations should be readily available. For example, POMS includes resources for contacting the 988 Suicide & Crisis Lifeline for US participants endorsing either of these questions. Additionally, most Institutional Review Boards and health systems will require that participants indicating suicidal thoughts receive follow-up contact to ensure their safety. These measures prioritize participant well-being and fulfill ethical and regulatory obligations for support and intervention.

Personality Traits

Personality traits such as risk-taking behaviors, delay discounting (i.e., tendency to devalue rewards or benefits that are delayed in favor of smaller, immediate rewards), and wisdom (e.g., resilience under stress, openness to different perspectives, and the tendency to seek advice) may influence the trajectory from prescription opioid use to misuse and OUD [114, 178–180]. Q94–111 ask about personality traits and behaviors, such as risk-taking behaviors, delay discounting, and wisdom.

Risk (Q94)

Identifying risk-taking tendencies provides insights into the personality traits that contribute to the initiation and escalation of opioid use [115]. Q94 asks “Do you like to take risks?” with response options that include “Yes” or “No.” Survey-based measures of general risk tolerance, such as this one, have been found to be good predictors of risky behaviors across various domains, including substance use [181–183].

Delay Discounting (Q95–104)

Delay discounting, the tendency to favor smaller immediate rewards over larger delayed ones, plays a critical role in the development of OUD and is notably different in individuals with SUD compared to controls [118, 184]. For instance, a person with high delay discounting may choose the immediate relief of opioid use over the long-term benefits of abstaining, despite potential risks such as OUD and health decline. This preference for immediate rewards is particularly impactful during periods of stress, where it can increase the likelihood of initiating or escalating opioid use, contribute to relapse, and complicate recovery efforts [116]. To assess delay discounting, POMS incorporates the well-established Monetary Choice Questionnaire (MCQ; [185, 186]). The full MCQ includes nine questions each at each of three magnitudes. POMS uses the shorter version of the MCQ, which has only the intermediate magnitude. This was chosen for its high concordance with the longer version and excellent psychometric properties [117], and consists of 10 questions (Q95–99; Q101–104) at one magnitude (intermediate) where participants choose between receiving a smaller amount of money immediately or a larger amount at a future date (e.g., “Would you rather have: USD 54 today or USD 55 in 111 days”). A greater preference for immediate rewards indicates higher delay discounting [117]. To identify careless responses, Q100 asks “Would you rather have: USD 60 today or USD 20 today”; individuals who select USD 20 today instead of USD 60 today are assumed to be inattentive and can be excluded. Participants’ preferences for immediate over delayed rewards indicate a higher rate of delay discounting. The responses are used to calculate the hyperbolic discounting constant, k, which quantifies how much an individual devalues future rewards. A higher value of k signifies a greater tendency toward immediate gratification, a trait observed in individuals at higher risk for opioid misuse and other addictive behaviors [117].

Wisdom (Q105–111)

Wisdom can be characterized by seven components – self-reflection, prosocial behaviors (i.e., voluntary actions that are intended to benefit others), emotional regulation, acceptance of diverse perspectives, decisiveness, social advising, and spirituality. This measure of wisdom is strongly associated with well-being [123]. This relationship is supported by studies using validated scales such as the San Diego Wisdom Scale (SD-WISE) and its abbreviated version, SD-WISE-7, known as the Jeste-Thomas Wisdom Index (JTWI; [123, 187, 188]). To assess wisdom, we used the abbreviated version that has seven questions from the SD-WISE, each designed to measure specific traits. For example, Q105 asks “I remain calm under pressure,” and Q107 asks “I enjoy being exposed to diverse viewpoints.” Other questions explore decision-making processes and social support. For example, Q108 asks “I tend to postpone making major decisions as long as I can” and “I often don't know what to tell people when they come to me for advice.” Q110 asks “My spiritual belief gives me inner strength.” Response options include “Strongly disagree”; “Disagree”; “Neutral”; “Agree”; “Strongly Agree.” Q105–111 are summed; each item is scored from 1 to 5, resulting in a total score ranging from 7 to 35. Q106, Q108–109, and Q111 are reverse coded. Higher total scores indicate greater wisdom [123]. These traits provide insights into stress coping, decision-making, and social support dynamics, essential for effective opioid use and recovery strategies [120–122, 189–196].

Basic Socio-Demographics (Q112–120)

Socio-demographic factors significantly influence the potential path from initial opioid exposure to the development of OUD. For example, males and females exhibit distinct pathways to opioid use, with males more likely to initiate use recreationally and progress quickly to higher doses and intravenous use, while females often start with prescribed opioid medications for chronic pain and progress more slowly due to emotional stressors and co-occurring mental health disorders [6]. Younger individuals may be more prone to experimenting due to peer influence and risk-taking behaviors, whereas older adults might develop dependency due to chronic use for medical reasons [125]. Racial and ethnic minorities may face disparities in access to treatment and pain management resources, which may affect their risk of misuse and paths to recovery [8]. Marital status and education level impact opioid misuse and the transition to OUD by influencing social support, economic stability, and health literacy [126, 197]. Married individuals typically have more social support, reducing the likelihood of opioid misuse. Higher education levels correlate with better health literacy and access to resources, which is associated with a lower risk of opioid misuse [65, 198]. Conversely, those who are single or have lower education levels may face higher stress and limited access to healthcare, increasing their risk of opioid misuse [126, 199]. Socioeconomic conditions such as poverty may lead to economic hardships that limit educational and employment opportunities, increasing stress levels and making opioid misuse a more likely coping mechanism [124, 200, 201]. Poverty, often associated with limited access to healthcare, leaves individuals with fewer options for pain management outside of opioids [124]. This lack of access may lead individuals to rely on non-prescribed opioids as a more accessible solution to manage their pain [8].

To better understand these influences, Q112–120 include basic socio-demographic questions covering age, sex assigned at birth, gender identity, sexual orientation, race/ethnicity, marital status, education level, and income that were developed based on recent guides for using inclusive socio-demographic survey questions [202, 203]. However, the socio-demographic information can be adjusted based on the goals of the study and the population of interest.

Discussion

Integrity and Responsiveness

As described throughout this paper, implementing the following quality control measures will help ensure the integrity of the data collected using POMS. First, eligibility screening should ensure that only individuals who report a history of prescription opioid use proceed to the full survey (Q1–2). Responses completed in an implausibly short duration (e.g., less than 5 min) should be flagged and removed. Additionally, response patterns should be assessed for logical inconsistencies (e.g., scoring low on problematic opioid use [Q41–51] but endorsing OUD [Q52]). To further improve data quality, embedded attention checks are included. For example, in a hypothetical monetary choice question (Q100; “Would you rather have: USD 60 today or USD 20 today?”), individuals who select the obviously lower amount (USD 20 today) should be assumed to be inattentive and excluded. Implementing these measures will help ensure the reliability and validity of the dataset.

To assess participant responsiveness, we examined survey completion rates and response consistency within the UCSD Health cohort. Out of 1,275 patients who were interested in the study and completed the eligibility screener (UCSD Health patient, age >18, and self-reported lifetime prescription opioid use), 1,049 patients completed the full survey, yielding a completion rate of 82%. This completion rate provides insight into participant engagement and the feasibility of data collection within this population. We recommend that researchers using the POMS survey assess missing data patterns to determine whether data are missing completely at random (MCAR), missing at random (MAR), or not missing at random (NMAR). Little’s [204] MCAR test can be used to evaluate randomness in missingness. If data are MCAR, complete case analysis may be appropriate. If data are MAR, we recommend multiple imputation by chained equations to estimate missing values [205, 206]. If data are not MAR, sensitivity analyses, such as pattern-mixture modeling, should be conducted to assess and adjust for potential biases [207]. These approaches will help ensure robust analyses and minimize biases introduced by missing data.

Limitations and Future Directions

POMS is a comprehensive tool designed to capture a wide range of information on patterns of prescription opioid use and misuse, as well as associated risk factors. It is designed for online use and can be deployed to very large study cohorts, including those with complementary data such as genotypes or electronic health records (EHRs) also available. For example, integrating survey responses with EHR-based opioid phenotyping enhances the characterization of opioid exposure patterns, treatment histories, and associated health outcomes. Survey responses provide context on patient-reported experiences, including pain management, opioid effects, and reasons for discontinuation or continued use – factors that may not be fully captured in structured EHR data. Additionally, linking survey data with EHRs allows for validation of self-reported opioid use and facilitates identification of potential misclassification or missing data in medical records.

While POMS provides valuable insights into opioid use and progression to OUD, several limitations must be addressed. First, the reliance on self-reported data introduces the potential for recall bias. Participants may have difficulty accurately remembering and reporting their past prescription opioid use and related experiences [37, 41, 208, 209], especially if significant time has passed since their initial and longest period of use. Second, because individuals are asked to recall prescription opioids they may have used recently or even decades ago, POMS does not include measurements of opioid dose. Consequently, some differences in subjective effects may stem from variations in opioid dosage (e.g., low vs. high doses). POMS also does not capture the setting in which opioids were used, the concurrent use of other drugs, or the expected drug effects. Another form of recall bias is also possible, where subsequent experiences with prescription opioids might shape or alter participants’ memories of their initial use. Some questions, such as those in AUDIT, GAD-2, or PHQ-2, focus on the last year and may overlook significant lifetime patterns or historical risk factors.

Participants’ subjective effects may also be influenced by preconceived notions about the effects of opioids, which are sometimes called expectancy effects [210]. Subjective effects are also known to evolve over time, particularly in individuals with prolonged use. Longitudinal research suggests that the balance of positive (euphoric, stimulating) and negative (sedating, unpleasant) effects may shift as opioid use progresses. According to the opponent process theory of addiction, individuals may initially use opioids for their rewarding properties [211, 212] but later continue to use to mitigate withdrawal symptoms [213]. However, research by Agrawal et al. [147] on retrospectively assessed subjective effects of initial opioid use suggests that individuals who later develop OUD report greater initial euphoria than those who do not progress to OUD. This finding supports our observation that individuals with OUD continue to report experiencing euphoria, even at later stages of use. Future studies employing longitudinal designs could further clarify how subjective experiences change over the course of opioid use.

Another limitation is the inability to measure opioid-induced hyperalgesia, where opioid use paradoxically increases sensitivity to pain and may escalate dependency [65, 66]. Assessing this phenomenon requires clinical evaluation and testing, which are not feasible in large-scale surveys, limiting insights into its role in OUD development. Additionally, it is important to note that opioids are not always used for analgesic purposes. For example, codeine is often used as a cough suppressant, making questions about pain relief irrelevant in such cases.

POMS also does not account for temporal trends in opioid prescribing practices, which have shifted significantly over time due to changes in medical guidelines, policy regulations, and public health interventions. Participants’ responses may reflect experiences with opioid prescriptions from different time periods and geographic locations. Coupling survey responses with external data sources, such as prescription records from EHRs or Prescription Drug Monitoring Programs, could provide a more comprehensive understanding of prescribing trends and their influence on OUD risk.

While the instruments used in this study are widely applied in clinical practice and research, many of them have not been specifically adapted for different languages or cultural backgrounds. Future research should explore the psychometric properties of POMS across different populations, assessing test-retest reliability, factor structure, and cultural validity. These tools have also traditionally been administered and validated independently. By integrating them into the POMS framework, we have the potential to uncover broader patterns and relationships that may not be apparent when the surveys are used in isolation. For example, phenotypes derived from POMS can support future genetic studies of multiple traits across the opioid use spectrum that have not been studied before (e.g., opioid sensitivity, opioid withdrawal). Deploying this comprehensive survey across diverse populations will enable us to capture a wide range of opioid use behaviors, spanning from initial exposure to potential misuse, rather than focusing solely on OUD.

Conclusion

POMS may serve as a pivotal tool in the study of OUD development, offering a comprehensive framework to examine the complexities of opioid use and its progression. By integrating a diverse set of validated questions addressing the subjective effects of prescription opioids, problematic use, medical history, behavioral patterns, and socio-demographic factors, POMS provides a nuanced and holistic view of the factors contributing to OUD.

One of the most salient applications of POMS lies in its ability to identify key precursors and risk factors for OUD, enabling the design of targeted prevention and intervention strategies. By emphasizing the most critical and clinically relevant questions, POMS can be adapted for streamlined implementation in clinical settings, assisting healthcare providers in assessing opioid use patterns and associated risks efficiently. This adaptability ensures that the survey remains relevant and scalable across diverse populations and healthcare contexts.

Moreover, POMS’s ability to integrate with other data sources, such as EHR, enhances the accuracy and utility of the collected data. This dynamic approach allows for continuous refinement of the survey, facilitating the discovery of novel insights into OUD risk factors and progression. By bridging the gap between research and clinical application, POMS provides a robust tool for improving our understanding and management of opioid use and its associated challenges.

Statement of Ethics

An ethics statement was not required for this study type since no human or animal subjects or materials were used.

Conflict of Interest Statement

Sandra Sanchez-Roige is an editorial board member for Complex Psychiatry but has no other conflicts of interest to declare. Anna R. Docherty was a member of the journal’s Editorial Board at the time of submission. Natasia S. Courchesne-Krak is a consultant and holds stock options in CARI Health, Inc. Vinh Tran is employed by 23andMe, Inc. and holds stock or stock options in 23andMe, Inc. The authors have no conflicts of interest to declare.

Funding Sources

Natasia S. Courchesne-Krak is supported by the California Tobacco-Related Disease Research Program (TRDRP; T33KT6694) and the National Institutes of Health Loan Repayment Program (NIH LRP; 1L40AA031140-01). Jean Gonzalez and Sandra Sanchez-Roige are supported by the National Institute on Drug Abuse (NIDA; DP1DA054394). Abraham A. Palmer and Sandra Sanchez-Roige are also supported by NIDA (P50DA037844). Jean Gonzalez, Sandra Sanchez-Roige, and Abraham A. Palmer are supported by NIDA (R01DA061977). Jean Gonzalez is also funded by the National Institute of General Medical Sciences (NIGMS; T32GM139790)

Author Contributions

Abraham A. Palmer and Sandra Sanchez-Roige conceived the idea. Natasia S. Courchesne-Krak, Vinh Tran, Eric O. Johnson, Vanessa Troiani, John M. Hettema, Murray Stein, Hilary Coon, Anna Docherty, Wade Berretini^, James Mackillop, Harriet de Wit, Carla Marienfeld, Abraham A. Palmer, and Sandra Sanchez-Roige contributed to the design of the survey. Natasia Courchesne-Krak and Sandra Sanchez-Roige wrote the first draft of the article. All authors including Anirudh R. Chandrasekaran, Jean Gonzalez, Sevim B. Bianchi, and Abraham A. Palmer edited the article.

Funding Statement

Natasia S. Courchesne-Krak is supported by the California Tobacco-Related Disease Research Program (TRDRP; T33KT6694) and the National Institutes of Health Loan Repayment Program (NIH LRP; 1L40AA031140-01). Jean Gonzalez and Sandra Sanchez-Roige are supported by the National Institute on Drug Abuse (NIDA; DP1DA054394). Abraham A. Palmer and Sandra Sanchez-Roige are also supported by NIDA (P50DA037844). Jean Gonzalez, Sandra Sanchez-Roige, and Abraham A. Palmer are supported by NIDA (R01DA061977). Jean Gonzalez is also funded by the National Institute of General Medical Sciences (NIGMS; T32GM139790)

Data Availability Statement

The survey is available via REDCap and has been provided in the online supplementary materials.