Faculty Bibliography

OBJECTIVE:The COVID-19 pandemic contributed to healthcare disruptions for patients with chronic pain. Following initial disruptions, national policies were enacted to expand access to long-term opioid therapy (LTOT) for chronic pain and opioid use disorder (OUD) treatment services, which may have modified risk of opioid overdose. We examined associations between LTOT and/or OUD with fatal and non-fatal opioid overdoses, and whether the pandemic moderated overdose risk in these groups. METHODS:We analyzed New York State Medicaid claims data (3/1/2019-12/31/20) of patients with chronic pain (N = 236,391). We used generalized estimating equations models to assess associations between LTOT and/or OUD (neither LTOT or OUD [ref], LTOT only, OUD only, and LTOT and OUD) and the pandemic (03/2020-12/2020) with opioid overdose. RESULTS:The pandemic did not significantly (ns) affect opioid overdose among patients with LTOT and/or OUD. While patients with LTOT (vs. no LTOT) had a slight increase in opioid overdose during the pandemic (pre-pandemic: aOR:1.65, 95% CI:1.05, 2.57; pandemic: aOR:2.43, CI:1.75,3.37, ns), patients with OUD had a slightly attenuated odds of overdose during the pandemic (pre-pandemic: aOR:5.65, CI:4.73, 6.75; pandemic: aOR:5.16, CI:4.33, 6.14, ns). Patients with both LTOT and OUD also experienced a slightly reduced odds of opioid overdose during the pandemic (pre-pandemic: aOR:5.82, CI:3.58, 9.44; pandemic: aOR:3.70, CI:2.11, 6.50, ns). CONCLUSIONS:Findings demonstrated no significant effect of the pandemic on opioid overdose among people with chronic pain and LTOT and/or OUD, suggesting pandemic policies expanding access to chronic pain and OUD treatment services may have mitigated the risk of opioid overdose.

BACKGROUND:Chronic pain has been extensively explored as a risk factor for opioid misuse, resulting in increased focus on opioid prescribing practices for individuals with such conditions. Physical disability sometimes co-occurs with chronic pain but may also represent an independent risk factor for opioid misuse. However, previous research has not disentangled whether disability contributes to risk independent of chronic pain. METHODS:Here, we estimate the independent and joint adjusted associations between having a physical disability and co-occurring chronic pain condition at time of Medicaid enrollment on subsequent 18-month risk of incident opioid use disorder (OUD) and non-fatal, unintentional opioid overdose among non-elderly, adult Medicaid beneficiaries (2016-2019). RESULTS:We find robust evidence that having a physical disability approximately doubles the risk of incident OUD or opioid overdose, and physical disability co-occurring with chronic pain increases the risks approximately sixfold as compared to having neither chronic pain nor disability. In absolute numbers, those with neither a physical disability nor chronic pain condition have a 1.8% adjusted risk of incident OUD over 18 months of follow-up, those with physical disability alone have an 2.9% incident risk, those with chronic pain alone have a 3.6% incident risk, and those with co-occurring physical disability and chronic pain have a 11.1% incident risk. CONCLUSIONS:These findings suggest that those with a physical disability should receive increased attention from the medical and healthcare communities to reduce their risk of opioid misuse and attendant negative outcomes.

BACKGROUND:Identifying community characteristics associated with firearm assault could facilitate prevention. We investigated the effect of community firearm dealer and alcohol outlet densities on individual risk of firearm assault injury. METHODS:In this density-sampled case-control study of Californians, January 2005-September 2015, cases comprised all residents with a fatal or nonfatal firearm assault injury. For each month, we sampled controls from the state population in a 4:1 ratio with cases. Exposures were monthly densities of county-level pawn and nonpawn firearm dealers and ZIP code-level off-premises alcohol outlets and bars and pubs ("bars/pubs"). We used case-control-weighted G-computation to estimate risk differences (RD) statewide and among younger Black men, comparing observed exposure densities to hypothetical interventions setting these densities to low. We estimated additive interactions between firearm and alcohol retailer density. Secondary analyses examined interventions targeted to high exposure density or outcome burden areas. RESULTS:There were 67,850 cases and 268,122 controls. Observed (vs. low) densities of pawn firearm dealers and off-premises alcohol outlets were individually associated with elevated monthly risk of firearm assault per 100,000 people (RD pawn dealers : 0.06, 95% CI: 0.05, 0.08; RD off-premises outlets : 0.01, 95% CI: 0.01, 0.03), but nonpawn firearm dealer and bar/pub density were not; models targeting only areas with the highest outcome burden were similar. Among younger Black men, estimates were larger. There was no interaction between firearm and alcohol retailer density. CONCLUSIONS:Our results are consistent with the hypothesis that limiting pawn firearm dealers and off-premises alcohol outlet densities can reduce interpersonal firearm violence.

BACKGROUND:Cannabis use disorder is associated with considerable comorbidity and impairment in functioning, and prevalence is increasing among adults with chronic pain. We aimed to assess the effect of introduction of medical cannabis laws (MCL) and recreational cannabis laws (RCL) on the increase in cannabis use disorder among patients in the US Veterans Health Administration (VHA). METHODS:Data from patients with one or more primary care, emergency, or mental health visit to the VHA in 2005-19 were analysed using 15 repeated cross-sectional VHA electronic health record datasets (ie, one dataset per year). Patients in hospice or palliative care were excluded. Patients were stratified as having chronic pain or not using an American Pain Society taxonomy of painful medical conditions. We used staggered-adoption difference-in-difference analyses to estimate the role of MCL and RCL enactment in the increases in prevalence of diagnosed cannabis use disorder and associations with presence of chronic pain, accounting for the year that state laws were enacted. We did this by fitting a linear binomial regression model stratified by pain, with time-varying cannabis law status, fixed effects for state, categorical year, time-varying state-level sociodemographic covariates, and patient covariates (age group [18-34 years, 35-64 years, and 65-75 years], sex, and race and ethnicity). FINDINGS:Between 2005 and 2019, 3 234 382-4 579 994 patients were included per year. Among patients without pain in 2005, 5·1% were female, mean age was 58·3 (SD 12·6) years, and 75·7%, 15·6%, and 3·6% were White, Black, and Hispanic or Latino, respectively. In 2019, 9·3% were female, mean age was 56·7 (SD 15·2) years, and 68·1%, 18·2%, and 6·5% were White, Black, and Hispanic or Latino, respectively. Among patients with pain in 2005, 7·1% were female, mean age was 57·2 (SD 11·4) years, and 74·0%, 17·8%, and 3·9% were White, Black, and Hispanic or Latino, respectively. In 2019, 12·4% were female, mean age was 57·2 (SD 13·8) years, and 65·3%, 21·9%, and 7·0% were White, Black, and Hispanic or Latino, respectively. Among patients with chronic pain, enacting MCL led to a 0·135% (95% CI 0·118-0·153) absolute increase in cannabis use disorder prevalence, with 8·4% of the total increase in MCL-enacting states attributable to MCL. Enacting RCL led to a 0·188% (0·160-0·217) absolute increase in cannabis use disorder prevalence, with 11·5% of the total increase in RCL-enacting states attributable to RCL. In patients without chronic pain, enacting MCL and RCL led to smaller absolute increases in cannabis use disorder prevalence (MCL: 0·037% [0·027-0·048], 5·7% attributable to MCL; RCL: 0·042% [0·023-0·060], 6·0% attributable to RCL). Overall, associations of MCL and RCL with cannabis use disorder were greater in patients with chronic pain than in patients without chronic pain. INTERPRETATION:Increasing cannabis use disorder prevalence among patients with chronic pain following state legalisation is a public health concern, especially among older age groups. Given cannabis commercialisation and widespread public beliefs about its efficacy, clinical monitoring of cannabis use and discussion of the risk of cannabis use disorder among patients with chronic pain is warranted. FUNDING:NIDA grant R01DA048860, New York State Psychiatric Institute, and the VA Centers of Excellence in Substance Addiction Treatment and Education.

OBJECTIVES:This study aimed to examine trends in cannabis-positive urine drug screens (UDSs) among emergency department (ED) patients from 2008 to 2019 using data from the Veterans Health Administration (VHA) health care system, and whether these trends differed by age group (18-34, 35-64, and 65-75 years), sex, and race, and ethnicity. METHOD:VHA electronic health records from 2008 to 2019 were used to identify the percentage of unique VHA patients seen each year at an ED, received a UDS, and screened positive for cannabis. Trends in cannabis-positive UDS were examined by age, race and ethnicity, and sex within age groups. RESULTS:Of the VHA ED patients with a UDS, the annual prevalence positive for cannabis increased from 16.42% in 2008 to 27.2% in 2019. The largest increases in cannabis-positive UDS were observed in the younger age groups. Male and female ED patients tested positive for cannabis at similar levels. Although the prevalence of cannabis-positive UDS was consistently highest among non-Hispanic Black patients, cannabis-positive UDS increased in all race and ethnicity groups. DISCUSSION:The increasing prevalence of cannabis-positive UDS supports the validity of previously observed population-level increases in cannabis use and cannabis use disorder from survey and administrative records. Time trends via UDS results provide additional support that previously documented increases in self-reported cannabis use and disorder from surveys and claims data are not spuriously due to changes in patient willingness to report use as it becomes more legalized, or due to greater clinical attention over time.

Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision supports for public health practitioners. To facilitate practitioner use of machine learning as decision support for area-level intervention, this study developed and applied four practice-based predictive model evaluation criteria (implementation capacity, preventive potential, health equity, and jurisdictional practicalities). We used a case study of overdose prevention in Rhode Island to illustrate how these criteria could inform public health practice and health equity promotion. We used Rhode Island overdose mortality records from January 2016 to June 2020 (N=1,408) and neighborhood-level Census data. We learned two disparate machine learning models, Gaussian process and random forest, to illustrate the comparative utility of our criteria to guide interventions. Our models predicted 7.5-36.4% of overdose deaths during the test period, illustrating the preventive potential of overdose interventions assuming 5-20% statewide implementation capacities for neighborhood-level resource deployment. We described the health equity implications of predictive modeling to guide interventions along urbanicity, racial/ethnic composition, and poverty. In sum, our study discussed considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice.

In the United States, cannabis is increasingly used to manage chronic pain. Veterans Health Administration (VHA) patients are disproportionately affected by pain and may use cannabis for symptom management. Because cannabis use increases the risk of cannabis use disorders (CUDs), we examined time trends in CUD among VHA patients with and without chronic pain, and whether these trends differed by age. From VHA electronic health records from 2005 to 2019 (∼4.3-5.6 million patients yearly), we extracted diagnoses of CUD and chronic pain conditions (International Classification of Diseases [ICD]-9-CM, 2005-2014; ICD-10-CM, 2016-2019). Differential trends in CUD prevalence overall and age-stratified (<35, 35-64, or ≥65) were assessed by any chronic pain and number of pain conditions (0, 1, or ≥2). From 2005 to 2014, the prevalence of CUD among patients with any chronic pain increased significantly more (1.11%-2.56%) than those without pain (0.70%-1.26%). Cannabis use disorder prevalence increased significantly more among patients with chronic pain across all age groups and was highest among those with ≥2 pain conditions. From 2016 to 2019, CUD prevalence among patients age ≥65 with chronic pain increased significantly more (0.63%-1.01%) than those without chronic pain (0.28%-0.47%) and was highest among those with ≥2 pain conditions. Over time, CUD prevalence has increased more among VHA patients with chronic pain than other VHA patients, with the highest increase among those age ≥65. Clinicians should monitor symptoms of CUD among VHA patients and others with chronic pain who use cannabis, and consider noncannabis therapies, particularly because the effectiveness of cannabis for chronic pain management remains inconclusive.

BACKGROUND:In the United States, methadone treatment for opioid use disorder is only available at opioid treatment programs (OTPs). In addition to federal regulations, states can enact laws which shape access to OTPs. We aimed to define classes of states according to restrictiveness of state OTP laws and examine population characteristics associated with class membership. METHODS:A set of laws was extracted from a database of statutes and regulations governing OTPs in 49 states and the District of Columbia as of June 2021. Latent class analysis of laws was used to estimate the probability of class membership for each state. Class-weighted multinomial logistic regression analysis assessed state-level correlates of class membership and adjusted Relative Risk Ratio (aRRR) and 95% confidence intervals (95%CI) were generated. RESULTS:States (n = 50) were assigned to three classes; Class 1) High restrictiveness on patient experience, low restrictiveness on access to service (n = 13); Class 2) Medium restrictiveness on patient experience, high restrictiveness on access to service (n = 14); Class 3) Low restrictiveness on patient experience, low restrictiveness on access to service (n = 23). States with a higher probability of membership in Classes with higher restrictiveness had higher rates of unemployment (Class 1 vs Class 3, aRRR:1.24; 95%CI:1.06-1.45), and Black residents (Class 2 vs Class 3, aRRR:1.10; 95%CI:1.04-1.15), and lower likelihood of Medicaid coverage of methadone (Class 1 vs Class 3, aRRR:0.25; 95%CI:0.07-0.88). States with a higher probability of membership in Classes with higher restrictiveness also had higher rates of potential indicators for opioid use disorder treatment need, including rates of opioid dispensing (Class 1 vs Class 3, aRRR:1.06; 95%CI:1.02-1.10, Class 2 vs Class 3, aRRR:1.07; 95%CI:1.03-1.11) and HIV diagnoses attributed to injection (Class 1 vs Class 3, aRRR:3.92; 95%CI:1.25-12.22). CONCLUSIONS:States with indicators of greater potential need for opioid use disorder treatment have the most restrictions, raising concerns about unmet treatment need.

BACKGROUND:Despite evidence that the U.S. "War on Drugs" is associated with increases in drug-related harm and other negative outcomes, all U.S. states have long criminalized most drug possession. In early 2021, both Oregon and Washington became exceptions to this rule when they fully (Oregon) or partially (Washington) decriminalized possession of small amounts of all drugs. METHODS:We obtained arrest data for 2019 to 2021 for intervention states (Oregon and Washington) and control states (Colorado, Idaho, Montana, and Nevada). We calculated monthly rates for arrests overall and for violent crimes, drug possession, equipment possession, non-drug crimes, and a set of low-level crimes termed displaced arrests. Using an interrupted time series analysis, we examined changes in monthly arrest rates after the implementation of policy change in Oregon and Washington compared to control states. RESULTS:In Oregon, there were 3 fewer drug possession arrests per 100,000 in the month after the policy change; the rate decreased throughout the post-implementation period. In Washington, there were almost 5 fewer drug possession arrests per 100,000 in the month following policy change, and the rate remained stable thereafter. Both declines were significantly greater than in comparison states. There were also statistically significant reductions in arrests for possession of drug equipment in Washington and a significant increase in displaced arrests in Oregon. There were no significant changes in overall arrests, non-drug arrests or arrests for violent crime in either state, relative to controls. CONCLUSION:This analysis demonstrates that it is possible for state drug decriminalization policies to dramatically reduce arrests for drug possession without increasing arrests for violent crimes, potentially reducing harm to people who use drugs and their communities. Additional research is needed to determine whether these legal reforms were associated with changes in overdose rates and other drug-related harms.